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Dynamic process scheduling and resource allocation in distributed environment: an agent-based modelling and simulation

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MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 2018年第5期24卷 485-505页

作者： Dalapati, Poulami Agarwal, Piyush Dutta, Animesh Bhattacharya, Swapan Natl Inst Technol Durgapur Dept CSE Mahatma Gandhi AveA Zone Durgapur 713209 W Bengal India Arcesium India Private Ltd Hyderabad India Jadavpur Univ Dept CSE Kolkata India

This paper addresses the issues concerning resource allocation and process scheduling in a dynamic environment, where resources are distributed and availability of them is uncertain. In this context, we introduce a new multi-agent-based resource allocation and process scheduling approach, where agents communicate and cooperate among themselves to produce an optimal schedule. A distributed constraint optimization problem-based model in accordance with Markov Decision Process is proposed in this regard. We overcome the hardship of existing centralized approach and our technique optimizes not only the process completion delay but also the number of resources being idle, which is much more beneficial. Apart from the theoretical approach, we take a case study in its practical application domain to validate our claim. Analysis and experimental results show that this proposed method outperforms the state-of-the-art methods and bridges the gap between theory and its applications.

关键词： Multi-agent systems resource allocation process scheduling distributed constraint optimization agent-based modelling

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Applying max-sum to teams of mobile sensing agents

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 71卷 87-99页

作者： Yedidsion, Harel Zivan, Roie Farinelli, Alessandro Ben Gurion Univ Negev Ind Engn & Management Dept Beer Sheva Israel Univ Verona Comp Sci Dept Verona Italy

Multi-agent applications that include teams of mobile sensing agents are challenging since they are inherently dynamic and a single movement of a mobile sensor can change the problem that the entire team is facing. A variation of the distributed constraint optimization model for Mobile Sensor Teams (DCOP_MST) was previously adjusted to represent such problems along with local search algorithms that were enhanced with exploration methods. This paper considers the use of the Max-sum algorithm for solving problems of deploying a mobile sensor team in an unknown environment to track and monitor points of interest (targets), represented by the DCOPMST model. The DCOP_MST model allows the representation of different functions for aggregating the joint coverage of targets by multiple sensors. The use of different functions has a dramatic effect on the complexity of the Max-sum algorithm. When using cardinality functions, Max-sum can be performed efficiently regardless of the arity of constraints. When Max-sum is used to solve applications that require other (more complex) aggregation functions, its complexity is exponential in the arity of the constraints and thus, its usefulness is limited. In this paper we investigate the performance of the Max-sum algorithm on two implementations of the DCOPMST model. Each implementation considers a different joint credibility function for determining the coverage for each target, with respect to the locations and the credibility of agents. In the first, the coverage is calculated according to the number of agents that are located within sensing range from the target. This function can be calculated efficiently. The second takes the angle between the lines of sight of different agents to a target into consideration. The larger the difference in the angle between the lines of sight, the higher the coverage efficiency. We analyze the challenges in adjusting the Max-sum algorithm in both scenarios and propose enhancements of the algorithm

关键词： distributed constraint optimization Incomplete algorithms GDL Max-sum Multi-agent systems Exploration Mobile sensor networks

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A Tutorial on optimization for Multi-Agent Systems

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COMPUTER JOURNAL 2014年第6期57卷 799-824页

作者： Cerquides, Jesus Farinelli, Alessandro Meseguer, Pedro Ramchurn, Sarvapali D. Spanish Council Sci Res CSIC Artificial Intelligence Res Inst IIIA Bellaterra Spain Univ Verona I-37100 Verona Italy Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

Research on optimization in multi-agent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multi-agent application domains. Multi-agent optimization focuses on casting MAS problems into optimization problems. The solving of those problems could possibly involve the active participation of the agents in a MAS. Research on multi-agent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely scattered. These two factors dramatically hinder access to a basic, general view of the foundations of the field. This tutorial is intended to ease such access by providing a gentle introduction to fundamental concepts and techniques on multi-agent optimization.

