Abstract
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.
Highlights
Nowadays, cloud computing has become a backbone for government enterprises and education sectors because of providing continuous resource allocation service to ensure their application service reliability. e cloud service supplier shares the resources among end-users based on cost function’s value (CF) to meet the demand of system performance
As per the Gartner report, the cloud service provider (CSP) market would grow approximately 331.2 billion dollars in 2022 [1]. e cloud global report [2] confines 623.3-billion-dollar market growth rate in 2023 for data computation. e statistical analysis states that cloud computing has a notable impact on Security and Communication Networks the Internet of ings (IoT), blockchain, and soft computing measurement systems with artificial intelligence models. e tasks are divided into subtasks with relative attribute definitions through dynamic acyclic graph (DAG) theory. e DAG approach shows a prominent impact while dealing with complex workflow applications such as systematic mathematical applications [3,4,5]
Data analytic languages such as Hive and Pig [6,7,8] platforms handle the MapReduce model queries. us, the DAG theory’s importance tremendously changed over the past decade since it influences the service execution time and resource usage. erefore, this issue is formulated as NPhard [9], and many heuristic approaches resolved the same issue through resource usage consolidation [10,11,12]
Summary
Cloud computing has become a backbone for government enterprises and education sectors because of providing continuous resource (memory, CPU, and bandwidth) allocation service to ensure their application service reliability. e cloud service supplier shares the resources among end-users based on cost function’s value (CF) to meet the demand of system performance. (1) Develop a data analytic weight measurement (DAWM) approach to optimize service quality and price of CSP during an effective resource slicing process by considering each machine cost and revenue, and profit. X[i, j] matrix identifies the errand evolution time of all VMs under different instances To address all these issues, we design a data analytic weight measurement (DAWM) approach to optimize a cloud service provider’s quality and price during an effective resource slicing process by considering each machine’s cost and revenue, and profit. We design a multiobjective heuristic user service demand (MHUSD) algorithm based on the CPS profit estimation model and the user service demand (USD) model to measure the user demand service rate and cost by considering service demand weight, service tenant cost, and machine energy cost
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