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Journal articles on the topic 'Load balancing in heterogeneous networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Hyukmin Son, Sanghoon Lee, Soo-Chang Kim, and Yeon-Seung Shin. "Soft Load Balancing Over Heterogeneous Wireless Networks." IEEE Transactions on Vehicular Technology 57, no. 4 (2008): 2632–38. http://dx.doi.org/10.1109/tvt.2007.912324.

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2

Ray, Ranada Prasad, and Lun Tang. "Hysteresis Margin and Load Balancing for Handover in Heterogeneous Network." International Journal of Future Computer and Communication 4, no. 4 (2015): 231–35. http://dx.doi.org/10.7763/ijfcc.2015.v4.391.

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3

Elsässer, Robert, Burkhard Monien, and Robert Preis. "Diffusion Schemes for Load Balancing on Heterogeneous Networks." Theory of Computing Systems 35, no. 3 (2002): 305–20. http://dx.doi.org/10.1007/s00224-002-1056-4.

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4

Aghazadeh, Yasin, Hashem Kalbkhani, Mahrokh G. Shayesteh, and Vahid Solouk. "Cell Selection for Load Balancing in Heterogeneous Networks." Wireless Personal Communications 101, no. 1 (2018): 305–23. http://dx.doi.org/10.1007/s11277-018-5689-2.

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5

LINCKE, SUSAN J., and CYNTHIA S. HOOD. "LOAD SHARING VERSUS SERVICE BALANCING IN INTEGRATED NETWORKS." Journal of Interconnection Networks 06, no. 01 (2005): 35–50. http://dx.doi.org/10.1142/s0219265905001307.

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As third and fourth generation cellular/wireless networks evolve, operators must learn to efficiently manage diverse services, and multiple networks consisting of varying technologies, cell sizes, and frequency bands. Architectural studies on integrated heterogeneous networks suggest that vertical handovers can be used to increase network efficiency. We propose that carefully-controlled load distribution can also promote Quality of Service (QoS) goals for the diverse services. This study compares session overflow and session placement algorithms in order to determine their effects on efficiency and QoS.

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6

Gandhi, Anshul, Naman Mittal, and Xi Zhang. "Optimal Load-Balancing for Heterogeneous Clusters." ACM SIGMETRICS Performance Evaluation Review 43, no. 3 (2015): 43. http://dx.doi.org/10.1145/2847220.2847232.

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7

Alam, Mahfooz, and Mohammad Shahid. "A Load Balancing Strategy with Migration Cost for Independent Batch of Tasks (BoT) on Heterogeneous Multiprocessor Interconnection Networks." International Journal of Applied Evolutionary Computation 8, no. 3 (2017): 74–92. http://dx.doi.org/10.4018/ijaec.2017070104.

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In high performance computing, heterogeneous Multiprocessor Interconnection Networks (MINs) are used for processing of compute intensive applications. These applications are distributed on the heterogeneous computational processors of MINs arranged in specific geometrical shape. MINs are also used for transfer task between two processors in a heterogeneous multistage network for better load balancing. Load balancing algorithm plays a vital role in interconnection network in order to minimize the load imbalance on the processors. In this paper, a Load Balancing Strategy with Migration cost (LBSM) is proposed to execute an independent batch of tasks on various heterogeneous MINs viz. MetaCube, X-Torus and Folded Crossed Cube having the objective of minimizing the load imbalance on processors. In simulation study, LBSM is compared with its previous work DLBS and superior performance is shown with the considered parameters under study. Further, the performance analysis of LBSM has been conducted on MetaCube, X-Torus and Folded Crossed Cube and results have been reported accordingly.

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8

Lee, Kyoung-Jae, and Han-Shin Jo. "Interference Cancellation and Load Balancing in Heterogeneous Cellular Networks." Journal of the Institute of Electronics and Information Engineers 51, no. 10 (2014): 45–49. http://dx.doi.org/10.5573/ieie.2014.51.10.045.

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9

Zhao, Chenggui. "Parameter-invariant models for load balancing on heterogeneous networks." Linear Algebra and its Applications 471 (April 2015): 369–82. http://dx.doi.org/10.1016/j.laa.2015.01.002.

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10

Ye, Qiaoyang, Beiyu Rong, Yudong Chen, Mazin Al-Shalash, Constantine Caramanis, and Jeffrey G. Andrews. "User Association for Load Balancing in Heterogeneous Cellular Networks." IEEE Transactions on Wireless Communications 12, no. 6 (2013): 2706–16. http://dx.doi.org/10.1109/twc.2013.040413.120676.