关键词： optimization multi-agent systems coalition formation resource allocation auctions distributed constraint optimization DCOP

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DCSACA: distributed constraint service-aware collaborative access algorithm based on large-scale access to the Internet of Things

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JOURNAL OF SUPERCOMPUTING 2018年第12期74卷 6408-6427页

作者： Yi Meng Chen QingKui Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China Univ Shanghai Sci & Technol Sch Management Shanghai Peoples R China

With the rapid development of the smart city and the Internet Plus-themed multi-network applications, it is becoming increasingly difficult for the data access center for the Internet of Things (DACIOT) to meet large-scale users' service requirements with low latency and high quality while sending service access requests. This paper first converts the problem of a large number of access requests to DACIOT into a distributed constraint optimization problem. Then, in order to address the optimization problem, a dynamic multi-constraint service-aware collaborative access algorithm is proposed based on dynamic load feedback from the access nodes, which can effectively reduce network congestion through load feedback and improve access performance. The algorithm firstly defines the dynamic context load sensing model, which is able to detect the load metrics of access clusters and assist access servers to work together to improve the availability of DACIOT, then it uses a heuristic falling search algorithm to search for the optimal resource on the basis of this model, after which it analyzes the convergence of the access algorithm. Experimental results show that the algorithm can effectively improve the rate of success, lower the network delay of access requests and reduce network jitter when accessing DACIOT.

关键词： Data access center for the Internet of Things distributed constraint optimization Load sensing Heuristic search Collaboration access

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Leximin Multiple Objective DCOPs on Factor Graphs for Preferences of Agents

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FUNDAMENTA INFORMATICAE 2018年第1-3期158卷 63-91页

作者： Matsui, Toshihiro Okimoto, Tenda Yokoo, Makoto Silaghi, Marius Hirayama, Katsutoshi Matsuo, Hiroshi Nagoya Inst Technol Showa Ku Gokiso Cho Nagoya Aichi 4668555 Japan Kobe Univ Higashinada Ku 5-1-1 Fukaeminami Machi Kobe Hyogo 6580022 Japan Kyushu Univ Nishi Ku 744 Motooka Fukuoka 8190395 Japan Florida Inst Technol Melbourne FL 32901 USA

distributed constraint optimization Problem (DCOP) has been studied as a fundamental component of multiagent systems. With DCOPs, various applications on multiagent systems are formalized as constraint optimization problems where variables and functions are distributed among agents. Leximin AMODCOP has been proposed as a class of Multiple Objective DCOPs, where multiple objectives for individual agents are optimized based on the leximin operator. This problem also relates to Asymmetric DCOPs based on its the criteria of fairness among agents. Previous studies explore only Leximin AMODCOPs on constraint graphs limited to functions with unary or binary scopes. We address the Leximin AMODCOPs on factor graphs that directly represent n-ary functions. A dynamic programming method on factor graphs is investigated as an exact solution method. In addition, for relatively dense problems, we also investigate several approximate/inexact algorithms.

关键词： distributed constraint optimization asymmetric multiple objectives leximin egalitarian

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Message Passing for distributed QoS-Security Tradeoffs

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COMPUTER JOURNAL 2014年第6期57卷 840-855页

作者： Mostafa, Hala Pal, Partha Hurley, Patrick Raytheon BBN Technol Cambridge MA 02138 USA Air Force Res Lab Rome NY 13441 USA

Information assurance (IA) is a growing concern, since almost every aspect of our lives depends on distributed information systems and the frequency and sophistication of cyber attacks targeting these systems is on the rise. However, IA cannot be considered in isolation, as it also affects the Quality of Service (QoS);with limited resources, the security mechanisms employed for IA (e.g. firewalls, antivirus, encryption) usually adversely affect QoS levels delivered by a system. The system therefore needs to make tradeoffs between IA and QoS. These tradeoffs are complicated by the facts that users' relative preferences over QoS-IA change based on the situation, the preferences of different users conflict and tradeoff decisions made at one node in the distributed system typically affect other nodes as well. We address the problem of distributed computation of tradeoffs among various aspects of QoS and IA in a way that maximizes the satisfaction of all stakeholders. Specifically, we want the nodes in the system to make coordinated decisions as to what local actions to take to optimize QoS/IA levels delivered by the system. Our first contribution is formulating this problem as a distributed constraint optimization problem (DCOP). This entails quantifying various notions involved in tradeoffs to be able to compare options in the course of optimization, as well as encoding the effects of various decisions on the quantities we want to optimize. The DCOPs we obtain have cost functions where multiple local configurations result in the same cost. In addition, the corresponding factor graphs contain many cycles. To deal with these issues, our second contribution is a value propagation (VP) algorithm that helps nodes reach a consistent set of decisions even in cyclic factor graphs with non-unique local optima. We present experimental results comparing the performance of the max-sum algorithm with and without VP against other algorithms when applied to domain-inspired and random

关键词： distributed constraint optimization information assurance value propagation

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distributed constrained optimization for multi-agent networks with nonsmooth objective functions

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SYSTEMS & CONTROL LETTERS 2019年 124卷 60-67页

作者： Chen, Gang Yang, Qing Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China

This paper investigates the distributed constrained optimization problem in multi-agent systems, where agents cooperatively minimize an objective function being the sum of each agent's objective function while meeting equality and inequality constraints. By virtue of nonsmooth analysis and graph theory, a novel distributed continuous-time algorithm is proposed to solve such a kind of optimization problems. Different from existing continuous-time results relying on the differentiability or strict (strong) convexity of local objective functions, the proposed approach considers more general local objective functions which are only convex and not necessarily smooth. In addition, the proposed approach considers more general local constraints, not just box constraints considered in most existing studies. The optimality of the proposed algorithm is ensured under certain initial condition. Based on set-valued LaSalle invariance principle, the convergence of the proposed scheme is rigorously proved. Finally, simulation examples are applied to validate the effectiveness of the proposed algorithm. (C) 2018 Elsevier B.V. All rights reserved.

