Optimized task scheduling in cloud computing using improved multi-verse optimizer

Otair, Mohammed; Alhmoud, Areej; Jia, Heming; Altalhi, Maryam; Hussein, Ahmad MohdAziz; Abualigah, Laith

doi:10.1007/s10586-022-03650-y

Cited by 20 publications

(7 citation statements)

References 39 publications

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“…Mohammed Otair [19] have introduced an Improved Multi-Objective Multi-Verse Optimizer (IMOMVO) to solve the task scheduling problems which occur in a cloud environment. The suggested approach rectifies the issues related to average positioning by improving the position based on the best solution.…”

Section: Related Workmentioning

confidence: 99%

“…Where 𝜀 is the quality of the food source and random number in the range 0 to 1 is denoted as 𝑟𝑎𝑛𝑑 and predator effect is 𝑃𝐸 that is represented in Eq. (19).…”

Section: Exploitation Stagementioning

confidence: 99%

“…Here, the efficiency of MOPDOA is assessed on the basis of makespan time and the execution time for different task counts. The performance of MOPDOA is assessed with PSGWO [17], IMOMVO [19] and ANN-BPSO [20]. The simulation setup of the proposed approach is based on three cases.…”

Section: Comparative Analysismentioning

confidence: 99%

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Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

2024

IJIES

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An efficient task scheduling plays an important role in facilitating the virtual resource in a cloud computing environment by minimalizing make span and enhancing the allocation of resources. Requests for resources are treated as tasks, and appropriate resources are allocated based on user requirements. But, due to high demand and requests, the cloud has difficulty allocating resources. To overcome the issues, this research introduced an optimization-based task scheduling approach. The Multi-Objective Prairie Dog Optimization (MOPDO) algorithm is introduced which considers the makespan time and the execution time as the major objective while allocating resources in IoT. The proposed MOPDOA effectively allocates the resource to the Virtual Machines (VMs) by choosing the host with maximized resources. The search mechanism with the help of MOPDO helps to detect a suitable VM for resource allocation will be continued. After the process of allotting the resources to VMs, the load balancing process must be initiated to schedule the tasks for VMs. When the task count is assigned as 100, the makespan time of MOPDOA is 12s while Particle Swarm Gray Wolf Optimization (PSGWO) obtains a makespan time of the 20s. Similarly, for different VMs, the proposed approach is 175.45s for execution whereas the existing Improved Multi-Objective Multi-Verse Optimizer obtains 186.33s to execute for 10 VMs.

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Section: Related Workmentioning

confidence: 99%

“…Where 𝜀 is the quality of the food source and random number in the range 0 to 1 is denoted as 𝑟𝑎𝑛𝑑 and predator effect is 𝑃𝐸 that is represented in Eq. (19).…”

Section: Exploitation Stagementioning

confidence: 99%

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Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

2024

IJIES

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“…Technique Used Addressed Parameters [7] APSO Makespan, throughput [8] LJ-PSO, M-PSO makespan, total execution time, degree of imbalance [9] GAGELS makespan, resource utilization [10] MPSO Makespan, resource utilization [11] IT2FCM Data movements, data placement, makespan [12] PSO-RDAL Response time, task deadline, penalty cost [13] EPSOCHO Makespan, processing cost, resource utilization [14] GSOS Makespan, cost [15] AINN-BPSO makespan, cost, degree of imbalance [16] QPSO Scheduling efficiency [17] MVO-GA Task transfer time [18] NSGAIII runtime, cost, power consumption [19] Hybrid Lion-GA Load balancing [20] GSAGA Makespan [21] GBO Makespan, accuracy of scheduling [22] HWOA-MBA Makespan, cost [23] IWHOLF-TSC Makespan, cost [24] HWACOA Makespan, cost ELHHO Schedule length, execution cost, resource utilization [28] RATSA Failure rate [29] SOATS Cost, energy consumption [30] HunterPlus Energy consumption, job completion rate [31] IQSSA QOS parameters [32] RAO Makespan [33] HFSGA Makespan, cost [34] DRL Makespan, throughput [35] IMOMVO Execution time, throughput [36] HBSFD Task processing time, turnaround time [37] Wale Disk space [38] Docker Containers Disk space…”

Section: Authorsmentioning

confidence: 99%

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

Mangalampalli,

Karri,

Gupta

et al. 2023

Sensors

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Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.

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“…This simulation conducted on Cloudsim with random workload and results proved that penalty related to SLA violation minimized with ABFSOS. Authors in 9 formulated a task scheduling mechanism which uses improved version of MVO approach by adjusting average position of solution in scheduling. These simulations are conducted on Cloudsim and evaluated parameters i.e.…”

Section: Existing Related Workmentioning

confidence: 99%

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

Mangalampalli,

Karri,

Mohanty

et al. 2023

Sci Rep

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Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.

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Optimized task scheduling in cloud computing using improved multi-verse optimizer

Cited by 20 publications

References 39 publications

Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

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