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Relevant bibliographies by topics / Load balancing

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Academic literature on the topic 'Load balancing'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Journal articles on the topic "Load balancing"

1

x, Kuldeep, and Ravinder Kumar. "Load Balancing Techniques ? A Review." International Journal of Scientific Engineering and Research 5, no. 12 (December 27, 2017): 26–29. https://doi.org/10.70729/ijser172078.

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Load balancing"

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Burrows, Richard B. P. "Dynamic load balancing." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363886.

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Nagel, Lars. "Randomised load balancing." Thesis, Durham University, 2011. http://etheses.dur.ac.uk/3207/.

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Due to the increased use of parallel processing in networks and multi-core architectures, it is important to have load balancing strategies that are highly efficient and adaptable to specific requirements. Randomised protocols in particular are useful in situations in which it is costly to gather and update information about the load distribution (e.g. in networks). For the mathematical analysis randomised load balancing schemes are modelled by balls-into-bins games, where balls represent tasks and bins computers. If m balls are allocated to n bins and every ball chooses one bin at random, the gap between maximum and average load is known to grow with the number of balls m. Surprisingly, this is not the case in the multiple-choice process in which each ball chooses d > 1 bins and allocates itself to the least loaded. Berenbrink et al. proved that then the gap remains ln ln(n) / ln(d). This thesis analyses generalisations and variations of the multiple-choice process. For a scenario in which batches of balls are allocated in parallel, it is shown that the gap between maximum and average load is still independent of m. Furthermore, we look into a process in which only predetermined subsets of bins can be chosen by a ball. Assuming that the number and composition of the subsets can change with every ball, we examine under which circumstances the maximum load is one. Finally, we consider a generalisation of the basic process allowing the bins to have different capacities. Adapting the probabilities of the bins, it is shown how the load can be balanced over the bins according to their capacities.

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Clifton, Christopher W. "Dynamic load balancing." Thesis, Massachusetts Institute of Technology, 1986. http://hdl.handle.net/1721.1/81504.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1986.<br>MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING<br>Bibliography: leaves 72-74.<br>by Christopher W. Clifton.<br>M.S.

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Friedetzky, Thomas. "Randomised dynamic load balancing." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=96937979X.

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Wang, Chunpu. "Distributed random load balancing." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61801.

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Low latency is highly desirable for cloud services spanning thousands of servers. With the rapid development of cloud market, the size of server farms grows fast. Hence, stringent timing requirements are needed for task scheduling in a large-scale server farm. Conventionally, the Join-the-Shortest-Queue (JSQ) algorithm, which directs an arriving task to the least loaded server, is adopted in scheduling. Despite its excellent delay performance, JSQ is throughput-limited, and thus doesn't scale well with the number of servers. There are two distributed algorithms proposed as "approximations" of the idealized JSQ. The first one is the Power-of-d-choices (Pod) algorithm, which selects d servers at random and routes a task to the least loaded server of the d servers. Despite its scalability, Pod suffers from long tail response times. The second one is the distributed Join-the-Idle-Queue (JIQ), which take advantages idle servers for task scheduling. In this thesis, we are interested in exploring Pod and JIQ further. First, a hybrid scheduling strategy called Pod-Helper is proposed. It consists of a Pod scheduler and a throughput-limited helper. Hybrid scheduling takes the best of both worlds, enjoying scalability and low tail response times. In particular, hybrid scheduling has bounded maximum queue size in the large-system regime, which is in sharp contrast to the Pod scheduling whose maximum queue size is unbounded. Second, we conduct an in-depth analysis for distributed Join-the-Idle-Queue (JIQ), a promising new approximation of an idealized task-scheduling algorithm. In particular, we derive semi-closed form expressions for the delay performance of distributed JIQ. Third, we propose a new variant of distributed JIQ that offers clear advantages over alternative algorithms for large systems.<br>Applied Science, Faculty of<br>Engineering, School of (Okanagan)<br>Graduate

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Montag, David. "Load balancing of IP telephony." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16066.

