A comprehensive survey for scheduling techniques in cloud computing

Kumar, Mohit; Sharma, S. C.; Goel, Anubhav; Singh, Santar Pal

doi:10.1016/j.jnca.2019.06.006

Cited by 276 publications

(118 citation statements)

References 121 publications

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“…This work requires various resources needed by each tasks are known and constants in amount, which does not match reality. The resources are both considered as VMs for offloaded task processing in this work which ignores the heterogeneity between edge and cloud resources, may lead to resource inefficiency [126].…”

Section: ) All Offloading A: Response Time Optimizationmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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Section: ) All Offloading A: Response Time Optimizationmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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“…According to these advantages, several studies established that the MH methods provide good results for the task scheduling problems in cloud computing than other traditional methods [16,30]. e authors in [31,32] provided a comprehensive review of various metaheuristics that have been developed for solving the task scheduling problem in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Attiya

Elaziz

Xiong

2020

Computational Intelligence and Neuroscience

100

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In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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“…Scheduling algorithms can be divided into static and dynamic algorithms [29,36]. Static scheduling algorithms before execution (upfront) require detailed information regarding the tasks, such as length, number of tasks, and the deadlines for its execution, as well as information regarding the resources (VMs) that should be provided, such as available processing power, memory capacity, energy consumption, etc.…”

Section: Resource Scheduling In Cloud Computingmentioning

confidence: 99%

“…We adjusted the basic WOA-AEFS parameters as follows: size of population to 30 (N = 30) and maximum number of iterations in one run to 1000 (maxIter = 1000). Parameter limit was set to 33 (round(1000/30)), while the dynamic parameters a, eir, and α were adjusted during the course of one run by expressions (28), (33) and (36), respectively. The other WOA-AEFS parameters were adjusted as shown in Table 3.…”

Section: Simulations With Real Data Setmentioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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A comprehensive survey for scheduling techniques in cloud computing

Cited by 276 publications

References 121 publications

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

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