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Journal articles on the topic 'Load balancing'

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Published: 4 June 2021

Last updated: 8 June 2024

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1

Suguna, Dr S., and R. Barani. "Simulation of Dynamic Load Balancing Algorithms." Bonfring International Journal of Software Engineering and Soft Computing 5, no. 1 (July 31, 2015): 01–07. http://dx.doi.org/10.9756/bijsesc.8061.

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2

SA MVPanduranga Rao, Kavya. "Grid Computing for Load Balancing Strategies." International Journal of Science and Research (IJSR) 1, no. 3 (March 5, 2012): 1–7. http://dx.doi.org/10.21275/ijsr12120309.

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3

Chi-Chung Hui and S. T. Chanson. "Hydrodynamic load balancing." IEEE Transactions on Parallel and Distributed Systems 10, no. 11 (1999): 1118–37. http://dx.doi.org/10.1109/71.809572.

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4

Friedrich, Tobias, Martin Gairing, and Thomas Sauerwald. "Quasirandom Load Balancing." SIAM Journal on Computing 41, no. 4 (January 2012): 747–71. http://dx.doi.org/10.1137/100799216.

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5

Pearce, Olga, Todd Gamblin, Bronis R. de Supinski, Martin Schulz, and Nancy M. Amato. "Decoupled load balancing." ACM SIGPLAN Notices 50, no. 8 (December 18, 2015): 267–68. http://dx.doi.org/10.1145/2858788.2688539.

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6

Bonald, T., M. Jonckheere, and A. Proutiére. "Insensitive load balancing." ACM SIGMETRICS Performance Evaluation Review 32, no. 1 (June 2004): 367–77. http://dx.doi.org/10.1145/1012888.1005729.

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7

Grosof, Isaac, Ziv Scully, and Mor Harchol-Balter. "Load Balancing Guardrails." ACM SIGMETRICS Performance Evaluation Review 47, no. 1 (December 17, 2019): 9–10. http://dx.doi.org/10.1145/3376930.3376937.

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8

Grosof, Isaac, Ziv Scully, and Mor Harchol-Balter. "Load Balancing Guardrails." Proceedings of the ACM on Measurement and Analysis of Computing Systems 3, no. 2 (June 19, 2019): 1–31. http://dx.doi.org/10.1145/3341617.3326157.

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9

Alkassar, Eyad, Mark A. Hillebrand, Dirk C. Leinenbach, Norbert W. Schirmer, Artem Starostin, and Alexandra Tsyban. "Balancing the Load." Journal of Automated Reasoning 42, no. 2-4 (March 28, 2009): 389–454. http://dx.doi.org/10.1007/s10817-009-9123-z.

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10

Huang, Weihua, Zhong Ma, Xinfa Dai, Mingdi Xu, and Yi Gao. "Fuzzy Clustering with Feature Weight Preferences for Load Balancing in Cloud." International Journal of Software Engineering and Knowledge Engineering 28, no. 05 (May 2018): 593–617. http://dx.doi.org/10.1142/s021819401850016x.

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Load balancing, which redistributes dynamic workloads across computing nodes within cloud to improve resource utilization, is one of the main challenges in cloud computing system. Most existing rule-based load balancing algorithms failed to effectively fuse load data of multi-class system resources. The strategies they used for balancing loads were far from optimum since these methods were essentially performed in a combined way according to load state. In this work, a fuzzy clustering method with feature weight preferences is presented to overcome the load balancing problem for multi-class system resources and it can achieve an optimal balancing solution by load data fusion. Feature weight preferences are put forward to establish the relationship between prior knowledge of specific cloud scenario and load balancing procedure. Extensive experiments demonstrate that the proposed method can effectively balance loads consisting of multi-class system resources.

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11

Batumalai, Sathis Kumar, Joseph Ng Poh Soon, Choo Peng Yin, Wong See Wan, Phan Koo Yuen, and Lim Eng Heng. "IP Redundancy and Load Balancing With Gateway Load Balancing Protocol." International Journal of Scientific Engineering and Technology 4, no. 3 (March 1, 2015): 218–22. http://dx.doi.org/10.17950/ijset/v4s3/321.

