Evaluation of Discrete Voltage Level for Fixed Priority Framework Energy-Efficient Scheduling

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: 136(3), P. 1637 - 1649

Published: June 1, 2024

Language: Английский

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Reducing Makespan and Enhancing Resource Usage in Cloud Computing With ESJFP Method: A New Dynamic Approach

Internet Technology Letters, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 12, 2024

ABSTRACT A well‐designed web‐based tool or application allows consumers to access on‐demand services on a pay‐per‐use basis via the internet in cloud computing service paradigm. Cloud continues be leading technology trend, with primary focus optimizing and enhancing end‐user applications. The increasing challenge of meeting diverse needs clints for providers is propelling development load scheduling algorithms. Some current algorithms used include First Come Serve (FCFS), Shortest Job (SJF) Round Robin (RR). response time makespan—the interval between start end times consecutive tasks same machine—are two most crucial balancing elements. Response refers duration server takes respond client's request. Measured milliseconds, this timer starts when client sends request stops its initial response. This study examines many methods suggests improvements computing's methodology. To minimize makespan maximize resource usage, we provide solution that Enhanced Priority (ESJFP) Under ESJFP method, computers more processing power are assigned longest higher MIPS (million instructions per second) needs, while machines lesser capacity shortest jobs lower requirements. By giving equal priority all activities, technique makes sure neither high‐MIPS nor low‐MIPS have wait an extended period allocation.

Language: Английский

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Efficient Hybrid DDPG task scheduler for HPC and HTC in cloud environment

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 108897 - 108920

Published: Jan. 1, 2024

Task Scheduling is a crucial challenge in cloud computing as diversified tasks come rapidly onto console dynamically from heterogeneous resources which consists of different task lengths, processing capacities. Generating schedules for these type Cloud Service Provider(CSP).Therefore, to generate paradigm effectively by considering arising and match it with respective Virtual Machine (VM), scheduler formulated using Deep Deterministic Policy Gradient (DDPG) algorithm used methodology design scheduler. This works three stages. In the initial stage, are classified based on length capacity identify them whether they High Performance Computing (HPC) or Throughput (HTC) tasks. After classification, second be tracked matches corresponding nature Finally, third according VM priorities calculated electricity unit cost mapped VMs. Simulations conducted Cloudsim fabricated workload distributions realtime worklogs. our proposed Hybrid scheduler(HDDPGTS) evaluated over DQN, A2C algorithms. From results, proved that HDDPGTS significantly improved makespan, Energy consumption, scheduling overhead, scalability baseline approaches.

Language: Английский

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Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm

Network Computation in Neural Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 31

Published: Oct. 9, 2024

An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud planning approaches lack support for the emerging paradigm regarding asset management speed optimization. The use of computing relies heavily on task allocation scheduling issue is more crucial in arranging allotting application jobs supplied by customers Virtual Machines (VM) a specific manner. needs to be specifically stated increase efficiency. environment model developed using optimization techniques. This intends optimize both VM placement over environment. In this model, new hybrid-meta-heuristic algorithm named Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). multi-objective function considered with constraints like cost, time, utilization, makespan, throughput. proposed further validated compared against existing methodologies. total time required 30.23%, 6.25%, 11.76%, 10.44% reduced than ESO, RSO, LO, GOA 2 VMs. simulation outcomes revealed that effectively resolved VL issues.

Language: Английский

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