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<p>In today's world, more and more phone calls are made over IP. This results in an increasing demand for scalable IP telephony equipment.</p><p>Ingate Systems AB produces firewalls specialized in handling IP telephony. They have an inherent limit in the number of concurrent phone calls that they can handle. This can be a bottleneck at high loads. There is a load balancing solution available in the platform, but it has a number of drawbacks, such as media latency and client capability requirements, limiting its usage.</p><p>Many companies provide load balancing solutions for SIP. However, it appears few handle all the problematic scenarios that the Ingate firewall does. This master's thesis aims to add load balancing functionality to the Ingate firewall, so that it can handle all types of clients.</p><p>By splitting the firewall into two completely separate layers - a SIP layer and a firewall layer - the concept of a virtual machine emerges. A machine is no longer restricted to its physical SIP and firewall layers. Instead, virtual machines are used to process calls. They still have SIP and firewall layers, but the layers can reside on different physical machines.</p><p>This thesis demonstrates the operation of an innovative load balancing implementation. The implementation was evaluated, and using four machines the test setup performed 50% better than the original Ingate platform, while still retaining all functionality -- something that was not possible with the original platform. This surpassed both the company's and my own expectations.</p>

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Mohammad, Malik Adeel, and Saeed Muhammad Sheharyar. "Load Balancing in Microwave Networks." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-121698.

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Microwave links are very commonly used in carrier networks especially towards the access side. They not only ease deployment of a network but are also very cost effective. However, they bring along a multitude of challenges which are characteristic of the wireless technology. Microwave links are fickle. Being exposed to varying weather conditions, they experience bandwidth fluctuations. This is true especially in the case of links operating at higher frequencies. The unpredictable nature of microwave links makes it quite challenging to plan capacity in a network beforehand. Radio links employ adaptive modulation. They operate on a range on modulation schemes each of which offers different throughput and bit error rates. When operating at a low bit rate modulation scheme, a situation may arise where the microwave link is not able to support the entire traffic incident from the backbone network. As a result, the microwave link will suffer from congestion and packets arriving at the microwave link will eventually be dropped. The switching nodes that precede the microwave link along a communication path are unaware of the microwave link conditions and, therefore, continue to transmit traffic at a high rate. Large carrier networks cannot afford to have performance inconsistencies like data loss and increased latency. Service degradation, even for a very short duration, can have dire consequences in terms of customer dissatisfaction and revenue loss. The goal of this thesis is to use MPLS-TP Linear Protection to load balance traffic across alternative paths in a network where links use adaptive modulation. Rerouted traffic must take other paths so that the congested microwave link is completely avoided. The idea is augmented by the use of a radio condition signaling mechanism between the packet switching node and the microwave node that precede a microwave link. The microwave node sends radio condition control messages to the preceding packet switching node to rate limit traffic and avoid congestion at the microwave link. The result of this thesis work is a system prototype that achieves the stated goal. Evaluation of the prototype is carried out through graphical results, generated by a traffic generator, that advocate the correctness, performance and robustness of the system.

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Macharia, Geoffrey Muragori. "Cellular load distribution : dynamic load balancing in scalable multicomputers." Thesis, University of York, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.276343.

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Do, Manh Duc. "Green Cloud - Load Balancing, Load Consolidation using VM Migration." TopSCHOLAR®, 2017. https://digitalcommons.wku.edu/theses/2059.

