A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems

A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems

Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing...

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Main Authors: Jixiang Yang, Ling Ling, Haibin Liu
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2016/5641831
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spelling doaj-cf838fbd7e0b4028b2bdf5e5d7806a002020-11-24T21:13:24ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472016-01-01201610.1155/2016/56418315641831A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing SystemsJixiang Yang0Ling Ling1Haibin Liu2School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Business Administration, Hebei Normal University of Science & Technology, Qinhuangdao 066004, ChinaLoad balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: (1) the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, (2) node computation rate and communication delay randomness imposed by the communication medium are considered, and (3) communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient.http://dx.doi.org/10.1155/2016/5641831
collection DOAJ
language English
format Article
sources DOAJ
author Jixiang Yang
Ling Ling
Haibin Liu
spellingShingle Jixiang Yang
Ling Ling
Haibin Liu
A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems
Mathematical Problems in Engineering
author_facet Jixiang Yang
Ling Ling
Haibin Liu
author_sort Jixiang Yang
title A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems
title_short A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems
title_full A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems
title_fullStr A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems
title_full_unstemmed A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems
title_sort hierarchical load balancing strategy considering communication delay overhead for large distributed computing systems
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2016-01-01
description Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: (1) the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, (2) node computation rate and communication delay randomness imposed by the communication medium are considered, and (3) communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient.
url http://dx.doi.org/10.1155/2016/5641831
work_keys_str_mv AT jixiangyang ahierarchicalloadbalancingstrategyconsideringcommunicationdelayoverheadforlargedistributedcomputingsystems
AT lingling ahierarchicalloadbalancingstrategyconsideringcommunicationdelayoverheadforlargedistributedcomputingsystems
AT haibinliu ahierarchicalloadbalancingstrategyconsideringcommunicationdelayoverheadforlargedistributedcomputingsystems
AT jixiangyang hierarchicalloadbalancingstrategyconsideringcommunicationdelayoverheadforlargedistributedcomputingsystems
AT lingling hierarchicalloadbalancingstrategyconsideringcommunicationdelayoverheadforlargedistributedcomputingsystems
AT haibinliu hierarchicalloadbalancingstrategyconsideringcommunicationdelayoverheadforlargedistributedcomputingsystems
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