Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
3 results
Search Results
Item Automating the Selection of Container Orchestrators for Service Deployment(Institute of Electrical and Electronics Engineers Inc., 2022) Chaurasia, P.; Nath, S.B.; Addya, S.K.; Ghosh, S.K.With the ubiquitous usage of cloud computing, the services are deployed as a virtual machine (VM) in cloud servers. However, VM based deployment often takes more amount of resources. In order to minimize the resource consumption of service deployment, container based lightweight virtualization is used. The management of the containers for deployment is a challenging problem as the container managers need to consume less amount of resources while also catering to the needs of the clients. In order to choose the right container manager, we have proposed an architecture based on the application and user needs. In the proposed architecture, we have a machine learning based decision engine to solve the problem. We have considered docker containers for experimentation. The experimental results show that the proposed system can select the proper container manager among docker compose based manager and Kubernetes. © 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 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.