A Comparative Study of Load Balancing Algorithms in Computational Grid Environment

Goswami, Sukalyan; Sarkar, Ajanta De

doi:10.1109/cimsim.2013.24

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…Teknik load balancing bertujuan untuk mendistribusikan beban kerja secara merata di antara node dalam jaringan sehingga tidak ada satu node pun yang terbebani secara berlebihan [3]. Terdapat dua metode load balancing yang populer diantaranya adalah Least-Connection dan Round-Robin [4].…”

Section: Pendahuluanunclassified

Evaluasi Load Balancing: Studi Komparatif Least-Connection dan Round-Robin dalam Konteks Cloud Computing

Ariestiandy,

Suhery,

Jusmawati

et al. 2023

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Teknologi cloud computing membutuhkan metode load balancing yang efisien untuk mendistribusikan beban kerja secara merata ke seluruh node dalam jaringan. Penelitian ini bertujuan untuk menganalisis perbandingan efektivitas antara algoritma load balancing yaitu Round-Robin dan Least-Connection. Pengujian dilakukan melalui beberapa tingkat koneksi yang berbeda, dari 1000/600 hingga 5000/1000 untuk menilai hasil rata-rata response time yang diukur dalam millisecond (ms). Data empiris yang terkumpul mengindikasikan bahwa terjadi peningkatan linear response time sejalan dengan peningkatan jumlah koneksi pada kedua algoritma. Meskipun keduanya menunjukkan kinerja yang hampir serupa pada fase awal, dengan rata-rata response time Round-Robin pada 1000/600 koneksi adalah 2.8 ms, sedangkan Least-Connection mencapai 2.9 ms. Algoritma Least-Connection menunjukkan keunggulan yang konsisten di koneksi yang lebih tinggi. Pada 5000/1000 koneksi, Round-Robin tercatat memiliki response time 3.275 ms, sedangkan Least-Connection memiliki rata-rata response time 3.34 ms, yang mengindikasikan bahwa Least-Connection memiliki skalabilitas yang lebih baik dalam mengelola koneksi bertingkat tinggi. Temuan ini menyarankan bahwa untuk sistem yang mengharapkan jumlah koneksi yang relatif stabil dan dapat diprediksi maka direkomendasikan menggunakan Round-Robin. Namun, untuk sistem dengan beban kerja yang dinamis dan tidak terprediksi, maka Least-Connection memiliki kemampuan yang lebih baik.

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Section: Pendahuluanunclassified

Evaluasi Load Balancing: Studi Komparatif Least-Connection dan Round-Robin dalam Konteks Cloud Computing

Ariestiandy,

Suhery,

Jusmawati

et al. 2023

View full text Add to dashboard Cite

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“…Salah satu komponen dalam komputasi Grid yaitu load balancing. Load balancing merupakan proses untuk membagi load atau proses ke komputer-komputer yang terhubung [4,5]. Komponen ini merupakan salah satu komponen penting dalam grid, karena untuk load atau proses harus didistribusikan ke komputer-komputer sesuai dengan keadaan dan spesifikasi dari komputerkomputer tersebut, sehingga sumber daya yang ada dapat dioptimalkan secara maksimal [6,7].…”

Section: Pendahuluanunclassified

Implementasi Algoritma Load Balancing PLBA Komputasi Grid pada Lab Environment Menggunakan PVM3

Putra

Widiarto

Wiharto

2020

RESTI

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Load balancing is one of the main parts of scheduling Grid resources. One of the load balancing models on Grid resources is the hierarchical model. This model has the advantage that it requires minimal communication costs between one resource and another. The PLBA load balancing algorithm uses a hierarchical model with dynamically obtained threshold values, so that it can adjust conditions at a time, both the state of the resource, the state of the computer network, and the state of the recipient or client. PVM3 is a software system capable of optimizing heterogeneous resources, so that resources can work in parallel. Resources can also complete tasks well, even though they are very large and complex tasks. This research has implemented the PLBA load balancing algorithm, with the aim of optimizing Grid resources. This research has also developed the PLBA load balancing algorithm by changing the arguments for NPEList, so that resources can be grouped more optimally. The PLBA load balancing algorithm has been successfully developed by modifying the arguments for NPEList, so that the running time required to complete the given tasks is shorter, because resources can be grouped more optimally. This has been shown by the shorter average running time when using the modified NPEList argument (0.75 * threshold1 <= ALCi <= 1.25 * threshold1) is shorter, than using the NPEList argument in previous research (ALCi = threshold1). Comparison of the average running time has been obtained as follows : (82513.63740 : 67837.71720); (63869.92450 : 50722.17210); (858,96710 : 207,33680); (321.88000 : 126.89100); (768.54560 : 468.27190); (780.22770 : 279.43730).

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“…The works presented in [13], [14], [15] specifically tackle the issue of load sharing from a SLA point of view. The work in [13] explicitly relies on a self-control loop to monitor the state of virtual machines.…”

Section: Related Workmentioning

confidence: 99%

Proactive Cloud Management for Highly Heterogeneous Multi-cloud Infrastructures

Pellegrini

Sanzo

Avresky³

2016

2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework.

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A Comparative Study of Load Balancing Algorithms in Computational Grid Environment

Cited by 12 publications

References 15 publications

Evaluasi Load Balancing: Studi Komparatif Least-Connection dan Round-Robin dalam Konteks Cloud Computing

Evaluasi Load Balancing: Studi Komparatif Least-Connection dan Round-Robin dalam Konteks Cloud Computing

Implementasi Algoritma Load Balancing PLBA Komputasi Grid pada Lab Environment Menggunakan PVM3

Proactive Cloud Management for Highly Heterogeneous Multi-cloud Infrastructures

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