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12

Boulmier, Anthony, Nabil Abdennadher, and Bastien Chopard. "Optimal load balancing and assessment of existing load balancing criteria." Journal of Parallel and Distributed Computing 169 (November 2022): 211–25. http://dx.doi.org/10.1016/j.jpdc.2022.07.002.

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13

Al-Najjar, Anees, Marius Portmann, Siamak Layeghy, and Jadwiga Indulska. "Flow-level Load Balancing of HTTP Traffic using OpenFlow." Australian Journal of Telecommunications and the Digital Economy 6, no. 4 (December 31, 2018): 75–95. http://dx.doi.org/10.18080/ajtde.v6n4.166.

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In this paper, we explore the concept of flow-based load balancing of network traffic on multi-homed hosts. In contrast to existing approaches such as MultipathTCP, our approach is a client-side-only solution, and can therefore easily be deployed. We specifically explore flow-based load balancing for web and video traffic use cases. Experimental evaluations of our OpenFlow-based load balancer demonstrate the potential of flow-based load balancing.

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14

Al-Najjar, Anees, Marius Portmann, Siamak Layeghy, and Jadwiga Indulska. "Flow-level Load Balancing of HTTP Traffic using OpenFlow." Journal of Telecommunications and the Digital Economy 6, no. 4 (December 31, 2018): 75–95. http://dx.doi.org/10.18080/jtde.v6n4.166.

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In this paper, we explore the concept of flow-based load balancing of network traffic on multi-homed hosts. In contrast to existing approaches such as MultipathTCP, our approach is a client-side-only solution, and can therefore easily be deployed. We specifically explore flow-based load balancing for web and video traffic use cases. Experimental evaluations of our OpenFlow-based load balancer demonstrate the potential of flow-based load balancing.

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15

Yadav, Ram Kumar, and Deepak Kumar. "Productivity Improvement by Load Balancing of Machines." International Journal of Scientific Research 2, no. 3 (June 1, 2012): 168–69. http://dx.doi.org/10.15373/22778179/mar2013/53.

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16

Klymyshyn, Nazarii. "The Network Load Balancer in Decentrilized Systems." Advances in Cyber-Physical Systems 8, no. 1 (May 10, 2023): 25–34. http://dx.doi.org/10.23939/acps2023.01.025.

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This article explores the implementation of network load balancing in decentralized systems using OpenWrt, Quality of Service (QoS), and traffic balancing techniques. The increasing demand for high-quality net- work services and the surge in network traffic requires the adoption of more efficient load-balancing methods to main- tain network performance. This paper discusses the use of OpenWrt, an open-source firmware for network routers, to configure and manage network traffic. The article also covers the implementation of QoS and traffic balancing techniques to optimize network performance and reduce network congestion. The study employs iperf3 to evaluate network performance and demonstrates the effectiveness of the proposed network load-balancing approach. The index terms include OpenWrt, QoS, balancing, traffic, and ip- erf3.

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17

Fadila, Aina, Muhammad Nasir, and Safriadi Safriadi. "Implementasi Sistem Load Balancing Web Server Pada Jaringan public Cloud Computing Menggunakan Least Connection." Journal of Artificial Intelligence and Software Engineering (J-AISE) 3, no. 2 (October 28, 2023): 50. http://dx.doi.org/10.30811/jaise.v3i2.4578.

