Game theory-enhanced chaos engineering system and method for robust cloud computing operations

Abstract

The present disclosure provides a system (100) and a method (700) for implementing chaos engineering in a cloud environment (106). The system (100) monitors the cloud environment (106), introducing disruptions such as network latency, resource depletion, and service failures. The system models operational strategies for each cloud component in response to disruptions using game theory techniques, evaluates the payoffs of these strategies based on predefined performance metrics, and identifies optimal strategies via Nash Equilibrium. These strategies are then implemented across the cloud environment. Additionally, the system monitors the response to the implemented strategies and uses this feedback to refine future strategy modeling and payoff calculations, enhancing decision-making capabilities over time. The system is communicatively coupled to computing devices (110) for displaying performance metrics, operational strategies, payoffs, and health of the cloud environment.

Description

GAME THEORY-ENHANCED CHAOS ENGINEERING SYSTEM AND METHODFOR ROBUST CLOUD COMPUTING OPERATIONSTECHNICAL FIELD

[0001] The present disclosure relates to the field of computer technology. In particular, the present disclosure provides a system and method for implementing chaos engineering in cloud environments using game theory principles to optimize operational strategies and enhance system robustness and adaptability.BACKGROUND

[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.

[0003] Chaos engineering is a disciplined approach to identifying potential system failures before they become critical outages. This is particularly important in cloud computing, where systems are inherently distributed and complex, requiring robust methods to ensure reliability and resilience. Traditional chaos engineering practices include deliberately introducing faults or stressors into the system in a controlled manner, followed by manual analysis and adjustments to enhance system robustness. However, these traditional methods often lack real-time adaptability and struggle to predict the complex interactions that can occur under stress in modem cloud environments.

[0004] One of the main limitations of traditional chaos engineering is its reactive nature, which relies heavily on post-event analysis to improve system performance. This often requires significant manual intervention for both the analysis and adjustment processes, which can be time-consuming, prone to human error, and not scalable for large and complex cloud environments. Furthermore, traditional chaos engineering practices face challenges in predicting all potential failure modes due to the intricate and dynamic nature of cloud systems. As cloud infrastructures grow in size and complexity, scaling these traditional methods becomes increasingly difficult, often rendering them insufficient to handle the dynamism and intricacy of large-scale cloud environments. Additionally, traditional methods do not typically support real-time, automated adaptation to operational changes or stressors, preventing cloud systems from promptly adjusting to maintain optimal performance and resilience in rapidly evolving conditions.

[0005] There is, therefore, a need to provide a solution that ensures higher system stability, efficiency, and scalability, ultimately leading to more resilient and reliable cloud computing environments.OBJECTS OF THE PRESENT DISCLOSURE

[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0007] An object of the present disclosure is to provide a solution for obviating above mentioned issues and enhancing resilience of cloud environments by effectively managing operational disruptions.

[0008] Another object of the present disclosure is to provide a system that enables dynamic adaptability in cloud environments, allowing for real-time adjustments to changing operational conditions.

[0009] Another object of the present disclosure is to provide a system that automates the decision-making process in response to chaos, reducing the need for manual intervention.

[0010] Another object of the present disclosure is to provide a system that uses predictive analytics to preemptively adjust strategies, thereby preventing potential failures or performance degradation.

[0011] Another object of the present disclosure is to provide a system that integrates game theory algorithms to model interactions and strategic decision-making processes of cloud components.

[0012] Another object of the present disclosure is to provide a system that dynamically and autonomously reconfigures system settings using machine learning and automation scripts.

[0013] Another object of the present disclosure is to provide a system that learns from past adaptations and improve future decision-making.

[0014] Another object of the present disclosure is to provide a system that ensures higher operational stability, even under fluctuating and unpredictable conditions.

[0015] Another object of the present disclosure is to provide a system that optimizes resource utilization and operational efficiency, leading to cost-effective cloud management.

[0016] Another object of the present disclosure is to provide a system that effectively scales with the complexity and size of cloud infrastructures.

[0017] Another object of the present disclosure is to provide a system that minimizes downtime through proactive and timely adaptations.

