Priority based task scheduling by mapping conflict-free resources and Optimized workload utilization in cloud computing

Yadav, Anagha; Rathod, Santanu

doi:10.1109/iccubea.2016.7859987

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Yadav et al (20), through the mapping of non-conforming resources and working loads into the CC environment, suggested priority task based planning technology. This leads to a priority and balanced resource distribution based on the workload.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybridized Bat Algorithm for effective Task

Udhayakumar¹,

Jeyaseelan²,

Pradeepa³

et al. 2022

JUSST

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The recently developed distributed framework Cloud Computing (CC) offers versatile and dynamically scalable low-cost computing services. In CC environment Task Scheduling (TS) is the key issues to be tackled to boost system performance and increase cloud satisfaction for customers. Advanced programming techniques should be introduced to ensure an efficient cloud mapping of tasks to fulfil the complex necessities of end-users applications. Having regard to a timing problem, unrelated MTs are responsible to perform individual tasks, is the key motivation behind this study. There are many subtasks to a mission. Only after completion of its predecessor subtask can a successor subtask be started. The subtasks may be carried out in the same machine or in another machine independently. It is known in advance how long each machine is run. Due to the continuous processing of other work, each computer has known release time (i.e., the time available to perform current tasks). We also considered two variants of problems for two separate objective functions in this research paper. The first version of the problem takes note of minimising the overall finishing time target while the second version considers minimising the Making-Time goal. In this paper we proposed an algorithm for hybridised bat optimization for this multi-target TS. Unlike current algorithms, an optimum solution can be sought by using fewer iterations. We have compared to determine the performance of this enhanced Bat Algorithm with the existing systems. The experiment conclude that the proposed procedureacts well in comparison with other TS algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybridized Bat Algorithm for effective Task

Udhayakumar¹,

Jeyaseelan²,

Pradeepa³

et al. 2022

JUSST

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“…However, the benefits brought by fulfilling the task are not always proportional to the costs of computing resources [15,16]. In addition, resource utilization also needs to be considered, as service providers optimize the computing resource allocation to ensure the maximum efficiency of the resource usage [17]. Therefore, service providers are concerned about how to dynamically provide computing resources for multiple computing tasks in a cost-effective manner.…”

Section: Introductionmentioning

confidence: 99%

Application-Aware Resource Allocation Based on Benefit–Cost Ratio in Computing Power Network with Heterogeneous Computing Resources

Wang,

Li,

Guo

et al. 2023

Photonics

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The computing power network (CPN) is expected to realize the efficient provisioning of heterogeneous computing power through the collaboration between cloud computing and edge computing. Heterogeneous computing resources consist of CPU, GPU, and other types of computing power. Different types of applications may have diverse requirements for heterogeneous computing resources, such as general applications, CPU-intensive applications, and GPU-intensive applications. Service providers are concerned about how to dynamically provide heterogeneous computing resources for different applications in a cost-effective manner, and how to deploy more applications as much as possible with limited resources. In this paper, the concept of the benefit–cost ratio (BCR) is proposed to quantify the usage efficiency of CPU and GPU in CPNs. An application-aware resource allocation (AARA) algorithm is designed for processing different types of applications. With massive simulations, we compare the performance of the AARA algorithm with a benchmark. In terms of blocking probability, resource utilization, and BCR, AARA achieves better performance than the benchmark. The simulation results indicate that more computing tasks can be accommodated by reducing 3.7% blocking probability through BCR-based resource allocation.

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“…Cloud computing is inter-based computing system, which is highly dynamic. Task Scheduling (TS) and resource allocation are main issues in cloud computing [1]. Based on the aspects of Platform as a Service (PaaS), Software as a Service (SaaS), Infrastructure as a Service (IaaS), these computing platforms provides a collection of host networks through the internet.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Task Scheduling Using Modified Ant Colony Optimization in Cloud Computing

Reddy¹,

Phanikumar²

2018

IJIES

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Abstract:Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as a commercial implementation of such computer science concepts. One of the main issues in a cloud computing environment is Task scheduling (TS). In Cloud task scheduling, many Non deterministic Polynomial time-hard optimization problem, and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on Modified Ant Colony Optimization (MACO) algorithm. The main contribution of recommended method is to minimize makespan and to perform Multi Objective Task Scheduling (MOTS) process by assigning pheromone amount relative to corresponding virtual machine efficiency. MACO algorithm improves the performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than a basic ACO algorithm by its multi-objective and deliberate nature. Experimental outcomes have shown that proposed MACO to have makespan 350 milliseconds and average utilization of 0.51 for a set of 100 tasks.

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Priority based task scheduling by mapping conflict-free resources and Optimized workload utilization in cloud computing

Cited by 7 publications

References 9 publications

A Hybridized Bat Algorithm for effective Task

A Hybridized Bat Algorithm for effective Task

Application-Aware Resource Allocation Based on Benefit–Cost Ratio in Computing Power Network with Heterogeneous Computing Resources

Multi Objective Task Scheduling Using Modified Ant Colony Optimization in Cloud Computing

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