Method for managing a set of data packet compression rules

Abstract

The invention relates to a method for managing a set (RS) of compression rules applied to data packets, referred to as an initial set, characterised in that the method comprises the following steps: • a step of determining (S1) a set of rules, referred to as effective rules, for a batch (L) of data packets, referred to as a given batch; • a step of obtaining (S2) a reduced set (RRS) of compression rules by deleting from the initial set (RS) the compression rules that do not belong to the set of effective rules for the given batch (L).

Description

Method for managing a set of data packet compression rules

[0001] The technical field is that of data compression and decompression.

[0002] More specifically, the invention relates to a method for managing a set of compression rules for data packets. These data packets are intended to be transmitted over a computer or telecommunications network. The method according to the invention aims in particular to reduce the size of the compression rule set.

[0003] Modern networked computing and digital telecommunications operate on the principle of exchanging data packets between senders and receivers using protocols such as the IP protocol (Internet Protocol). A set of data packets related to a communication is sent by the sender to a receiver. The packets contain the receiver's address so that it can be addressed using various routing algorithms, and the content of the communication is reconstructed by the receiver by regrouping the data contained in the different packets. To achieve complete communication, numerous protocols must be used, each fulfilling a role from the physical transmission of bits—the atomic elements that constitute digital data—to the interpretation of data for computer applications. Bits are typically grouped into bytes, which are sets of eight bits.The various protocols are traditionally viewed as being organized in layers, with a protocol at one layer relying on a protocol at a lower layer. The OSI (Open Systems Interconnection) model and the Internet model describe how the various protocols interact between different layers.

[0004] For example, in a simplified version, the HTTP (Hypertext Transfer Protocol) protocol is an Application layer protocol that allows data to be transferred to or from a website. The data to be transferred is organized into data packets. The HTTP protocol uses the TCP (Transmission Control Protocol), which belongs to the Transport layer, for example, to request the transfer of data packets representing the content of a website to a user. The TCP protocol, for example, operates in three phases: establishing a connection, transferring data, and terminating the connection. Transport layer protocols will use a Network layer protocol, generally the IP (Internet Protocol) protocol in one of its IPv4 or IPv6 versions.This protocol is only concerned with the transmission of data organized into packets from one address to another, without dealing with establishing the connection or the order in which the data packets arrive within a transmission; these considerations are handled by the protocol of the higher Transport layer. The IP protocol will use a protocol of the Data Link layer, for example, the Ethernet protocol, which will organize the sending of the elementary data that constitutes the packet. This elementary data is bits, transmitted using the Physical layer, either via fiber optic cable or wireless communication, depending on the case.

[0005] Other protocols in the Application layer will use other protocols from lower layers to perform their functions. For example, the CoAP (Constrained Application Protocol) protocol used in the Internet of Things generally relies on the UDP (User Datagram Protocol) protocol of the Transport layer.

[0006] A common feature of all protocols across all layers is the organization of exchanged data into data packets. A data packet is organized into two parts: the first part is the packet header, which contains the information necessary for the protocol responsible for processing the packet; the second part is the payload, which contains the data transported within the packet itself. If packets of a given protocol contain one or more encapsulated packets of another protocol, the payload of a packet from the first protocol may contain the header of that other protocol. At the Data Link layer, we speak of frames rather than packets, and, in addition to the header, there is a frame footer indicating the end of the frame.

[0007] Packet headers contain fields. A field is a continuous set of bits of either a fixed or variable length. When a field is variable in length, mechanisms exist to determine the actual length of the field transmitted. When the value in a fixed-length field is variable, padding bits are used to extend the value up to the field's fixed length. Each field has an identifier that indicates the nature of the value it contains. The values ​​in the field are used by the relevant protocol to process the packet appropriately. For example, in an IP packet header, a field with the identifier "Version" will contain the version number of the protocol used. This field is four bits long. Other fields include, for example, the source and destination IP addresses of the packet.A header is therefore naturally divided into several sections, namely the header fields. The packet payload can also be divided into several sections, for example by defining sections of a constant length expressed in number of bits.

[0008] A packet analysis tool such as Wireshark or tcpdump allows you to capture and analyze data packets. This analysis separates a data packet exchanged over a network into its header and payload, reads the information contained in the header and the data in the payload, and identifies the various sections of a packet, both in its header and payload. It's important to remember that, following the encapsulation of data packets, the typical header of a packet will consist of several concatenated headers, one for each encapsulated protocol. For example, an Ethernet frame encapsulating an IP packet will begin with the IP header, followed by the UDP header. A UDP datagram carried by an Ethernet frame will then successively present the Ethernet header, followed by the IP header, and then the UDP header.

[0009] There is a clear need for data compression in computing and digital telecommunications to optimize the use of hardware and energy resources consumed by computer and telecommunications networks. This need explains the creation of protocols dedicated to compressing exchanged data packets. Examples of compression protocols include ROHC (Robust Header Compression), defined by the IETF (Internet Engineering Task Force) in RFC 3095, and SCHC (Static Context Header Compression), defined by the IETF in RFC 8724.

[0010] These protocols operate using compression rules that apply or not to a particular data packet depending on the values ​​held by the different sections of that given packet. The compression rules used in the ROHC or SCHC protocols, or in other similar protocols, will, for example, attempt to erase a known value held by a given section of a packet, and a subsequent decompression rule will restore the erased value upon reception of the packet. Other rules may replace a given value with the index of a dictionary, or may only erase a prefix of the value represented as a sequence of bits, a prefix that will be restored during decompression. This results in very efficient compression of data packets, even if the compression is limited to the headers.

[0011] In all cases, the operation of data packet compression protocols relies on sets of compression rules that describe the compression operations to be performed on header field values, or more generally, on sections of data packets, provided that these values ​​meet conditions that trigger the application of the compression operations contained in a rule. A compression rule is said to apply to a packet when all the compression operations present in the rule can be applied to the values ​​of the fields in that packet. For a given packet, there exists, within the set of compression rules, one or more rules that achieve the most efficient compression when applied to that particular packet. The most efficient compression is the greatest compression achieved by the various rules that can be applied to the packet in question.These specific rules can be called the optimal rules for the packet. In general, the compression protocol will seek to apply, for each data packet it must compress, a rule that is optimal for that packet in order to achieve the best compression for that given packet. Operations that do not perform any compression but potentially apply to all possible values ​​can be defined for completion purposes, so that it is possible, for any given packet, to find a compression rule that applies to that packet, even if no compression is actually performed.

