Conference Papers

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    Construing microservice architectures: State-of-the-art algorithms and research issues
    (Springer Verlag service@springer.de, 2019) Nene, A.V.; Joseph, C.T.; Chandrasekaran, K.
    Cloud Computing is one of the leading paradigms in the IT industry. Earlier, cloud applications used to be built as single monolithic applications, and are now built using the Microservices Architectural Style. Along with several advantages, the microservices architecture also introduce challenges at the infrastructural level. Five such concerns are identified and analysed in this paper. The paper presents the state-of-art in different infrastructural concerns of microservices, namely, load balancing, scheduling, energy efficiency, security and resource management of microservices. The paper also suggests some future trends and research domains in the field of microservices. © Springer Nature Switzerland AG 2019.
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    Fuzzy Reinforcement Learning based Microservice Allocation in Cloud Computing Environments
    (Institute of Electrical and Electronics Engineers Inc., 2019) Joseph, C.T.; Martin, J.P.; Chandrasekaran, K.; Kandasamy, A.
    Nowadays the Cloud Computing paradigm has become the defacto platform for deploying and managing user applications. Monolithic Cloud applications pose several challenges in terms of scalability and flexibility. Hence, Cloud applications are designed as microservices. Application scheduling and energy efficiency are key concerns in Cloud computing research. Allocating the microservice containers to the hosts in the datacenter is an NP-hard problem. There is a need for efficient allocation strategies to determine the placement of the microservice containers in Cloud datacenters to minimize Service Level Agreement violations and energy consumption. In this paper, we design a Reinforcement Learning-based Microservice Allocation (RL-MA) approach. The approach is implemented in the ContainerCloudSim simulator. The evaluation is conducted using the real-world Google cluster trace. Results indicate that the proposed method reduces both the SLA violation and energy consumption when compared to the existing policies. © 2019 IEEE.
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    LEASE: Leveraging Energy-Awareness in Serverless Edge for Latency-Sensitive IoT Services
    (Institute of Electrical and Electronics Engineers Inc., 2024) Verma, A.; Satpathy, A.; Das, S.K.; Addya, S.K.
    Resource scheduling catering to real-time IoT services in a serverless-enabled edge network is particularly challenging owing to the workload variability, strict constraints on tolerable latency, and unpredictability in the energy sources powering the edge devices. This paper proposes a framework LEASE that dynamically schedules resources in serverless functions catering to different microservices and adhering to their deadline constraint. To assist the scheduler in making effective scheduling decisions, we introduce a priority-based approach that offloads functions from over-provisioned edge nodes to under-provisioned peer nodes, considering the expended energy in the process without compromising the completion time of microservices. For real-world implementations, we consider a testbed comprising a Raspberry Pi cluster serving as edge nodes, equipped with container orchestrator tools such as Kubernetes and powered by OpenFaaS, an open-source serverless platform. Experimental results demonstrate that compared to the benchmarking algorithm, LEASE achieves a 23.34% reduction in the overall completion time, with 97.64% of microservices meeting their deadline. LEASE also attains a 30.10% reduction in failure rates. © 2024 IEEE.