Cuckoo-inspired Job Scheduling Algorithm for Cloud Computing

Aloboud, Ebtesam; Kurdi, Heba

doi:10.1016/j.procs.2019.04.153

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…Schedulers are aware of data limitations like data transfer time. In Aloboud and Kurdi, 25 Cuckoo-inspired is presented for job scheduling with application in cloud computing. Some of the scheduling algorithms combine several other meta-heuristic algorithms.…”

Section: Job Scheduling Algorithmsmentioning

confidence: 99%

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Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Beigrezaei

Haghighat

Mirtaheri

2020

Int J Communication

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Summary In Data Grid systems, quick data access is a challenging issue due to the high latency. The failure of requests is one of the most common matters in these systems that has an impact on performance and access delay. Job scheduling and data replication are two main techniques in reducing access latency. In this paper, we propose two new neighborhood‐based job scheduling strategies and a novel neighborhood‐based dynamic data replication algorithm (NDDR). The proposed algorithms reduce the access latency by considering a variety of practical parameters for decision making and the access delay by considering the failure probability of a node in job scheduling, replica selection, and replica placement. The proposed neighborhood concept in job scheduling includes all the nodes with low data transmission costs. Therefore, we can select the best computational node and reduce the search time by running a hierarchical and parallel search. NDDR reduces the access latency through selecting the best replica by performing a hierarchical search established based on the access time, storage queue workload, storage speed, and failure probability. NDDR improves the load balancing and data locality by selecting the best replication place considering the workload, temporal locality, geographical locality, and spatial locality. We evaluate our proposed algorithms by using Optorsim Simulator in two scenarios. The simulations confirm that the proposed algorithms improve the results compared with similar existing algorithms by 11%, 15%, 12%, and 10% in terms of mean job time, replication frequency, mean data access latency, and effective network usage, respectively.

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Section: Job Scheduling Algorithmsmentioning

confidence: 99%

“…A better algorithm executes jobs faster that leads to lower mean job time. This can be obtained using Equation (25). In Equation 25, t j (st), t j (ct), and NJ are the job submission time, the job completion time and the total number of the jobs.…”

Section: The Evaluation Parametersmentioning

confidence: 99%

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Beigrezaei

Haghighat

Mirtaheri

2020

Int J Communication

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“…The modified PSO reduces the response time of the algorithm by more than 10%, and can be used for fast-scheduling of resources in the cloud. Heuristics based scheduling is proposed using the max-min algorithm in [6], wherein researchers have applied maximum sized tasks to minimum capacity virtual machines. By doing this, the minimum sized tasks execute on higher capacity virtual machines, thereby the processing speed of the system improves drastically.…”

Section: Literature Reviewmentioning

confidence: 99%

Statistical Evaluation of Task Scheduling Algorithms in Cloud Environments

Kantale¹

2020

IJATCSE

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Task scheduling algorithms in cloud have come a long way, from simplistic algorithms like first come first serve, to bio-inspired & machine learning algorithms like Q-learning and genetic algorithms. The main objective of any task scheduling algorithm is to minimize the number of execution cycles needed to completely and effectively execute a given set of tasks. In this work, we present a comparison of different scheduling algorithms and their performance evaluation. The proposed research takes into consideration different nuances of cloud task scheduling, which include task length, task completion time, virtual machine configuration and task deadline. Moreover, the review also includes containers, which ensures that the scheduler works effectively on a container environment to improve efficiency of the cloud deployment. We propose different comparisons of the algorithms which are under different task conditions. In our analysis, we reviewed all the algorithms on different tasks and under different virtual machine (VM) configurations. As per the research, the machine learning based algorithms perform better in terms of overall task scheduling efficiency when compared with others.

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“…The results of the shark smell job scheduling method outperformed the Firefly Algorithms'. A cuckoo-inspired cloud job scheduling mechanism is proposed in [35].…”

Section: Related Workmentioning

confidence: 99%

Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm

Hassan¹,

Yousif²

2020

Eng. Technol. Appl. Sci. Res.

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Cloud computing technology success comes from its manner of delivering information ‎technology services, how they are designed, propagated, maintained and scaled. Job Scheduling ‎on cloud computing is a crucial ‎research area and is known to be an NP-complete problem. Scheduling refers to assigning user requests to underlying resources effectively. ‎This paper proposes a new Job Scheduling mechanism for cloud computing ‎environment. The proposed mechanism is based on the Ions Motion Optimization (IMO) algorithm. IMO has two phases, liquid, and crystal. These two phases balance the algorithm behavior ‎between convergence and local optima avoidance. To evaluate the proposed mechanism, a ‎simulation with different scenarios using the CloudSim simulator is conducted. The performance of ‎the proposed algorithm is compared with two metaheuristic algorithms known as Cat Swarm ‎Optimization (CSO) and Glowworm Swarm Optimization (GSO). Furthermore, the proposed IMO ‎mechanism is compared with First Come First Served and random solution. The experimental ‎results demonstrated that the proposed mechanism outperformed both CSO ‎and GSO and produced the shortest execution time in all experimental scenarios.

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Cuckoo-inspired Job Scheduling Algorithm for Cloud Computing

Cited by 11 publications

References 5 publications

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Statistical Evaluation of Task Scheduling Algorithms in Cloud Environments

Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm

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