[0012] All of these elements are well known in the state of the art and will not be described further. State of the art

[0013] Compression rule sets are generally defined by experts based on their knowledge and an analysis of representative packets from a given traffic pattern. The idea that compression rule sets can be obtained through machine learning is beginning to emerge in the state of the art. Obtaining a compression rule set through machine learning would also start with a set of packets representative of the expected traffic, which would then be classically called the training set.

[0014] To ensure optimal performance of compression protocols, the quality of compression rule sets is crucial. This quality is judged by the compression ratio achieved by a given rule set when deployed to execute on a specific traffic sample. The quality of a compression rule set can therefore be objectively measured by comparing the compression ratios achieved by different rule sets on the same traffic sample.

[0015] Another quality criterion for a compression rule set is its size. The smaller the rule set, for a given compression ratio, the better, as it will take up less memory on both receiving and transmitting devices. Furthermore, a smaller rule set allows the compression protocol to determine which rule to apply more quickly. Finally, when a rule is used, its identifier must generally be transmitted with the compressed packet to enable the reverse decompression operation upon packet reception. The more rules there are, the larger the rule identifier will be, and therefore the greater the number of bits used to encode this information in the transmitted packet, which defeats the purpose of compression.

[0016] The two criteria of size and compression ratio are therefore used to judge the quality of a set of compression rules, whether this set is provided by an expert or obtained through machine learning. The size criterion is particularly important in the context of connected objects, which have limited memory and for which a maximum size for the set of compression rules may be imposed. The management method according to the invention aims to reduce the size of a given set of compression rules until it reaches a specified size while maintaining good performance with regard to the compression ratio.

[0017] In the current state of the art, compression rule sets are often determined by experts. If the rule set has a maximum size, the expert ensures that this size is not exceeded during its construction. This process is therefore performed manually and prevents the development of compression rule sets that adapt to traffic, since changing a compression rule set requires expert intervention.

[0018] When compression rules are obtained through machine learning, the algorithm will generally aim to produce compression rules tailored to the training set being fed into the machine learning algorithm, and will tend to add increasingly more suitable rules. Consequently, compression rule sets obtained through machine learning are typically very large, making them impractical.

[0019] The invention improves the situation.

[0020] According to a first functional aspect, the invention relates to a method for managing a set of compression rules applying to data packets, called the initial set, characterized in that it comprises the following steps: A step of determining a set of compression rules, called effective rules, for a batch of data packets, called the given batch; A step of obtaining a reduced set of compression rules by removing from the initial set the compression rules that do not belong to the set of effective rules for the given batch.

[0021] Thanks to the invention, the size of the initial set of compression rules is reduced. In this way, an initial set that was too large to be deployed in a connected device or used by telecommunications equipment is reduced in size. This size reduction takes into account a given batch of data packets, which are representative of the expected traffic that must be compressed by the compression rule set. The reduction is achieved by first targeting the rules that are not efficient at compressing the packets belonging to this given batch.

[0022] The advantage of the invention is therefore to reduce the size of the compression rule set by removing rules from it, while ensuring that the effective rules for packets in a given batch—packets representative of the expected traffic—are retained. The reduction in the size of the compression rule set is thus achieved while preserving its quality in terms of the compression performed by the retained rules.

[0023] According to one embodiment of this functional aspect of the invention, the process further includes a step of supplying the reduced set of compression rules.

[0024] After this reduction, the reduced set of compression rules can be provided for use in performing packet compression according to a compression protocol.

[0025] According to another embodiment of this functional aspect of the invention, a rule is determined to belong to the set of effective rules when there is a packet in the given batch for which said compression rule achieves the greatest compression of the packet among the compression rules of the initial set.

[0026] This implementation defines an objective criterion that determines which efficient rules to retain for a given set of packets. The rules considered efficient are those that are optimal for at least one data packet within that set. A rule is optimal for a packet when it achieves the greatest compression for that packet compared to the compressions achievable across all available compression rules. Generally, the optimal rule for a packet will be the one chosen by the compression protocol for application. By retaining the optimal rules from among the efficient rules retained by the management process, the process minimizes disruption to the compression protocol's operation, as it preserves the most frequently used and efficient rules.

[0027] According to another embodiment, which may be implemented alternatively with the previous embodiment, a rule is determined to belong to the effective rule set when said rule achieves compression above a given threshold of the packets in the given batch to which said rule applies.

[0028] This embodiment provides an alternative method for determining which effective rules to retain. A rule is considered effective here when it achieves compression exceeding a given threshold for the packets to which it applies. In this embodiment, the rules achieving the greatest compression are retained.

[0029] According to another embodiment of this functional aspect of the invention, which may be implemented alternatively or cumulatively with the preceding embodiments, the method further comprises the following steps: A step of obtaining a compression penalty caused by the deletion of a rule from the reduced set; A step of deleting a rule from the reduced set which minimizes the penalty obtained.

[0030] If simply removing ineffective or inefficient rules is insufficient to adequately reduce the rule set, the two steps described above are performed to further reduce its size while maintaining good compression quality relative to the given batch. To achieve this, a penalty is applied to assess the impact of removing a rule from the reduced set. Removing a given rule indicates that some packets within the expected traffic will no longer be compressed by that rule. A simple way to calculate the penalty is to count the bits erased by a rule in a packet when it is applied and compresses that packet.

[0031] One rule is then removed, whichever has the lowest penalty. In case of a tie, the removed rule can be randomly selected from those with the lowest penalty, or chosen according to the order in which the rule appears in the reduced set. The sequence of these two operations ensures that the size of the reduced set of compression rules decreases by at least one rule.

[0032] According to another embodiment, which can be implemented cumulatively with the previous embodiment, a size of a set of compression rules, called the target size, is provided and the process includes an iteration of the steps of obtaining a compression penalty and deleting a compression rule until the size of the reduced set is less than the target size.

[0033] In this embodiment, the sequence of two operations—obtaining a penalty and removing a rule to minimize the penalty—is repeated until a target size is reached. In other embodiments, the reduced set can then be provided for use in compressing data packets as expected.

[0034] According to another embodiment, which can be implemented cumulatively with the previous embodiment, the step of obtaining a compression penalty takes into account that a compression rule removed from the reduced set will be replaced by another rule from the reduced set to perform data packet compressions.

