30 results about "CPU-bound" patented technology

The invention discloses a multi-queue peak-alternation scheduling model and a multi-queue peak-alteration scheduling method based on task classification in cloud computing. The multi-queue peak-alternation scheduling model is characterized by comprising a task manager, a local resource manager, a global resource manager and a scheduler. The multi-queue peak-alternation scheduling method comprises the following steps: firstly, according to demand conditions of a task to resources, dividing tasks into a CPU (central processing unit) intensive type, an I/O (input/output) intensive type and a memory intensive type; sequencing the resources according to the CPU, the I/O and the memory load condition, and staggering a resource using peak during task scheduling; scheduling a certain parameter intensive type mask to a resource with relatively light index load, scheduling the CPU intensive type mask to the resource with relatively low CPU utilization rate. According to the multi-queue peak-alternation scheduling model and the multi-queue peak-alternation scheduling method disclosed by the invention, load balancing can be effectively realized, scheduling efficiency is improved and the resource utilization rate is increased.

The invention provides a dynamic MapReduce dispatching method and system based on a task type. The dynamic MapReduce dispatching method based on task type comprises the steps as follows: A. entering the waiting queue, B. classifying the work task, C. moving the work task, and D. dispatching the work task, independently dispatching a CPU (Central Processing Unit) intensive queue and an I/O intensive queue respectively, dispatching the work task to a work node cluster, and executing the task. The method and the system have the benefits that the dynamic MapReduce dispatching method based on the task type arranges queue to the task with various types respectively and independently dispatching the queues through the prediction to the work task type. The dynamic MapReduce dispatching method based on the task type improves the throughput of the cluster under task environment with various types.

The invention discloses a method for classifying operated load in a virtual machine under cloud computing environment. The method includes firstly acquiring monitoring parameters in 5 minutes of load operation and subjecting the monitored parameters to normalization processing; classifying load monitored into four categories such as CPU (central processing unit)-intensive load, memory intensive load, I/O (input/output) intensive load and network intensive load by means of TSRSVM (training sets refresh SVM); providing corresponding customized optimizing strategies for operation systems which running the four categories of intensive loads, and monitoring operating state of the systems through a performance comparison device; indicating that classification strategies are correct if performance of the systems is improved, otherwise, indicating that the classification strategies are incorrect. By the method, accuracy of load classification is high and system performance loss is low.

The invention relates to the field of parallel computation, and discloses a parallel computation dispatch method in the heterogeneous environment. According to the method, a plurality of JVM task slots having different internal storages, and idle task slot sets are built, tasks in the parallel computation are divided into I/O intensities and CPU intensities, the tasks are appointed to suitable task slots for calculation, and parallel calculation efficiency in the heterogeneous environment is optimized. The parallel computation dispatch method has the advantages that the sizes and the types of the internal storages required by the tasks are determined dynamically, resource using efficiency of heterogeneous clusters is improved, total operation time of the parallel calculation work is shortened, and the situation that the internal storages overflow in a task operation process is avoided.

Methods and machines which increase image processing performance by efficiently copying image data from input memory to main memory before performing CPU intensive operations, such as image enhancement, compression, or encryption, and by efficiently copying image data from main memory to output memory after performing CPU intensive operations, such as decryption, decompression, image enhancement, or reformatting.

The invention discloses a Docker dynamic scheduling algorithm for a typical container, includes the following steps: S1: Typical application container scenarios include CPU intensive/batch, Memory-intensive/batch, I/O-intensive/batch and CPU-intensive/real-time, which respectively select the corresponding application containers and analyze the resource usage and performance of each application container when it runs separately and concurrently in the Docker environment; S2: The scheduling algorithm comprises a container static scheduling mode and a container dynamic scheduling mode based on runtime monitoring; Container static scheduling mode and container dynamic scheduling mode are adopted according to user's demand. The invention has the beneficial effect that the dynamic scheduling algorithm can improve the utilization rate of the system resources without affecting the running performance of the application container.

A technique for enabling real-time CPU-bound in-kernel workloads to run infinite loops while keeping read-copy update (RCU) grace periods finite. In an embodiment, a per-CPU indicator may be set to indicate that a CPU running the CPU-bound in-kernel workload has not reported an RCU quiescent state within a defined time. A function may be invoked from within the workload that causes an RCU quiescent state to be reported on behalf of the CPU if the per-CPU indicator is set. If the RCU quiescent state is not reported within a longer defined time, the CPU may be rescheduled.

