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parallel physics-inspired waterflow particle mechanics algorithm for load rebalancing

COMPUTER NETWORKS 2010年第11期54卷 1767-1777页

作者： Feng, Xiang Lau, Francis C. M. E China Univ Sci & Technol Dept Comp Sci Shanghai 200237 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

The Load Rebalancing Problem (LRP) that reassigns tasks to processors so as to minimize the maximum load arises in the context of dynamic load balancing. Many applications such as on Web based environment, parallel computing on clusters can be stated as LRP. Solving LRP successfully would allow us to utilize resources better and achieve better performance. However LRP has been proven to be NP-hard, thus generating the exact solutions in tractable amount of time becomes infeasible when the problems become large. We present a new nature-inspired approximation algorithm based on the Waterflow Particle Mechanics (W-PM) model to compute in parallel approximate efficient solutions for LRPs. Just like other Nature-inspired algorithms (NAs) drawing from observations of physical processes that occur in nature, the W-PM algorithm is inspired by kinematics and dynamics of waterflow. The W-PM algorithm maps the classical LRP to the flow of water flows in channels by corresponding mathematical model in which all water flows flow according to certain defined rules until reaching a stable state. By anti-mapping the stable state, the solution to LRP can be obtained. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Load rebalancing Approximation algorithm Nature-inspired algorithm Waterflow particle mechanics model distributed and parallel algorithm

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A novel bio-inspired approach based on the behavior of mosquitoes

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INFORMATION SCIENCES 2013年 233卷 87-108页

作者： Feng, Xiang Lau, Francis C. M. Yu, Huiqun E China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

This paper proposes a new nature-inspired algorithm (NA)-mosquito host-seeking algorithm (MHSA)-the inspiration for which comes from the host-seeking behavior of mosquitoes. Applying the algorithm to the traveling salesman problem (TSP), every city pair is treated as an artificial mosquito, and the TSP solving process is transformed into the host-seeking behavior of a swarm of artificial mosquitoes. We study the evolution of "swarms", the artificial mosquitoes' microcosmic actions, and macroscopic swarm intelligence, and present efficient solutions to TSP using MHSA. The proposed MHSA is fundamentally different from the other popular NAs in its motivation, principle, the optimization mechanism, its elements and their states, and the biological model, the mathematical model and theoretical foundation on which it is based. We show that (1) MHSA can converge;(2) its parameter setting does not depend on algorithm learning or prior knowledge;and (3) MHSA can describe complex behaviors and dynamics. The properties of MHSA, including correctness, convergence and stability, are discussed in details. Simulation results attest to the effectiveness and suitability of MHSA. (c) 2013 Elsevier Inc. All rights reserved.

关键词： Traveling salesman problem (TSP) Mosquito host-seeking algorithm (MHSA) distributed and parallel algorithm

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A new economic generalized particle model for flow control

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COMPUTER NETWORKS 2010年第3期54卷 506-524页

作者： Feng, Xiang Lau, Francis C. M. E China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

The problem of bandwidth allocation in computer networks can be likened to the supply-demand problem in economics. This paper presents the economic generalized particle model (EGPM) approach to intelligent allocation of network bandwidth. EGPM is a significant extension and further development of the generalized particle model (GPM) [1]. The approach comprises two major components: (1) dynamic allocation of network bandwidth based on GPM: and (2) dynamic modulation of price and demands of network bandwidth. The resulting algorithm can be easily implemented in a distributed fashion. Pricing being the network control mechanism in EGPM is carried out by a tatonnement process. We discuss the EGPM's convergence and show that the approach is efficient in achieving the global Pareto optimum. Via simulations, we test the approach, analyze its parameters and compare it with GPM and a genetic-algorithm-based solution. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Intelligent bandwidth allocation Economic generalized particle model (EGPM) Price and demands dynamic modulation distributed and parallel algorithm Dynamical process Computer networks

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Group mosquito host-seeking algorithm

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APPLIED INTELLIGENCE 2016年第3期44卷 665-686页

作者： Feng, Xiang Liu, Xiaoting Yu, Huiqun E China Univ Sci & Technol Dept Comp Sci & Engn 130 Meilong Rd Shanghai 200237 Peoples R China

The host-seeking behavior of mosquitoes is very interesting. This paper is motivated by the following general observation on mosquito groups and their host-seeking behavior in nature: (1) Mosquitoes' behavior has possession of the parallelism, openness, local interactivity and self-organization. (2) Mosquito groups seek host very fast. (3) The host-seeking behavior is similar to the producer-scrounger process, which assumes that group members search either for "finding" (producer) or for "joining" (scrounger) opportunities. It stimulates us to extend a mosquito system model in nature to group mosquito host-seeking model (GMHSM) and algorithm (GMHSA) for intelligent computing. In this paper, we propose GMHS approach and show how to use it. By GMHSM, the TSP is transformed into the kinematics and dynamics of mosquito groups host-seeking process. The properties of GMHSM and GMHSA, including the correctness, convergence and stability, have been discussed in this paper. The GMHS approach has many advantages in terms of multiple objective optimization, large-scale distributed parallel optimization, effectiveness of problem-solving and suitability for complex environment. Via simulations, we test the GMHS approach and compare it with other state-of-art algorithms.