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Recently, cloud computing is a new trend emerging in computer technology with a massive demand from the clients. To meet all requirements, a lot of cloud data centers have been constructed since 2008 when Amazon published their cloud service. The rapidly growing data center leads to the consumption of a tremendous amount of energy even cloud computing has better improved in the performance and energy consumption, but cloud data centers still absorb an immense amount of energy. To raise company’s income annually, the cloud providers start considering green cloud concepts which gives an idea about how to optimize CPU’s usage while guaranteeing the quality of service. Many cloud providers are paying more attention to both load balancing and load consolidation which are two significant components of a cloud data center. Load balancing is taken into account as a vital part of managing income demand, improving the cloud system’s performance. Live virtual machine migration is a technique to perform the dynamic load balancing algorithm. To optimize the cloud data center, three issues are considered: First, how does the cloud cluster distribute the virtual machine (VM) requests from clients to all physical machine (PM) when each computer has a different capacity. Second, what is the solution to make CPU’s usage of all PMs to be nearly equal? Third, how to handle two extreme scenarios: rapidly rising CPU’s usage of a PM due to sudden massive workload requiring VM migration immediately and resources expansion to respond to substantial cloud cluster through VM requests. In this chapter, we provide an approach to work with those issues in the implementation and results. The results indicated that the performance of the cloud cluster was improved significantly. Load consolidation is the reverse process of load balancing which aims to provide sufficient cloud servers to handle the client requests. Based on the advance of live VM migration, cloud data center can consolidate itself without interrupting the cloud service, and superfluous PMs are turned to save mode to reduce the energy consumption. This chapter provides a solution to approach load consolidation including implementation and simulation of cloud servers.

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Garcia, Gasulla Marta. "Dynamic load balancing for hybrid applications." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/406040.

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It is well known that load imbalance is a major source of efficiency loss in HPC (High Performance Computing) environments. The load imbalance problem has very different sources, from static ones related to the data distribution to very dynamic ones, for example, the noise of the system. In this thesis, we present DLB: Dynamic Load Balancing library. DLB is a framework to improve the efficient use of the computational resources of a computational node. With DLB we offer a dynamic solution to load imbalance problems. DLB is applied at runtime and does not need previous information to solve load imbalance problems, for this reason, it can deal with load imbalances coming from any source. The DLB framework includes a novel load balancing algorithm: LeWI (Lend When Idle). The main idea of LeWI is to use the computational resources assigned to a process or thread when it is idle, to speed up another process running on the same node that it is still doing computation. We will see how this idea although being quite simple it is powerful and flexible to obtain an efficient use of resources close to the ideal one.<br>En aquesta tesi presentem DLB (Dynamic Load Balancing), una llibreria que ajuda a fer un us eficient dels recursos d'un node de càlcul. Dins de DLB hem implementat un algoritme de balanceig original: LeWI (Lend When Idle). LeWI està basat en la idea que quan un procés MPI està esperant en una crida MPI bloquejant els recursos de càlcul que té assignats no estan ocupats. Per tant, aquests recursos els poden fer servir altres processos que s'estiguin executant al mateix node per acabar el seu càlcul més ràpid. DLB intercepta les crides MPI i canvia el nombre de threads OpenMP com calgui. Quan un procés arriba a una crida MPI bloquejant cedirà les seves CPUs a un altre procés que s'estigui executant al mateix node. Quan el primer procés MPI acabi la crida MPI bloquejant recuperarà les seves CPUs. Hem implementat LeWI a DLB i avaluat el seu rendiment, amb aquesta avaluació hem vist que DLB i LeWI poden millorar el rendiment d'aplicacions híbrides. LeWI pot balancejar aplicacions amb patrons regulars o irregular de desbalanceig sense modificar l'aplicació. Hem observat que la mal·leabilitat del model de programació i de l'aplicació pot afectar el rendiment que s'obté amb l'algoritme de balanceig. Tot i que OpenMP és mal·leable té una limitació, el nombre de threads només es pot canviar fora d'una regió paral·lela. El model de programació OmpSs és més mal·leable, ja que el nombre de threads es pot canviar en qualsevol punt. L'avaluació ens va demostrar que la mal·leabilitat del model de programació que es fa servir te un impacte substancial en el rendiment que obté l'algoritme de balanceig. Per defecte els diferents processos MPI es distribueixen de manera consecutiva entre els nodes de càlcul, però hem observat que en les aplicacions científiques la tendència és que els processos més carregats siguin consecutius. Per aquest motiu fer una distribució cíclica (Round Robin) dels processos MPI entre els nodes permet a l'algoritme de balanceig obtenir un millor rendiment. També hem observat que lligar els threads a CPUs o no fer-ho afecta al rendiment de les aplicacions i en especial quan es fa servir l'algoritme de balanceig. Per a permetre que LeWI pugui gestionar CPUs concretes hem modificat la llibreria perquè utilitzi mascares de CPUs. Amb l'avaluació hem vist que lligar els threads a CPUs té un impacte important en el rendiment que s'obté. Però també que l'impacte depèn de la mida del node (nombre de CPUs per node) i l'estructura de la memòria. Hem integrat DLB amb un runtime parallel, Nanos++. Aquesta integració ens ha mostrat el potencial d'aquest tipus de col·laboracions entre runtimes. Ens ha permès identificar els punts clau de coordinació necessaris i ens ha demostrat que DLB està preparat per a ser integrat amb altres runtimes paralels. L'avaluació ha mostrat el potencial d'aquest tipus d'integracions i col·laboracions. Finalment, hem fet una avaluació exhaustiva de l'entorn i l'algoritme amb una aplicació en producció: Alya. Hem vist que podem reduir fins a un 40% el temps d'execució per a situacions amb un alt desbalanceig. I en el cas de situacions sense desbalanceig l'ús de DLB no penalitza el rendiment de l'aplicació. També hem vist que el rendiment de la paral·lelització OpenMP de l'aplicació té un alt impacte en el rendiment de DLB i LeWI. Hem pogut provar que DLB i LeWI estan llestos per a ser utilitzats en execucions reals. I en executar proves d'escalabilitat fins a 16.000 cores hem vist que no només LeWI pot escalar fins a milers de cores sinó que l'algoritme de balanceig que només s'aplica dins del node de càlcul pot millorar el rendiment d'execucions en milers de nodes de càlcul.

