An efficient Tri Queue job Scheduling using dynamic quantum time for cloud environment

Karthick, A. V.; Ramaraj, E.; Kannan, R. Jagadeesh

doi:10.1109/icgce.2013.6823557

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…This may lead to under utilization of available resources. 11 Task scheduling algorithms mainly focus on minimizing waiting time and response time, while task starvation problem is neglected. 12 Starvation problem is common among the priority-based cloud schedulers and occurs when a task waits for the requested resource for a very long time.…”

Section: Motivation Of the Papermentioning

confidence: 99%

A multi‐queue priority‐based task scheduling algorithm in fog computing environment

Fahad

Shojafar

Abbas

et al. 2022

Concurrency and Computation

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Fog computing is a novel, decentralized and heterogeneous computing environment that extends the traditional cloud computing systems by facilitating task processing near end-users on computing resources called fog nodes. These diverse and resource-constrained fog devices process a large volume of tasks generated by various fog applications. These tasks are generated by various applications, some of which may be latency-sensitive, while others may tolerate some degree of delay in their normal functions. Task scheduling determines when a task should be allocated to a computing resource and how long that task can occupy the assigned resource.The majority of task scheduling algorithms focus on prioritizing the latency-sensitive tasks only, which results in the long waiting time for the other type of tasks. Hence, these priority-based schedulers cause task starvation for less important tasks while achieving delay-optimal results for latency-sensitive tasks. As a result, in this paper, we propose MQP, a multi-queue priority-based preemptive task scheduling approach that achieves a balanced task allocation for those applications that can tolerate a certain amount of processing delay and the latency-sensitive fog applications. At run-time, the MQP algorithm categorizes tasks as short and long based on their burst time. MQP algorithm maintains a separate task queue for each task category and dynamically updates the time slot value for preemption. The proposed technique's major purpose is to reduce response time for those data-intensive applications in the fog computing environment, which include both latency-sensitive tasks and tasks which are less latency-sensitive, thereby addressing the starvation problem for less latency-sensitive tasks. A smart traffic management case study is created to model a scenario with both latency-sensitive short and less latency-sensitive long tasks. We implement the MQP algorithm using iFogSim and confirm that it reduces the service latencies for long tasks.Simulation results show that the MQP algorithm allocates tasks to a fog device more efficiently and reduces the service latencies for long tasks. The average value of percentage reduction in the latency across all experimental configurations achieved is 22.68% and 38.45% in comparison to First Come-First Serve and shortest job first algorithms.

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Section: Motivation Of the Papermentioning

confidence: 99%

A multi‐queue priority‐based task scheduling algorithm in fog computing environment

Fahad

Shojafar

Abbas

et al. 2022

Concurrency and Computation

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show abstract

“…Efficient resource planning will as a whole enable the service providers to manage the resources and solve several related problems. [2] Various resource allocation strategies are available for collaborating the cloud provider activities for allocating and utilizing resources with the limitation to meet the needs of the cloud application of the cloud environment. Keeping in view the amount of resources needed by each application for fulfilling a user job the task can b completed.…”

Section: Importance Of Resource Allocationmentioning

confidence: 99%

A Quantum Optimization Model for Dynamic Resource Allocation in Cloud Computing

Alyas

2017

Research Journal Of Computer Science And Information Technology

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Quantum Computing and Cloud Computing technologieshave potential capability to change the dynamic of futurecomputing. Similarly, both Complexities, time and Space are the basicconstraints which can determine the efficient cloud service performance.Quantum optimization for the cloud resources in dynamic environmentprovides a way to deal with the present classical cloud computationmodel’s challenges. By combining the fields of quantum computing andcloud computing, will result in evolutionary technology. Virtual resourceallocation is a major challenge facing cloud computing with dynamiccharacteristics, a single aspect for the evaluation of resource allocationstrategy cannot satisfy the real world demands in this case. QuantumOptimization resource allocation mechanism on the cloud computingenvironment based two-way factors, improving user satisfaction and bestuse of resource utilization of cloud computing systems.A dynamic resource allocation mechanism for cloud services, based onnegotiation by keeping the focus on preferences and pricing factor istherefore proposed.

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“…To guarantee the delay requirement, an enhanced backfill scheduling algorithm called the conservative backfilling algorithm is commonly used in cloud computing [8] [9]. The algorithm solves the problem by limiting the number of jobs to be backfilled.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm solves the problem by limiting the number of jobs to be backfilled. However, it is not able to maximally utilize resources because it only allows low priority job to execute when it does not cause any slowdowns of all the other jobs with higher priority [7] [8].…”

Section: Related Workmentioning

confidence: 99%

Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing

Shin

Kim

Lee

2015

2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC)

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Cloud computing is computing in which large groups of remote servers are networked to allow centralized data storage and on-line access to computer services. Cloud computing providers install and operate application software in the cloud through Software as a Service (SaaS) model. Cloud users access the software and utilizes the services by cloning tasks onto multiple virtual machines (VMs). In recent years, offering high-performance computing capacity reminds cloud providers to utilize resources fully due to limitation of resource. Enhancing resource utilization is also essential for achieving cost effectiveness. In addition, SaaS applications support various services such as email, FTP, and multimedia services with different delay requirement. Among these application multimedia service has relatively low delay requirements. Therefore, in this paper, we propose a scheduling algorithm to enhance both deadline guarantee and resource utilization. We modified the conservative backfilling algorithm by utilizing the earliest deadline first (EDF) algorithm and the largest weight first (LWF) algorithm. The proposed algorithm first score all the jobs arrived at the data center(DC) and sort the jobs in ascending order to serve high priority job first. The proposed algorithm then select the largest possible backfill job as guaranteeing deadline.Simulation results show that the proposed algorithm significantly improve the performance in terms of resource utilization and deadline guarantee.

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An efficient Tri Queue job Scheduling using dynamic quantum time for cloud environment

Cited by 12 publications

References 17 publications

A multi‐queue priority‐based task scheduling algorithm in fog computing environment

A multi‐queue priority‐based task scheduling algorithm in fog computing environment

A Quantum Optimization Model for Dynamic Resource Allocation in Cloud Computing

Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing

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