[0035] This implementation makes the compression penalty that governs the removal of a rule from the reduced set more precise. Indeed, if a rule is removed from the reduced set, it does not mean that the packets that would have been compressed by that rule will no longer be compressed at all. They will generally be compressed by another rule, which may be less efficient. The penalty can therefore be calculated as the difference between the compression performed by the removed rule and the compression performed by the rule that will be chosen by the compression protocol to compress packets in place of the removed rule.

[0036] According to another embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, the step of obtaining a compression penalty further takes into account that several compression rules may be removed from the reduced set and replaced by another rule, present in the initial set and absent from the reduced set, and that the removal step then includes the removal of said several rules and the addition of said other rule if this removal combined with this addition minimizes the penalty obtained during the obtaining step.

[0037] Thanks to this implementation, the compression penalty that guides rule removals is further refined. Indeed, at some point, a rule may have been removed during previous steps of the process and therefore be absent from the reduced set, but it can still provide a useful replacement for a rule that would otherwise be removed. Simply put, if a previously removed rule is reintroduced into the reduced set, at least two rules must be removed to ensure that the number of rules in the reduced set always decreases by one. The penalty thus takes into account the possibility of removing several rules from the reduced set and reintroducing a rule that is no longer in the reduced set but was in the initial set, in order to replace the removed rules when the compression protocol uses the reduced set of rules to compress data packets.The penalty is calculated here by subtracting the penalty of the added rule from the sum of the penalties of the several deleted rules.

[0038] According to another embodiment of the invention, which may be implemented alternatively or cumulatively with the preceding embodiments, the initial set of compression rules is represented by a data structure in which a compression rule, called the first rule, is linked to another compression rule, called the second rule, when, if the first rule applies to a given packet, the second rule applies to the same packet.

[0039] Thanks to this embodiment, the execution of the process is simplified. Having such a data structure allows one to quickly determine whether a rule is more generic than a more specialized one, meaning that it can be applied to all data packets to which the latter can be applied. This information can be used to calculate compression penalties because it indicates which rule can replace a deleted one. Obtaining this information directly, as it is stored within the data structure itself, greatly accelerates and simplifies the implementation of the process according to the invention.

[0040] According to another embodiment, which may be implemented alternatively or cumulatively with the previous embodiments, obtaining a compression penalty takes into account the number of packets in the given batch for which the deleted rule(s) achieve the greatest compression among the rules in the reduced set.

[0041] This implementation further refines the penalty that guides the removal of rules from the reduced set. To achieve this, the penalty considers, within the given batch (which is representative of the expected traffic), the packets for which the rules to be removed achieve the greatest compression and will therefore be chosen by the compression protocol to compress the packets of the expected traffic. A rule that achieves the greatest compression for a given packet among a set of possible rules is said to be optimal for that packet. If a rule compresses a packet by ten bits but is optimal for only one packet in the given batch, the penalty can be ten bits; whereas a rule that compresses a packet by only one bit but is optimal for one hundred packets in the given batch will have a total penalty of one hundred bits.This consideration of the number of packets for which the deleted rule(s) are optimal must also take into account that a deleted rule will be replaced by another rule from the reduced set to effectively compress these packets.

[0042] According to another embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, the size of a set of compression rules is the number of compression rules belonging to the set of compression rules.

[0043] This implementation provides a simple way to calculate the size of a compression rule set. Naturally, this size is the number of rules in the set. The process will therefore reduce the number of rules in the managed rule set until a target number is reached.

[0044] According to another embodiment, which may be implemented alternatively or cumulatively with the previous embodiments, the size of a set of compression rules is calculated by taking into account the number of compression rules belonging to the set of compression rules as well as the complexity of the rules belonging to the set.

[0045] It is possible to calculate the size in a more elaborate way, taking into account the complexity of the compression rules within the rule set. In this case, a rule with many more detailed compression operations will be larger than a simpler rule, and this difference will be reflected in the calculation of the rule set's size. An example calculation might be as follows: the size of a rule is the number of different effective compression operations within that rule, and the size of the rule set is the sum of the sizes of the individual rules in the set. More complex formulas can be used, taking into account the complexity of the compression operations.

[0046] According to another embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, the determination step includes calculating for a rule in the initial set a score obtained by multiplying the compression achieved by said rule by the number of packets in the given batch for which said rule achieves the greatest compression among the rules in the initial set.

[0047] This implementation provides comprehensive information on the compression capabilities of the rules in the initial set. The effectiveness of a compression rule depends on the compression it achieves when applied to a packet, multiplied by the number of packets for which this rule achieves the greatest compression among all possible rules and will therefore likely be used for compression. Since this calculation is performed on packets in the given batch, which is representative of the expected traffic, the resulting score indicates the amount of compression a given rule will achieve on expected traffic. This score also reveals which rules do not achieve the greatest compression for any packet in the given batch, as these rules will have a score of zero.

[0048] According to another embodiment, which can be implemented alternatively or cumulatively with the previous embodiments, the step of obtaining the reduced set includes the removal from the initial set of compression rules whose score is below a given threshold.

[0049] This implementation offers an alternative. Rather than directly removing rules that do not achieve the highest compression for any package, this implementation removes rules whose score is below a given threshold. In this way, more rules are removed, with the risk of removing rules that are optimal for some packages within the given batch.

[0050] According to another embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, a size of a set of compression rules, called the target size, is provided and the step of obtaining the reduced set includes removing the initial set of compression rules with the lowest score until the size of the reduced set is less than the target size.

[0051] This embodiment also offers another alternative implementation of the invention. Here, the rules are removed one after another based on the calculated score until the target size is reached. The advantage of this embodiment is its simplicity, at the expense of accounting for the replacement of removed rules with other rules in the actual packet compression, a factor addressed in other embodiments.

[0052] According to another embodiment, which can be implemented alternatively or cumulatively with the previous embodiments, the initial set of compression rules is obtained by a machine learning algorithm and the given batch was included in the training set used to obtain the initial set.

[0053] Thanks to this embodiment, the method according to the invention is used in the context of machine learning of compression rules. The method according to the invention thus solves a significant problem in machine learning, namely that it produces large sets of compression rules. After the machine learning phase, the method according to the invention automatically reduces the size of the resulting compression rule set to a target size in certain embodiments, for example, to allow deployment in telecommunications equipment that imposes a specific size on the deployed compression rule set.

