Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

Abstract

In today's world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.

Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

“…Cost-Effective Optimal Task Scheduling Model (CEOTS) was introduced in [21] for minimizing the cost and make span. However, the energy usage was not considered.…”

Dynamic Cloud Resource Allocation: A Broker-Based Multi-Criteria Approach for Optimal Task Assignment

“…In general, the service contract (or SLA) specifies the cloud service's cost based on the quantity and quality of service. According to SLA standards, identifying an effective solution for handling consumers' requests is required even as the number of cloud consumers grows [1]. Service providers such as Amazon and Google have introduced a variety of cloud services in response to the rapid development of cloud computing, enabling users to manage enormous datasets operating on remote servers without having to install and execute on local devices [2].…”