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Web adalah sebuah perangkat lunak dengan berbasis data yang berfungsi untuk menerima permintaan dari client dan tanggapan permintaan dengan mentranfer melalui browser yang merupakan halaman situs web. Dibalik kemudahan pengaksesan segala informasi terdapat permasalahan yang terjadi pada trafik yang menuju web server yaitu dengan meningkatnya permintaan informasi akan dapat menjadikan trafik menuju web server menjadi overload dan akhirnya menjadi down karena tidak mampu menjalankan permintaan yang berlebihan. Untuk mengatasi permasalahan tersebut adalah dengan menggunakan load balancing yang bertugas untuk mendistribusikan beban trafik kebanyak server. Rumusan masalah yang terdapat adalah Bagaimana sitem monitoring jalanya trafik secara real time dan Bagaimana performa web server yang menggunakan load balancing dan web server tidak menggunakan load balancing. Tujuannya untuk melihat system monitoring secara real time dan mengetahui performa web server menggunakan load balancing dan tidak menggunakan load balancing.Pada penelitian ini diselesaikan dengan menerapkan load balancing pada jaringan public dan menerapkan load balancing Haproxy pada server serta didukung algoritma least connetion. Bedasarkan analisis, dapat diperoleh hasil bahwa keberhasilan system jalannya traffic secara real time yaitu 90 % dan hasil uji performa dari web server menggunakan aplikasi jmeter dengan jumlah traffic 1000 permintaan dalam satu waktu dengan looping 1,10,50 dan 100 pada load balancing nilai rata-rata throughput 630.2/sec dan tidak menggunakan load balancing nilai rata-rata throughput 354.5/sec.Kata Kunci : Load balancing, Web Server, Apache, JMeter, DockerAbstractWeb is a software with data-based that functions to receive requests from clients and respond to requests by transferring through a browser which is a website page. Behind the ease of accessing all information, there are problems that occur in traffic to the web server, namely with the increase in requests for information, it will be able to make traffic to the web server become overloaded and eventually down because it is unable to carry out excessive requests. To overcome this problem is to use load balancing which is in charge of distributing traffic loads to many servers. The formulation of the problem is how the system monitors traffic in real time and how the performance of web servers that use load balancing and web servers do not use load balancing. The goal is to see the monitoring system in real time and find out the performance of the web server using load balancing and not using load balancing. This research was completed by applying load balancing on public networks and applying Haproxy load balancing on servers and supported by least connetion algorithms. Based on the analysis, and the results of performance tests from the web server using the JMet application with the number of traffic 1000 requests at one time with looping 1, 10, 50 and 100 on load balancing average throughput value of 164.2 / sec and not using load balancing average throughput value of 612.2 / sec.Keywords— Load balancing, Web Server, Apache JMeter, Docker.

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18

Sufiev, Hadar, Yoram Haddad, Leonid Barenboim, and José Soler. "Dynamic SDN Controller Load Balancing." Future Internet 11, no. 3 (March 21, 2019): 75. http://dx.doi.org/10.3390/fi11030075.

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The software defined networking (SDN) paradigm separates the control plane from the data plane, where an SDN controller receives requests from its connected switches and manages the operation of the switches under its control. Reassignments between switches and their controllers are performed dynamically, in order to balance the load over SDN controllers. In order to perform load balancing, most dynamic assignment solutions use a central element to gather information requests for reassignment of switches. Increasing the number of controllers causes a scalability problem, when one super controller is used for all controllers and gathers information from all switches. In a large network, the distances between the controllers is sometimes a constraint for assigning them switches. In this paper, a new approach is presented to solve the well-known load balancing problem in the SDN control plane. This approach implies less load on the central element and meeting the maximum distance constraint allowed between controllers. An architecture with two levels of load balancing is defined. At the top level, the main component called Super Controller, arranges the controllers in clusters, so that there is a balance between the loads of the clusters. At the bottom level, in each cluster there is a dedicated controller called Master Controller, which performs a reassignment of the switches in order to balance the loads between the controllers. We provide a two-phase algorithm, called Dynamic Controllers Clustering algorithm, for the top level of load balancing operation. The load balancing operation takes place at regular intervals. The length of the cycle in which the operation is performed can be shorter, since the top-level operation can run independently of the bottom level operation. Shortening cycle time allows for more accurate results of load balancing. Theoretical analysis demonstrates that our algorithm provides a near-optimal solution. Simulation results show that our dynamic clustering improves fixed clustering by a multiplicative factor of 5.

