Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Exploring the support for high performance applications in the container runtime environment(Springer Berlin Heidelberg, 2018) Martin, J.P.; Kandasamy, A.; Chandrasekaran, K.Cloud computing is the driving power behind the current technological era. Virtualization is rightly referred to as the backbone of cloud computing. Impacts of virtualization employed in high performance computing (HPC) has been much reviewed by researchers. The overhead in the virtualization layer was one of the reasons which hindered its application in the HPC environment. Recent developments in virtualization, especially the OS container based virtualization provides a solution that employs a lightweight virtualization layer and promises lesser overhead. Containers are advantageous over virtual machines in terms of performance overhead which is a major concern in the case of both data intensive applications and compute intensive applications. Currently, several industries have adopted container technologies such as Docker. While Docker is widely used, it has certain pitfalls such as security issues. The recently introduced CoreOS Rkt container technology overcomes these shortcomings of Docker. There has not been much research on how the Rkt environment is suited for high performance applications. The differences in the stack of the Rkt containers suggest better support for high performance applications. High performance applications consist of CPU-intensive and data-intensive applications. The High Performance Linpack Library and the Graph500 are the commonly used computation intensive and data-intensive benchmark applications respectively. In this work, we explore the feasibility of this inter-operable Rkt container in high performance applications by running the HPL and Graph500 applications and compare its performance with the commonly used container technologies such as LXC and Docker containers. © 2018, The Author(s).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 Fog Assisted Personalized Dynamic Pricing for Smartgrid(Institute of Electrical and Electronics Engineers Inc., 2023) Joseph, C.T.; Martin, J.P.; Chandrasekaran, K.; Raja, S.P.Unit electricity pricing is of vital importance in an electric grid network. It is essential to charge the customers in a fair manner. Traditional pricing models are found to be inadequate in the ability to charge customers fairly due to a lack of support for real-time communication between customers and electricity providers. With the introduction of smart devices in the electric grid domain, the real-time gathering of information is a seamless process. Such an electric network that uses smart devices is called a smart grid. In a smart grid network, electricity providers can monitor the electricity usage pattern of customers in a real-time manner, which can then be analyzed to determine the appropriate prices. To analyze the customer's history of usage and price the electricity in a real-time manner, the computation must be performed with minimal latencies. Adoption of a fog computing layer in the smart grids can aid in the attainment of this goal. In this article, we propose a novel method for the pricing of electricity. In our approach, the electric demand of a household is predicted based on their past usage patterns. Users are then clustered into different bins based on their demands, and an evolutionary algorithm is used to generate the prices for the users present in different bins in a real-time manner to ensure the maximum attainable profit to a service provider. © 2014 IEEE.