[0018] Another object of the present disclosure is to provide a system that offers usercentric metrics and performance data through a user-friendly dashboard, enhancing visibility and control for system administrators.SUMMARY

[0019] Aspects of the present disclosure relates to the field of computer technology. In particular, the present disclosure provides a system and method for implementing chaos engineering in cloud environments using game theory principles to optimize operational strategies and enhance system robustness and adaptability. This significantly enhances resilience, adaptability, and operational efficiency of cloud environments under various stress conditions.

[0020] An aspect of the present disclosure pertains to a system that enhances chaos engineering in cloud computing by employing a processor and memory to monitor and obtain performance metrics and operational data, introduce disruptions via chaos agents, and model operational strategies using game theory techniques. The processor simulates various operational strategies, such as resource allocation, traffic rerouting, and load balancing, to optimize system performance and resilience. The system evaluates the payoff of each strategy based on predefined metrics, identifies optimal strategies through Nash Equilibrium analysis, and implements these strategies across the cloud environment.

[0021] In an aspect, the system may be configured to monitor response to implemented optimal strategies, feeding the outcomes into a learning engine to refine the modeling of operational strategies and payoff evaluations.

[0022] In an aspect, the system may be communicatively coupled to a computing device that displays information from the processor, including performance metrics, operational strategies, payoffs, and the health of the cloud environment.

[0023] In an aspect, the system may be configured to evaluate the payoff of each operational strategy by assessing performance, calculating a score based on predefined performance metrics, and using the score to determine the payoff.

[0024] Another aspect of the present disclosure pertains to a method for monitoring cloud environment to gather performance metrics and status data, introducing disruptions via chaos agents, modeling operational strategies using game theory, evaluating the payoff of each strategy, identifying optimal strategies using Nash Equilibrium, and implementing these strategies across the cloud environment.

[0025] In an aspect, the method includes modeling operational strategies by simulating resource allocation schemes, traffic rerouting strategies, and load balancing methods.

[0026] In an aspect, the method includes monitoring the response to the implemented optimal strategies and feeding the outcomes into a learning engine to refine strategy modeling and payoff evaluations.

[0027] In an aspect, the method includes displaying information from the processor, such as performance metrics, operational strategies, payoffs, and cloud environment health, on a computing device communicatively coupled to the processor.

[0028] In an aspect, the method includes evaluating the payoff of each operational strategy involves assessing performance, calculating a score by comparing performance against predefined metrics, and using the score to determine the payoff.

[0029] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0031] FIG. 1 illustrates an exemplary network architecture of the proposed game theory-based chaos engineering system, in accordance with an embodiment of the present disclosure.

[0032] FIG. 2 illustrates an exemplary architecture of the proposed game theorybased chaos engineering system to disclose working at multiple layers, in accordance with an embodiment of the present disclosure.

[0033] FIG. 3 illustrates interaction between components of proposed system and cloud environment, in accordance with an embodiment of the present disclosure.

[0034] FIG. 4 illustrates an exemplary flow diagram to disclose data flow in the system, in accordance with an embodiment of the present disclosure.

[0035] FIGs. 5 A and 5B illustrate interaction between components of proposed system at multiple layers, in accordance with an embodiment of the present disclosure.

[0036] FIG. 6 illustrates an exemplary flow diagram to disclose working of proposed system, in accordance with an embodiment of the present disclosure.

[0037] FIG. 7 illustrates a flow diagram of a method for implementing chaos engineering in a cloud environment, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION

[0038] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosures as defined by the appended claims.

[0039] Embodiments explained herein relate to the field of computer technology. In particular, the present disclosure provides a system and method for implementing chaos engineering in cloud environments using game theory principles to optimize operational strategies and enhance system robustness and adaptability.

[0040] Referring to FIG. 1, an exemplary network architecture of the proposed game theory -enhanced chaos engineering system 100 (interchangeably referred to as system 100, hereinafter) for robust cloud computing operations is disclosed. The proposed system 100 integrates game theory principles, particularly focusing on Nash Equilibrium concepts, to dynamically adjust cloud environment or system operations in response to varying degrees of operational stress, disruptions, or chaos in the system. By utilizing Nash Equilibrium, the system can identify optimal strategies for balancing workloads, reallocating resources, and mitigating failures, thereby maintaining operational efficiency and resilience. This approach allows the cloud computing environment to adapt in real-time to unexpected challenges, ensuring continuous and reliable service.

