Agent-Based Decentralized Grid Model

Decentralization of grid systems plays an important role in improving their efficiency and fault tolerance. To enhance the performance and stability of grid and mitigate the problems of centralized grid, an agent-based decentralized gird model (ADGM) with universality and functional integrity is proposed. In this paper, we build an agent-based grid structure and propose an agent-based grid consensus algorithm (AGCA). A group membership protocol, a consistency protocol and a view change protocol in AGCA are also designed. Furthermore, based on Pi calculus, the information registration, resource sharing and error recovery of the grid model in the decentralized environment are formally specified. Finally, we analyze the performance of the AGCA algorithm and compare it with other consensus algorithms, the simulation results demonstrate that the AGCA algorithm achieves better comprehensive performance and more symmetrical time performance, space performance and fault tolerance than other consensus algorithms.

[1]  Salama A. Mostafa,et al.  Norm-Based Behavior Regulating Technique for Multi-Agent in Complex Adaptive Systems , 2019, IEEE Access.

[2]  Bryan Ford,et al.  Enhancing Bitcoin Security and Performance with Strong Consistency via Collective Signing , 2016, USENIX Security Symposium.

[3]  Keun Ho Ryu,et al.  Mixture of Activation Functions With Extended Min-Max Normalization for Forex Market Prediction , 2019, IEEE Access.

[4]  Yuhao Wang,et al.  Study of Blockchains’s Consensus Mechanism Based on Credit , 2019, IEEE Access.

[5]  Ian T. Foster,et al.  The anatomy of the grid: enabling scalable virtual organizations , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[6]  Ying Zhang,et al.  Latency-Aware In-Network Computing for Internet of Battery-Less Things , 2018, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[7]  Sathya Chinnathambi,et al.  Scheduling and checkpointing optimization algorithm for Byzantine fault tolerance in cloud clusters , 2018, Cluster Computing.

[8]  Rajendran Parthiban,et al.  A Semantic Impact in Decentralized Resource Discovery Mechanism for Grid Computing Environments , 2012, ICA3PP.

[9]  D. Parkes,et al.  A Decentralized Auction Framework to Promote Efficient Resource Allocation in Open Computational Grids , 2007 .

[10]  Hyungmin Cho,et al.  ASIC-Resistance of Multi-Hash Proof-of-Work Mechanisms for Blockchain Consensus Protocols , 2018, IEEE Access.

[11]  Taeweon Suh,et al.  Mobility-aware balanced scheduling algorithm in mobile Grid based on mobile agent , 2014, The Knowledge Engineering Review.

[12]  ROBIN MILNER,et al.  Edinburgh Research Explorer A Calculus of Mobile Processes, I , 2003 .

[13]  Ying Zhang,et al.  Collaborative In-Network Processing for Internet of Battery-Less Things , 2019, IEEE Internet of Things Journal.

[14]  G. Plotkin,et al.  Proof, language, and interaction: essays in honour of Robin Milner , 2000 .

[15]  Roland Siegwart,et al.  The impact of agent definitions and interactions on multiagent learning for coordination in traffic management domains , 2019, Autonomous Agents and Multi-Agent Systems.

[16]  Martín Abadi,et al.  The Applied Pi Calculus , 2016, J. ACM.

[17]  Xiaoli Ma,et al.  Performance Analysis of the Raft Consensus Algorithm for Private Blockchains , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[18]  Benjamin C. Pierce,et al.  Pict: a programming language based on the Pi-Calculus , 2000, Proof, Language, and Interaction.

[19]  Xuan Wang,et al.  Resource Clustering Based Decentralized Resource Discovery Scheme in Computing Grid , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[20]  Thouraya Louati,et al.  PastryGridCP: A Decentralized Rollback-Recovery Protocol for Desktop Grid Systems , 2013, ICA3PP.

[21]  Karl Andersson,et al.  A Survey of Blockchain From the Perspectives of Applications, Challenges, and Opportunities , 2019, IEEE Access.

[22]  David Sarne,et al.  Summarizing agent strategies , 2019, Autonomous Agents and Multi-Agent Systems.

[23]  Xin Xu,et al.  Decentralised grid scheduling approach based on multi-agent reinforcement learning and gossip mechanism , 2018, CAAI Trans. Intell. Technol..

[24]  Peter Sewell,et al.  Nomadic pict: Programming languages, communication infrastructure overlays, and semantics for mobile computation , 2010, TOPL.

[25]  Florin Pop.  Communication Model for Decentralized Meta-Scheduler in Grid Environments , 2008, 2008 International Conference on Complex, Intelligent and Software Intensive Systems.

[26]  Long Wang,et al.  Recent Advances in Consensus of Multi-Agent Systems: A Brief Survey , 2017, IEEE Transactions on Industrial Electronics.

[27]  Sorina Camarasu-Pop,et al.  Enabling Large Scale Data Production for OpenDose with GATE on the EGI Infrastructure , 2019, 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[28]  John K. Ousterhout,et al.  In Search of an Understandable Consensus Algorithm , 2014, USENIX ATC.

[29]  Sergey Sakulin,et al.  User Interface Distribution Method Based on Pi-Calculus , 2019, Int. J. Distributed Syst. Technol..

[30]  Hugues Benoit-Cattin,et al.  Simulating Application Workflows and Services Deployed on the European Grid Infrastructure , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[31]  Yun Zhou,et al.  Identifying vital nodes in complex networks by adjacency information entropy , 2020, Scientific Reports.