Non-live Task Migration Approach for Scheduling in Cloud Based Applications

Panwar, Neelam; Negi, Sarita; Rauthan, Man Mohan Singh

doi:10.1007/978-981-10-8660-1_9

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…Cloud computing was the important method for enabling the connection capability of the VMs for which Task Migration based Scheduling methodology [12] using the enhanced-FCFS (TM-eFCFS) were used. Non-live operation or task migration method were also used for transmitting the tasks which were executed partially to another virtual machines for quick operations.…”

Section: Review Of Existing Workmentioning

confidence: 99%

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Shaheen¹,

kumar²

2023

Intelligent Automation &Amp; Soft Computing

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Cloud computing plays a significant role in Information Technology (IT) industry to deliver scalable resources as a service. One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling. The main advantage of this scheduling is to maximize the performance and minimize the time loss. Various researchers examined numerous scheduling methods to achieve Quality of Service (QoS) and to reduce execution time. However, it had disadvantages in terms of low throughput and high response time. Hence, this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines (VMs). For this purpose, the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task (PSO-BATS) scheduling with Multi-Layered Regression Host Employment (MLRHE) to sort out the issues of task scheduling and ease the scheduling operation by load balancing. The proposed efficient scheduling provides benefits to both cloud users and servers. The performance evaluation is undertaken with respect to cost, Performance Improvement Rate (PIR) and makespan which revealed the efficiency of the proposed method. Additionally, comparative analysis is undertaken which confirmed the performance of the introduced system than conventional system for scheduling tasks with high flexibility.

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Section: Review Of Existing Workmentioning

confidence: 99%

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Shaheen¹,

kumar²

2023

Intelligent Automation &Amp; Soft Computing

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“…In batch dynamic based approaches, if a task with shorter deadline arrives in later batches; and all the VMs are already overloaded, the newly arrived task need to wait until the completion of previously mapped tasks. Majority of the batch dynamic scheduling heuristics [17,[24][25][26][27] provide dynamism at the batch level only and suffer from issues related to batch formation delays, and under-utilization of resources (during the batch formation). Dynamic scheduling mechanisms are much more flexible than the previously described categories [20,39].…”

Section: Gap Analysismentioning

confidence: 99%

“…Additionally, the employed approach is unable to achieve improved resource utilization, load balance, and minimized makespan. TM-eFCFS: a task migration-based task scheduling heuristic proposed by Panwar and Negi in [27] that employs First Come First Serve heuristic as the base algorithm. To achieve faster execution and minimized the makespan, this approach uses non-live migration of tasks in the queue (waiting for the execution turn) or partially executed task to fastest idle VMs.…”

Section: Dynamic Scheduling Heuristicsmentioning

confidence: 99%

“…Dynamic schedulers have the capability to check, estimate, and update the VMs load during the execution of the tasks either in the reactive or proactive manner [17]. These schedulers can allow prioritization and migration [23,27] of the already assigned tasks. Moreover, the dynamic scheduling mechanisms generally have the capability of new VMs creation [18,24,46], removal the existing VMs and migration of VMs [28] at run time.…”

Section: Introductionmentioning

confidence: 99%

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RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks

et al. 2022

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Cloud service providers acquire the computing resources and allocate them to their clients. To effectively utilize the resources and achieve higher user satisfaction, efficient task scheduling algorithms play a very pivotal role.A number of task scheduling technique have been proposed in the literature. However, majority of these scheduling algorithms fail to achieve efficient resource utilization that causes them to miss tasks deadlines. This is because these algorithms are not resource and deadline-aware. In this research, a Resource and deadline Aware Dynamic Load-balancer (RADL) for Cloud tasks has been presented. The proposed scheduling scheme evenly distribute the incoming workload of compute-intensive and independent tasks at run-time. In addition, RADL approach has the capability to accommodate the newly arrived tasks (with shorter deadlines) efficiently and reduce task rejection. The proposed scheduler monitors/updates the task and VM status at run-time. Experimental results shows that the proposed technique has attained up to 67.74%, 303.57%, 259.2%, 146.13%, 405.06%, and 259.14% improvement for average resource utilization, meeting tasks deadlines, lower makespan, task response time, penalty cost, and task execution cost respec-

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“…Since, the Cloud computing was the important method for enabling the connection capability of the VMs by TM-eFCFS -Task Migration based Scheduling methodology [12] using the enhanced-FCFS. This work make use of Non-live operation/task migration method for transmitting the tasks which were executed partially to another virtual machines for quick operations.…”

Section: Introductionmentioning

confidence: 99%

Tasks Scheduling in Cloud environment using PSO-BATS

Shaheen

kumar

2022

Preprint

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Cloud computing plays an important role in the IT industry to deliver scalable resources as a service. Cloud users can virtually access resources over the internet. One of the most important factor to increase the performance and efficiency of the cloud server by maximizing resource utilization as task scheduling. The performance of the cloud mainly depends on resource management and optimally job scheduling. Task scheduling is a technique that defines the choosing of most appropriate resources for the execution of tasks which means the distribution of tasks on available resources by taking some parameters. The main advantage of this scheduling is to maximize the performance and minimize the time loss. There are various researchers are examined numerous scheduling methods to achieve QoS (Quality of Service) and to reduce execution time. However, it had the disadvantages of low throughput and high response time. The main objective of the proposed system is to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the VMs. This research proposed the novel PSO-BATS with MLRHE (Multi-Layered Regression Host Employment) to sort out the issues of task scheduling and ease the scheduling operation with balancing of the load. The proposed efficient scheduling provides benefits to both cloud users and servers. The performance evaluation was done in terms of Makespan, cost, and PIR and found that the proposed method surpassed the other taken methods. In a comparative analysis, the proposed method proved that the best performance compared to existing methods.

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Non-live Task Migration Approach for Scheduling in Cloud Based Applications

Cited by 6 publications

References 17 publications

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks

Tasks Scheduling in Cloud environment using PSO-BATS

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