[0041] In an exemplary embodiment, a cloud environment encompasses a wide range of components and services. This includes computing resources such as virtual machines, containers, and server less functions that execute applications and services, as well as storage systems, which provide block storage, object storage, and file storage for data management. Additionally, networking infrastructure, including virtual networks, load balancers, firewalls, and VPNs, facilitates communication and data transfer. Furthermore, cloud environments host various databases, such as managed relational databases, NoSQL databases, and datawarehouses for data storage and retrieval. Application services offer platforms for deploying and managing applications, and monitoring and management tools track performance and handle resource management. Moreover, identity and access management systems control user identities and permissions, while backup and disaster recovery solutions ensure data integrity and availability. Development and deployment tools, including CI / CD pipelines and configuration management systems, support software development, and security services provide threat detection, encryption, compliance, and security policy management.

[0042] In an embodiment, the system 100 includes one or more processor(s) 102 (interchangeably referred to as processor 102, hereinafter) and a memory 104, with the processor 102 housing a game theory engine 107, and a learning engine 108. The one or more processor(s) 102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 102 may be configured to fetch and execute computer-readable instructions stored in the memory 104. The memory 104 may store one or more computer- readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 104 may include any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as an Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.

[0043] Additionally, the processor 102 is communicatively coupled to one or more computing devices 110-1, 110-2, and ...110-N (interchangeably referred to as computing devices 110, hereinafter) through a communication unit 108. The computing devices 110 can include any device such as, but not limited to, a smartphone, laptop, tablet, or desktop computer. The computing devices 110 can be configured to interface with the processor to present essential information to users. It receives data from the processor and displays it through a user interface.

[0044] The game theory engine 107 is specifically responsible for applying game theory principles to model, analyze, and optimize operational strategies. It uses techniques such as Nash Equilibrium to identify optimal strategies for managing disruptions and performance metrics in the cloud environment.

[0045] The learning engine 108 within the processor 102 is configured for analyzing data and making decisions based on game theory principles, particularly Nash Equilibrium, to dynamically adjust cloud environment operations.

[0046] In an embodiment, the communication unit 110 may be wired communication means, or wireless communication means, or a combination thereof. In some embodiments, the wired communication means may include, but not limited to, wires, cables, data buses, optical fibre cables, and the like. In some embodiments, the wireless communication means may include, but not be limited to, telecommunication networks, Near Field Communication (NFC), Bluetooth, Internet, Local Area Networks (LAN), Wide Area Networks (WAN), Light Fidelity (Li-FI) networks, a carrier network, and the like. In some embodiments, the form factor of the data transmitted through the communication unit 110 may be any one or combination of including, but not limited to, analogue signals, electrical signals, digital signals, radio signals, infrared signals, data packets, and the like. This communication unit 108 ensures seamless interaction and data exchange between the processor 102 and the computing devices 110, facilitating coordinated responses to operational stress and disruptions.

[0047] In an embodiment, the processor 102 is connected to a server 112 that serves as an additional resource for data processing, storage, and management, further enhancing the system's ability to maintain robust cloud computing operations.

[0048] In an embodiment, the processor 102 is configured to monitor the cloud environment to obtain performance metrics and data about the system's operational status, including system uptime, service availability, and resource allocation. These performance metrics can be such as but not limited to, CPU usage, memory utilization, network bandwidth, and response times. Additionally, the processor tracks environmental factors and potential stressors that could impact the cloud environment. This monitoring helps in maintaining optimal performance and identifying any issues that could disrupt the system.

[0049] In an embodiment, the processor 102 is configured to introduce disruptions into a cloud environment using chaos agents which are specialized tools designed to simulate faults and stress conditions to evaluate system resilience. These disruptions can include various types of intentional disturbances such as network latency, which slows down data transmission; resource depletion, which includes exhausting system resources like CPU, memory, or storage; and service failures, which entail the intentional shutdown or malfunction of services. For instance, resource depletion includes artificially exhausting critical resources such as CPU, memory, or storage to test the system's ability to cope with limited capacity. Additionally, service failures are simulated by causing specific services or components to fail, which helps in understanding how the system manages and recovers fromsuch events. By implementing these disruptions, the cloud environment can be thoroughly tested for its robustness and recovery capabilities under various stress conditions.