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Books on the topic "Load balancing"

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Membrey, Peter, David Hows, and Eelco Plugge. Practical Load Balancing. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-3681-8.

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W, Wah Benjamin, ed. Load balancing: An automated learning approach. River Edge, NJ: World Scientific Pub., 1995.

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Biswas, R. Load balancing sequences of unstructured adaptive grids. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1997.

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Leonid, Oliker, and Research Institute for Advanced Computer Science (U.S.), eds. Load balancing sequences of unstructured adaptive grids. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1997.

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Membrey, Peter. Practical Load Balancing: Ride the Performance Tiger. Berkeley, CA: Apress, 2012.

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Kameda, Hisao, Jie Li, Chonggun Kim, and Yongbing Zhang. Optimal Load Balancing in Distributed Computer Systems. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0969-3.

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Delisle, Pierre. A load balancing facility for distributed systems. Toronto: University of Toronto, Dept. of Computer Science, 1989.

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Priyanthi, Fernando, and Porter Gina, eds. Balancing the load: Women, gender, and transport. London: Zed Books in association with International Forum for Rural Transport and Development, 2002.

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Tumuluri, Chaitanya. Locality-conscious load balancing: Connectionist architectural support. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1996.

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Ruud, Schoonderwoerd, and Hewlett-Packard Laboratories, eds. Ant-based load balancing in telecommunications networks. Palo Alto, CA: Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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Book chapters on the topic "Load balancing"

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Sahay, Rahul. "Load Balancing." In Microsoft Azure Architect Technologies Study Companion, 497–559. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6200-9_14.

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Sharma, Rahul, and Akshay Mathur. "Load Balancing." In Traefik API Gateway for Microservices, 67–97. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6376-1_3.

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Epstein, Leah. "Load Balancing." In Encyclopedia of Algorithms, 457–59. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_206.

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Vohra, Deepak. "Load Balancing." In Docker Management Design Patterns, 219–39. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2973-6_12.

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Castro, José Román Bilbao. "Load Balancing." In Encyclopedia of Systems Biology, 1140. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1273.

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Weik, Martin H. "load balancing." In Computer Science and Communications Dictionary, 911. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_10419.

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Dongarra, Jack, Piotr Luszczek, Paul Feautrier, Field G. Zee, Ernie Chan, Robert A. Geijn, Robert Bjornson, et al. "Load Balancing." In Encyclopedia of Parallel Computing, 1043. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2027.