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19

Shivaliya, Shikha, and Vijay Anand. "Design of Load Balancing Technique for Cloud Computing Environment." ECS Transactions 107, no. 1 (April 24, 2022): 2911–18. http://dx.doi.org/10.1149/10701.2911ecst.

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Cloud computing allows for the provision of IT resources on-demand and has various advantages. Because the majority of firms have shifted their activities to the cloud, data centers are frequently flooded with sporadic loads. When dealing with high network traffic in the cloud, it is necessary to balance the load among servers. This is something that load balancing can help with. The primary goal of load balancing is to distribute demand evenly among all available servers such that no server is under or overloaded. Load balancing is the process of dispersing load among several nodes to make the best use of resources when work is overwhelmed. When a node is overburdened to support the load, load balancing is essential. When a node becomes overcrowded, the load is dispersed to the remaining optimal nodes.

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20

Liu, Zhenhua, Minghong Lin, Adam Wierman, Steven Low, and Lachlan L. H. Andrew. "Greening Geographical Load Balancing." IEEE/ACM Transactions on Networking 23, no. 2 (April 2015): 657–71. http://dx.doi.org/10.1109/tnet.2014.2308295.

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21

Hofmeyr, Steven, Costin Iancu, and Filip Blagojević. "Load balancing on speed." ACM SIGPLAN Notices 45, no. 5 (May 2010): 147–58. http://dx.doi.org/10.1145/1837853.1693475.

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22

Anselmi, J., and N. S. Walton. "Decentralized Proportional Load Balancing." SIAM Journal on Applied Mathematics 76, no. 1 (January 2016): 391–410. http://dx.doi.org/10.1137/140969361.

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23

Hiraishi, Tasuku, Masahiro Yasugi, Seiji Umatani, and Taiichi Yuasa. "Backtracking-based load balancing." ACM SIGPLAN Notices 44, no. 4 (February 14, 2009): 55–64. http://dx.doi.org/10.1145/1594835.1504187.

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24

Doroudi, Sherwin, Esa Hyytiä, and Mor Harchol-Balter. "Value driven load balancing." Performance Evaluation 79 (September 2014): 306–27. http://dx.doi.org/10.1016/j.peva.2014.07.019.

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25

Nahir, Amir, Ariel Orda, and Danny Raz. "Replication-Based Load Balancing." IEEE Transactions on Parallel and Distributed Systems 27, no. 2 (February 1, 2016): 494–507. http://dx.doi.org/10.1109/tpds.2015.2400456.

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26

Canright, Geoffrey, Andreas Deutsch, and Tore Urnes. "Chemotaxis-Inspired Load Balancing." Complexus 3, no. 1-3 (2006): 8–23. http://dx.doi.org/10.1159/000094184.

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27

Delgosha, Payam, and Venkat Anantharam. "Load Balancing in Hypergraphs." Journal of Statistical Physics 173, no. 3-4 (March 7, 2018): 546–625. http://dx.doi.org/10.1007/s10955-018-1977-1.

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28

Buchbinder, Niv, and Joseph Naor. "Fair online load balancing." Journal of Scheduling 16, no. 1 (March 10, 2011): 117–27. http://dx.doi.org/10.1007/s10951-011-0226-0.

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29

Lee, Kangbok, Joseph Y. T. Leung, and Michael L. Pinedo. "Two dimensional load balancing." Operations Research Letters 42, no. 8 (December 2014): 539–44. http://dx.doi.org/10.1016/j.orl.2014.09.006.

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30

Kamble, Sachin, and R. H. Borhade. "Load Balancing Through Multipathing." International Journal of Engineering Trends and Technology 36, no. 5 (June 25, 2016): 219–23. http://dx.doi.org/10.14445/22315381/ijett-v36p241.