[0050] In an embodiment, the processor 102 is further configured to create and simulate various operational strategies customized for each individual component such as, but not limited to virtual machines, storage systems, databases, and network elements within the cloud environment. The processor 102 models operational strategies by simulating at least one of resource allocation schemes, traffic rerouting strategies, load balancing methods, and the like. The operational strategies are specifically designed to address and mitigate the effects of the intentional disruptions introduced by chaos agents, such as network latency, resource depletion, and service failures. The modeling of strategies is based on real-time data collected from the cloud environment. This data includes performance metrics (such as response time, throughput, and error rates) and operational status information (such as current resource utilization and health of services).

[0051] Further, the processor 102 utilizes game theory techniques to model interactions and outcomes of different operational strategies. Game theory provides a structured framework to predict the behavior of various cloud components under stress, identify optimal strategies, and ensure that the cloud environment remains resilient and efficient.

[0052] In an embodiment, the processor 102 is further configured to assess effectiveness (i.e. payoff) and benefits of different operational strategies in the cloud environment. The evaluation is based on a predefined set of performance metrics, which are predefined and established as benchmarks. These performance metrics could include response time, throughput, error rates, resource utilization, and other relevant indicators of system performance. To evaluate the payoff of each operational strategy, the processor 102 monitors and measures how each operational strategy performs in the cloud environment. This includes collecting data on how the strategies impact various aspects of the system's operation. Further, the processor 102 compares the measured performance data against the predefined performance metrics. Based on this comparison, the processor 102 calculates a score that represents the effectiveness of each operational strategy. Further, the processor 102 utilizes the calculated scores to evaluate the payoff of each strategy. The payoff represents the benefit or value that the strategy provides in maintaining or improving the cloud environment's performance and resilience. This helps in identifying which strategies are most effective and should be implemented to ensure optimal system performance.

[0053] In an embodiment, the processor 102 is further configured to identify an equilibrium point using the Nash Equilibrium concept. This includes analyzing each operational strategy and their respective payoffs through at least one game theory technique. The equilibrium point represents the optimal strategy for each cloud component. By finding this equilibrium point, the processor 102 can determine the best course of action for each part of the cloud environment, ensuring that the system 100 operates efficiently and effectively under given conditions. This helps in optimizing the performance and resilience of the cloud environment by strategically balancing the various operational strategies.

[0054] In an embodiment, the processor 102 is configured to implement the identified optimal strategy across the cloud environment. This indicates that after determining the best strategies for each component of the cloud environment, the processor 102 deploys these strategies to actively manage and optimize the cloud environment. Additionally, the processor 102 is further configured to monitor how the cloud environment responds to the implemented strategies. The outcomes of this monitoring are then sent to a learning engine 108. The learning engine 108 uses this feedback to refine and improve the modeling of operational strategies and the evaluation of their payoffs. Essentially, the system learns from the results of the implemented strategies to enhance future decision-making and strategy optimization. This feedback loop allows for continuous improvement and adaptation, leading to more effective management of the cloud environment over time.

[0055] Further, the processor 102 displays information on the computing device 110. This information encompasses performance metrics, which quantify system performance such as uptime and response times; operational strategies, which detail the approaches used to manage and optimize cloud components; payoffs, representing the benefits and effectiveness of each strategy in addressing disruptions; and the overall health of the cloud environment, including stability and availability. This comprehensive display enables users to monitor the cloud environment’s status, evaluate the success of strategies, and ensure system health. This information is further stored and managed by the server 112. The server 112 consolidates and retains the displayed data for long-term analysis and reference, ensuring that all relevant details about the cloud environment are available for review and further action. This allows for a comprehensive overview and historical tracking of the cloud system's performance and strategic adjustments.

[0056] Referring to FIG. 2, an exemplary architecture 200 of the proposed system 100 to disclose working at multiple layers (i.e. functional modules) at the processor 102 is disclosed. Each module performs distinct tasks, which are essential for operation of thesystem. The system architecture is designed to seamlessly integrate game theory principles into chaos engineering for enhanced cloud computing operations. The architecture is organized into several interconnected layers, each contributing to automated, real-time adaptability and increased resilience in cloud environments. A data layer 202 continuously gathers and stores performance data from the cloud system. This data includes essential metrics such as response times, error rates, and resource utilization, providing a comprehensive view of the system's operational status.