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11

Ran, Chen, Shaowei Wang, and Chonggang Wang. "Balancing backhaul load in heterogeneous cloud radio access networks." IEEE Wireless Communications 22, no. 3 (2015): 42–48. http://dx.doi.org/10.1109/mwc.2015.7143325.

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12

Haldar, Kuheli Louha, g. Hyun Jun, Talmai Oliveira, and Dharma P. Agrawal. "Exploring load balancing in heterogeneous networks by rate distribution." International Journal of Autonomous and Adaptive Communications Systems 3, no. 3 (2010): 284. http://dx.doi.org/10.1504/ijaacs.2010.033384.

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13

Kaur, Sukhkirandeep, Roohie Naaz Mir, Aditya Khamparia, Poonam Rani, Deepak Gupta, and Ashish Khanna. "Heterogeneous load balancing clustering protocol for Wireless Sensor Networks." Cognitive Systems Research 70 (December 2021): 10–17. http://dx.doi.org/10.1016/j.cogsys.2021.07.001.

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14

Thiagarajah, Siva Priya, Mohamad Yusoff Alias, and Wooi-Nee Tan. "QoS controlled capacity offload optimization in heterogeneous networks." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2667–80. http://dx.doi.org/10.11591/eei.v9i6.2706.

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An efficient resource allocation mechanism in the physical layer of wireless networks ensures that resources such as bandwidth and power are used with high efficiency in spite of low delay and high edge user data rate. Microcells in the network are typically set with bias settings to artificially increase the Signal-to-Interference-Plus-Noise Ratio, thus encouraging users to offload to the microcell. However, the artificial bias settings are tedious and often suboptimal. This work presents a low complexity algorithm for maximization of network capacity with load balancing in a heterogeneous network without the need for bias setting. The small cells were deployed in a grid topology at a selected distance from macrocell to enhance network capacity through coverage overlap. User association and minimum user throughput were incorporated as constraints to enable closer simulation to real word Quality of Service requirements. The results showed that the proposed algorithm was able to maintain less than 10% user drop rate. The proposed algorithm can increase user confidence as well as maintain load balancing, maintain the scalability, and reduce power consumption of the wireless network.

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15

Murugesan. "ENHANCED LOAD BALANCING STRATEGY IN HETEROGENEOUS PEER-TO-PEER NETWORKS." Journal of Computer Science 9, no. 9 (2013): 1197–204. http://dx.doi.org/10.3844/jcssp.2013.1197.1204.

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16

Hong, Myung-Hoon, and Seung-Young Park. "Load Balancing Scheme for Heterogeneous Cellular Networks Using e-ICIC." Journal of Korean Institute of Communications and Information Sciences 39A, no. 5 (2014): 280–92. http://dx.doi.org/10.7840/kics.2014.39a.5.280.

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17

Vu, Trung Kien, Mehdi Bennis, Sumudu Samarakoon, Merouane Debbah, and Matti Latva-aho. "Joint Load Balancing and Interference Mitigation in 5G Heterogeneous Networks." IEEE Transactions on Wireless Communications 16, no. 9 (2017): 6032–46. http://dx.doi.org/10.1109/twc.2017.2718504.

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18

Hasan, MD Mehedi, and Sungoh Kwon. "Cluster-Based Load Balancing Algorithm for Ultra-Dense Heterogeneous Networks." IEEE Access 8 (2020): 2153–62. http://dx.doi.org/10.1109/access.2019.2961949.

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19

Zhao, Chenggui, and Wenjun Xiao. "Improved Diffusion Strategies for Load Balancing on General Heterogeneous Networks." Advanced Science Letters 5, no. 2 (2012): 901–5. http://dx.doi.org/10.1166/asl.2012.1783.

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20

Jin, Ling. "A Novel Adaptive Load Balancing Algorithm in Heterogeneous Wireless Networks." Journal of Information and Computational Science 11, no. 7 (2014): 2213–23. http://dx.doi.org/10.12733/jics20103195.

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21

Sohn, Illsoo, and Sang Hyun Lee. "Distributed Load Balancing via Message Passing for Heterogeneous Cellular Networks." IEEE Transactions on Vehicular Technology 65, no. 11 (2016): 9287–98. http://dx.doi.org/10.1109/tvt.2016.2519921.

