Evaluation of gang scheduling performance and cost in a cloud computing system. J Supercomput

The Journal of Supercomputing (Impact Factor: 0.86). 02/2010; 59(2):975-992. DOI: 10.1007/s11227-010-0481-4
Source: DBLP


Cloud Computing refers to the notion of outsourcing on-site available services, computational facilities, or data storage
to an off-site, location-transparent centralized facility or “Cloud.” Gang Scheduling is an efficient job scheduling algorithm
for time sharing, already applied in parallel and distributed systems. This paper studies the performance of a distributed
Cloud Computing model, based on the Amazon Elastic Compute Cloud (EC2) architecture that implements a Gang Scheduling scheme.
Our model utilizes the concept of Virtual Machines (or VMs) which act as the computational units of the system. Initially,
the system includes no VMs, but depending on the computational needs of the jobs being serviced new VMs can be leased and
later released dynamically. A simulation of the aforementioned model is used to study, analyze, and evaluate both the performance
and the overall cost of two major gang scheduling algorithms. Results reveal that Gang Scheduling can be effectively applied
in a Cloud Computing environment both performance-wise and cost-wise.

KeywordsCloud computing–Gang scheduling–HPC–Virtual machines

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Available from: Ioannis A. Moschakis, Feb 19, 2015
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    • "The paper contains detailed evaluations of the proposed model, and offers explanations with regard to physical cluster and cloud resources. While estimating the performance of cloud through application of Gang scheduling algorithms, Moschakis and Karatza (2012) have also discussed frequent communication in the context of which such algorithms have been deemed appropriate. Their study involves only a single public cloud consisting of a cluster of virtual machines on which parallel jobs are processed. "

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    • "The group schedulers, Adapted First Come First Served (AFCFS) and Largest Gang First Served (LGFS), used in this work, have been studied in a distributed environment, [7][9][10][11]. The aim of this study is to make them more efficient in scheduling of parallel tasks, since these algorithms cause fragmentation in the system. In [7][10][11][12], there are proposed migration mechanisms, which are utilized in order to minimize the fragmentation caused by the schedulers group in the distributed environments. "
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    ABSTRACT: Task scheduling is a problem which seeks to allocate, over time, various tasks from different resources. In this paper, we consider group task scheduling upon a heterogeneous multi-cluster system. Two types of job tasking are considered, parallel and sequential. In order to reduce fragmentation caused by the scheduler group, migration mechanisms were implemented. Moreover, the dispatchers global and local use distribution of jobs in order to minimize delays in the task queues, as well as in response time. To analyze the different situations, performance metrics were applied, aiming to compare schedulers in different situations.
    ICNS 2014 : The Tenth International Conference on Networking and Services; 04/2014
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    • "Based on the research works, in paper [24] it was proven that its proposed algorithm contribute to reduce the energy consumption in cloud resources with the adoption of Dynamic Voltage Scaling (DVS) technique. In [26] the algorithm improves the waiting time especially when the workload had increased. However, the works only run in one scenario and not for different scenarios with variety of workload especially for High Performance Application (HPC). "
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    ABSTRACT: The cloud provider plays a major role especially providing resources such as computing power for the cloud subscriber to deploy their applications on multiple platforms anywhere; anytime. Hence the cloud users still having problem for resource management in receiving the guaranteed computing resources on time. This will impact the service time and the service level agreements for various users in multiple applications. Therefore there is a need for a new resolution to resolve this problem. This survey paper conducts a study in resource allocation and monitoring in the cloud computing environment. We describe cloud computing and its properties, research issues in resource management mainly in resource allocation and monitoring and finally solutions approach for resource allocation and monitoring. It is believed that this paper would benefit both cloud users and researchers for further knowledge on resource management in cloud computing.
    02/2014; 4(1):31. DOI:10.7763/IJMLC.2014.V4.382
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