[0057] Continuing further, a monitoring and chaos injection layer 204 introduces controlled disruptions into the cloud environment, such as simulating high traffic or network issues through chaos agents. This layer also includes monitoring tools that track and assess the cloud system’s response to these faults, capturing how well the system handles various stressors.

[0058] Continuing further, in an analysis and decision layer 206, the game theory engine 107 processes real-time data to model and evaluate potential strategies for each cloud component in response to the chaos introduced. This layer calculates the payoff of each strategy based on its effectiveness and uses game theory techniques, including Nash Equilibrium, to identify which strategies will optimize performance under the given conditions.

[0059] Continuing further, an execution layer 208 implements the optimal strategies identified by the analysis layer. Automation scripts apply these strategies across the system components, and the results are monitored and fed back into the system. This feedback allows machine learning algorithms in the game theory engine 107 to refine and enhance future strategy predictions and implementations.

[0060] Further, a presentation layer 210 updates a real-time dashboard that displays important information such as current reliability scores, the nature of chaos events, and the strategies being employed. This layer ensures that system administrators or end-users have a clear, up-to-date view of the system's health and performance, enabling continuous monitoring and adjustment as needed.

[0061] This architecture integrates chaos engineering and game theory principles to ensure sophisticated, real-time responses to disruptions, maintaining system stability and optimizing performance while providing clear, informative updates on system health.

[0062] In an exemplary implementation, the proposed system 100 is utilized to handle a surge in traffic on an e-commerce website hosted on a cloud platform during a major sales event, as depicted in FIG. 3. The process begins with the monitoring system tracking thewebsite's performance and detecting a sudden spike in user traffic, which poses a risk of overload. In response, chaos agents simulate high-traffic conditions and feed this scenario into a game theory engine 107. The game theory engine 107 further adjusts the load balancing strategy and accelerates resource allocation to manage the increased demand.

[0063] During this simulation, load balancers evaluate the incoming traffic and dynamically adjust their strategies to better distribute the load across servers, including web servers. Additionally, certain servers automatically switch to a high-performance mode, reallocating additional resources to handle the surge. This proactive strategy ensures that the website can manage the traffic increase efficiently, avoiding significant latency or downtime. Consequently, the user experience remains seamless and uninterrupted, showcasing the system's effectiveness in adapting to high-traffic situations and maintaining optimal performance.

[0064] In an exemplary implementation, flow of data in the system is depicted in FIG. 4. Initially, the system continuously monitors the cloud environment, capturing real-time data related to system performance, resource utilization, and environmental conditions by a data collection & monitoring layer. This data is then transmitted to both a chaos injection layer and a user interface & reporting layer. In the chaos injection layer, controlled chaos events, such as network latency or high load simulations, are introduced to test the system’s resilience. Details about these events, including their type, intensity, and impact, are forwarded to an analysis & game theory decision layer. In the analysis & game theory decision layer, the chaos data is analyzed to formulate potential response strategies and calculate their respective payoffs. Using Nash Equilibrium principles, the optimal strategy for each system component is determined under the current conditions. These optimal strategies are further communicated to an execution & adaptation layer. Concurrently, operational strategies and metrics are also sent to the user interface & reporting layer.

[0065] Upon receiving the optimal strategies, the execution & adaptation layer implements them across the cloud infrastructure, adjusting operational parameters as needed. Feedback on the effectiveness of these strategies is sent back to a learning & optimization layer. This learning & optimization layer uses the feedback to refine and enhance decisionmaking algorithms, improving future strategy selections. The improved algorithms and insights are then fed back to the analysis & game theory decision layer. The user interface & reporting layer provides a real-time dashboard for system administrators, displaying current operational strategies, performance metrics, and resilience scores. Administrators can interactwith the system by providing feedback or adjusting parameters based on the observed performance.

[0066] In an exemplary implementation, when a cloud-based service experiences significant network latency and partial outages due to external network issues, the stability and responsiveness of the service are threatened. The proposed system operates as depicted in FIG. 5A, illustrating the system workflow for chaos engineering in a cloud environment, from user interactions to the implementation of optimal strategies. Users access services that are continuously monitored. The monitoring system detects issues such as latency or outages and reports these to the Game Theory Decision Layer. This layer then requests and receives additional network data to comprehensively analyze the situation.

