Faculty Publications

Now showing 1 - 4 of 4

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. Â© 2024 IEEE.
Containerized deployment of micro-services in fog devices: a reinforcement learning-based approach
(Springer, 2022) Nath, S.B.; Chattopadhyay, S.; Karmakar, R.; Addya, S.K.; Chakraborty, S.; Ghosh, S.K.
The real power of fog computing comes when deployed under a smart environment, where the raw data sensed by the Internet of Things (IoT) devices should not cross the data boundary to preserve the privacy of the environment, yet a fast computation and the processing of the data is required. Devices like home network gateway, WiFi access points or core network switches can work as a fog device in such scenarios as its computing resources can be leveraged by the applications for data processing. However, these devices have their primary workload (like packet forwarding in a router/switch) that is time-varying and often generates spikes in the resource demand when bandwidth-hungry end-user applications, are started. In this paper, we propose pick–test–choose, a dynamic micro-service deployment and execution model that considers such time-varying primary workloads and workload spikes in the fog nodes. The proposed mechanism uses a reinforcement learning mechanism, Bayesian optimization, to decide the target fog node for an application micro-service based on its prior observation of the system’s states. We implement PTC in a testbed setup and evaluate its performance. We observe that PTC performs better than four other baseline models for micro-service offloading in a fog computing framework. In the experiment with an optical character recognition service, the proposed PTC gives average response time in the range of 9.71 sec–50 sec, which is better than Foglets (24.21 sec–80.35 sec), first-fit (16.74 sec–88 sec), best-fit (11.48 sec–57.39 sec) and mobility-based method (12 sec–53 sec). A further scalability study with an emulated setup over Amazon EC2 further confirms the superiority of PTC over other baselines. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
ESMA: Towards elevating system happiness in a decentralized serverless edge computing framework
(Academic Press Inc., 2024) Datta, S.; Addya, S.K.; Ghosh, S.K.
Due to the rapid growth in the adoption of numerous technologies, such as smartphones and the Internet of Things (IoT), edge and serverless computing have started gaining momentum in today's computing infrastructure. It has led to the production of huge amounts of data and has also resulted in increased network traffic, which if not managed well can cause network congestion. To address this and maintain the quality of service (QoS), in this work, a novel dispatch (destination selection) algorithm called Egalitarian Stable Matching Algorithm (ESMA) for faster data processing has been developed while also considering the best use of server resources in a decentralized Serverless-Edge environment. This will allow us to effectively utilize the enormous volumes of data that are generated. The proposed algorithm has been able to achieve lower overall dissatisfaction scores for the entire system. Individually, the client's happiness as well as the server's happiness have improved over the baseline. Moreover, there has been a drop of 25.7% in the total execution time and the total network resources consumed are lower as compared to the baseline algorithm as well as random-allocation algorithm. © 2023 Elsevier Inc.
CSMD: Container state management for deployment in cloud data centers
(Elsevier B.V., 2025) Nath, S.B.; Addya, S.K.; Chakraborty, S.; Ghosh, S.K.
As the containers are lightweight in resource usage, they are preferred for cloud and edge computing service deployment. Containers serve the requests whenever a user sends a query; however, they remain idle when no user request comes. Again, improving the consolidation ratio of container deployments is essential to ensure fewer servers are used in a cloud data center with an optimal resource balance. To increase the consolidation ratio of a cloud data center, in this paper, we propose a system called Container State Management for Deployment (CSMD) to manage the container states. CSMD uses an algorithm to checkpoint the idle containers so that their resources can be released. The new containers are deployed using the released resources in a server. In addition, CSMD uses an algorithm to check the container status periodically, and the containers are resumed from the checkpoint state when the user requests them. We evaluate CSMD in Amazon Elastic Compute Cloud (Amazon EC2) by performing efficient state management of the containers. The experiments in the Amazon cloud show that the proposed CSMD system is superior to the existing algorithms as the proposed system increases the consolidation ratio of data centers. © 2024 Elsevier B.V.

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