Energy-Efficient Task Scheduling for Heterogeneous Cloud Computing Systems

Zhao, Hui; Qi, Ge; Wang, Quan; Yang, Pengfei; Qiao, Lei

doi:10.1109/hpcc/smartcity/dss.2019.00137

Cited by 16 publications

(11 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous competent scheduling approaches to optimize energy utilization have been researched. [30][31][32][33][34] called the enhancing HEFT (EHEFT) and the enhancing critical path on a processor, addresses the energy-efficient and schedule length in workflow scheduling. But these two approaches, only find power inefficient processors to reduce energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Energy and cost aware workflow scheduling in clouds with deadline constraint

Medara

Singh

Sompalli

2022

Concurrency and Computation

View full text Add to dashboard Cite

Cloud computing is a promising platform for executing scientific workflow applications.Commonly, the scientific workflow applications are complex in size and computation intensive. Task scheduling in clouds is a familiar NP-complete problem. Efficient workflow application scheduling is critical for meeting multi-objectives in cloud environments. By reason of its pivotal role, this problem has been widely studied, and many approaches have been developed. Most of the algorithms focus on schedule makespan and execution cost. The cloud data centers consume a large amount of energy while running workflow applications due to a lack of efficient scheduling algorithms to perform the task to a virtual machine (VM) mapping. Hence, there is a significant need to address energy utilization in the cloud data center (CDC) for two reasons such as data center operational cost optimization and to improve the environment. This article presents an energy and cost-aware scheduling (ECWS) approach with the objectives of decreasing energy utilization, execution cost, and maximizing utilization of the resources. The ECWS algorithm is a heterogeneous earliest finish time (HEFT) based energy-efficient heuristic for cloud scheduler. The ECWS algorithm includes three sub-algorithms like RE calculation, RE Threshold selection, and slack algorithm.The effectiveness of the proposed algorithm is evaluated on the WorkflowSim tool using distinct workloads from various scientific areas. The experimental outcome exhibit that the proposed algorithm achieved significant energy conservation, maximized resource utilization, and cost-saving when compared with related well-known algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Energy and cost aware workflow scheduling in clouds with deadline constraint

Medara

Singh

Sompalli

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Consumption of energy and delay process in network is handled using NOVEL system design algorithm. It shows how better novel algorithm works to provide various simulation results [32]. Placing the virtual machines in the network is addressed using Swarm modified Salp algorithm.…”

Section: Related Workmentioning

confidence: 99%

Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

Kumar¹,

Venkatachalam²,

Masud³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Huge data processing applications are stored efficiently using cloud computing platform. Few technologies like edge computing, Internet of Things (IoT) model helps cloud computing framework for executing data with less energy and latencies for better infrastructure. Recently researches focused on providing excellent services to cloud computing users. Also, cloud-based services are highly developed over IT field. Energy a level varies based on the cloud setup like speed, memory, service capability and bandwidth. The user job requirements are varied based its nature. The process of identifying efficient energy resources for the user job is main aim of this research work. Initially IoT, Edge devices capture the job and process to help cloud infrastructure. Data transferring process is a Non-deterministic Polynomial (NP) hard problem which can be easily supported by technologies like IoT and Edge computing. Main issue is, tremendous increase of data over the cloud causes difficult to manage consumption of energy. In this research work server clouds with edge devices and centralized cloud servers are combined to work for providing efficient energy consumption. Here, in this paper we implement novel dynamic speed scaling (NDS) algorithm. The CPU workload is first computed using NDS algorithm for incoming application. The less energy computation is achieved using energy internet of things (EIoT). Fine methodology called speed processor scaling, helps to consume less energy and less computation price. High energy consumption of processor is due to high computation speed. likewise, less energy is consumed during less processor computation speed. This technique is building in NDS algorithm and computed in Edge devices for less energy consumption. The result evaluation proves that proposed technique consumes only 10 s for data computation when compared to other existing techniques.

show abstract

“…A power-aware scheduling approach for a heterogeneous cloud network was suggested to solve the issue of high energy consumption. The results show that the average power consumption in this system is 23.9-6.6% lower than in modern technology [23]. An abstract model was proposed that uses piecewise linear functions to handle data analytics workload in a distributed cluster architecture.…”

Section: Related Workmentioning

confidence: 99%

An optimized cost-based data allocation model for heterogeneous distributed computing systems

Tarun

Dubey

Batth

et al. 2022

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.</span>

show abstract

Energy-Efficient Task Scheduling for Heterogeneous Cloud Computing Systems

Cited by 16 publications

References 19 publications

Energy and cost aware workflow scheduling in clouds with deadline constraint

Energy and cost aware workflow scheduling in clouds with deadline constraint

Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

An optimized cost-based data allocation model for heterogeneous distributed computing systems

Contact Info

Product

Resources

About