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22

Ma, Dong, and Maode Ma. "Proactive load balancing with admission control for heterogeneous overlay networks." Wireless Communications and Mobile Computing 13, no. 18 (2011): 1671–80. http://dx.doi.org/10.1002/wcm.1224.

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23

Singh, Surjit, and Rajeev Mohan Sharma. "Heuristic Based Coverage Aware Load Balanced Clustering in WSNs and Enablement of IoT." International Journal of Information Technology and Web Engineering 13, no. 2 (2018): 1–10. http://dx.doi.org/10.4018/ijitwe.2018040101.

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Integration of heterogeneous communication systems is the major objective of Future Internet. Heterogeneous systems such as wired and wireless assert the concept of Internet of Things (IoT). Communication standard protocols based global network of identical addressable interconnected resources conferred to IoT. In this article, it is described how wireless sensor networks (WSN) are one of the keys enabling the IoT. Clustering in WSNs plays an important role since it allows altering the network topology and helps in balancing of traffic loads. Load balancing improves the lifespan and scalability of the WSNs. Here, a heuristic approach, coverage aware load balanced clustering, has been developed in order to increase the lifespan of the WSNs. Moreover, simulation results are demonstrated for a sample network and the results show that the network lifespan has increased and simultaneously energy consumption is reduced compared to the other existing techniques.

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24

Gadam, M. A., Maryam Abdulazeez Ahmed, Chee Kyun Ng, Nor Kamariah Nordin, Aduwati Sali, and Fazirulhisyam Hashim. "Review of Adaptive Cell Selection Techniques in LTE-Advanced Heterogeneous Networks." Journal of Computer Networks and Communications 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7394136.

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Poor cell selection is the main challenge in Picocell (PeNB) deployment in Long Term Evolution- (LTE-) Advanced heterogeneous networks (HetNets) because it results in load imbalance and intercell interference. A selection technique based on cell range extension (CRE) has been proposed for LTE-Advanced HetNets to extend the coverage of PeNBs for load balancing. However, poor CRE bias setting in cell selection inhibits the attainment of desired cell splitting gains. By contrast, a cell selection technique based on adaptive bias is a more effective solution to traffic load balancing in terms of increasing data rate compared with static bias-based approaches. This paper reviews the use of adaptive cell selection in LTE-Advanced HetNets by highlighting the importance of cell load estimation. The general performances of different techniques for adaptive CRE-based cell selection are compared. Results reveal that the adaptive CRE bias of the resource block utilization ratio (RBUR) technique exhibits the highest cell-edge throughput. Moreover, more accurate cell load estimation is obtained in the extended RBUR adaptive CRE bias technique through constant bit rate (CBR) traffic, which further improved load balancing as against the estimation based on the number of user equipment (UE). Finally, this paper presents suggestions for future research directions.

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25

Li, Zhi Jun, and Ming Hong Liao. "Modeling Load Balancing in Heterogeneous Unstructured P2P Systems." Journal of Computer Science 1, no. 3 (2005): 323–31. http://dx.doi.org/10.3844/jcssp.2005.323.331.

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26

Sudarmani., R., and Dr K. R. Shankar Kumar. "Performance Evaluation of Heterogeneous Sensor Networks with ATPC and Load Balancing." International Journal of Computer Applications 12, no. 3 (2010): 49–54. http://dx.doi.org/10.5120/1655-2226.

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27

Rabiul Alam, Md Golam. "A Reliable Semi-Distributed Load Balancing Architecture Of Heterogeneous Wireless Networks." International journal of Computer Networks & Communications 4, no. 1 (2012): 97–111. http://dx.doi.org/10.5121/ijcnc.2012.4108.

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28

Wenfeng, Zhu, Qiu Ling, Chen Zheng, and Liang Xiaowen. "Joint load balancing and interference coordination in multi-antenna heterogeneous networks." Journal of China Universities of Posts and Telecommunications 23, no. 6 (2016): 34–40. http://dx.doi.org/10.1016/s1005-8885(16)60067-5.

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29

Abbas, Ziaul Haq, Fazal Muhammad, and Lei Jiao. "Analysis of Load Balancing and Interference Management in Heterogeneous Cellular Networks." IEEE Access 5 (2017): 14690–705. http://dx.doi.org/10.1109/access.2017.2732498.

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30

Li, Jingya, Emil Bjornson, Tommy Svensson, Thomas Eriksson, and Merouane Debbah. "Joint Precoding and Load Balancing Optimization for Energy-Efficient Heterogeneous Networks." IEEE Transactions on Wireless Communications 14, no. 10 (2015): 5810–22. http://dx.doi.org/10.1109/twc.2015.2443044.

