Cloudlet Scheduling with Particle Swarm Optimization

Al-Olimat, Hussein S.; Alam, M. S.; Green, Robert C.; Lee, Jong Kwan

doi:10.1109/csnt.2015.252

Cited by 42 publications

(24 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For further studies, we intend to apply the proposed algorithm to practical applications, such as application switch problem [ 32 ] and task scheduling problem [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, it is infeasible to solve them with exhaustive method. So as to optimize these problems, such as unit commitment [ 1 ], feature selection [ 2 , 3 ], task scheduling [ 4 , 5 ], and 0-1 knapsack problem [ 6 , 7 ], binary algorithms are proposed to generate binary solutions. For example, Mirjalili et al [ 8 ] adapted the standard continuous BA algorithm to be applied to binary spaces and then BBA combined with k -Nearest Neighbor (KNN, k = 1) method was used to solve feature selection problem [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

Huang

Zeng

Han

2017

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.

show abstract

“…For further studies, we intend to apply the proposed algorithm to practical applications, such as application switch problem [ 32 ] and task scheduling problem [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

Huang

Zeng

Han

2017

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Simulations were carried out to compare the optimization ability of the proposed algorithm (CBA) [14] in scheduling problem with the classical BA [5], GA [4], PSO with dynamic inertia weight [15], [16] and Symbiotic organism [2]. We use chaotic maps for Performance improvements.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Bi-Objective Task Scheduling in Cloud Computing using Chaotic Bat Algorithm

Farkar¹,

Kazem²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Cloud computing is a technology for providing services over the Internet. It gives approach to renting IT infrastructures on a short-term pay-per-usage basis. One of the service provider's goals is to use the resources efficiently and gain maximum profit. Cloud processes a huge amount of data, so tasks scheduling is a vital role in the cloud computing. The purpose of this paper is to propose a method based on chaos theory and bat algorithm for task scheduling in Cloud computing. Task scheduling is a core and challenging issue in cloud computing. The nature of the scheduling issue is as an NPHard problem and because of the success of heuristic algorithms in optimization and NP-Hard problems, the authors use a newly inspired bat algorithm and chaos theory to scheduling the tasks in the cloud. In this method, bat or candidate solutions are represented by a one-dimensional array. The fitness function is calculated based on makespan and energy consumption. The results show that the proposed method can schedule the received tasks in proper time than other compared heuristic algorithms, also the proposed method has better performance in term of makespan and energy consumption than compared methods.

show abstract

“…e results of TSDQ-FLPSO outperform those of the other methods including the TSDQ-SAPSO. Other works related to the application of PSO to task scheduling in cloud computing have been reported in the literature [36][37][38].…”

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

View full text Add to dashboard Cite

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.

show abstract

Cloudlet Scheduling with Particle Swarm Optimization

Cited by 42 publications

References 19 publications

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

Bi-Objective Task Scheduling in Cloud Computing using Chaotic Bat Algorithm

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

Contact Info

Product

Resources

About