Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Chandrasekaran, K.Subject: search.filters.subject.Scheduling

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    Analysis of MapReduce scheduling and its improvements in cloud environment
    (Institute of Electrical and Electronics Engineers Inc., 2015) D'Souza, S.; Chandrasekaran, K.
    MapReduce has become a prominent Parallel processing model used for analysing large scale data. MapReduce applications are increasingly being deployed in the cloud along with other applications sharing the same physical resources. In this scenario, efficient scheduling of MapReduce applications is of utmost importance. Also, MapReduce has to consider various other parameters like energy efficiency and meeting SLA goals besides achieving performance when executing jobs in cloud environments. In this work, we have classified MapReduce Scheduling as Cluster based Scheduling and Objective based Scheduling. We then summarize and analyse the different class of schedulers highlighting the strong points and limitations of each of the scheduling approaches. The Adaptive scheduling techniques provide dynamic resource management and meet performance goals. The Energy efficient scheduling techniques aim to cut data centre costs by using different approaches. Finally, we discuss the current challenges and future work. © 2015 IEEE.
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    Analysis of emerging workflow scheduling algorithms in cloud
    (Institute of Electrical and Electronics Engineers Inc., 2016) Raghavan, S.; Chandrasekaran, K.
    Workflow has a great impact on several diverse fields of science such as Physics, Bioinformatics, Astronomy etc. The computational requirements of the world is increasing exponentially and so the use of workflows. These requirements are expected to increase by many folds. Thus workflow can be considered as one of the important areas to be explored. Workflow is used extensively by the scientific community for automation. Similarly these workflows have a significant impact on grid computing, cloud computing and other distributed environments. Moreover it is because of grid computing technology that power of workflow is realizable. Similarly cloud computing also supports workflows. Workflow scheduling is considered to be the core when it comes to workflow thus it is highly important to know what are workflow scheduling algorithms available for scheduling workflow in cloud. This paper gives an overview about workflow, the impact of workflows on cloud and other important issues related to it are presented. This paper primarily focuses on Workflow scheduling algorithms in cloud and analysis of the same is presented. © 2015 IEEE.
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    Optimization-Aware scheduling in cloud computing
    (Association for Computing Machinery acmhelp@acm.org, 2016) Binu, A.; George, N.; Chandrasekaran, K.
    Cloud computing allows delivery of computational resources via internet. Some of the computational resources include signals, codes and physical resources. Cloud users require computational resources for execution of their tasks. The computational elements, when thoroughly assigned to the cloud users according to requirement facilitate an efficient scheduling mechanism. The efficiency is dependent on the parameters chosen to promote the assignment of task to resources. Here, computational cost is chosen as the optimization factor, and the solution with the least cost is selected as the best assignment. A Cuckoo Search inspired technique is used, and task assignment to resources is done by validating the solution with the least value of cost. © 2016 ACM.
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    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.