[0054] To ensure that reducing the size of the compression rule set does not compromise the quality of the compressions performed by the reduced set, the method according to the invention is implemented using a given batch of packets. This given batch is representative of the expected traffic that will be compressed by the reduced rule set. When the set to be reduced is derived from machine learning, the given batch can be the training set used for the machine learning or can be included within the training set. In this way, the machine learning and the reduction of the compression rule set use the same set of packets representative of the expected traffic and are consistent with each other.

[0055] The method according to the invention can be carried out after another method of managing a set of compression rules in which, after machine learning, a set of compression rules is checked against a set of packets that were not used during machine learning, in order to ensure that the machine learning is not too adapted to the training set.In this case, the overall acquisition of a set of compression rules is achieved through a succession of three distinct processes: Automatic learning of a set of compression rules by a machine learning process using a batch of data packets, representative of the expected traffic, as the training set; Automatic control of the generalization of the set of compression rules obtained by machine learning using another batch of data packets, disjoint from the training set, also representative of the expected traffic, as the control set; Automatic reduction of the size of the set of compression rules to adapt it to the constraints of the equipment where the set will be deployed, this reduction using at least the training set to take into account the expected traffic, and potentially the merging of the training set and the disjoint control set.This reduction is the object of the process according to the invention.

[0056] In one embodiment, the given batch used in the process according to the invention comprises, in addition to the training set, another batch of packets, also representative of the expected traffic, used as a control set. In this way, a maximum number of packets representative of the expected traffic are used in the process according to the invention.

[0057] According to another embodiment of the invention, which may be implemented alternatively or cumulatively with the preceding embodiments, the data structure representing the initial set was calculated by the machine learning algorithm that enabled the initial set to be obtained.

[0058] A compression rule learning algorithm can start with general rules and search for more specialized rules that apply to the data packets in the training set. This type of learning algorithm can produce, as a side effect, a data structure that retains information about which rule was created from which others, and more generally, which rule is more specialized than another. In this embodiment, this information, which was previously an afterthought of the machine learning algorithm, becomes very useful for accelerating the management process according to the invention.

[0059] According to another embodiment, which can be implemented alternatively or cumulatively with the previous embodiments, the compression rules of the initial set are compression rules according to the SCHC protocol.

[0060] Thanks to this embodiment, the process applies to the SCHC compression protocol and allows compression according to this protocol with small sets, suitable for Internet of Things equipment.

[0061] In other embodiments, the compression rules are those of other compression protocols.

[0062] According to a first material aspect, the invention relates to a management entity managing a set of compression rules applying to data packets, called the initial set, comprising the following modules: Module for determining a set of compression rules, called effective rules, for a batch of data packets, called the given batch; Module for obtaining a reduced set of compression rules by removing from the initial set compression rules that do not belong to the set of effective rules for the given batch.

[0063] According to a first embodiment of this first material aspect, the management entity further includes the following modules: Module for obtaining a compression penalty caused by the deletion of a rule from the reduced set; Module for deleting a rule from the reduced set which minimizes the penalty obtained.

[0064] In some embodiments, the management entity further includes a module for supplying the reduced set.

[0065] According to another embodiment of this first material aspect, the invention relates to telecommunications equipment comprising a management entity according to the invention.

[0066] According to another embodiment, the invention relates to a mobile terminal comprising a management entity according to the invention.

[0067] According to another material aspect, the invention relates to a computer program, the program comprising code instructions which, when executed by a processor, carries out the steps of the management process defined above.

[0068] Finally, according to another material aspect, the invention relates to a data carrier on which is recorded a computer program comprising sequences of instructions for the implementation of the management process defined above.

[0069] Data storage media can be any entity or device capable of storing programs. For example, media can include a storage medium, such as a ROM (e.g., a CD-ROM or a microelectronic circuit ROM), or a magnetic recording medium such as a hard drive. Alternatively, media can be transmissible, such as an electrical or optical signal, which can be transmitted via an electrical or optical cable, by radio, or by other means. Programs according to the invention can, in particular, be downloaded from a network such as the Internet. Alternatively, the information storage medium can be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the process in question.The program according to the invention can use any type of computer technology in terms of compiled programming languages, interpreted languages, or a combination of both, as well as in terms of operating systems. Brief description of the figures

[0070] The invention will be better understood upon reading the following description, given by way of example, and made with reference to the accompanying drawings in which:

[0071] represents a management entity carrying out the process according to the invention.

[0072] represents a series of steps carried out during the process according to the invention.

[0073] represents an example of a data structure used in the process according to the invention. Detailed description

[0074] Lare represents a management entity 100 capable of carrying out a management process according to the invention.

[0075] In the example implementation shown in the diagram, management entity 100 includes the following modules: A module 101 for determining a set of rules, called effective rules, for a batch L of data packets, called the given batch; A module 102 for obtaining the reduced set RRS by removing from the initial set RS the rules that do not belong to the effective rule set for the given batch L; A module 103 for providing the reduced set RRS; A module 104 for calculating the compression penalty caused by removing a rule from the reduced set RRS; A module 105 for removing a rule from the reduced set RRS which minimizes the calculated penalty.

[0076] In other implementation examples, the management entity does not include module 103 for supplying the reduced RRS set and can, for example, be reduced to modules 101 and 102.

[0077] Management entity 100 presents the hardware architecture of a conventional computer. It includes, in particular, a processor, RAM and read-only memory such as Flash memory, ROM (not shown in the figure), as well as input / output devices such as, in some cases, keyboards and / or screens (not shown in the figure), and network ports allowing communication with other entities and servers via a communication network such as the Internet (not shown in the figure).

[0078] The management entity could be, for example, a computer server that performs the management process by establishing sets of compression rules before deploying them to one or more devices that will subsequently apply the rules of the managed set to data packets they process in real-world traffic. The management entity can also be deployed in a cloud architecture, where the user does not have a dedicated server but requests computing, memory, and network resources that perform the functions of the management entity.

[0079] In other implementation examples, the management entity will itself be part of a device that applies compression rules. The management entity is then a component of the device responsible for improving, in successive steps, the set(s) of rules that the component uses to compress the traffic processed by the device.

