Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Author: search.filters.author.Satpathy, A.Subject: search.filters.subject.Serverless Computing

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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).