Task scheduling and resource allocation in cloud computing using a heuristic approach

Gawali, Mahendra Bhatu; Shinde, Subhash K.

doi:10.1186/s13677-018-0105-8

Cited by 170 publications

(97 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [26] the heuristic approach is used to schedule the tasks through proper distribution of the resources. In this approach every incoming task is processed using modified analytic hierarchy process then the resources are scheduled using differential evolution algorithm.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

Bhargavi

Babu²

2019

Cybernetics and Information Technologies

View full text Add to dashboard Cite

Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application of Neutrosophic Soft-Set (NSS) and Fuzzy Neutrosophic Soft-Set (FNSS). The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering the RUBiS, RUBBoS, and Olio benchmark applications.

show abstract

Section: Related Workmentioning

confidence: 99%

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

Bhargavi

Babu²

2019

Cybernetics and Information Technologies

View full text Add to dashboard Cite

show abstract

“…It directly affects the efficiency of the entire cloud system. The major parameters to be considered while scheduling the tasks are the overall completion time, overall execution cost, throughput, load balance and consumption of resource [2]. Efficient task scheduling results in better utilization of the machines with minimized overall execution time and cost.…”

Section: Introductionmentioning

confidence: 99%

PE2 - A Service Oriented Meta Task Scheduling Framework in Cloud Environment

Krishnaveni*,

Janita

2019

IJITEE

View full text Add to dashboard Cite

Cloud computing is an emerging computing environment which facilitates on demand services. As it contributes a large pool of computing resources, scheduling of tasks in an efficient manner is one of the main problems. Poor allocation of tasks affects the performance of the whole system. Hence it is very important to schedule the tasks for better utilization of resources by allocating them properly to particular resources in particular time. Efficient scheduling algorithms fulfill the user requirements and also satisfy the needs of the cloud service providers without affecting the performance of the environment. Execution Time based Sufferage Algorithm (ETSA), Cost and Completion Time based Sufferage Algorithm (CCTSA) and Modified Artificial Fish Swarm(MAFSA) Algorithm are efficient task scheduling approaches developed in cloud environment. These algorithms considered the parameters such as makespan, cost and resource utilization while scheduling the tasks and produced better performance. This paper presents a scheduling framework which converts the above said algorithms in to services and deployed in the cloud. Depends on the user’s requirements, the services will be delivered.

show abstract

“…In (Gawali and Shinde, 2018), authors proposed a heuristic algorithm that performs task scheduling and allocates resources efficiently in cloud computing environments. They used real Cybershake and Epigenomics scientific workflows as input tasks for the system and have also considered the bandwidth as a resource.…”

Section: Related Workmentioning

confidence: 99%

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

View full text Add to dashboard Cite

Abstract:As the cloud computing technology is gaining popularity with time, more and more users and applications are shifting towards it. This is why clouds are experiencing high load, which demands for load balancing of user tasks submitted to cloud for execution. This makes load balancing of non-preemptive tasks a key issue in cloud computing. Superior task scheduling leads to balanced loads among cloud nodes, which results in faster execution of tasks. Task scheduling in cloud environment is an instance of NP-hard optimization problem. When few nodes in a cloud are overloaded whereas other nodes are under loaded then in such situation the performance of overloaded VMs is diminished. It demands a task scheduling so that the incoming tasks can be distributed uniformly across virtual machines (VMs) for proper utilization of available resources. In this study, we propose a novel load balancing algorithm named Viral System Based Load Balancing (VSB-LB) algorithm, which is based on bio-inspired viral system algorithm that distributes the tasks uniformly among VMs. The proposed algorithm is compared with basic load balancing algorithms such as First Come First Serve (FCFS), Weighted Round Robin (WRR) as well as newer bio-inspired Load balance Aware Genetic Algorithm (LAGA) to show its effectiveness. Simulation results proved that VSBLB outperforms FCFS and WRR and LAGA for performing load balancing.

show abstract

Task scheduling and resource allocation in cloud computing using a heuristic approach

Cited by 170 publications

References 22 publications

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

PE2 - A Service Oriented Meta Task Scheduling Framework in Cloud Environment

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Contact Info

Product

Resources

About