An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems

Panda, Sanjaya Kumar; Jana, Prasanta K.

doi:10.1007/s10586-018-2858-8

Cited by 90 publications

(31 citation statements)

References 28 publications

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“…The rationality behind this threshold is that energy consumption is drastically increased beyond this threshold. Panda and Jana [17] have addressed the demerits associated with task consolidation and scheduling, and proposed an energy-efficient task scheduling without considering nodes. In the above works, the RE-based sources are not used to reduce energy consumption.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Renewable Energy-Based Scheduling Algorithm for Cloud Computing

Nayak

Panda

Das

et al. 2020

Distributed Computing and Internet Technology

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The global growth of cloud computing services is witnessing a continuous surge, starting from storing data to sharing information with others. It makes cloud service providers (CSPs) efficiently utilize the existing resources of datacenters to increase adaptability and minimize the unexpected expansion of datacenters. These datacenters consume enormous amounts of energy generated using fossil fuels (i.e., non-renewable energy (NRE) sources), and emit a substantial amount of carbon footprint and heat. It drastically impacts the environment. As a result, CSPs are pledged to decarbonize the datacenters by adopting renewable energy (RE) sources, such as solar, wind, hydro and biomass. However, these CSPs have not completely ditched fossil fuels as RE sources are subjected to inconsistent atmospheric conditions. Recent studies have suggested using both NRE and RE sources by the CSPs to meet user requirements. However, these studies have not considered flexible duration, nodes and utilization of the user requests (URs) with respect to datacenters. Therefore, we consider these URs' properties and propose a RE-based scheduling algorithm (RESA) to efficiently assign the URs to the datacenters. The proposed algorithm determines both the earliest completion time and energy cost, and takes their linear combination to decide a suitable datacenter for the URs. We conduct extensive simulations by taking 1000 to 16000 URs and 20 to 60 datacenters. Our simulation results are compared with other algorithms, namely roundrobin (RR) and random, which show that RESA is able to reduce the overall completion time (i.e., makespan (M )), energy consumption (EC), overall cost (OC) and the number of used RE (|URE|) resources.

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Section: Related Workmentioning

confidence: 99%

“…Note that existing scheduling algorithms do not model the URs in flexible duration, nodes and utilization; hence, those algorithms are not directly comparable to the proposed algorithm. Therefore, we compare with RR and random as compared in [10,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Renewable Energy-Based Scheduling Algorithm for Cloud Computing

Nayak

Panda

Das

et al. 2020

Distributed Computing and Internet Technology

View full text Add to dashboard Cite

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“…The current iteration is represented as X, the position vector is represented as ⃗ S and the best solution position vector is represented as − → S * . The coefficient vector is denoted as − → T The current best search agent's position is represented in Equation (6). The Equation 7calculates the new position.…”

Section: The Proposed Load Balancing Algorithmmentioning

confidence: 99%

“…Due to the reason in the iteration of the solution till reaching the stop criteria or accomplishing the maximum number of iterations (4,5) . However, the main purpose to implement the metaheuristics algorithms is to augment the heuristic algorithm efficiency (6) . The users of the cloud service provider use the pay per use model for gaining the cloud resources.…”

Section: Introductionmentioning

confidence: 99%

A QoS Aware FTOPSIS-WOA based task scheduling algorithm with load balancing technique for the cloud computing environment

Samriya¹,

Kumar²

2020

IJST

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Objectives: To perform task scheduling with minimising the makespan through implementing an effective load balancing approach. Methods: In this study, the Fuzzy Topsis algorithm (FTPOSIS) is used for the task scheduling and the makespan is minimised with the effective load balancing by modelling the whale optimization algorithm (WOA). Findings: This proposed model controls the admittance of the requests by achieving target QoS in terms of response time. Hence, the admittance is controlled so that the requests which are accepted do not face a delay greater than the time limit stated in the SLA. Using CloudSim tool the simulation is done and the results are exhibited. The effectiveness of the intended algorithm is compared with the existing methods. Novelty: The novelty of this study includes increasing the throughput of the cloud system by reducing the makespan of the cloud scheduling process. Reducing SLA violations and improving the QoS can efficiently give assurance to reduce the delay of transmission, packet loss rate of data. Attaining a balance between constrained resources and QoS.

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“…It provides the flexibility of resources for everyone. These services are provided via data centers, which are located in various parts of the world [2,3]. Cloud computing makes access to these resources to everyone on a global scale at a very minimal cost and significantly higher speed.…”

Section: Introductionmentioning

confidence: 99%

A Study on Classifications of Cloud

Tayade¹,

Khan²

2020

Int. J. Eng. Tech. Mgmt. Res.

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“The interesting thing about cloud computing is that we’ve redefined cloud computing toinclude everything that we already do.” - Larry EllisonCloud computing is an emerging field that is expected to change the entire informationtechnology processes and IT market in the future. It is changing how we develop, deploy, anduse the information technology and the infrastructure that it runs on. Cloud Computing canbe defined as Internet-based computing in which virtually shared servers that are data centersprovide software, platform, infrastructure, policies and many resources.Clouds can be classified in various types: Public, Private, Hybrid and Virtual Private. In thispaper we will understand the Different Classifications of Clouds – IaaS, PaaS, SaaS – Public,Private, Community, Hybrid and also match Cloud Providers to Our Needs – Amazon, Google,Microsoft.

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An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems

Cited by 90 publications

References 28 publications

An Efficient Renewable Energy-Based Scheduling Algorithm for Cloud Computing

An Efficient Renewable Energy-Based Scheduling Algorithm for Cloud Computing

A QoS Aware FTOPSIS-WOA based task scheduling algorithm with load balancing technique for the cloud computing environment

A Study on Classifications of Cloud

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