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Abstract

The growth of cloud computing has resulted in uneconomic energy consumption, which has negatively impacted the environment through the generation of carbon emissions. This project proposes a distributed Locust-inspired scheduling algorithm to reduce cloud computing consumed energy (LACE). It schedules and optimizes the allocation of virtual machines (VMs) based on behavior derived from locusts. LACE distributes scheduling among servers; each server is responsible for allocating and migrating its VMs. Therefore, the scheduling load is distributed between servers rather than being centralized in one component. LACE was thoroughly evaluated by equaling it with long-standing VM scheduling algorithms: dynamic voltage–frequency scaling (DVFS), energy-aware scheduling using the workload-aware consolidation technique, and the static threshold with minimum utilization policy. In addition, this project proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) and Platform as services clouds (PaaS). This project presents an algorithm based on the Superior Element Multitude Optimization (SEMO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. The main scope of the project is used to analyze best available resource in the cloud environment depends upon the total execution time and total execution cost which is compare between one process to another process. If the provider satisfies the time least time, then the process becomes to termination.

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How to Cite
P.Ranganathan, & S.Jagadeesan. (2019). Improve cloud base Wi-Fi network using load base mobile cloud computing model. International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 674–681. Retrieved from https://ijiarec.com/ijiarec/article/view/994