The invention relates to a multi-JVM deployment method based on a non-uniform memory accessing technology. The method comprises the following steps of 1, confirming the starting of a NUMA service in a BIOS option; 2, confirming the installation of a numactl rpm package in a Linux system; 3, using numactl-hardware to check the number of nodes and a CPU core and memory information which are contained in each node; 4, using a numactl order to carry out node binding. A JVM is bound on a specific node or a specific CPU by the adoption of a CPU binding strategy; the problems are effectively avoided that when the node binding is not conducted on the JVM, each brought JVM memory is randomly distributed among the nodes, and therefore accessing time is increased when the CPU accesses the memory by crossing nodes, so that the Java application performance is prone to severe influence.

The invention relates to the technical field of real-time data processing, in particular to a quick statistical method based on a Discuz community. The method comprises the following steps that: S1: constructing a data memory calculation mechanism; S2: constructing a forum topic Hash table; and S3: carrying out statistical processing. By use of the method, the processing performance of a communityplatform with huge historical data can be remarkably improved, a database intensive operation is transferred into a memory CPU (Central Processing Unit) intensive operation, database resources, including MySQL and the like, are released in time, lock table situations, including exclusive locks and the like, are reduced, a response success rate is improved, and the method can be used for the business fields with a large handling capacity, wherein the business fields include medium-and-small-scale instant query, statistics and the like with high frequency; an advantageous guarantee is providedfor arrival instantaneity and accuracy required by a user to use business; and the secondary extension development of personalized statistical item requirements is supported. The method opens a new approach for a data statistical processing platform, and a new research idea is provided for the business application research and development of a community forum.

The invention discloses a firewall multi-core architecture intelligent detection method, and belongs to the technical field of multi-core architecture intelligent detection methods. The intelligent detection method comprises the following steps: (1) according to hardware configuration of a system, retrieving the number of support cores, the number of network card queues and a memory of the system to obtain the maximum number of start monitoring processes SNUM; (2) setting a network card monitoring queue number according to the maximum start monitoring process number SNUM obtained in the step (1), and interrupting CPU binding; (3) binding the started process with a CPU (Central Processing Unit), and starting an intelligent detection engine; and (4) optimizing the scheduling condition among the process services by utilizing a CPU isolation technology, and detecting concurrent data packets among the plurality of network cards. According to the method and the device, the scheduling condition among the process services is optimized, so that a multi-network-card concurrent data packet detection process with higher performance is finally realized.

The invention relates to a CPU-intensive request processing method and system for a VRP cloud service system, wherein the method comprises the steps of: a message generation step: receiving differentrequests of service systems of various requests in a unified manner, then, generating solving messages uniform in format according to pre-settings of a client, and simultaneously, pushing the solvingmessages into a message queue; a message queue processing step: performing processing by adopting a message queue RabbitMQ; a to-be-solved message obtaining step: starting as the role of a message consumer, and solving by actively pulling a message from the message queue while being idle; a VRP system starting and calculating step: calculating the reasonable iteration time as the calculation parameter based on the solving problem complexity of the client; a solving result message generation step: organizing solving results of a VRP system into a uniform result message, and pushing the result message into the message queue; and a message queue consumption middleware processing step: pulling a winding displacement result from the message queue in a timed manner, and then, pushing the windingdisplacement result to the rear winding displacement end. Thereby, the high availability of the service system is improved; and furthermore, the CPU utilization rate of the service system is increased.

The invention discloses a load balancing method, which comprises the steps of: controlling a plurality of network card queues to receive flow data distributed through using an ECMP protocol by a load balancing server; adopting a CPU bound with each network card queue to decapsulate the flow data to obtain to-be-forwarded data; and packaging and forwarding the to-be-forwarded data by adopting a lightweight IP protocol stack through a configured Nginx load balancing function. According to the load balancing method of the invention, the received flow data is decapsulated through the bound CPU, and the data is further encapsulated and forwarded by adopting the lightweight IP protocol stack instead of adopting the network protocol stack of the Linux kernel through the load balancing function of the Nginx, so that the load balancing processing capacity is improved, and the load balancing performance is further improved. The invention further discloses a load balancing device, a server and a computer readable storage medium, which have the above beneficial effects.