[0067] Using game theory, particularly Nash Equilibrium analyses, this layer determines optimal strategies to address detected issues, which are then sent to an execution layer. The execution layer is responsible for implementing these strategies across the relevant network components to restore or improve the service. This entire process is cyclic, aiming to continuously optimize cloud service performance in response to real-world conditions and simulated disruptions. This system ensures that user experience is maintained or enhanced through proactive management of potential and actual service disruptions.

[0068] Referring to FIG. 5B, a cloud optimization workflow using chaos engineering and game theory decision-making is disclosed. Initially, a user interacts with cloud services, which are continuously monitored for performance and operational stability by a monitoring system (i.e. monitoring module). This monitoring is essential in detecting any disruptions or irregularities in service delivery. If a disruption, termed a "chaos event," such as network latency or resource overload, is detected, the Chaos Agent intervenes by triggering a specific response mechanism. Upon recognizing a chaos event, the Chaos Agent alerts a game theory decision layer (GTDE), prompting it to request more detailed system data from the Monitoring System. The received data is analyzed using game theory principles, particularly focusing on Nash Equilibrium to devise optimal strategies for mitigating the impact of the chaos event on service performance.

[0069] These strategies are further conveyed to an execution layer, which implements them across the cloud environment to stabilize and optimize service operations effectively. Following strategy implementation, the system's performance is continually monitored. Performance outcomes are fed into a feedback and learning system (i.e. feedback and learning module), which utilizes this data to refine and improve future responses and operational strategies.

[0070] In scenarios where the monitoring system does not detect a chaos event, the cloud services continue their normal operations without activating the chaos response mechanisms. This ensures that the system remains highly adaptive, continuously learning from both real disruptions and simulated events to enhance resilience and efficiency over time.

[0071] Referring to FIG. 6, the flow diagram illustrates the working of the proposed system. At step 602, the processor continuously monitors system performance to detect any anomalies or chaos events. When such events are detected at step 604, the system proceeds to gather detailed performance data at step 608. This data is then analyzed in the game theory decision layer at step 610. At step 612, the system calculates the payoff for different strategies based on the analyzed data. The next step, 614, involves determining the Nash Equilibrium to identify the optimal strategy for each component. Once the optimal strategy is established, adjustments are executed at step 616, and the effectiveness of these adjustments is monitored at step 618. Feedback on the effectiveness is provided for machine learning optimization at step 620. If no anomalies or chaos events are detected at step 606, the system skips to step 616 to continue monitoring and adjusting the system as needed.

[0072] Referring to FIG. 7, a flow diagram of a method 700 for implementing chaos engineering in a cloud environment is disclosed.

[0073] At step 702, the method 700 includes monitoring, by a processor 102, a cloud environment by collecting and analyzing various performance metrics and operational status data. This includes monitoring key indicators such as system load, resource utilization, and response times to assess the overall health and efficiency of the cloud system. Additionally, the processor 102 tracks environmental factors and potential stressors, which include external and internal conditions that could impact the cloud system's performance, such as network latency, resource constraints, and other operational challenges. This monitoring helps in identifying current conditions and potential issues within the cloud environment, providing a basis for further analysis and strategic decision-making.

[0074] At step 704, the method 700 includes introducing, by the processor 102, disruptions into the cloud environment by chaos agents. These disruptions are designed to simulate various types of stress and failure scenarios to test the resilience of the system. The disruptions can include network latency, which involves intentionally slowing down network communication to assess how well the system manages delays; resource depletion, where resources such as CPU, memory, or storage are intentionally exhausted to evaluate system response to resource shortages; and service failures, which simulate the sudden unavailabilityof services or components to determine the system's ability to recover from unexpected outages. This step is essential for understanding how the cloud environment reacts under adverse conditions and for identifying areas where improvements are needed.