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31

Chai, Rong, Huili Zhang, Xiaoyu Dong, Qianbin Chen, and Tommy Svensson. "Optimal joint utility based load balancing algorithm for heterogeneous wireless networks." Wireless Networks 20, no. 6 (2014): 1557–71. http://dx.doi.org/10.1007/s11276-014-0695-0.

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32

De Schepper, Tom, Steven Latré, and Jeroen Famaey. "Scalable Load Balancing and Flow Management in Dynamic Heterogeneous Wireless Networks." Journal of Network and Systems Management 28, no. 1 (2019): 133–59. http://dx.doi.org/10.1007/s10922-019-09502-2.

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33

Alotaibi, Modhawi, and Amiya Nayak. "Linking handover delay to load balancing in SDN-based heterogeneous networks." Computer Communications 173 (May 2021): 170–82. http://dx.doi.org/10.1016/j.comcom.2021.04.001.

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34

Rotaru, Tiberiu, and Hans-Heinrich Nägeli. "Dynamic load balancing by diffusion in heterogeneous systems." Journal of Parallel and Distributed Computing 64, no. 4 (2004): 481–97. http://dx.doi.org/10.1016/j.jpdc.2004.02.001.

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35

De Schepper, Tom, Steven Latre, and Jeroen Famaey. "Flow Management and Load Balancing in Dynamic Heterogeneous LANs." IEEE Transactions on Network and Service Management 15, no. 2 (2018): 693–706. http://dx.doi.org/10.1109/tnsm.2018.2804578.

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36

Gardner, Kristen, Jazeem Abdul Jaleel, Alexander Wickeham, and Sherwin Doroudi. "Scalable Load Balancing in the Presence of Heterogeneous Servers." ACM SIGMETRICS Performance Evaluation Review 48, no. 3 (2021): 37–38. http://dx.doi.org/10.1145/3453953.3453961.

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In large-scale computer systems, deciding how to dispatch arriving jobs to servers is a primary factor affecting system performance. Consequently, there is a wealth of literature on designing, analyzing, and evaluating the performance of load balancing policies. For analytical tractability, most existing work on dispatching in large-scale systems makes a key assumption: that the servers are homogeneous, meaning that they all have the same speeds, capabilities, and available resources. But this assumption is not accurate in practice. Modern computer systems are instead heterogeneous: server farms may consist of multiple generations of hardware, servers with varied resources, or even virtual machines running in a cloud environment. Given the ubiquity of heterogeneity in today's systems, it is critically important to develop load balancing policies that perform well in heterogeneous environments. In this paper, we focus on systems in which server speeds are heterogeneous.

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37

Lan, Zhiling, Valerie E. Taylor, and Greg Bryan. "Dynamic Load Balancing of Samr Applications on Distributed Systems." Scientific Programming 10, no. 4 (2002): 319–28. http://dx.doi.org/10.1155/2002/254206.

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Dynamic load balancing(DLB) for parallel systems has been studied extensively; however, DLB for distributed systems is relatively new. To efficiently utilize computing resources provided by distributed systems, an underlying DLB scheme must address both heterogeneous and dynamic features of distributed systems. In this paper, we propose a DLB scheme for Structured Adaptive Mesh Refinement(SAMR) applications on distributed systems. While the proposed scheme can take into consideration (1) the heterogeneity of processors and (2) the heterogeneity and dynamic load of the networks, the focus of this paper is on the latter. The load-balancing processes are divided into two phases: global load balancing and local load balancing. We also provide a heuristic method to evaluate the computational gain and redistribution cost for global redistribution. Experiments show that by using our distributed DLB scheme, the execution time can be reduced by 9%- to using parallel DLB scheme which does not consider the heterogeneous and dynamic features of distributed systems.

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38

Su, Gongchao, Bin Chen, Xiaohui Lin, Hui Wang, and Lemin Li. "A Submodular Optimization Framework for Outage-Aware Cell Association in Heterogeneous Cellular Networks." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4567625.

