Fuzzy Reinforcement Learning based Microservice Allocation in Cloud Computing Environments

Joseph, Christina Terese; Martin, John P.; Chandrasekaran, K.; Kandasamy, A.

doi:10.1109/tencon.2019.8929586

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…Various Middleware Frameworks of Containers [21] Programming Sensors and IoT Devices (Small VMs Supported by Python Runtime Environments such as Spring and NodeJS) [22][23][24][25] Application Efficiency (Resource Virtualization Technology for Hardware Flexibility) [26] Lightweight Virtualization Solution [27] Virtualization Application to Lower Overhead [28] Definition of Container [29] Strengths of Containers, Lightening Up [30] The Connection between Lightweight Virtualization and Microservice [31] Strategies to Strengthen The Fuzzy Distribution of Microphone Services [32] Cluster Intelligence-Based Strategies [33] Reinforcement…”

Section: Cloud Computingmentioning

confidence: 99%

“…Research related to the implementation of smart containers, dockers, Apache, and Kubernetes is currently being actively conducted. For example, studies have proposed fuzzy enhancement learning strategies for microservice allocation [32]. Research has also been conducted to suggest cluster intelligence-based strategies that can contribute to scheduling in big data applications [33].…”

Section: Container Computingmentioning

confidence: 99%

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Improved Q Network Auto-Scaling in Microservice Architecture

et al. 2022

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Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning. A common approach used in both commercial and open-source computing platforms is workload-based automatic scaling, which expands instances by increasing the number of incoming requests. Concurrency is a request-based policy that has recently been proposed in the evolving microservice framework; in this policy, the algorithm can expand its resources to the maximum number of configured requests to be processed in parallel per instance. However, it has proven difficult to identify the concurrency configuration that provides the best possible service quality, as various factors can affect the throughput and latency based on the workloads and complexity of the infrastructure characteristics. Therefore, this study aimed to investigate the applicability of an artificial intelligence approach to request-based auto-scaling in the microservice framework. Our results showed that the proposed model could learn an effective expansion policy within a limited number of pods, thereby showing an improved performance over the underlying auto expansion configuration.

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Section: Cloud Computingmentioning

confidence: 99%

Section: Container Computingmentioning

confidence: 99%

Improved Q Network Auto-Scaling in Microservice Architecture

et al. 2022

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“…Currently just two major works [19,20] can be found in relation to smart containers' scheduling and their implementations in Docker Swarm, Apache Mesos and Google Kubernetes. On the one hand, in [19] a fuzzy reinforcement learning strategy is proposed for microservices allocation. On the other hand, [20] suggests the consideration of a Swarm Intelligence-based strategy for scheduling in big data applications.…”

Section: Smart Containers' Scheduling Strategies In Scientific Literamentioning

confidence: 99%

“…Although these techniques have been largely proved effective in the scheduling of tasks and VMs in the last years [14][15][16][17][18], their adaptation and adoption in containers' scheduling represent multiple challenges as well as opportunities. These challenges and opportunities have scarcely been explored and analyzed at the time of writing and motivates this work [19,20]. • Secondly, the possible benefits of specific smart containers' schedulers for Docker Swarm, Apache Mesos and Google Kubernetes, for the different interfaces in cloud-fog-IoT networks and type of microservices are analyzed.…”

Section: Introductionmentioning

confidence: 99%

Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities

Prado

García-Galán

Expósito

et al. 2020

Sensors

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Docker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers' scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intelligent containers' schedulers based on soft-computing in the dominant open-source containers' management platforms: Docker Swarm, Google Kubernetes and Apache Mesos. Secondly, the need for specific intelligent containers' schedulers for the different interfaces in cloud-fog-IoT networks: cloud-to-fog, fog-to-IoT and cloud-to-fog. The goal of this work is to support the optimal allocation of microservices provided by the main cloud service providers today and used by millions of users worldwide in applications such as smart health, content delivery networks, smart health, etc. Particularly, the improvement is studied in terms of quality of service (QoS) parameters such as latency, load balance, energy consumption and runtime, based on the analysis of previous works and implementations. Moreover, the scientific-technical impact of smart containers' scheduling in the market is also discussed, showing the possible repercussion of the raised opportunities in the research line.

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“…In the physical resources, a managing tool namely, Docker, lightweight virtualization technology, or warden will be set up, which is accountable for operating the containers 12,13 . In contrast to VMs, container‐oriented virtualization allows an existing operating system to offer further isolation 14–17 . Thus, a container simplifies the exploitation of cloud appliances and satisfies the most important needs of cloud, that is, density and elasticity 7,18–21 …”

Section: Introductionmentioning

confidence: 99%

Self‐improved moth flame for optimal container resource allocation in cloud

Vhatkar¹,

Bhole²

2022

Concurrency and Computation

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SUMMARY Resource allocation in the cloud is becoming more complicated and challenging due to the rising necessities of cloud services. Effective management of virtual resources in the cloud is of large significance since it has a great impact on both the operational cost and scalability of the cloud environment. Nowadays, containers are becoming more popular in this regard due to their characteristics like reduced overhead and portability. Conventional resource allocation schemes are usually modeled for the migration and allocation of virtual machines (VM), as a result; the question may arise on, “how these strategies can be adapted for the management of a containerized cloud”. This work evolves the solution to this issue by introducing a new fitness oriented moth flame algorithm (F‐MFA) for optimizing the allocation of containers. Further in this work, the optimal allocation relies on certain constraints like balanced cluster use, system failure, total network distance (TND), security and threshold distance, and credibility factor as well. In the end, the supremacy of the presented model is computed to the conventional models in terms of cost and convergence analysis.

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Fuzzy Reinforcement Learning based Microservice Allocation in Cloud Computing Environments

Cited by 7 publications

References 12 publications

Improved Q Network Auto-Scaling in Microservice Architecture

Improved Q Network Auto-Scaling in Microservice Architecture

Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities

Self‐improved moth flame for optimal container resource allocation in cloud

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