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 preserving is a challenging task. Most of existing approaches consider task deadline violation rate rather than performance server size ratio during estimation, which impacts causes high cost. To address this issue, we develop two algorithms for maximization adequate reliability. First, belief propagation-influenced cost-aware asset scheduling approach derived based on data analytic weight measurement (DAWM) model effective optimization. Second, multiobjective heuristic user (MHUSD) formulated CPS estimation (USD) with dynamic acyclic graph (DAG) phenomena DAWM classifies prominent servers to preserve resource usage an slicing process considering each machine execution factor (remaining energy, cost, workload rate, configuration (CSC), requirement level agreement (SLAV) penalty rate). MHUSD algorithm measures USD models weight, tenant simulation results show that proposed system has accomplished average gain 35%, 51%, 39% state-of-the-art approaches.