[0075] At step 706, the method 700 includes modeling, by the processor, operational strategies for each cloud component of the cloud environment in response to the disruptions, obtained performance metrics, and operational status data, using at least one game theory technique. The modeling process includes simulating various strategies to address the identified disruptions. Specifically, this may inclyde developing and testing resource allocation schemes to optimize the use of available resources, traffic rerouting strategies to manage data flow and mitigate bottlenecks, and load balancing methods to evenly distribute workloads across the cloud environment. By employing game theory techniques, the processor aims to find the most effective strategies that balance competing needs and improve overall system performance in response to the disruptions.

[0076] At step 708, the method 700 includes evaluating, by the processor, payoff of each operational strategy. This evaluation process starts with assessing the performance of each strategy to determine how effectively it addresses the disruptions and meets predefined performance metrics. The processor calculates a score for each operational strategy by comparing its assessed performance against these predefined metrics. This score helps in quantifying the effectiveness of the strategy. The calculated score is then used to determine the payoff of each operational strategy, providing a measure of its success or failure in improving the system’s performance under the given conditions.

[0077] At step 710, the method 700 includes identifying, by the processor, an equilibrium point by Nash Equilibrium. After analyzing each operational strategy and their respective payoffs with at least one game theory technique, the processor determines the equilibrium point. This equilibrium point represents the optimal strategy or set of strategies for each cloud component. In essence, it identifies the strategies that would lead to the best overall performance of the cloud environment by balancing the various factors and constraints involved.

[0078] At step 712, the method 700 includes implementing, by the processor, the identified at least one optimal strategy across the cloud environment. Once these strategies are in place, the processor monitors the system’s response to these strategies to ensure they are functioning as intended. The outcomes of this monitoring are fed into a learning engine 108, which uses this information to refine the modeling of operational strategies and payoff evaluations. This ongoing process helps in continuously improving the system’s resilienceand efficiency by learning from results of implemented strategies and making necessary adjustments.

[0079] The method 700 includes the step of displaying information received from the processor 102, which includes showing key data and insights related to the cloud environment. This information includes performance metrics, operational strategies, payoffs, and the overall health of the cloud environment. The processor 102 communicates this data to computing devices 110, such as a display screen or user interface. By presenting this information, the method provides users with a clear view of the cloud system’s performance and effectiveness of implemented strategies, facilitating better decision-making and management of the cloud environment, chaos engineering in cloud computing by integrating game theory techniques, such as Nash Equilibrium, to enhance resilience and operational efficiency. The system involves monitoring the cloud environment to collect performance metrics and operational status, introducing disruptions to test system responses, and using game theory to model and evaluate operational strategies. By identifying and implementing optimal strategies based on real-time data and equilibrium analysis, the system can dynamically adapt to changing conditions, reduce manual intervention, and optimize resource management, leading to a more robust and efficient cloud environment.

[0080] Thus, the present disclosure provides system method that enhance chaos engineering in cloud computing by employing game theory techniques like Nash Equilibrium to improve cloud system resilience and operational efficiency. It involves monitoring the cloud environment to gather performance metrics and operational data, introducing controlled disruptions to test system responses, and using game theory to model and evaluate strategies for each cloud component. By identifying optimal strategies through this analysis and implementing them, the system can dynamically adjust to changing conditions, minimize manual intervention, and optimize resource management, thereby creating a more robust and efficient cloud environment.

[0081] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.ADVANTAGES OF THE PRESENT DISCLOSURE

[0082] The present disclosure provides a solution that strengthens the resilience of cloud environments by effectively handling operational disruptions.

[0083] The present disclosure provides a solution that allows for dynamic adaptability in cloud environments, enabling real-time adjustments to changing operational conditions.

[0084] The present disclosure provides a solution that automates decision-making in response to chaos, thereby minimizing the need for manual intervention.

[0085] The present disclosure provides a solution that utilizes predictive analytics to adjust strategies proactively, preventing potential failures or performance degradation.

[0086] The present disclosure provides a solution that incorporates game theory algorithms to model the interactions and strategic decision-making processes of cloud components.

[0087] The present disclosure provides a solution that continuously monitors performance and uses real-time data to inform decision-making.

[0088] The present disclosure provides a solution that uses Nash Equilibrium to identify the optimal operational strategies for each cloud component.

[0089] The present disclosure provides a solution that autonomously and dynamically reconfigures settings using machine learning and automation scripts.

[0090] The present disclosure provides a solution that includes a feedback mechanism, enabling it to learn from past adaptations and improve future decision-making.

