A Multiconstrained Grid Scheduling Algorithm with Load Balancing and Fault Tolerance

Keerthika, P.; Suresh, P.

doi:10.1155/2015/349576

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Response time is calculates using the following Equation 8. (8) Makespan is defined as the completion time of the last job to leave the virtual machine [18]. It is formulated IN Equation 9.…”

Section: Results and Performance Evaluationmentioning

confidence: 99%

Enhanced Resource Allocation and Workload Management using Reinforcement Learning Method for Cloud Environment

Suresh*,

Keerthika,

Logeswaran

et al. 2019

IJRTE

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Cloud computing is a delivery model of IT resources such as computing servers, storage, databases, networking and software over the Internet. It offers the resources as services based on demand with more faster, flexible and economies of scale. The major challenges in the cloud computing are resource allocation and workload management due to the scalability of the cloud users and the services deployed in it. Even though there are various approaches available to manage workload and resource allocation, unfortunately most of them fail to mange it properly. This paper proposes a Reinforcement Learning based Enhanced Resource Allocation and Workload Management (RL-ERAWM) approach to increase the performance of cloud with large number of tasks and users. It implements the Q-Learning approach which effectively considers arrival rate of the requests and workload of the virtual machine. Experimental results prove that the proposed method alleviates the performance of task scheduling and workload management process compared with other approaches in terms of response time, makespan and virtual machine utilization.

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Section: Results and Performance Evaluationmentioning

confidence: 99%

Enhanced Resource Allocation and Workload Management using Reinforcement Learning Method for Cloud Environment

Suresh*,

Keerthika,

Logeswaran

et al. 2019

IJRTE

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“…In grid computing, Ritu Garg et al [16] suggested a scheduling technique for dependent tasks. P. Keerthika and P. Suresh [19] presented a load-balancing-based scheduling technique that improves resource efficiency. Rakesh Kumar et al [20] discussed Virtualization, Cloud, Grid, and Cluster Computing along with their characteristics, advantages, shortcomings, benefits, and downsides along with a comparison of cloud, cluster, and grid computing, as well as a comparison of grid and cluster computing.…”

Section: ░ 2 Literature Reviewmentioning

confidence: 99%

Designing and Implementation of Failure-Aware Based Approach for Task Scheduling in Grid Computing

Singh

Kumar

2022

IJEER

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Grid computing makes large-scale computations easier to handle. In heterogeneous systems like grid computing, failure is inevitable. Because of the volume and diversity of the resources, scheduling algorithm is among the most difficult challenges to overcome in grid computing. To reduce the make-span of the job to be executed a thorough understanding of scheduling in grid is important. Say there are two computing nodes that aren't being used right now. The scheduler may choose the node that has higher computing strength (for example, higher CPU speed, higher free memory), even though this node may also have high potential of failure. High potential of failure refers to the possibility of the failure occurring at execution time, resulting in the decrease of system performance. Therefore, awareness of failure is also very important in scheduling. This work proposes and implements a failure-aware scheduling method to schedule the tasks which uses both performance factors and failure factors of resources while making scheduling decision. The proposed algorithm is analyzed over various performance matrices and it shows considerably improved performance over existing algorithm

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“…Initially, using efficiently dividing the hierarchical features of the grid framework, the resource in the intensive grids are classified and scheduled based on the resource characteristics data, thus the grid resource could be hierarchically scheduled, the resource scheduling efficacy could be enhanced by attaining the hierarchical features of the grid, and the grid scheduling approach of the intensive framework could be added by the max and min approach, and lastly, the optimization of the grid resource scheduling approach can be accomplished. Keerthika et al [17] designed a RA approach i.e., users fulfillment, fault tolerance, budget limited, and target LB by taking into account the aforementioned conditions. The presented MLFT decreases the task failure rate, schedule cost, and makespan also enhances resource consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

Duhayyim¹,

Eltahir²,

Issaoui³

et al. 2022

Computers, Materials &Amp; Continua

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The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid platform. The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer. It also derives an objective function with candidate solution (schedule) as input and the outcome is the makespan value denoting the quality of the candidate solution. The simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.

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A Multiconstrained Grid Scheduling Algorithm with Load Balancing and Fault Tolerance

Cited by 11 publications

References 19 publications

Enhanced Resource Allocation and Workload Management using Reinforcement Learning Method for Cloud Environment

Enhanced Resource Allocation and Workload Management using Reinforcement Learning Method for Cloud Environment

Designing and Implementation of Failure-Aware Based Approach for Task Scheduling in Grid Computing

Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

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