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In cellular heterogeneous networks (HetNets), offloading users to small cell base stations (SBSs) leads to a degradation in signal to interference plus noise ratio (SINR) and results in high outage probabilities for offloaded users. In this paper, we propose a novel framework to solve the cell association problem with the intention of improving user outage performance while achieving load balancing across different tiers of BSs. We formulate a combinatorial utility maximization problem with weighted BS loads that achieves proportional fairness among users and also takes into account user outage performance. A formulation of the weighting parameters is proposed to discourage assigning users to BSs with high outage probabilities. In addition, we show that the combinatorial optimization problem can be reformulated as a monotone submodular maximization problem and it can be readily solved via a greedy algorithm with lazy evaluations. The obtained solution offers a constant performance guarantee to the cell association problem. Simulation results show that our proposed approach leads to over 30% reduction in outage probabilities for offloaded users and achieves load balancing across macrocell and small cell BSs.

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39

Lee, HyungJune. "Optimal Cell Selection Scheme for Load Balancing in Heterogeneous Radio Access Networks." Journal of Korea Information and Communications Society 37B, no. 12 (2012): 1102–12. http://dx.doi.org/10.7840/kics.2012.37b.12.1102.

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40

Park, Jin-Bae, and Kwang Soon Kim. "Load-Balancing Scheme With Small-Cell Cooperation for Clustered Heterogeneous Cellular Networks." IEEE Transactions on Vehicular Technology 67, no. 1 (2018): 633–49. http://dx.doi.org/10.1109/tvt.2017.2748570.

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41

Hassan, Noha, and Xavier Fernando. "Interference Mitigation and Dynamic User Association for Load Balancing in Heterogeneous Networks." IEEE Transactions on Vehicular Technology 68, no. 8 (2019): 7578–92. http://dx.doi.org/10.1109/tvt.2019.2919812.

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42

Ge, Xin, Xiuhua Li, Hu Jin, Julian Cheng, and Victor C. M. Leung. "Joint User Association and User Scheduling for Load Balancing in Heterogeneous Networks." IEEE Transactions on Wireless Communications 17, no. 5 (2018): 3211–25. http://dx.doi.org/10.1109/twc.2018.2808488.

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43

Shen, Haiying, and Cheng-Zhong Xu. "Hash-based proximity clustering for efficient load balancing in heterogeneous DHT networks." Journal of Parallel and Distributed Computing 68, no. 5 (2008): 686–702. http://dx.doi.org/10.1016/j.jpdc.2007.10.005.

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44

Cho, Sung-rae, and Wan Choi. "Coverage and Load Balancing in Heterogeneous Cellular Networks with Minimum Cell Separation." IEEE Transactions on Mobile Computing 13, no. 9 (2014): 1955–66. http://dx.doi.org/10.1109/tmc.2013.87.

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45

Taboada, Ianire, Samuli Aalto, Pasi Lassila, and Fidel Liberal. "Delay- and energy-aware load balancing in ultra-dense heterogeneous 5G networks." Transactions on Emerging Telecommunications Technologies 28, no. 9 (2017): e3170. http://dx.doi.org/10.1002/ett.3170.

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46

Xiao, Lin, Fahui Wu, Xiaolan Zhang, and Tiankui Zhang. "Mobility Load Balancing Scheme Based on Cell Priority in Heterogeneous Cellular Networks." International Journal of Hybrid Information Technology 9, no. 7 (2016): 61–70. http://dx.doi.org/10.14257/ijhit.2016.9.7.07.

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47

Summakieh, MHD Amen, Chee Keong Tan, Ayman, A. El-Saleh, and Teong Chee Chuah. "Improved Load Balancing for LTE-A Heterogeneous Networks using Particle Swarm Optimization." International Journal of Technology 10, no. 7 (2019): 1407. http://dx.doi.org/10.14716/ijtech.v10i7.3253.

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48

Matinkhah, S. Mojtaba, Siavash Khorsandi, and Shantia Yarahmadian. "A load balancing system for autonomous connection management in heterogeneous wireless networks." Computer Communications 97 (January 2017): 111–19. http://dx.doi.org/10.1016/j.comcom.2016.10.003.

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49

Demirci, İlhan, and Ömer Korçak. "Cell breathing algorithms for load balancing in Wi-Fi/cellular heterogeneous networks." Computer Networks 134 (April 2018): 140–51. http://dx.doi.org/10.1016/j.comnet.2018.01.045.

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50

Gardner, Kristen, Jazeem Abdul Jaleel, Alexander Wickeham, and Sherwin Doroudi. "Scalable load balancing in the presence of heterogeneous servers." Performance Evaluation 145 (January 2021): 102151. http://dx.doi.org/10.1016/j.peva.2020.102151.

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