Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

Mohammed, Abdullahi; Ngadi, Md. Asri

doi:10.1371/journal.pone.0158229

Cited by 67 publications

(53 citation statements)

References 30 publications

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“…The algorithm first orders all the tasks based on increasing order of their deadlines (line 1) and initially allocates all the tasks in increasing order of their deadlines to the lowest cost virtual machine (lines [2][3][4][5][6][7][8]. Note that if all the tasks can complete on the lowest cost machine without violating any of the constraints, then it is the optimal solution since the communication cost (CC) will be zero and the execution cost (CE) is minimal.…”

Section: Greedy Algorithmmentioning

confidence: 99%

“…The pay-per-usage approach eliminates the requirement for investment in the acquisition of hardware and software making cloud computing an attractive option for many organizations [2,3]. In cloud computing, services are provisioned in the forms of Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS).…”

Section: Introductionmentioning

confidence: 99%

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Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

2017

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Abstract:In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality.

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Section: Greedy Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

2017

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“…The mutualism between organism X i and a randomly selected organism X j with i = j is modeled in Equations (19)- (20).…”

Section: Mutualismmentioning

confidence: 99%

“…Pooranian et al (2015) [19] hybridized a gravitational emulation local search strategy with particle swarm optimization to improve the obtained solution. [20] proposed an SA-based SOS in order to improve the convergence rate and quality of solution.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

Anwar

Deng

2018

Applied Sciences

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Cloud computing has emerged as a high-performance computing environment with a large pool of abstracted, virtualized, flexible, and on-demand resources and services. Scheduling of scientific workflows in a distributed environment is a well-known NP-complete problem and therefore intractable with exact solutions. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The aim of this study is to optimize multi-objective scheduling of scientific workflows in a cloud computing environment based on the proposed metaheuristic-based algorithm, Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO). The strong global exploration ability of the nature-inspired metaheuristic Symbiotic Organisms Search (SOS) is enhanced by involving an efficient list-scheduling heuristic, Predict Earliest Finish Time (PEFT), in the proposed algorithm to obtain better convergence and diversity of the approximate Pareto front in terms of reduced makespan, minimized cost, and efficient load balance of the Virtual Machines (VMs). The experiments using different scientific workflow applications highlight the effectiveness, practicality, and better performance of the proposed algorithm.

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“…In simple, it is the ability to handle or process work in cloud computing. Degree of imbalance is used for calculating the load of work in data centers [146].…”

Section: Analysis Of Resources and Parameters Used In Current Studiesmentioning

confidence: 99%

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

et al. 2016

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There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

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Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

Cited by 67 publications

References 30 publications

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

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