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Sanders, Peter, Kurt Mehlhorn, Martin Dietzfelbinger, and Roman Dementiev. "Load Balancing." In Sequential and Parallel Algorithms and Data Structures, 419–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25209-0_14.

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Kumar, Jitendra, Ashutosh Kumar Singh, Anand Mohan, and Rajkumar Buyya. "Load Balancing." In Machine Learning for Cloud Management, 141–54. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003110101-8.

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Membrey, Peter, David Hows, and Eelco Plugge. "Network Load Balancing." In Practical Load Balancing, 153–74. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-3681-8_10.

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Conference papers on the topic "Load balancing"

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Panda, Subramanyam, Sukant Kishoro Bisoy, Rohit Kumar, Maasoom Barik, and Pritam Kumar Jena. "Load Balancing in Cloud Computing." In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icspcre62303.2024.10674973.

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Javani, Alireza, and Zhiying Wang. "Load Balancing in Federated Learning." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference, 3950–55. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901731.

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Kaur, Simranjit, and Tejinder Sharma. "Efficient load balancing using improved central load balancing technique." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8398857.

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Friedrich, Tobias, Martin Gairing, and Thomas Sauerwald. "Quasirandom Load Balancing." In Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611973075.132.

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Bonald, T., M. Jonckheere, and A. Proutiére. "Insensitive load balancing." In the joint international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1005686.1005729.

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Juva, Ilmari. "Robust Load Balancing." In IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference. IEEE, 2007. http://dx.doi.org/10.1109/glocom.2007.513.

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Grosof, Isaac, Ziv Scully, and Mor Harchol-Balter. "Load Balancing Guardrails." In SIGMETRICS '19: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3309697.3331514.

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Pearce, Olga, Todd Gamblin, Bronis R. de Supinski, Martin Schulz, and Nancy M. Amato. "Decoupled load balancing." In PPoPP '15: 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2688500.2688539.

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Spicuglia, Sebastiano, Mathias Bjöerkqvist, Lydia Y. Chen, Giuseppe Serazzi, Walter Binder, and Evgenia Smirni. "On load balancing." In the ACM/SPEC international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2479871.2479884.

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Kapidakis, Sarantos, and Marios Mavronicolas. "Load balancing networks." In the fourteenth annual ACM symposium. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/224964.225013.

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Reports on the topic "Load balancing"

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Hendrickson, B., and R. Leland. Multidimensional spectral load balancing. Office of Scientific and Technical Information (OSTI), January 1993. http://dx.doi.org/10.2172/6691328.

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Pearce, Olga Tkachyshyn. Load Balancing Scientific Applications. Office of Scientific and Technical Information (OSTI), December 2014. http://dx.doi.org/10.2172/1178404.

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Volz, B., S. Gonczi, T. Lemon, and R. Stevens. DHC Load Balancing Algorithm. RFC Editor, February 2001. http://dx.doi.org/10.17487/rfc3074.

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Heirich, Alan, and Stephen Taylor. A Parabolic Load Balancing Method. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada442993.

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Gabler, Jason. Better Bonded Ethernet Load Balancing. Office of Scientific and Technical Information (OSTI), September 2006. http://dx.doi.org/10.2172/883778.

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Heirich, Alan. Scalable Load Balancing by Diffusion. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada448706.

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Brisco, T. DNS Support for Load Balancing. RFC Editor, April 1995. http://dx.doi.org/10.17487/rfc1794.

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Filsfils, C., P. Mohapatra, and C. Pignataro. Load-Balancing for Mesh Softwires. RFC Editor, August 2009. http://dx.doi.org/10.17487/rfc5640.

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Bershad, Brian. Load Balancing with Maitre d'. Fort Belvoir, VA: Defense Technical Information Center, December 1985. http://dx.doi.org/10.21236/ada185092.

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Cai, Y., H. Ou, S. Vallepalli, M. Mishra, S. Venaas, and A. Green. PIM Designated Router Load Balancing. RFC Editor, April 2020. http://dx.doi.org/10.17487/rfc8775.

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