Faculty Publications
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Item A probe into the technological enablers of microservice architectures(Springer Verlag service@springer.de, 2019) Joseph, C.T.; Chandrasekaran, K.Microservice architectures (MSA), composed of loosely coupled and autonomous units called microservices, are gaining wide adoption in the software field. With characteristics that are loyal to the requirements of the Cloud environment, such as inherent support for continuous integration/continuous deployment (CI/CD), MSA are actively embraced by the Cloud computing community. Containers employing lightweight virtualization have also been increasingly adopted in the Cloud environment. The containers wrap applications along with their dependencies into self-contained units, which can be deployed independently. These features make it the unanimously accepted technology to enable seamless execution of microservices in the Cloud. With this outlook, this chapter undertakes a study on how containers may be used to support the execution of microservices. The study also includes other technologies that, in collaboration with container technologies, provide the support required for running microservices in the Cloud. An interesting concern for applications running on containers is resource management. Nevertheless, this is a significant aspect for supporting microservices as well. Such issues have been identified and research works addressing all or some of these issues, have been considered. The various relevant studies have been classified into different categories and the future directions have been identified, which can be used by researchers aiming to enhance the technological support for microservices in Cloud. © Springer Nature Singapore Pte Ltd. 2019.Item Improving the efficiency of genetic algorithm approach to virtual machine allocation(Institute of Electrical and Electronics Engineers Inc., 2014) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.Virtual machine (VM) allocation is the process of allocating virtual machines to suitable hosts. This problem is an NP-Hard problem. It can be considered as a variation of the bin-packing problem. Among various solutions that attempt to solve this problem, several approaches that apply Genetic Algorithm have been proposed. This paper proposes a method to improve the efficiency of such approaches. Implementation of the proposed approach shows significant improvements in the runtime, memory used, energy efficiency and SLA violations. © 2014 IEEE.Item A perspective study of virtual machine migration(Institute of Electrical and Electronics Engineers Inc., 2014) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.Cloud Computing is one of the leading technologies. As a solution to many of the challenges faced by Cloud providers, virtualization is employed in Cloud. Virtual machine migration is a tool to utilize virtualization well. This paper gives an overview of the different works in literature that consider virtual machine migration. The different works related to virtual migration are classified into different categories. Some of the works that consider less explored areas of virtual machine migration are discussed in detail. © 2014 IEEE.Item A novel family genetic approach for virtual machine allocation(Elsevier B.V., 2015) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.The concept of virtualization forms the heart of systems like the Cloud and Grid. Efficiency of systems that employ virtualization greatly depends on the efficiency of the technique used to allocate the virtual machines to suitable hosts. The literature contains many evolutionary approaches to solve the virtual machine allocation problem, a broad category of which employ Genetic Algorithm. This paper proposes a novel technique to allocate virtual machines using the Family Gene approach. Experimental analysis proves that the proposed approach reduces energy consumption and the rate of migrations, and hence offers much scope for future research. © 2015 Published by Elsevier B.V.Item Machine Learning Approaches for Resource Allocation in the Cloud: Critical Reflections(Institute of Electrical and Electronics Engineers Inc., 2018) Murali, A.; Das, N.N.; Sukumaran, S.S.; Chandrasekaran, K.; Joseph, C.T.; Martin, J.P.Resource Allocation is the effective and efficient use of a Cloud's resources and is a very challenging problem in cloud environments. Many attempts have been made to make Resource Allocation automated and optimal in terms of profit. The best of these methods used Machine Learning, but this comes with an overhead for computation. A lot of research has been done in this domain to find more efficient methods. Distributed Neural Networks (DNN) is the future of computation and will soon be used to make the computation of large-scale data faster and easier. DNN is currently the most researched area. This paper will summarize the major research works in these fields. A new taxonomy is proposed and can be used as a reference for all future research in this domain. The paper also proposes some areas that need more research in the foreseeable future. © 2018 IEEE.Item Construing microservice architectures: State-of-the-art algorithms and research issues(Springer Verlag service@springer.de, 2019) Nene, A.V.; Joseph, C.T.; Chandrasekaran, K.Cloud Computing is one of the leading paradigms in the IT industry. Earlier, cloud applications used to be built as single monolithic applications, and are now built using the Microservices Architectural Style. Along with several advantages, the microservices architecture also introduce challenges at the infrastructural level. Five such concerns are identified and analysed in this paper. The paper presents the state-of-art in different infrastructural concerns of microservices, namely, load balancing, scheduling, energy efficiency, security and resource management of microservices. The paper also suggests some future trends and research domains in the field of microservices. © Springer Nature Switzerland AG 2019.Item Fuzzy Reinforcement Learning based Microservice Allocation in Cloud Computing Environments(Institute of Electrical and Electronics Engineers Inc., 2019) Joseph, C.T.; Martin, J.P.; Chandrasekaran, K.; Kandasamy, A.Nowadays the Cloud Computing paradigm has become the defacto platform for deploying and managing user applications. Monolithic Cloud applications pose several challenges in terms of scalability and flexibility. Hence, Cloud applications are designed as microservices. Application scheduling and energy efficiency are key concerns in Cloud computing research. Allocating the microservice containers to the hosts in the datacenter is an NP-hard problem. There is a need for efficient allocation strategies to determine the placement of the microservice containers in Cloud datacenters to minimize Service Level Agreement violations and energy consumption. In this paper, we design a Reinforcement Learning-based Microservice Allocation (RL-MA) approach. The approach is implemented in the ContainerCloudSim simulator. The evaluation is conducted using the real-world Google cluster trace. Results indicate that the proposed method reduces both the SLA violation and energy consumption when compared to the existing policies. © 2019 IEEE.Item Concurrency analysis of go and java(Institute of Electrical and Electronics Engineers Inc., 2020) Abhinav, P.Y.; Bhat, A.; Joseph, C.T.; Chandrasekaran, K.There has been tremendous progress in the past few decades towards developing applications that receive data and send data concurrently. In such a day and age, there is a requirement for a language that can perform optimally in such environments. Currently, the two most popular languages in that respect are Go and Java. In this paper, we look to analyze the concurrency features of Go and Java through a complete programming language performance analysis, looking at their compile time, run time, binary sizes and the language's unique concurrency features. This is done by experimenting with the two languages using the matrix multiplication and PageRank algorithms. To the extent of our knowledge, this is the first work which used PageRank algorithm to analyse concurrency. Considering the results of this paper, application developers and researchers can hypothesize on an appropriate language to use for their concurrent programming activity.Results of this paper show that Go performs better for fewer number of computation but is soon taken over by Java as the number of computations drastically increase. This trend is shown to be the opposite when thread creation and management is considered where Java performs better with fewer computation but Go does better later on. Regarding concurrency features both Java with its Executor Service library and Go had their own advantages that made them better for specific applications. © 2020 IEEE.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.