Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A Preliminary Study of Serverless Platforms for Latency Sensitive Applications(Institute of Electrical and Electronics Engineers Inc., 2022) Sarathi, T.V.; Sai Nischal Reddy, J.; Shiva, P.; Saha, R.; Satpathy, A.; Addya, S.K.Serverless computing is the new-age cloud delivery model wherein resources are provisioned only during event-triggered functions. It dramatically improves the flexibility and scalability of applications compared to virtual machine (VM)/container-based service delivery models. As serverless computing is gaining significant impetus, major cloud providers such as Amazon, Microsoft Azure, and Google have launched their respective serverless computing platforms. However, for a user selecting an appropriate service provider (SP), meeting its desired quality-of-services (QoS) is challenging. Moreover, there is not enough public information available to assist the users in making such accurate decisions. Hence, we provide preliminary analysis via real-time experimentation for the users in this work, acting as a stepping stone in selecting an appropriate SP. To evaluate, we consider execution time and execution cost as evaluation metrics to assess different real-world SPs' performance by considering different workloads. Experimental results show that Azure functions achieved lower execution times than AWS Lambda and Google Cloud Functions, but in terms of execution cost, AWS Lambda costs much lower than the other two platforms. © 2022 IEEE.Item LCS : Alleviating Total Cold Start Latency in Serverless Applications with LRU Warm Container Approach(Association for Computing Machinery, 2023) Sethi, B.; Addya, S.K.; Ghosh, S.K.Serverless computing offers "Function-as-a-Service"(FaaS), which promotes an application in the form of independent granular components called functions. FaaS goes well as a widespread standard that facilitates the development of applications in cloud-based environments. Clients can solely focus on developing applications in a serverless ecosystem, passing the overburden of resource governance to the service providers. However, FaaS platforms have to bear the degradation in performance originating from the cold starts of executables i.e. serverless functions. The cold start reflects the delay in provisioning a runtime container that processes the functions. Each serverless platform is handling the problem of cold start with its own solution. In recent times, approaches to deal with cold starts have received the attention of many researchers. This paper comes up with an extensive solution to handle the cold start problem. We propose a scheduling approach to reduce the cold start occurrences by keeping the containers alive for a longer period of time using the Least Recently Used warm Container Selection (LCS ) approach on Affinity-based scheduling. Further, we carried out an evaluation and compared the obtained results with the MRU container selection approach. The proposed LCS approach outperforms by approximately 48% compared to the MRU approach. © 2023 ACM.Item Function Scheduling with Data Security in Serverless Computing Systems(Institute of Electrical and Electronics Engineers Inc., 2025) Saha, S.; Pandey, A.; Addya, S.K.; Brata Nath, S.In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user's QoS. © 2025 IEEE.Item Adaptive Workload Management for Enhanced Function Performance in Serverless Computing(Association for Computing Machinery, Inc, 2025) Birajdar, P.A.; Harsha, V.; Satpathy, A.; Addya, S.K.Serverless computing streamlines application deployment by removing the need for infrastructure management, but fluctuating workloads make resource allocation challenging. To solve this, we propose an adaptive workload manager that intelligently balances workloads, optimizes resource use, and adapts to changes with auto-scaling, ensuring efficient and reliable serverless performance. Preliminary experiments demonstrate an ≈ 0.6X% and 2X% improvement in execution time and resource utilization compared to the First-Come-First-Serve (FCFS) scheduling algorithm. © 2025 Copyright held by the owner/author(s).