Cloud computing through dynamic resource allocation scheme

Gupta, Priya; Samvatsar, Makrand; Singh, Upendra

doi:10.1109/iceca.2017.8212723

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…The execution of the high priority tasks shouldn't be delayed by the low priority tasks. In this situation, low and high priority jobs compete for resources to access the resources [7]. A cost-based resource allocation strategy that gives priority to tasks depending on the price the user has paid the service provider for the resource being utilized.…”

Section: Year 2017mentioning

confidence: 99%

A Survey on Resource Allocation Techniques in Cloud Computing

Priyadarshini¹,

Nithya²,

Reka³

2022

IJRITCC

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Cloud is an important and emerging technology utilized by various fields for storing, processing and retrieving of data anywhere and anytime without any interruption. Cloud is now acting as a platform for many companies for storing and other computational purposes to reduce infrastructure and maintenance cost similarly they can utilize their application widely based on pay per use. To make available of data to all cloud users Resource Allocation (RA) is mandatory process. In cloud hardware, software and platform are the resources utilized to satisfy user needs hence sharing these resources according to users need is a difficult task. Cloud service provider and cloud service consumer plays the major role in RA. The parameters under resource allocation, its issues and challenges are needed to be analyzed deeply before implementing any optimizing approach in RA. Hence in this work various resource allocation methods have been studied and issues in it is analyzed and presented as a survey. This work is useful for both cloud users and researchers in overcoming the challenges faced in RA.

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Section: Year 2017mentioning

confidence: 99%

A Survey on Resource Allocation Techniques in Cloud Computing

Priyadarshini¹,

Nithya²,

Reka³

2022

IJRITCC

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“…These results showcase the superiority of using a pre-calculated metric, rather than fixating on a given policy. Value of service (VoS) is a novel metric defined in [8], wherein the value of the services given by a cloud provider are decided by the virtual machine's previous ability to complete a task within a given deadline and the energy needed by the VM to execute the given task. Using these parameters, a VoS based scheduling algorithm is defined in [8], which uses soft and hard thresholds for scheduling tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Value of service (VoS) is a novel metric defined in [8], wherein the value of the services given by a cloud provider are decided by the virtual machine's previous ability to complete a task within a given deadline and the energy needed by the VM to execute the given task. Using these parameters, a VoS based scheduling algorithm is defined in [8], which uses soft and hard thresholds for scheduling tasks. The VoS based algorithm is compared with a non-VoS based system, and it is observed that the VoS based system is 50% more responsive, and 40% more energy efficient.…”

Section: Literature Reviewmentioning

confidence: 99%

Statistical Evaluation of Task Scheduling Algorithms in Cloud Environments

Kantale¹

2020

IJATCSE

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Task scheduling algorithms in cloud have come a long way, from simplistic algorithms like first come first serve, to bio-inspired & machine learning algorithms like Q-learning and genetic algorithms. The main objective of any task scheduling algorithm is to minimize the number of execution cycles needed to completely and effectively execute a given set of tasks. In this work, we present a comparison of different scheduling algorithms and their performance evaluation. The proposed research takes into consideration different nuances of cloud task scheduling, which include task length, task completion time, virtual machine configuration and task deadline. Moreover, the review also includes containers, which ensures that the scheduler works effectively on a container environment to improve efficiency of the cloud deployment. We propose different comparisons of the algorithms which are under different task conditions. In our analysis, we reviewed all the algorithms on different tasks and under different virtual machine (VM) configurations. As per the research, the machine learning based algorithms perform better in terms of overall task scheduling efficiency when compared with others.

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Best-KFF: a multi-objective preemptive resource allocation policy for cloud computing systems

Fathalla

Salah

2021

Cluster Comput

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Cloud computing through dynamic resource allocation scheme

Cited by 4 publications

References 4 publications

A Survey on Resource Allocation Techniques in Cloud Computing

A Survey on Resource Allocation Techniques in Cloud Computing

Statistical Evaluation of Task Scheduling Algorithms in Cloud Environments

Best-KFF: a multi-objective preemptive resource allocation policy for cloud computing systems

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