A Survey of Task Allocation and Load Balancing in Distributed Systems

Jiang, Yichuan

doi:10.1109/tpds.2015.2407900

Cited by 166 publications

(92 citation statements)

References 93 publications

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“…The duplex public goods game, D-PGG, studied in this article can be an appropriate basis for this study. Then we also hope this series of studies can help the research of agent coordination [57,58], since agents always need to interact through multiple network layers concurrently.…”

Section: Discussionmentioning

confidence: 99%

Effect of local information within network layers on the evolution of cooperation in duplex public goods games

Zhou

Zheng

2015

Chaos, Solitons & Fractals

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Section: Discussionmentioning

confidence: 99%

Effect of local information within network layers on the evolution of cooperation in duplex public goods games

Zhou

Zheng

2015

Chaos, Solitons & Fractals

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“…There are several inspiring load balancing approaches which have presented interesting results. Some of them are proposed for distributed systems [8], grid [9], [10], P2P systems [11], [12], and also for cloud computing systems [13], [14], and heterogeneous computing systems [15], [16], [17]. The main idea behind the considered challenge is the effective method for task assignment.…”

Section: Related Workmentioning

confidence: 99%

A Mobile Agent Team Works based on Load-Balancing Middleware for Distributed Computing Systems

Benchara¹,

Youssfi²

2019

IJACSA

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The aim of this paper is to present a load balancing middleware for parallel and distributed systems. The great challenge is to balance the tasks between heterogeneous distributed nodes for parallel and distributed computing models based distributed systems, by the way to ensure HPC (High performance computing) of these models. Accordingly, the proposed middleware is based on mobile agent team work which implements an efficient method with two strategies: (i) Load balancing Strategy that determines the node tasks assignment based on node performance, and (ii) Rebalancing Strategy that detects the unbalanced nodes and enables tasks migration. The paper focuses on the proposed middleware and its cooperative mobile agent team work model strategies to dynamically balance the nodes, and scale up distributed computing systems. Indeed, some experimental results that highlight the performance and efficiency of the proposed middleware are presented.

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“…On the other hand, the computational power or other resources in the controllers may have spare capacity to process additional tasks. The study of assigning these existing extra resources to time sensitive tasks and overloaded computer systems becomes an interesting alternative to cloud processing [10,11]. Such systems as part of a network infrastructure could efficiently deal with front line demands [12].…”

Section: Introductionmentioning

confidence: 99%

Modeling industry 4.0 based fog computing environments for application analysis and deployment

Verba

Chao

Lewandowski

et al. 2019

Future Generation Computer Systems

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The extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical runtime parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with optimization methods can instruct application deployment and migration and improve the overall system reliability, delay and constraint violations. An Industry 4.0 based case study with different scenarios was used to analyze and validate the effectiveness of the proposed model. Tests were deployed on physical and virtual environments with different scales. The advantages of the model based optimization methods were validated in real physical environments. Based on these tests, we have found that our model is 92% accurate on load and delay predictions for application deployments in both cloud and edge. AbstractThe extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical runtime parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with optimization methods can instruct application deployment and migration and improve the overall system reliability, delay and constraint violations. An Industry 4.0 based case study with different scenarios was used...

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A Survey of Task Allocation and Load Balancing in Distributed Systems

Cited by 166 publications

References 93 publications

Effect of local information within network layers on the evolution of cooperation in duplex public goods games

Effect of local information within network layers on the evolution of cooperation in duplex public goods games

A Mobile Agent Team Works based on Load-Balancing Middleware for Distributed Computing Systems

Modeling industry 4.0 based fog computing environments for application analysis and deployment

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