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31

Kaluzny, Bohdan L., and R. H. A. David Shaw. "Optimal aircraft load balancing." International Transactions in Operational Research 16, no. 6 (November 2009): 767–87. http://dx.doi.org/10.1111/j.1475-3995.2009.00723.x.

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32

Berenbrink, Petra, Tom Friedetzky, Leslie Ann Goldberg, Paul W. Goldberg, Zengjian Hu, and Russell Martin. "Distributed Selfish Load Balancing." SIAM Journal on Computing 37, no. 4 (January 2007): 1163–81. http://dx.doi.org/10.1137/060660345.

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33

Zhou, Xingyu. "Asymptotically Optimal Load Balancing." ACM SIGMETRICS Performance Evaluation Review 47, no. 3 (January 23, 2020): 34–37. http://dx.doi.org/10.1145/3380908.3380919.

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34

Azar, Yossi, Andrei Z. Broder, and Anna R. Karlin. "On-line load balancing." Theoretical Computer Science 130, no. 1 (August 1994): 73–84. http://dx.doi.org/10.1016/0304-3975(94)90153-8.

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35

Adler, Micah, Soumen Chakrabarti, Michael Mitzenmacher, and Lars Rasmussen. "Parallel randomized load balancing." Random Structures and Algorithms 13, no. 2 (September 1998): 159–88. http://dx.doi.org/10.1002/(sici)1098-2418(199809)13:2<159::aid-rsa3>3.0.co;2-q.

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36

Mekonnen, Dawit, Alemayehu Megersa, Rakesh Kumar Sharma, and Durga Prasad Sharma. "Designing a Component-Based Throttled Load Balancing Algorithm for Cloud Data Centers." Mathematical Problems in Engineering 2022 (October 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/4640443.

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Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, “available” and “free.” To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm’s performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm’s performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed “component-based throttled load balancing algorithm” is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization.

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37

Mondal, Ranjan Kumar, Enakshmi Nandi, and Debabrata Sarddar. "Load Balancing Scheduling with Shortest Load First." International Journal of Grid and Distributed Computing 8, no. 4 (August 31, 2015): 171–78. http://dx.doi.org/10.14257/ijgdc.2015.8.4.17.

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38

Ri, Sotetsu, Yusheng Ji, Shoichiro Asano, and Jun Matsukata. "Load balancing method based on load vector." Systems and Computers in Japan 25, no. 2 (1994): 8–21. http://dx.doi.org/10.1002/scj.4690250202.

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39

Maurya, Santosh Kumar, and Garima Sinha. "Load balancing in cloud computing: an analytical review and proposal." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1530. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1530-1537.

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Task scheduling <span>or mapping of requests to machines with specified requirements becomes a very important problem to get optimized results. With the requests of many users, the machine can go under different conditions such as heavy load and minimum load. These various aspects can lead to system failure and loss of data. Hence the load must be balanced to beat the losses of the system. A load balancing concept described how different types of loads could be balanced with each machine. The load can be of memory, computation, and network. The paper mainly focuses on all kinds of load to provide a big view for load balancing in cloud computing. Along with a review of all load balancing techniques, the paper is also presenting a proposal to balance the load with decision theory. The proposed paper introduces the basic techniques and types for load balancing and their implications for innovation.</span>

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40

Mishra, Swati, and Sanjaya Kumar Panda. "An Efficient Server Minimization Algorithm for Internet Distributed Systems." International Journal of Rough Sets and Data Analysis 4, no. 4 (October 2017): 17–30. http://dx.doi.org/10.4018/ijrsda.2017100102.

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The increasing use of online services leads to an unequal distribution of the loads among the servers. As a result, the problem is to balance the loads among the servers such that the total number of active servers is minimized. One of the possible solutions is to transfer the loads from the underutilized server to a suitable server and make the underutilized server to sleep mode. In this paper, a server minimization algorithm (SMA) is proposed for the solution of server minimization and the load balancing problem. The proposed algorithm reduces the number of servers by merging the loads of the two least loaded servers. Then it determines the standard deviation of the server loads for load balancing. The proposed SMA is compared with an existing load balancing algorithm using the number of minimized servers, load standard deviation and load factor. The simulation results show the efficacy of the SMA.

