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

Mahmood, Amjad; Khan, Salman A.; Bahlool, Rashed A.

doi:10.3390/computers6020015

Cited by 45 publications

(15 citation statements)

References 37 publications

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“…Allocation and scheduling of tasks on a VM is a difficult job in a hard RT environment. To resolve this problem, Amjad Mahmood and Khan 40 proposed adaptive genetic algorithm (AGA) in a cloud computing environment. The model consists of numerous tasks having the work pressure to complete its tasks while meeting the deadline.…”

Section: Adaptive Genetic Approachmentioning

confidence: 99%

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

Qureshi

Fayaz

et al. 2020

International Journal of Distributed Sensor Networks

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An efficient resource allocation scheme plays a vital role in scheduling applications on high-performance computing resources in order to achieve desired level of service. The major part of the existing literature on resource allocation is covered by the real-time services having timing constraints as primary parameter. Resource allocation schemes for the real-time services have been designed with various architectures (static, dynamic, centralized, or distributed) and quality of service criteria (cost efficiency, completion time minimization, energy efficiency, and memory optimization). In this analysis, numerous resource allocation schemes for real-time services in various high-performance computing (distributed and non-distributed) domains have been studied and compared on the basis of common parameters such as application type, operational environment, optimization goal, architecture, system size, resource type, optimality, simulation tool, comparison technique, and input data. The basic aim of this study is to provide a consolidated platform to the researchers working on scheduling and allocating high-performance computing resources to the real-time services. This work comprehensively discusses, integrates, analysis, and categorizes all resource allocation schemes for real-time services into five high-performance computing classes: grid, cloud, edge, fog, and multicore computing systems. The workflow representations of the studied schemes help the readers in understanding basic working and architectures of these mechanisms in order to investigate further research gaps.

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Section: Adaptive Genetic Approachmentioning

confidence: 99%

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

Qureshi

Fayaz

et al. 2020

International Journal of Distributed Sensor Networks

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“…Amjad et al [4] proposed 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. N.Moganarangan et al [5] presented a new Hybrid algorithm is proposed for reduction of energy consumption and make span by merging the advantages of ACO and cuckoo search algorithm. Deepika et al [6] proposed an algorithm that Categorize tasks based on their deadline and cost restrictions and assign them to different priority queue, and regarding the resource selection the scheduler select VM with the lowest turnaround time for each individual task.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Study On Cloud Computing Simulation Platforms

Alshathri

2020

International Journal of Systems Applications, Engineering &Amp; Development

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Experimentation in a real environment is quite problem due to the high financial cost and the time required to accomplish it. Above all that, the tests are not repeatable, because many variables cannot be controlled with in the test, which may affect the results. Therefore, using simulation frameworks to evaluate cloud applications is preferred. It is extremely difficult to use real infrastructures for benchmarking the application performance (throughput, cost benefits) under inconstant conditions. Therefore, we cannot execute benchmarking experiments using real-world Cloud environments. To overcome this challenge, the use of simulation tool is the best applicable choice to the developers with substantial resources and parallelized execution. These simulation tools offer the researchers the chance to evaluate the hypothesis in a measured environment and simply emulate the output results. This paper have reviewed and classified the most updates and extends simulation platforms in cloud computing and gave examples on each.

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“…They compared the performance the two algorithms in terms of makespan and energy consumption. Mahmood et al [15] proposed a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the works mentioned above were simulated or tested in a small scale. The number of tasks is up to decades in [10,17,18,20], hundreds in [9,15], or a little more than one thousand in [14]. Those algorithms may not work effectively under high pressure in Cloud environment.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments

Duan

Fong

et al. 2018

Symmetry

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Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.

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

Cited by 45 publications

References 37 publications

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

Comparative Study On Cloud Computing Simulation Platforms

Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments

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