关键词： distributed constraint optimization Multi-agent networks Nonsmooth analysis

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Extending Real-Time Challenge Balancing to Multiplayer Games: A Study on Eco-Driving

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IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES 2016年第1期8卷 27-32页

作者： Prendinger, Helmut Puntumapon, Kamthorn Madruga, Marconi Grad Univ Adv Studies Natl Inst Informat Tokyo 1018430 Japan Natl Inst Informat Tokyo 1018430 Japan

Multiplayer games are an important and popular game mode for networked players. Since games are played by a diverse audience, it is important to scale the difficulty, or challenge, according to the skill level of the players. However, current approaches to real-time challenge balancing (RCB) in games are only applicable to single-player scenarios. In multiplayer scenarios, players with different skill levels may be present in the same area, and hence adjusting the game difficulty to match the skill of one player may affect the other players in an undesirable way. To address this problem, we have previously developed a new approach based on distributed constraint optimization, which achieves the optimal challenge level for multiple players in real-time. The main contribution of this paper is an experiment that was performed with our new multiplayer real-time challenge balancing method applied to eco-driving. The results of the experiment suggest the effectiveness of RCB.

关键词： Challenge balancing distributed constraint optimization multiplayer games player experience

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Coordinated Intelligent Power Management and the Heterogeneous Sensing Coverage Problem

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COMPUTER JOURNAL 2011年第3期54卷 490-502页

作者： Tynan, Richard Muldoon, Conor O'Hare, Gregory O'Grady, Michael Univ Coll Dublin Sch Comp Sci & Informat CLARITY Ctr Sensor Web Technol Dublin 4 Ireland

One of the most important factors to be considered when developing an application for a wireless sensor network (WSN) is its power consumption. Intelligent power management (IPM) for a WSN is crucial in maximizing the operational longevity. An established regime for achieving this is through the opportunistic hibernation of redundant nodes. Redundancy, however, has various definitions within the field of WSNs and indeed multiple protocols, each operating using a different definition, coexist on the same node. In this paper, we advocate the use of an MAS as an appropriate mechanism by which different stake-holders, each desiring to hibernate a node in order to conserve power, can collaborate. The problem of node hibernation for the heterogeneous sensing coverage areas is introduced and the manner by which it can be solved using ADOPT, an algorithm for distributed constraint optimization, is described. We illustrate that the node hibernation strategy discussed here is more useful than the traditional stack-based approach and motivate our discussion using IPM as an exemplar.

关键词： coverage connectivity hibernation intelligent power management distributed constraint optimization

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Privacy loss in distributed constraint reasoning: A quantitative framework for analysis and its applications

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2006年第1期13卷 27-60页

作者： Maheswaran, RT Pearce, JP Bowring, E Varakantham, P Tambe, M Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Univ So Calif Inst Sci Informat Los Angeles CA 90089 USA

It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users' privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers have begun to propose metrics for analysis of privacy loss in such multiagent algorithms. Unfortunately, a general quantitative framework to compare these existing metrics for privacy loss or to identify dimensions along which to construct new metrics is currently lacking. This paper presents three key contributions to address this shortcoming. First, the paper presents VPS (Valuations of Possible States), a general quantitative framework to express, analyze and compare existing metrics of privacy loss. Based on a state-space model, VPS is shown to capture various existing measures of privacy created for specific domains of DisCSPs. The utility of VPS is further illustrated through analysis of privacy loss in DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings among users. In addition, VPS helps identify dimensions along which to classify and construct new privacy metrics and it also supports their quantitative comparison. Second, the article presents key inference rules that may be used in analysis of privacy loss in DCOP algorithms under different assumptions. Third, detailed experiments based on the VPS-driven analysis lead to the following key results: (i) decentralization by itself does not provide superior protection of privacy in DisCSP/DCOP algorithms when compared with centralization;instead, privacy protection also requires the presence of uncertainty about agents' knowledge of the constraint graph. (ii) one needs to carefu

关键词： distributed constraint reasoning privacy distributed constraint optimization

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