[0080] Such equipment could be, for example, a router. This router could be deployed in local area networks (LANs), metropolitan area networks (MANs), or wide area networks (WANs). The equipment could also be a gateway providing a link between distinct networks, for example, a home gateway or an access gateway for Internet of Things (IoT) devices. Alternatively, it could be a gateway providing a link between distinct protocols, for example, a gateway translating Voice over IP (VoIP) communications to an IP core network or, conversely, to a telephone access network or a fixed telephone network, such as a corporate network. The equipment could also be a base station used in a mobile telecommunications network.The equipment can also be a mobile terminal. In these different embodiments, management entity 100 reduces the size of an initial set of compression rules RS to obtain a reduced set RRS. In some embodiments, the process can ensure that the reduced set RRS is smaller than a given size S, referred to as the target size. In other embodiments, the reduction in size from the initial set RS to the reduced set RRS occurs without targeting a specific size.

[0081] The initial set of compression rules (RS) could be established by an expert or through a machine learning process. The RS set might be, for example, a set of compression rules based on the SCHC protocol and adapted to a specific traffic flow, that is, designed to compress data packets used in a given protocol context for specific equipment. The RS set could then be deployed for use in a device, and the management entity, in this case, could be a component of the device in question, which would apply the process to reduce its size to the target size (S) and provide the reduced set (RRS).Or, after obtaining a first version of the RS set of compression rules, it is transmitted to management entity 100 which is a server which will not use the RS set to perform compressions but is dedicated to reducing the sizes of the sets of compression rules.

[0082] In one implementation example, the RS set was obtained through machine learning, and a batch L, called the given batch, is then, in this example, the set of data packets used as representative examples of the traffic to be compressed for this machine learning; that is, the training set. The concept of a training set is well-known in the field of machine learning and is not explained in detail here. In a machine learning context, other packets may have been used as a control set to ensure that the RS set of compression rules is not too specialized. These other packets can be added to the training set to form the given batch L of data packets, which should be representative of the traffic expected to be compressed by the rules of the RS and RRS sets.

[0083] In a first step S1, visible on the diagram which will be described later, module 101 of management entity 100 determines a set of rules, called efficient rules, for the given batch L of data packets. The rules determined here from the set RS of compression rules are the most efficient rules for compressing the packets of batch L; therefore, these rules should be kept, if possible, in the reduced set RRS.

[0084] One way to determine the efficient rules during the first step S1 is to search for those rules in the initial set RS that are optimal for at least one packet in the given batch L—that is, those that achieve the best compression for a packet among all the RS rules. Indeed, an optimal rule will very likely be chosen by the compression algorithm to compress the packet in question. Since the packets in the given batch L are representative of the expected traffic, we can assume that a rule that is not optimal for any packet in the given batch L will rarely be used and will therefore not be efficient. Such a rule can be removed from the RS set to obtain the reduced set RRS without significantly impacting the overall efficiency of the reduced set RRS of compression rules for compressing real traffic.

[0085] It's worth noting that rules that aren't optimal—that is, rules for which there are no packets in the given set L that achieve the greatest compression compared to the compressions achieved by other rules in the RS set—can still be useful because they might prove optimal for real-world traffic packets not present in the given set L. The given set L aims to be representative of the traffic that the RS and RRS sets of compression rules will need to compress, but it cannot represent all possible examples of packets that might require compression.

[0086] Another way to determine the efficient rules during the first step S1 is to calculate a score for each rule by multiplying the compression achieved by the rule when applied to a packet by the number of packets in the given batch L for which the rule is optimal. This score corresponds to the compression achieved, that is, the number of bits erased, by a given rule on all packets in the given batch L. Rules that are not optimal for any packet in the given batch L then have a score of zero.

[0087] It is then possible to determine in the first step S1 the effective rules by choosing those which have a score greater than a given threshold and this method joins the previous one by choosing a zero threshold.

[0088] In a second step S2, visible on which will be described later, module 102 obtains a reduced set RRS by removing from the initial set RS the rules that were not determined during step S1 as belonging to the set of effective rules.

[0089] The production of the reduced set RRS consists of removing from the initial set RS the compression rules that were not determined during step S1 to be effective according to a certain criterion.

[0090] In a third step S3, visible in the diagram which will be described later, module 103 provides the reduced assembly RRS when it is smaller than a given size S, called the target size. This provisioning step S3 is present only in certain embodiments. In some embodiments, the provisioning step S3 takes place when the reduced assembly RRS has reached a size smaller than a given size S, called the target size.

[0091] This S3 provisioning step can be performed in several embodiments. In one embodiment, management entity 100 is part of a telecommunications device that uses the RS compression rule set to compress data packets of traffic carried by the device and seeks to reduce the size of the RS compression rule set to a target size S. In this case, module 103 will make the reduced RRS rule set available by replacing the compression rule set used by the device to compress data packets, which was initially the RS rule set, with the reduced RRS rule set. In one embodiment, module 103 can transfer a file containing the reduced RRS rule set, or any other computer object representing the reduced RRS rule set, to other entities that wish to use it.For example, a telecommunications device could transfer to the management entity 100, for size reduction, a set RS of compression rules along with a given batch L of data packets and a target size S, and, after implementation of the process, the module 103 provides the reduced set RRS in response. In another embodiment, the management entity 100 receives a set RS obtained by machine learning using a given batch L of data packets as the training set, and the management entity 100 will implement the process according to the invention in order to reduce the size of the set RS of compression rules to a target size S to obtain the reduced set RRS. This reduced set RRS will then be used in place of the initial set RS.

[0092] In other embodiments, management entity 100 reduces the size of the initial set RS to a reduced set RRS, and the latter is used by one or more telecommunications devices as the reduced set RRS is being downsized, without the need for a delimited step of supplying the reduced set RRS. Both the reduced set RRS and the initial set RS are simply available continuously throughout the operation of the process.

[0093] In a fourth step S4, visible on which will be described further below, module 104 receives a compression penalty caused by the removal of a rule from the reduced set RRS.

[0094] During step S2, if removing from the initial set RS of rules not identified as effective in step S1 is insufficient to obtain a sufficiently reduced set RRS, the process can continue by attempting to remove additional compression rules. This removal will be guided by a compression penalty obtained during step S4. In some embodiments, the objective of these removal operations is for the reduced set RRS to reach a target size S.

