Resource Scheduling Algorithms for Cloud Computing Environment: A Literature Survey

Arulkumar, V.; Bhalaji, N.

doi:10.1007/978-981-15-0146-3_102

Cited by 5 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ibrahim (2021) surveyed the load balancing approaches and categorized the different algorithms based on their performances in under loaded and over loaded conditions. In Arulkumar and Bhalaji (2020), the bargaining game algorithm is proposed for resource allocation by considering requirements of the clients and improving the performance with respect to deadline and budget. Song et al (2010) proposed improved scheduling algorithm for resources scheduling in cloud.…”

Section: Literature Surveymentioning

confidence: 99%

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

Marri

Rajalakshmi

2021

IJICC

View full text Add to dashboard Cite

PurposeMajority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approachCloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.FindingsThe energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/valueThis paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

show abstract

Section: Literature Surveymentioning

confidence: 99%

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

Marri

Rajalakshmi

2021

IJICC

View full text Add to dashboard Cite

show abstract

“…Although researchers have proposed a lot of resource scheduling algorithms still the cloud service providers find it hard to select a suitable algorithm for their implementations. This is because of the interdependencies, dispersion of assets, heterogeneity of resource types, and uncertainty in the cloud environment Arulkumar & Bhalaji (2020) .…”

Section: Introductionmentioning

confidence: 99%

Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

Sana¹,

Li²

2021

PeerJ Computer Science

View full text Add to dashboard Cite

In the last decade, cloud computing becomes the most demanding platform to resolve issues and manage requests across the Internet. Cloud computing takes along terrific opportunities to run cost-effective scientific workflows without the requirement of possessing any set-up for customers. It makes available virtually unlimited resources that can be attained, organized, and used as required. Resource scheduling plays a fundamental role in the well-organized allocation of resources to every task in the cloud environment. However along with these gains many challenges are required to be considered to propose an efficient scheduling algorithm. An efficient Scheduling algorithm must enhance the implementation of goals like scheduling cost, load balancing, makespan time, security awareness, energy consumption, reliability, service level agreement maintenance, etc. To achieve the aforementioned goals many state-of-the-art scheduling techniques have been proposed based upon hybrid, heuristic, and meta-heuristic approaches. This work reviewed existing algorithms from the perspective of the scheduling objective and strategies. We conduct a comparative analysis of existing strategies along with the outcomes they provide. We highlight the drawbacks for insight into further research and open challenges. The findings aid researchers by providing a roadmap to propose efficient scheduling algorithms.

show abstract

A Load Balancing-Based Container Migration Mechanism in the Smart Grid Cloud Platform

Wu,

Li,

2024

2024 9th International Conference on Computer and Communication Systems (ICCCS)

View full text Add to dashboard Cite

Resource Scheduling Algorithms for Cloud Computing Environment: A Literature Survey

Cited by 5 publications

References 17 publications

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

A Load Balancing-Based Container Migration Mechanism in the Smart Grid Cloud Platform

Contact Info

Product

Resources

About