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41

BOKIYE, Lencho M., and Ilker Ali OZKAN. "HYBRID LOAD BALANCING POLICY TO OPTIMIZE RESOURCE DISTRIBUTION AND RESPONSE TIME IN CLOUD ENVIRONMENT." International Journal of Applied Mathematics Electronics and Computers 10, no. 4 (December 31, 2022): 101–9. http://dx.doi.org/10.18100/ijamec.1158866.

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Load balancing and task scheduling are the main challenges in Cloud Computing. Existing load balancing algorithms have a drawback in considering the capacity of virtual machines while distributing loads among them. The proposed algorithm works toward solving existing issues, such as fair load distribution, avoiding underloading and overloading, and improving response time. It implements best practices of Throttled load balancing algorithm and Equally Shared Current Execution algorithm. Virtual machines are selected based on the ratio of their bandwidth and load allocation count. Requests are sent to a Virtual Machine with higher bandwidth and lower load allocation count. Proposed algorithm checks for the availability of VM based on their capacity. This process is performed by selecting two VMs and comparing their vmWeight capacity. The one with the least vmWeight is selected. CloudAnalyst is used for simulation, response time evaluation, and resource utilization evaluation. The simulation result of the proposed algorithm is compared with three well-known load-balancing algorithms. These are Round Robin, Throttled Load balancing algorithm, and Enhanced Active Monitoring. Load-balancing Proposed Algorithm selects VMs based on their Algorithm. The proposed algorithm has improved over other algorithms in load distribution, response time, and resource utilization. All virtual machines in the data centers are loaded with a relatively equal number of tasks according to their capacity. This resulted in fair resource sharing and load distribution.

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42

Ullah, Ibrar, Irshad Hussain, Khalid Rehman, Piotr Wróblewski, Wojciech Lewicki, and Balasubramanian Prabhu Kavin. "Exploiting the Moth–Flame Optimization Algorithm for Optimal Load Management of the University Campus: A Viable Approach in the Academia Sector." Energies 15, no. 10 (May 19, 2022): 3741. http://dx.doi.org/10.3390/en15103741.

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Unbalanced load condition is one of the major issues of all commercial, industrial and residential sectors. Unbalanced load means that, when different loads are distributed on a three-phase four-wire system, unequal currents pass through the three phases. Due to it, a heavy current flows in the neutral wire, which not only adds the losses, but also puts constraints on three phases’ loads. In this paper, we have presented a practical approach for load balancing. First, we have considered the existing three-phase load system where the supply is a three-phase unbalanced supply. Before balancing the load, it is necessary to compensate the current in neutral wire. A nature-inspired moth–flame optimization (MFO) algorithm is used to propose a scheme for balancing of current in neutral wire. The information of a distributed single-phase load was used to balance the currents in a three-phase system. The feeder phase and load profiles of each single-phase load are used to reconfigure the network using an optimization process. By balancing the current of three phases, the current of the neutral conductor in substation transformers was reduced to almost zero.

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43

Quereshi, Zeba, and Abhay Kothari. "Towards Investigation of Various Load Balancing Techniques in Cloud Computing." ECS Transactions 107, no. 1 (April 24, 2022): 2163–70. http://dx.doi.org/10.1149/10701.2163ecst.