[0091] The present disclosure provides a solution that ensures greater operational stability, even under fluctuating and unpredictable conditions.

[0092] The present disclosure provides a solution that optimizes resource utilization and operational efficiency, resulting in cost-effective cloud management.

[0093] The present disclosure provides a solution that effectively scales with the complexity and size of cloud infrastructures.

[0094] The present disclosure provides a solution that minimizes downtime through proactive and timely adaptations.

[0095] The present disclosure provides a solution that offers user-centric metrics and performance data via a user-friendly dashboard, enhancing visibility and control for system administrators.

Claims

I Claim:

1. A system (100) for implementing chaos engineering, the system (100) comprising: a processor (102); and a memory (104) storing instructions that, when executed by the processor, cause the processor to: monitor a cloud environment (106) to obtain performance metrics and operational status data, and track environmental factors and potential stressors; introduce disruptions into the cloud environment (106) by chaos agents, wherein the disruptions comprises at least one of network latency, resource depletion, and service failures; model operational strategies for each cloud component of the cloud environment in response to the disruptions, obtained performance metrics and operational status data, by at least one game theory technique; evaluate payoff of each operational strategy taking into consideration a predefined set of performance metrics; identify an equilibrium point by a Nash Equilibrium, upon analysis of each operational strategy and their payoffs by at least one game theory technique, wherein the equilibrium point represents at least one optimal strategy for each cloud component; and implement the identified at least one optimal strategy across the cloud environment.

2. The system (100) as claimed in claim 1, wherein the processor (102) models operational strategies by simulating at least one of resource allocation schemes, traffic rerouting strategies, and load balancing methods.

3. The system (100) as claimed in claim 1, wherein the processor (102) is further configured to: monitor response to the implemented at least one optimal strategy, and outcomes are fed to a learning engine and correspondingly refine the modeling of operational strategies and the payoff evaluations.

4. The system (100) as claimed in claim 1, wherein the processor (102) is communicatively coupled to one or more computing devices (110), wherein the one or more computing devices (110) are configured to display received information fromthe processor, and wherein the information comprises the performance metrics, the operational strategies, the payoffs, and health of the cloud environment.

5. The system (100) as claimed in claim 1, wherein to evaluate payoff of each operational strategy, the processor (102) is configured to: assess performance of each operational strategy; calculate a score for each operational strategy by comparing the assessed performance against the predefined set of performance metrics; and utilize the calculated score to determine the payoff of each operational strategy.

6. A method (700) for implementing chaos engineering in a cloud environment, the method comprising: monitoring (702), by a processor, the cloud environment to obtain performance metrics and operational status data, and tracking environmental factors and stressors; introducing (704), by the processor, disruptions into the cloud environment by chaos agents, wherein the disruptions comprise at least one of network latency, resource depletion, and service failures; modeling (706), by the processor, operational strategies for each cloud component of the cloud environment in response to the disruptions, obtained performance metrics, and operational status data, using at least one game theory technique; evaluating (708), by the processor, payoff of each operational strategy taking into consideration a predefined set of performance metrics; identifying (710), by the processor, an equilibrium point by Nash Equilibrium, upon analyzing each operational strategy and their payoffs using at least one game theory technique, wherein the equilibrium point represents at least one optimal strategy for each cloud component; and implementing (712), by the processor, the identified at least one optimal strategy across the cloud environment.

7. The method (700) as claimed in claim 6, wherein modeling of operational strategies comprises simulating at least one of resource allocation schemes, traffic rerouting strategies, and load balancing methods.

8. The method (700) as claimed in claim 6, further comprises monitoring response to the implemented at least one optimal strategy, and feeding outcomes to a learning engine to refine the modeling of operational strategies and payoff evaluations.

9. The method as claimed in claim 6, further comprises displaying information received from the processor, including performance metrics, operational strategies, payoffs, and health of the cloud environment, wherein the processor is communicatively coupled to the computing device.

10. The method (700) as claimed in claim 6, wherein evaluating the payoff of each operational strategy comprises: assessing performance of each operational strategy; calculating a score for each operational strategy by comparing the assessed performance against a predefined set of performance metrics; and utilizing the calculated score to determine the payoff of each operational strategy.

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