Cloud Computing Online Scheduling

Keshk, Arabi

doi:10.9790/3021-04360717

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Arabi Keshk et al [17] have introduced Modified Ant Colony Optimization for Load Balancing (MACOLB) algorithm to allocate the incoming jobs to the virtual machines (VMs). The tasks are allocated to the VMs based on the processing powers (i.e., tasks are allocated in descending order, starting from the most powerful VM, and so on) by considering balancing VMs' loads.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

et al. 2022

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In recent years, Cloud computing has been developed and become the foundation of a wide range of applications. It allows users to access a catalog of standardized services and respond to their business needs flexibly and adaptively, in the event of unforeseen demands, paying solely for the consumption they have made. Task scheduling problem is considered one of the most critical cloud computing challenges. The problem refers to how to reasonably order and allocate the applications tasks provided by the users to be executed on virtual machines. Furthermore, the quality of scheduling performance has a direct effect on customer satisfaction. The task scheduling problem in cloud computing must be more accurately described in order to improve scheduling performance. In this paper, a multi-objective task scheduling algorithm is proposed based on the decision tree in a heterogenous environment. We introduce a new Task Scheduling-Decision Tree (TS-DT) algorithm for allocating and executing an application's task. To evaluate the performance of the proposed TS-DT algorithm, a comparative study was conducted among the existing algorithms; Heterogeneous Earliest Finish Time (HEFT), Technique for Order of Preference by Similarity to Ideal Solution that incorporates the Entropy Weight Method (TOPSIS-EWM), and combining Q-Learning with the Heterogeneous Earliest Finish Time (QL-HEFT). Our results show that the proposed TS-DT algorithm outperforms the existing HEFT, TOPSIS-EWM, and QL-HEFT algorithms by reducing makespan by 5.21%, 2.54%, and 3.32%, respectively, improving resource utilization by 4.69%, 6.81%, and 8.27%, respectively, and improving load balancing by 33.36%, 19.69%, and 59.06%, respectively in average.

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Section: Related Workmentioning

confidence: 99%

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

et al. 2022

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“…Arabi Keshk et al [24] introduced the Modified Ant Colony Optimization for Load Balancing (MACOLB) method to efficiently distribute incoming workloads among virtual machines (VMs) in cloud computing environments. The MACOLB method employs a workload balancing strategy that considers the processing capacities of VMs.…”

Section: Related Workmentioning

confidence: 99%

“…Through this priority scheduling algorithm, processes are assigned execution based on their remaining time, prioritizing the shortest remaining time for execution. This approach aims to optimize the overall efficiency and reduce waiting times for processes [73][74][75] find process k with the maximum value wk = max_i{wt(i)-q * bt(i) 24.…”

Section: B Algorithm Of Priority Schedulingmentioning

confidence: 99%

“…This analysis of the idle time and job sequencing helps us gain insights into the optimal scheduling of jobs and the impact of different permutations on the overall performance. (24) In this analysis, we explore different scenarios and subcases that can impact the optimality of the job sequencing. Let us further delve into the subcases under Case (1.1.ii):…”

Section: Case (11ia)mentioning

confidence: 99%

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MTD-DHJS: Makespan-Optimized Task Scheduling Algorithm for Cloud Computing With Dynamic Computational Time Prediction

Banerjee,

Roy,

Sinha

et al. 2023

IEEE Access

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“…The work used direct user feedback to balance between the non-interactive VMs and interactive VMs by providing good average computation rates and fair service to both of them. In another work [16], Arabi E. Keshk(2014) has presented an online cloud task scheduling model based on virtual machine adaptive fault tolerance and load balancing using ant colony algorithm . The main contributed work is the addition of load balancing factor so that the system tolerates the faults by tracking the decision on the basis of reliability of the virtual machines in scheduling process.…”

Section: Review Of Literaturementioning

confidence: 99%

Design and Development of Efficient Cloud Scheduling Algorithm based on Load Balancing Analytics

Sridevi¹,

Varma²,

Krishna³

2016

IJCA

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Cloud computing involves sharing computing resources rather than having individual servers or personal devices to handle applications. Cloud computing architectures include the delivery of software, infrastructure, storage and technology enabled services over internet to the people and organizations on demand. Cloud scheduling is the process of allocating resources to the job requests in the form of Virtual Machines. In this paper we designed and developed a novel, efficient cloud scheduling algorithm based on load balancing analytics for allocation of physical resources in the form of virtual machine to the incoming job requests. In this paper we measured various cloud performance metrics like mean turnaround time and mean waiting time. The results obtained with this method compared with traditional methods like First Come First Serve (FCFS), two stage scheduling algorithms and observed considerable increase in the performance Metrics.

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Cloud Computing Online Scheduling

Cited by 7 publications

References 23 publications

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

MTD-DHJS: Makespan-Optimized Task Scheduling Algorithm for Cloud Computing With Dynamic Computational Time Prediction

Design and Development of Efficient Cloud Scheduling Algorithm based on Load Balancing Analytics

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