Research Area: Cloud Computing
Cloud computing, a multipurpose and high-performance internet-based computing, can model and transform a large range of application requirements into a set of workflow tasks. It allows users to represent their computational needs conveniently for data retrieval, reformatting, and analysis. However, workflow applications are big data applications and often take long hours to finish executing due to their nature and data size. In this paper, we study the cost optimised scheduling algorithms in cloud and proposed a novel task splitting algorithm named Cost optimised Heuristic Algorithm (COHA) for the cloud scheduler to optimise the execution cost. In this algorithm, the large tasks are split into sub-tasks to reduce their execution time. The design purpose is to enable all tasks to adequately meet their deadlines. We have carefully tested the performance of the COHA with a list of workflow inputs. The simulation results have convincingly demonstrated that COHA can effectively perform VM allocation and deployment, and well handle randomly arrived tasks. It can efficiently reduce execution costs while also allowing all tasks to properly finish before their deadlines. Overall, the improvements in our algorithm have remarkably reduced the execution cost by 32.5% for Sipht, 3.9% for Montage, and 1.2% for CyberShake workflows when compared to the state of art work.
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Author(s) Name: J. Kok Konjaang; Lina Xu
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Conferrence name: 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security
Publisher name: IEEE
DOI: 10.1109/BigDataSecurity-HPSC-IDS49724.2020.00038
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