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Cloud computing is an on-demand service where customers may access any time common IT resources, information, software and other equipment. It is a web-based development that offers virtual resources over the Internet as a service. The higher the cloud use, the higher the charge. The allocation of loads to components processing is a challenging phenomenon.In a multi-node system, there is a very good possibility that some nodes will be idle while others will be overloaded. The load balancing algorithms' objective is to keep the load on each processing element constant. A technique for Random Load Balancing in Cloud Computing is devised, in which the amount of user requests is instantly assigned to the resources. This method employs a random strategy to decrease the request's waiting time. This proposed load balancing technique is simulated in Cloud Analyst tool. The performance comparison study between proposed random method and other available load balancing methods is conducted. It is found that the performance of proposed random method is better in terms of response time.Keywords: Cloud Computing, Load Balancing, Response Time, Virtual Machine, Sorting

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44

Ravi Kumar, P., S. Rajagopalan, and Joseph Charles P. "Light Weight Native Edge Load Balancers for Edge Load Balancing." Green Intelligent Systems and Applications 3, no. 1 (June 13, 2023): 48–55. http://dx.doi.org/10.53623/gisa.v3i1.256.

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Edge computing has become an essential aspect of modern computing systems. Edge computing involves processing data at the edge of the network, closer to where the data is generated. The ability to process data in real-time at the edge provides various benefits such as lower latency, improved response times, and reduced network congestion. Load balancing is a critical component of edge computing, which distributes the workload across multiple edge devices, ensuring that the workload is evenly distributed. This paper discusses current trends in edge computing load balancing techniques, including static, dynamic, and hybrid load balancing approaches.

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45

Alistarh, Dan, Giorgi Nadiradze, and Amirmojtaba Sabour. "Dynamic Averaging Load Balancing on Cycles." Algorithmica 84, no. 4 (December 24, 2021): 1007–29. http://dx.doi.org/10.1007/s00453-021-00905-9.

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AbstractWe consider the following dynamic load-balancing process: given an underlying graph G with n nodes, in each step $$t\ge 0$$ t ≥ 0 , a random edge is chosen, one unit of load is created, and placed at one of the endpoints. In the same step, assuming that loads are arbitrarily divisible, the two nodes balance their loads by averaging them. We are interested in the expected gap between the minimum and maximum loads at nodes as the process progresses, and its dependence on n and on the graph structure. Peres et al. (Random Struct Algorithms 47(4):760–775, 2015) studied the variant of this process, where the unit of load is placed in the least loaded endpoint of the chosen edge, and the averaging is not performed. In the case of dynamic load balancing on the cycle of length n the only known upper bound on the expected gap is of order $$\mathcal {O}( n \log n )$$ O ( n log n ) , following from the majorization argument due to the same work. In this paper, we leverage the power of averaging and provide an improved upper bound of $$\mathcal {O} ( \sqrt{n} \log n )$$ O ( n log n ) . We introduce a new potential analysis technique, which enables us to bound the difference in load between k-hop neighbors on the cycle, for any $$k \le n/2$$ k ≤ n / 2 . We complement this with a “gap covering” argument, which bounds the maximum value of the gap by bounding its value across all possible subsets of a certain structure, and recursively bounding the gaps within each subset. We also show that our analysis can be extended to the specific instance of Harary graphs. On the other hand, we prove that the expected second moment of the gap is lower bounded by $$\Omega (n)$$ Ω ( n ) . Additionally, we provide experimental evidence that our upper bound on the gap is tight up to a logarithmic factor.

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Ohta, Satoru, and Ryuichi Andou. "WWW Server Load Balancing Technique Employing Passive Measurement of Server Performance." ECTI Transactions on Electrical Engineering, Electronics, and Communications 8, no. 1 (August 1, 2009): 59–66. http://dx.doi.org/10.37936/ecti-eec.201081.172018.

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Server load balancing is indispensable within World Wide Web (WWW) for providing high-quality service. In server load balancing, since the server loads and capacities are not always identical, traffic should be distributed by measuring server performance to improve the service quality. This study proposes a load balancing technique conducted by passive measurement, which estimates the server performance via user traffic passing through the load balancer. Since this method evaluates server performance without executing any programs on the server, no additional server or network load is generated. This paper first presents a server performance metric that can be passively measured. The presented metric utilizes the characteristics of TCP SYN and SYN ACK messages exchanged in the TCP connection establishment phase. An experiment shows that the metric correctly identifies server performance degradation. The paper then proposes a load balancing algorithm based on the metric, and its implementation issues. The proposed algorithm distributes fewer requests to servers that do not have su±cient capacities. Because of this, the algorithm achieves good performance in a heterogeneous environment where servers with different capacities coexist. The effectiveness of the proposed load balancing technique is confirmed experimentally.

