Task Deadline-Aware Energy-Efficient Scheduling Model for a Virtualized Cloud

Garg, Neha; Goraya, Major Singh

doi:10.1007/s13369-017-2779-5

Cited by 22 publications

(16 citation statements)

References 22 publications

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“…It simultaneously minimizes makespan and power consumption. Garg and Goraya 26 introduced an energy‐efficient heuristic for scheduling tasks under the deadline constraint in virtualized cloud. This method contains two instances to improve the energy efficiency by executing maximum workload of the VM and by maximum energy saving in the idle state of the VM.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA).Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well-known NPhard problem. To resolve this problem, this paper proposes an energy-efficient task-scheduling algorithm based on best-worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision-making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. K E Y W O R D Sbest-worst method (BWM), cloud computing, energy consumption, multi-criteria decision making, TOPSIS method

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Section: Related Workmentioning

confidence: 99%

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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“…includes network cables, server, lightning, and air conditioner etc. which consumes high electricity and emit a large amount of carbon-di-oxide ( which increase the environmental hazards such as global warming [4][5][6]. For example, in [7], seventy billion kilowatts per hour (kWh) energy is expended by datacenters in U.S.…”

Section: Such As Utilization Of the Resources Energy Minimization) Ementioning

confidence: 99%

“…resources use the energy to execute the workload. Like paper [4,18], energy consumption of only CPU is considered. Equation 2 describes the total energy consumptionas:…”

Section: A Energy Consumptionmentioning

confidence: 99%

VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

Garg¹,

Singh²,

Goraya³

2019

IJRTE

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Cloud computing, a metered based technology provides the services using virtualized technology over the internet. In the cloud environment, to improve the performance (such as utilization of the resources, energy minimization) extreme number of virtual machines (VMs) can be installed on the servers as per their resource capacity. In this way, servers can be overloaded. Overloaded servers consume more energythan normal status servers. VM migration (VMM) is an efficient technique to become a server in a normal state. VMM technique is used to consolidate the resources to increase resource utilization (RU) and reduceenergy usage. In the VMM technique, selection of VM such as which VM is migrated from one server to another server and allocation of VM on servers is an important aspect. Appropriate VM selection declines the numeral of VMMs and increasesenergy efficiency. Appropriate VM allocation declines the server to become overloaded. In this paper, the VM selection and allocation strategy is presented. CloudSim toolkit is used to verify the strength of proposed VM selection and allocation algorithm. Proposed VM Selection algorithm (MaMT) performs better than existing MiMT algorithm in terms of total energy consumption, number of hosts shut down, number of VMM, and average Service Level Agreement (SLA) violation rate. MaMT algorithm with resource aware provisioning (RAP) and MiMT+RAP algorithm combines both VM selection and allocation policies. RAP algorithm used both energy and RU parameters while allocating VM to the server.MaMTreduces the energy consumption up to 7.25% and reduces the SLA violation rate up-to 2.6% in comparison to MiMT algorithm. When VM selection and allocation policies combines together than more system performance is improved. MaMT+RAPreduces the energy consumption up to6.76% and reduces the SLA violation rate up-to 0.22% in comparison to MaMT algorithm.MiMT+RAPreduces the energy consumption up to15.23% and reduces the SLA violation rate up-to 0.95% in comparison to MiMT algorithm.

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“…Garg and Goraya [11] had presented another model for task deadline cognizant energy proficient development used in a virtualized haze. The independent and the deadline conscious errands have been programmed by revenues of virtualizing physical multitudes found in an information center.…”

Section: Related Workmentioning

confidence: 99%

An Energy and Deadline Aware Scheduling Using Greedy Algorithm for Cloud Computing

Venuthurumilli¹,

Mandapati²

2019

ISI

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Cloud computing has been providing various services to different users by means of an aid of large and scalable virtualized resources on the internet. Owing to all the recent and inventive developments that are found in the field, there are several scheduling algorithms which were developed in a cloud computing environment with the intention of decreasing the services given in cloud computing. For a very enormous gauge, assorted and the multiuser atmosphere in the cloud scheme where maximization of profit for that of the Cloud Service Provider (CSP) has been the primary objective. For the purpose of this work, the inclusive optimization problem in the operation of the cloud system by means of lowering the cost of procedure and by maximizing the efficiency of energy. At the same time, it satisfies the deadlines that are definite in Service Level Agreements (SLA) that has been addressed from a CSP perspective. The work proposes a Greedy algorithm for the environment of the cloud and this is compared to the scheduling of a First Come First Served (FCFS) and the Min-Min scheduling procedure. This system exploits the tasks and their heterogeneity and also the resources using a scheduler unit that schedules and allocates the tasks which are deadline-constrained which is delimited to the nodes that are energy conscious. After this, the CSP capitalizes on the parallelisms of data for every user workload and also effectively manages all collective user requests and also apply the custom optimization that creates a cost of global energy and a cloud platform which is deadline aware. The results of the experiment prove that this proposed Greedy algorithm which achieves a performance which is better (a guarantee ratio, utilization of resources and energy saving) compared to the FCFS and the Min-Min scheduling algorithm.

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Task Deadline-Aware Energy-Efficient Scheduling Model for a Virtualized Cloud

Cited by 22 publications

References 22 publications

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

An Energy and Deadline Aware Scheduling Using Greedy Algorithm for Cloud Computing

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