Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

Liu, Xiaocheng; Chen, Bin; Qiu, Xiaogang; Cai, Ying; Ke-di, Huang

doi:10.1155/2012/695757

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…Task scheduling optimization [19] was investigated to achieve better performance in terms of processing time while considering network contention and could cost. Another method to schedule parallel jobs using migration and consolidation was designed in [20] to reduce the average response time.…”

Section: Related Workmentioning

confidence: 99%

Load Optimized M/M/M Queueing for Parallel Job Scheduling in Multiple Cloud Centers

Antony¹,

Chandrasekar²

2017

IJERT

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The cloud computing environment enables cloud users to execute their applications in remote data centers. Many of these applications are considered to be highly complex in nature, requiring parallel processing capabilities. Parallel job scheduling techniques, mainly focus on improving throughput or the information processed by the cloud center in a given interval of time and reducing average task waiting time. For a data center that deals with parallel jobs, it is required to design an optimal scheduler resulting in minimal utilization of memory rate for scheduling each job. In this paper, we present M/M/M Queuing System and Load Optimized (QS-LO) model for parallel job scheduling in multiple cloud centers. The QS-LO model is designed as two algorithms, namely parallel job scheduling and load balancing with aiming at improving the throughput rate and reducing the average task waiting time in multiple cloud centers. We perform experimental analysis using benchmarks and synthetic datasets to measure the performance of the proposed algorithm. The experimental results are compared with the existing parallel job schedulers in terms of the job assigned, throughput memory utilization rate and scheduler time interval. The experimental results show that the QS-LO model is able to improve the throughput rate and also reduce the average task waiting time when compared to the state-of-the-art works.

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Section: Related Workmentioning

confidence: 99%

Load Optimized M/M/M Queueing for Parallel Job Scheduling in Multiple Cloud Centers

Antony¹,

Chandrasekar²

2017

IJERT

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“…Liu, Chen, Qiu, Cai and Huang [5] proposed a modified scheduling algorithm based on FCFS scheduling. They proposed two level scheduling, viz.…”

Section: Cloudlet Schedulingmentioning

confidence: 99%

Comparative Analysis of VM Scheduling Algorithms in Cloud Environment

Himthani¹,

Saxena²,

Manoria³

2015

IJCA

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Cloud computing is a pay per use model for providing convenient and on demand network access to sharable and configurable computing resources like networks, servers and applications that can be easily managed with minimal effort or service provider interaction. The various characteristics provided by cloud systems are Internet based computing, heterogeneity, on demand self service, pay per use model, location independent resource pooling, availability, scalability and ubiquitous network access.Virtualization is a way of abstracting the physical resources to improve their utilization. It logically divides the resources between different users. A virtual machine is a logical implementation of a computer that operates in the similar manner as the computer system. A cloud computing environment requires a suitable algorithm for executing the various jobs provided to the system in a cost effective manner based on certain constraints. This task is performed by a VM scheduler using a suitable scheduling algorithm.VM scheduling plays an important role in balancing the load of the system so that the utilization of the resources should be optimum. Better the scheduling policy, more the efficient operation of the cloud system.

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“…Load balancing seeks to improve user satisfaction. Because the number of users and their demands are growing by the day, the clouds should deliver services to clients that meet their highest expectations [5]. A good or ideal load balancing algorithm ensures that available resources are used efficiently and that no node is overloaded or underloaded.…”

Section: Introductionmentioning

confidence: 99%

In-depth analysis of dynamic degree load balancing technique in public cloud for heterogeneous cloudlets

Joshi

Devi

2022

IJEECS

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<span lang="EN-US">Load <span lang="EN-US">balancing is one of the challenges of the distributed computing worldview. With the enormous development in clients and their interest for different administrations on the distributed computing stage, compelling or productive asset usage in the cloud climate has turned into an urgent concern. Load balancing is critical to keeping cloud computing running smoothly. This study examines the research using four scheduling algorithms: dynamic degree balance CPU based (D2B_CPU), dynamic degree balanced membership based (D2B_Membership), dynamic degree memory balanced allocation (D2MBA) and hybrid dynamic degree balance (HDDB) algorithm. Central processing unit (CPU) utilisation, bandwidth utilisation, and memory utilisation are used as performance measures to verify the performance of these algorithms. The CloudSim simulation programme was used to simulate these algorithms. The primary goal of this work is to aid in the future construction of new algorithms by researching the behaviour of various existing algorithms.</span></span>

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Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

Cited by 7 publications

References 24 publications

Load Optimized M/M/M Queueing for Parallel Job Scheduling in Multiple Cloud Centers

Load Optimized M/M/M Queueing for Parallel Job Scheduling in Multiple Cloud Centers

Comparative Analysis of VM Scheduling Algorithms in Cloud Environment

In-depth analysis of dynamic degree load balancing technique in public cloud for heterogeneous cloudlets

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