Faculty Publications
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Item Machine Learning Powered Autoscaling for Blockchain-Based Fog Environments(Springer Science and Business Media Deutschland GmbH, 2022) Martin, J.P.; Joseph, C.T.; Chandrasekaran, K.; Kandasamy, A.Internet-of-Things devices generate huge amount of data which further need to be processed. Fog computing provides a decentralized infrastructure for processing these huge volumes of data. Fog computing environments provide low latency and location-aware alternative to conventional cloud computing by placing the processing nodes closer to the end devices. Co-ordination among end devices can become cumbersome and complex with the increasing amount of IoT devices. Some of the major challenges faced while executing services in the fog environment is the resource provisioning for the user services, service placement among the fog devices and scaling of fog devices based on the current load on the network. Being a decentralized infrastructure, fog computing is vulnerable to external threats such as data thefts. This work presents a blockchain based fog framework for making autoscaling decisions with the use of machine learning techniques. Evaluation is done by performing a series of experiments that show how the services are handled by the fog framework and how the autoscaling decisions are made. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Elucidating the challenges for the praxis of fog computing: An aspect-based study(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Martin, J.P.; Kandasamy, A.; Chandrasekaran, K.; Joseph, C.T.The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain. © 2019 John Wiley & Sons, Ltd.Item Mobility aware autonomic approach for the migration of application modules in fog computing environment(Springer Science and Business Media Deutschland GmbH, 2020) Martin, J.P.; Kandasamy, A.; Chandrasekaran, K.The fog computing paradigm has emanated as a widespread computing technology to support the execution of the internet of things applications. The paradigm introduces a distributed, hierarchical layer of nodes collaboratively working together as the Fog layer. User devices connected to Fog nodes are often non-stationary. The location-aware attribute of Fog computing, deems it necessary to provide uninterrupted services to the users, irrespective of their locations. Migration of user application modules among the Fog nodes is an efficient solution to tackle this issue. In this paper, an autonomic framework MAMF, is proposed to perform migrations of containers running user modules, while satisfying the Quality of Service requirements. The hybrid framework employing MAPE loop concepts and Genetic Algorithm, addresses the migration of containers in the Fog environment, while ensuring application delivery deadlines. The approach uses the pre-determined value of user location for the next time instant, to initiate the migration process. The framework was modelled and evaluated in iFogSim toolkit. The re-allocation problem was also mathematically modelled as an Integer Linear Programming problem. Experimental results indicate that the approach offers an improvement in terms of network usage, execution cost and request execution delay, over the existing approaches. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item CREW: Cost and Reliability aware Eagle-Whale optimiser for service placement in Fog(John Wiley and Sons Ltd cs-journals@wiley.co.uk, 2020) Paul Martin, J.; Kandasamy, A.; Chandrasekaran, K.Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability-aware Eagle-Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi-Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta-heuristics such as NSGA-II and MOWOA based placement strategies. © 2020 John Wiley & Sons Ltd