A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment

Alla, Said Ben; Touhafi, Abdellah; Ezzati, Abdellah

doi:10.1007/s10586-018-2811-x

Cited by 69 publications

(19 citation statements)

References 26 publications

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“…We denote LT as the loading of the workload request, and LTi is the prediction priority queue arrival time for workload request j. LTremain is the remaining prediction priority queue length. Assume there are LN requests that need to be loaded into the loading queue, then the loading cycle LT can be calculated by equation (3), in which LTn is the time when queue arrives at workload request LN, τLN is the loading time for the prediction priority queue load and n is the emergence in the queue. LNe (LN, 0) is the time for the queue, returning from the last load request n to load.…”

Section: Imentioning

confidence: 99%

“…Without it, the unequal burdens in server formation may cause asset wastage, execution corruption, and SLA violation [2]. Accordingly, using the right load balancing strategy can improve servers' usage and give better assurance of Quality of Service (QoS) [3]. A portion of the specialists in this field centers around the virtual machine distribution or virtual machine movement to accomplish load adjusting [4].…”

Section: Introductionmentioning

confidence: 99%

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A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

Sohani

Jain

2021

IEEE Access

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Section: Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

Sohani

Jain

2021

IEEE Access

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“…Dynamic requests for heterogeneous resources are defined to minimize execution time and increase resource utilization. Reference [22] aimed to solve the problem of dynamic load balancing by proposing a hybrid PSO algorithm. Workload is distributed across different resources via load balancing.…”

Section: Related Workmentioning

confidence: 99%

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

et al. 2019

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Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

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“…The result of the experiments indicates that the proposed algorithm can maintain the load balance in a dynamic environment. Last but not least, the work in [22] have proposed two algorithms based on hybrid meta-heuristic and Dynamic dispatch Queues (TSDQ). The hybrid meta-heuristic uses the Fuzzy Logic with Particle Swarm Optimization algorithm (TSDQ-FLPSO), while TSDQ involves the simulated annealing with Particle Swarm Optimization algorithm (TSDQ-SAPSO).…”

Section: Hybrid Strategymentioning

confidence: 99%

Performance Evaluation of Task Scheduling using Hybrid Meta-heuristic in Heterogeneous Cloud Environment

Saif¹

2019

IJATCSE

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Cloud computing is a ubiquitous platform that offers a wide range of online services to clients including but not limited to information and software over the Internet. It is an essential role of cloud computing to enable sharing of resources on-demand over the network including servers, applications, storage, services, and database to the end-users who are remotely connected to the network. Task scheduling is one of the significant function in the cloud computing environment which plays a vital role to sustain the performance of the system and improve its efficiency. Task scheduling is considered as an NP-complete problem in many contexts, however, the heterogeneity of resources in the cloud environment negatively influence on the job scheduling process. Furthermore, on the other side, the heuristic algorithms have satisfying performance but unable to achieve the desired level of the efficiency for optimizing the scheduling in a cloud environment. Thus, this paper aims at evaluating the effectiveness of the hybrid meta-heuristic that incorporate genetic algorithm along with DE algorithm (GA-DE) in terms of make-span. In addition, the paper also intends to enhance the performance of the task scheduling in the heterogeneous cloud environment exploiting the scientific workflows (Cybershake, Montage, and Epigenomics). The proposed algorithm (GA-DE) has been compared against three heuristic algorithms, namely: HEFT-Upward Rank, HEFT -Downward Rank, and HEFT -Level Rank. The simulation results prove that the proposed algorithm (GA-DE) outperforms the other existing algorithms in all cases in terms of make-span.

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A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment

Cited by 69 publications

References 26 publications

A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

Performance Evaluation of Task Scheduling using Hybrid Meta-heuristic in Heterogeneous Cloud Environment

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