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Pan, Jin Xue. "A Load Balancing Mechanism for Video Surveillance System." Advanced Materials Research 1049-1050 (October 2014): 2079–83. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.2079.

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In recent years, with the development of video surveillance systems, the cluster and load balancing technology need be applied to improve the system performance and the quality of service. In this paper, on the basis of common load balancing algorithms, considering the characteristics of the video surveillance system, design a new load balancing scheduling mechanism, by improving the weighted round robin algorithm and introducing the nodes cooperation strategy. The testing results show that the new mechanism can distribute the loads more reasonably and make use of the server resources more effectively.

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MUTHUKRISHNAN, S., and RAJMOHAN RAJARAMAN. "AN ADVERSARIAL MODEL FOR DISTRIBUTED DYNAMIC LOAD BALANCING." Journal of Interconnection Networks 03, no. 01n02 (March 2002): 35–47. http://dx.doi.org/10.1142/s0219265902000537.

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We study the problem of balancing the load on processors in an arbitrary network. If jobs arrive or depart during the process of load balancing, we have the dynamic load balancing problem; otherwise, we have the static load balancing problem. While static load balancing on a number of different networks has been well-studied, analytical work on dynamic load balancing is limited. The difficulty lies in modeling the arrivals and departures of jobs in a clean manner. In this paper, we introduce a new framework for dynamic load balancing in which the job traffic is modeled by an adversary. Our main result is that a simple local load balancing algorithm maintains the load of the network within a stable level against a powerful adversary. Our result holds for different models of traffic patterns and processor communication.

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Saber, Walaa, Walid Moussa, Atef M. Ghuniem, and Rawya Rizk. "Hybrid load balance based on genetic algorithm in cloud environment." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (June 1, 2021): 2477. http://dx.doi.org/10.11591/ijece.v11i3.pp2477-2489.

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Load balancing is an efficient mechanism to distribute loads over cloud resources in a way that maximizes resource utilization and minimizes response time. Metaheuristic techniques are powerful techniques for solving the load balancing problems. However, these techniques suffer from efficiency degradation in large scale problems. This paper proposes three main contributions to solve this load balancing problem. First, it proposes a heterogeneous initialized load balancing (HILB) algorithm to perform a good task scheduling process that improves the makespan in the case of homogeneous or heterogeneous resources and provides a direction to reach optimal load deviation. Second, it proposes a hybrid load balance based on genetic algorithm (HLBGA) as a combination of HILB and genetic algorithm (GA). Third, a newly formulated fitness function that minimizes the load deviation is used for GA. The simulation of the proposed algorithm is implemented in the cases of homogeneous and heterogeneous resources in cloud resources. The simulation results show that the proposed hybrid algorithm outperforms other competitor algorithms in terms of makespan, resource utilization, and load deviation.

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Singh, Jagdeep, and Mr Pawan Luthra. "CREDIT BASED LOAD BALANCING IN CLOUD ENVIRONMENT: A REVIEW." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 12 (August 24, 2016): 7258–62. http://dx.doi.org/10.24297/ijct.v15i12.4499.

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In present days cloud computing is one of the greatest platform which provides storage of data in very lower cost and available for all time over the internet.But it has more critical issue like security, load management and fault tolerance. In this paper we are discussing Load Balancing approach. Many types of load concern with cloud like memory load, CPU load and network load. Load balancing is the process of distributing load over the different nodes which provides good resource utilization when nodes are overloaded with job. Load balancing has to handle the load when one node is overloaded. When node is overloaded at that time load is distributed over the other ideal nodes. Many algorithms are available for load balancing like Static load balancing and Dynamic load balancing.

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