[0095] The S4 step of obtaining a compression penalty can be performed by calculations using module 104 or by receiving calculations performed by other entities not represented on the map. Rather than being calculated, penalties can be estimated or approximated using other methods. In one embodiment, obtaining an S4 compression penalty can be achieved by searching, for each potentially deleted compression rule, which packages the potentially deleted rule is the optimal rule for and which rule would become the optimal rule if the currently optimal rule were deleted. Once these packages are found, it is possible to calculate the difference in compression between the old, deleted optimal rule and the new optimal rule. This difference in compression constitutes the compression penalty.The higher the penalty, the more deleting a rule and replacing it with another rule as the optimal rule for certain packets will result in a decrease in the compression of those packets. The compression penalty is calculated by taking into account the number of packets for which the deleted rule is optimal, in order to determine the effective penalty in bits when a deleted rule is replaced by another to effectively compress packets.

[0096] In some embodiments, the compression penalty calculation takes into account all the rules present in the initial set RS. Indeed, rather than removing a single rule, it can be advantageous to remove several compression rules and reintroduce one that had been previously removed, for example, one that had not been determined S1 to be among the effective rules. The calculation of a compression penalty will then combine the compression losses due to the removal of two rules with the gain provided by the addition of a rule that was previously removed from the reduced set RRS of compression rules.

[0097] The description below provides a detailed example of the implementation of these methods.

[0098] In other, simpler embodiments, the compression penalty can be calculated solely by considering the compression scores achieved by the compression rules removed from the RS set, without taking into account any replacements of the removed rules by other compression rules. In these embodiments, the rules to be removed are determined by comparing scores attached to the compression rules, regardless of the packages to which the rules apply. The advantage of this embodiment is the greater simplicity in obtaining the minimum compression penalties and determining which rule to remove.

[0099] In a fifth step S5, visible on which will be described further, module 105 removes a rule from the reduced set RRS which minimizes the calculated penalty S4.

[0100] Once the compression penalties have been calculated, the process will remove a compression rule from the reduced RRS set that minimizes the compression penalty caused by this removal. This removal can involve simply deleting a single rule or deleting several rules while simultaneously reintroducing a rule from the original RS set that had been previously removed.

[0101] In some embodiments, the acquisition steps S4 and deletion steps S5 are iterated until the reduced set RRS reaches a target size S that has been provided. The reduced set RRS can then be provided in a step S3 in an example embodiment that includes this step.

[0102] The latter, on the other hand, represents a series of steps carried out during the process according to a particular method of implementation.

[0103] In the example shown, the embodiment begins with a step S1 of determining efficient rules from the initial set RS of compression rules for the given batch L. The initial set RS could be obtained using a machine learning algorithm with the given batch L as the training set. The objective of the method according to the invention is to obtain a reduced set RRS of compression rules starting from the set RS. The determination S1 of the efficient rules ensures that the efficient rules are preserved to the greatest extent possible, even when reducing the size of the initial set RS to that of the reduced set RRS. One possibility is to retain in the reduced set RRS the optimal compression rules, that is, the rules for which there exists a data packet in the given batch L for which the considered rule achieves the best compression among all the compression rules considered.

[0104] The embodiment continues with a step S2 of obtaining a reduced set RRS by removing from the set RS rules that were not determined S1 to be among the effective rules in the previous step S1. In the example embodiment shown, a target size S has been provided. If this step S2 produces a reduced set RRS of compression rules whose size is smaller than the target size S, then the process will continue with step S3, as shown by the test depicted in the figure. The size of a compression rule set can be simply measured by counting the number of compression rules present in the set under consideration. Other methods for defining the size of a compression rule set can be considered that take into account the number and complexity of the elementary compression operations that form the compression rules.

[0105] If the target size S is reached, the method according to the invention includes, in this embodiment, a step S3 for providing the reduced RRS set of compression rules. In other embodiments, the reduced RRS set can be used at any time to perform data packet compression without requiring a provisioning step S3. The reduced RRS set can be provided by transferring a file or any other relevant computer object from the management entity 100 to equipment that needs to use the RRS set of compression rules. In this context, the management entity 100 can be a server dedicated to managing and improving compression rule sets. The reduced RRS set can also be made available by providing access to requests to obtain the RRS set.In this context, management entity 100 can be a component of telecommunications equipment that uses the reduced RRS set of compression rules to compress the traffic it transmits. Management entity 100 then makes available to the telecommunications equipment that encompasses it an RRS version of the initial RS set of compression rules, reduced to the target size S.

[0106] If the target size S is not reached, the process, in this example embodiment, includes a step S4 for obtaining a compression penalty caused by the removal of a rule from the reduced set RRS. Since the rules remaining in the reduced set RRS after the removal step S2 are efficient rules for the packets in the given batch L, removing a rule from the reduced set will impact the compression efficiency of the rules in the reduced set RRS. Therefore, compression penalties associated with rule removals are obtained. Examples of penalty calculations will be given in the description. The compression penalties can take into account that only one rule is removed, but also that a previously removed rule can be reintroduced.In this case, two rules must be removed to compensate for the reintroduction of one rule and ensure a reduction in the size of the reduced set of compression rules.

[0107] The embodiment continues with step S5, which effectively removes at least one rule from the reduced set RRS of compression rules. In this embodiment, if the target size S is reached, the process continues with step S3, which provides the reduced set RRS. Otherwise, a further sequence of steps S4-S5 can take place.

[0108] The latter, for its part, represents a data structure used in certain embodiments of the process.

[0109] Figure 1 represents an example of a data structure used in certain embodiments of the method of the invention. Our example data structure consists of a set of nine compression rules, R1 through R9 (RS). These nine rules are organized into a tree-like data structure. Rule R1 has two descendant rules, R2 and R3. Rule R2 has two descendant rules, R4 and R5. Rule R3 has one descendant rule, R6. Rule R4 has two descendant rules, R7 and R8. Rule R8 has one descendant rule, R9. Rules R6, R7, and R9 have no descendant rules. We will explain later the conditions under which a rule is a descendant or parent of another. Such a data structure can be obtained through machine learning of the compression rule set RS.

[0110] The R1-R9 rules of the RS set are designed to compress data packets whose header contains five fields, F1 to F5. The compression rules consist of compression operations that can be applied to each of the F1 to F5 fields of the packets targeted by the compression rules. Each R1 to R9 rule therefore comprises five compression operations labeled F1 to F5. These compression operations can be of three types: An EQUL operation will be applied to packets where the field targeted by the operation takes exactly the value specified in the operation. In this case, the value specified by the operation is not sent. This value will be reconstructed upon reception of the compressed packet. A PREF operation will be applied to packets where the field targeted by the operation takes a value, expressed as a bit sequence, whose prefix has the value specified by the operation. In this case, the prefix specified by the operation is not sent.Again, the value will be reconstructed upon receipt of the compressed packet. An IGNR operation will ignore the field and apply it to all possible values, thus performing no compression. This type of operation ensures that all packets can be associated with a compression rule, even if that rule does not perform any compression.

