Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter

Kanwal, Samira; Iqbal, Zeshan; Al‐Turjman, Fadi; Irtaza, Aun; Khan, Muhammad Attique

doi:10.1016/j.ipm.2021.102676

Cited by 22 publications

(10 citation statements)

References 26 publications

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“…The resource allocation for moving nodes in CR-IoT networks is a challenging issue. In future research, we will focus resource allocation on vehicular-based IoT networks [33][34][35].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Latif

Akraam

Karamat

et al. 2022

Sensors

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The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

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“…The resource allocation for moving nodes in CR-IoT networks is a challenging issue. In future research, we will focus resource allocation on vehicular-based IoT networks [33][34][35].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Latif

Akraam

Karamat

et al. 2022

Sensors

Self Cite

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“…The quality of service will be preserved when the cloud provider follows the SLA promptly. For this purpose, in [ 17 ], the authors formulated a multi-phase scheduling mechanism formulated using the GA. In the initial phase, the individual fitness value was calculated and it was considered as the local fitness value; then, in the next phase, the global fitness value was identified.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Mangalampalli

Karri

Elngar

2023

Sensors

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Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency.

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“…The simulated results show this algorithm outperforms HEFT and CPOP in terms of cost and makespan of schedules. An intelligent scheduling mechanism has been proposed in [40] that uses genetic algorithm based multiphase fault tolerance (MFTGA) to schedule tasks over VMs. This strategy works through four phases namely, the individual phase, local phase, global phase, and fault tolerance phase.…”

Section: Related Workmentioning

confidence: 99%

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

et al. 2023

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In this paper, a task scheduling problem for a cloud computing environment is formulated by using the M/M/n queuing model. A priority assignment algorithm is designed to employ a new data structure named the waiting time matrix to assign priority to individual tasks upon arrival. In addition to this, the waiting queue implements a unique concept based on the principle of the Fibonacci heap for extracting the task with the highest priority. This work introduces a parallel algorithm for task scheduling in which the priority assignment to task and building of heap is executed in parallel with respect to the non-preemptive and preemptive nature of tasks. The proposed work is illustrated in a step-by-step manner with an appropriate number of tasks. The performance of the proposed model is compared in terms of overall waiting time and CPU time against some existing techniques like BATS, IDEA, and BATS+BAR to determine the efficacy of our proposed algorithms. Additionally, three distinct scenarios have been considered to demonstrate the competency of the task scheduling method in handling tasks with different priorities. Furthermore, the task scheduling algorithm is also applied in a dynamic cloud computing environment.

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Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter

Cited by 22 publications

References 26 publications

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

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