DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre
The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches. 2021 M. S. Mekala et al.
Mekala M.S.; Patan R.; Islam S.K.H.; Samanta D.; Mallah G.A.; Chaudhry S.A.
Security and Communication Networks, Vol-2021
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1155/2021/6688162 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107652607&doi=10.1155%2f2021%2f6688162&partnerID=40&md5=c0eb6a1806ecdede2546f3c2a5c0266e
Mekala M.S., Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38544, South Korea, RLRC for Autonomous Vehicle Parts and Materials Innovation, Yeungnam University, Gyeongsan, 38544, South Korea; Patan R., Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India; Islam S.K.H., Department of Computer Science and Engineering, Indian Institute of Information Technology, Kalyani, West Bengal, 741235, India; Samanta D., Department of Computer Science, CHRIST University, Bengaluru, 560029, India; Mallah G.A., Department of Computer Science, Shah Abdul Latif University, Khairpur, Sindh, 66020, Pakistan; Chaudhry S.A., Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Avcilar, Istanbul, 34310, Turkey