[0111] These three compression operations are inspired by the compression operations defined in the SCHC protocol, knowing that, in this protocol, a fourth type of operation exists in which values ​​of a field are replaced by the indices of a dictionary if they are equal to values ​​present in that dictionary.

[0112] Let's consider rule R4, which presents the three types of operation described above, and take a packet whose fields F1 to F5 take the following values: {F1 = 11010101; F2 = 01001000; F3 = 11; F4 = 11; F5 = 1}. Compression rule R4 can indeed be applied to this packet, and the effect of applying the compression operations would be as follows: For field F1, the prefix 110101 would not be transmitted. The last two bits of the value taken by field F1 in the packet would be transmitted, namely 01. For field F2, the entire value 01001000 would not be transmitted. For field F3, the prefix 1 would not be transmitted, and only the second bit, also with a value of 1, would be transmitted. For field F4, the value 11 would not be transmitted. For field F5, the operation ignores the field and transmits the unchanged value 1.

[0113] Finally, the packet transmitted after applying the R4 compression rule would be as follows: {F1 = 01; F2 = _; F3 = 1; F4 = _; F5 = 1}, where "_" indicates that a value is not transmitted for a given field (here, fields F2 and F4). Along with the compressed packet, the R4 rule identifier would be transmitted to allow the receiving entity to reconstruct the original value.

[0114] In this example, the five fields of the initial packet totaled 21 bits. The finally transmitted packet contained only 4 bits. Applying compression rule R4 therefore saves 17 bits during packet transmission (not counting the fact that the R4 rule identifier must also be transmitted). This number of bits saved will always be the same for any packet to which compression rule R4 can be applied. The score of the compression rule is displayed for each rule. This score ranges from 2 for rule R1, which includes only one prefix deletion rule (11) for field F1, to a score of 21 for rule R6, which deletes all fields F1 to F5 from a data packet as soon as they take the values ​​specified in the compression operations.

[0115] Rules R1 through R9 are organized according to a specialization relationship. When a rule is a parent of another, it is less specialized and will apply to all packages to which the descendant rule can apply. This specialization relationship can be determined by considering only the operations that constitute the compression rules.

[0116] During machine learning of a set of RS compression rules, such a data structure representing the specialization relationship between rules in the set can be obtained as a side effect of the learning process. Indeed, learning can begin with a very unspecialized rule, for example, a rule that ignores the values ​​of all fields and passes them unchanged, and then apply operational modifications that will increasingly specialize the rules.

[0117] In the example shown in Figure 1, the given set L, which may have been used as a training set in a machine learning algorithm, comprises nine packets p1 to p9. Each rule R1 to R9 of the set RS is associated with the packets within the given set L to which the rule applies. For example, rule R1 applies to all packets p1 to p9 in the given set L, while rule R7 applies only to packets p2 and p3, and rule R9 applies only to packet p8. The packets p1 to p9 are not further specified.

[0118] A final point presented here concerns the packets for which a given rule achieves the greatest or best compression among all available compression rules. These packets are then enclosed in the list of packets associated with a given rule. For example, rule R1 is optimal for packet p9, and therefore applies to this packet, and also applies to all packets p1 through p8. Similarly, rule R7 applies to packets p7 and p8 and is optimal for packet p7. Rule R2 applies to packets p2, p3, p6, p7, and p8 but is not optimal for any of these packets. Rule R9 applies only to packet p8 and is optimal for it, and so on. Often, a compression protocol will choose the optimal rule for a given packet, that is, the one that achieves the best compression for that given packet from among all available compression rules.This justifies the importance of having this information, which can also be obtained during machine learning of the RS set of compression rules.

[0119] We will now describe an example of an embodiment of the process according to the invention. Other embodiments are possible.

[0120] The first step is to determine the efficient rules for the batch L of data packets p1 to p9 from among the rules R1 to R9. In the example we will present, the rules chosen as efficient are those that achieve the best compression for at least one of the packets p1 to p9 of the given batch L. In the example, all the rules are efficient according to this criterion except for rules R2 and R4, which do not achieve the best compression for any packet of batch L among the rules R1 to R9 of the rule set RS.

[0121] In our example, these two rules R2 and R4 are therefore removed from the initial set RS to form the reduced set RRS. This therefore includes the rules R1, R3, R5, R6, R7, R8, R9.

[0122] In other examples, a rule can be considered efficient when it achieves a certain compression score for the given batch L. Such a score can be calculated by multiplying the compression performed by a rule, as displayed on the screen, by the number of packets within the given batch L for which the rule achieves the best compression. The score can also be calculated as the score achieved by the compression rule for any packet displayed on the screen, without considering the packets within batch L for which the rule achieves the best compression.

[0123] In some implementation examples, the size S of a set of compression rules is the number of rules belonging to the set. In other examples, the size S of a set of compression rules is measured by taking into account the number and complexity of the compression operations belonging to a given rule.

[0124] In our example, a compression rule set size has been provided, which is the target size S. The goal of the process is then to run it until a reduced rule set (RRS) smaller than the target size is obtained. In other examples, the process simply reduces the size of the compression rule set without forcing a target size S. Suppose the target size S, in our example, is 4 and corresponds to the number of rules in a compression rule set. The reduced rule set (RRS) then has a size of 7, and the process will continue in our example. The target size S will often be a power of 2 in order to encode the identifier of a rule belonging to a compression rule set using a minimal number of bits. Here, with a target size S of 4, two bits are sufficient to encode the compression rule identifiers.

[0125] To achieve this, in our example, a compression penalty is applied, corresponding to the removal of at least one rule from the reduced set RRS. In our example, the compression rules of the RS set are arranged in a tree-like data structure. This arrangement efficiently identifies rules that are more general than a given rule and could therefore replace it for compressing packets from the given batch L if a particular rule were removed.

[0126] For example, if rule R6 were removed, the process could read from the data structure represented in which rule R6 is most efficient for packets p1 and p4. If rule R6 were removed, the parent rule R3 in the tree would become the most efficient rule for these two packets p1 and p4. The compression penalty attached to removing rule R6 for the given batch L would then include the compression of both packets p1 and p4 by rule R3 instead of rule R4. When rule R3 compresses a packet, it erases 16 bits instead of the 21 bits erased by rule R4. The compression penalty for removing rule R4 can be obtained in our example as 10 bits (twice the five fewer bits compressed on packets p1 and p4 when replacing R4 with R3).

