Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Author: search.filters.author.Addya, S.K.Subject: search.filters.subject.Cloud computing

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    CoMCLOUD: Virtual Machine Coalition for Multi-Tier Applications over Multi-Cloud Environments
    (Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.
    Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model called CoMCLOUD to select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines. © 2013 IEEE.
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    Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets
    (Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.
    Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is $\mathcal {NP}$NP-Hard, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%-30% and electricity cost by approximately 25% compared to the baseline. © 2008-2012 IEEE.
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    Optimizing Completion Time of Requests in Serverless Computing
    (Springer, 2024) Sherawat, A.; Nath, S.B.; Addya, S.K.
    Serverless computing offers people with the liberty of not thinking about the backend side of the things in an application development. They are scalable and cost efficient as they provide pay-for-use service. Providing acceptable performance while having no knowledge about the kind of application is the main challenge the cloud providers have. Many applications may have the need to be completed before the deadline. In that case, the request has to be completed before the deadline or else it will lead to service level agreement violation. If the cloud provider completes the requests faster, there would be less SLA violations. This will also reduce cost for the user as the functions will be completed sooner. Therefore, improving the completion time of the requests will benefit the user as well as the provider. In this paper, we present a method to improve the completion time of requests using genetic algorithm for allocation of requests to virtual machines that could provide optimal completion time for them. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.