[0127] Finally, in this example, the lowest compression penalty is obtained by removing rule R9, which is then replaced by rule R8 as the rule providing the best compression for packet p8. The resulting compression penalty is only 1 bit. Note that rule R8 is present in the reduced RRS set.

[0128] Other compression penalty calculations can be performed, not taking into account the number of packets in the given batch L for which a given rule achieves the best compression, but just the compression score attached to each rule.

[0129] If the target size S is 4, removing rule R9 from the reduced set RRS brings its size, measured in number of rules, to 6 and two more removals must be found.

[0130] In the example shown, the lowest compression penalty, according to the previous calculation, will be found at this stage of the process execution by removing rules R7 and R8 from the reduced RRS set and adding rule R4, which had been previously removed because it was not among the effective rules. The compression penalty for this operation is, firstly, 4 bits, corresponding to the replacement of rule R7 by rule R4, which reduces the compression performed on packets p2 and p3 by 2 bits, and secondly, 1 bit, corresponding to the replacement of rule R8 by rule R4, which reduces the compression of packet p7 by 1 bit. The total compression penalty is therefore 5 bits, and this is the lowest penalty that can be found in this example at this stage of the process.

[0131] The reduced RRS set, after removing rules R7 and R8 and reintroducing rule R4, will then consist of rules R1, R3, R4, R5, and R6, each with a size of 5, which is greater than the target size S (4 in our example). The final removal, corresponding to the minimum compression penalty, will be to replace rule R6 with rule R3. This results in a penalty of 10 bits, corresponding to two 5-bit periods (the difference in score between rules R6 and R3), with the factor of 2 being due to the two packets p1 and p4.

[0132] The target size S of 4 is finally achieved with a reduced RRS set which includes the compression rules R1, R3, R4, R5.

[0133] This implementation example demonstrated how organizing the RS compression rule set into a tree structure, representing which rules are more general than others, simplifies the calculation of compression penalties. Such an organization can be achieved as a side effect of machine learning the RS compression rule set using the given L set as the training dataset.

[0134] Finally, it should be noted that, in this text, the term "module" can refer to a software component, a hardware component, or a set of hardware and software components. A software component itself corresponds to one or more computer programs or subprograms, or more generally, to any element of a program capable of implementing a function or set of functions as described for the modules in question. Similarly, a hardware component corresponds to any element of a hardware assembly capable of implementing a function or set of functions for the module in question (integrated circuit, smart card, memory card, etc.).

Claims

A method for managing a set (RS) of compression rules applying to data packets, called the initial set, characterized in that it comprises the following steps: A step of determining (S1) a set of compression rules, called effective rules, for a batch (L) of data packets, called the given batch; A step of obtaining (S2) a reduced set (RRS) of compression rules by removing from the initial set (RS) compression rules that do not belong to the set of effective rules for the given batch (L). Management method according to claim 1 characterized in that a compression rule is determined during step (S1) as belonging to the set of effective rules when there is a package in the given lot (L) for which said compression rule achieves the greatest compression of the package among the compression rules of the initial set (RS). Management method according to claim 1 or 2, characterized in that the method further comprises the following steps: A step of obtaining (S4) a compression penalty caused by the deletion of a rule from the reduced set (RRS); A step of deleting (S5) a rule from the reduced set (RRS) which minimizes the penalty obtained (S4). Management method according to claim 3 characterized in that a size (S) of a set of compression rules, called target size, is provided and in that the method includes an iteration of the steps of obtaining compression penalty (S4) and deleting (S5) a rule from the reduced set (RRS) until the size of the reduced set (RRS) is less than the target size (S). A management method according to any one of claims 3 or 4, characterized in that the step of obtaining a compression penalty (S4) takes into account that a compression rule removed from the reduced set (RRS) will be replaced by another rule from the reduced set (RRS) to perform data packet compressions. Management method according to claim 5 characterized in that the step of obtaining a compression penalty (S4) further takes into account that several compression rules can be removed from the reduced set (RRS) and replaced by another rule, present in the initial set (RS) and absent from the reduced set (RRS), and that the removal step (S5) then includes the removal of said several rules and the addition of said other rule if this removal combined with this addition minimizes the penalty obtained during the step of obtaining (S4). Management method according to any one of claims 3 to 6 characterized in that obtaining a compression penalty (S4) takes into account the number of packets in the given batch (L) for which the deleted rule(s) achieve the greatest compression among the rules in the reduced set (RRS). Management method according to any one of claims 1 to 7 characterized in that the determination step (S1) includes the calculation for a rule of the initial set (RS) of a score (SCO) obtained by multiplying the compression achieved by said rule by the number of packets in the given batch (L) for which said rule achieves the greatest compression among the rules of the initial set (RS). Management method according to claim 8 characterized in that a size (S) of a set of compression rules, called target size, is provided and in that the step of obtaining (S2) the reduced set (RRS) includes the removal of the initial set (RS) of compression rules whose score (SCO) is the lowest until the size of the reduced set (RRS) is less than the target size (S). Management method according to any one of claims 1 to 9 characterized in that the initial set (RS) is obtained by a machine learning algorithm and that the given batch (L) was included in the training set used to obtain the initial set (RS). Management entity (100) managing a set (RS) of compression rules applying to data packets, called the initial set, comprising the following modules: Module (101) for determining a set of compression rules, called effective rules, for a batch (L) of data packets, called the given batch; Module (102) for obtaining a reduced set (RRS) of compression rules by removing from the initial set (RS) compression rules that do not belong to the effective set of rules for the given batch (L). Management entity (100) according to claim 11 further comprising the following modules: Module (104) for obtaining a compression penalty caused by the deletion of a rule from the reduced set (RRS); Module (105) for deleting a rule from the reduced set (RRS) which minimizes the penalty obtained. Telecommunications equipment comprising a management entity (100) according to one of claims 11 or 12. Computer program capable of being implemented by a management entity (100) according to one of claims 11 or 12, the program comprising code instructions which, when executed by a processor, carries out the steps of the management process defined in claim 1. Data carrier on which is stored a computer program according to claim 14 comprising a sequence of instructions for implementing the management method according to claim 1 when loaded into and executed by a processor.

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