关键词： Group Mosquito Host-Seeking Model (GMHSM) and algorithm (GMHSA) Leader decision Traveling Salesman Problem (TSP) distributed and parallel algorithm

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Fast inexact decomposition algorithms for large-scale separable convex optimization

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OPTIMIZATION 2016年第2期65卷 325-356页

作者： Tran-Dinh, Q. Necoara, I. Diehl, M. Ecole Polytech Fed Lausanne Lab Informat & Inference Syst LIONS CH-1015 Lausanne Switzerland Univ Politehn Bucuresti Automat Control & Syst Engn Dept Bucharest Romania Univ Freiburg Inst Microsyst Engn IMTEK Freiburg Germany

In this paper, we propose a new inexact dual decomposition algorithm for solving separable convex optimization problems. This algorithm is a combination of three techniques: dual Lagrangian decomposition, smoothing and excessive gap. The algorithm has low computational complexity since it consists in only one primal step and two dual steps at each iteration and allows one to solve the subproblem of each component inexactly and in parallel. Moreover, the algorithmic parameters are updated automatically without any tuning strategy as it happens in augmented Lagrangian approaches. We analyse the convergence of the algorithm and estimate its O (1/ epsilon) analytical worst-case complexity for both the primal-dual suboptimality and the primal feasibility violation, where e is a given accuracy. Extensive numerical tests confirm that our method is numerically more efficient than the classical decomposition methods from the literature.

关键词： primal-dual algorithm smoothing technique excessive gap Lagrangian decomposition distributed and parallel algorithm separable convex optimization 90C06 90C25 90-08

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On Mixing and Edge Expansion Properties in Randomized Broadcasting

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algorithmICA 2010年第1期56卷 51-88页

作者： Sauerwald, Thomas Dept Comp Sci D-33102 Paderborn Germany

In this paper, we consider a popular randomized broadcasting algorithm called push-algorithm defined as follows. Initially, one vertex of a graph G = (V, E) owns a piece of information which is spread iteratively to all other vertices: in each timestep t = 1, 2, ... every informed vertex chooses a neighbor uniformly at random and informs it. The question is how many time steps are required until all vertices become informed (with high probability). For various graph classes, involved methods have been developed in order to show an upper bound of O(log N + diam(G)) on the runtime of the push-algorithm, where N is the number of vertices and diam(G) denotes the diameter of G. However, no asymptotically tight bound on the runtime based on the mixing time of random walks has been established. In this work we fill this gap by deriving an upper bound of O(T-mix+ logN), where T-mix denotes the mixing time of a certain random walk on G. After that we prove upper bounds that are based on certain edge expansion properties of G. However, for hypercubes neither the bound based on the mixing time nor the bounds based on edge expansion properties are tight. That is why we develop a general way to combine these two approaches by which we can deduce that the runtime of the push-algorithm is Theta (logN) on every Hamming graph.

关键词： Randomized algorithm distributed and parallel algorithm Markov chain

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A New Nature-inspired algorithm for Load Balancing

A New Nature-inspired Algorithm for Load Balancing

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11th IEEE Singapore International Conference on Communication Systems

作者： Feng, Xiang Lau, Francis C. M. Shuai, Dianxun East China Univ Sci & Technol Dept Comp Sci Shanghai 200237 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781424424238

The classical Load Balancing Problem (LBP) is to map tasks to processors so as to minimize the maximum load. Solving the LBP successfully would lead to better utilization of resources and better performance. The LBP has been proven to be NP-hard, thus generating the exact solutions in a tractable amount of time becomes infeasible when the problems become large. We present a new nature-inspired approximation algorithm based on the Particle Mechanics (PM) model to compute in parallel approximate efficient solutions for LBPs. Just like other Nature-inspired algorithms (NAs) drawing from observations of physical processes that occur in nature, the PM algorithm is inspired by physical models of particle kinematics and dynamics. The PM algorithm maps the classical LBP to the movement of particles in a force field by a corresponding mathematical model in which all particles move according to certain defined rules until reaching a stable state. By anti-mapping the stable state, the solution to LBP can be obtained.

关键词： Load balancing approximation algorithm nature-inspired algorithm particle mechanics model distributed and parallel algorithm

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Cache Allocation in CDN: An Evolutionary Game Generalized Particle Model

Cache Allocation in CDN: An Evolutionary Game Generalized Pa...

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1st International Conference on Complex Sciences: Theory and Applications (Complex 2009)

作者： Feng, Xiang Lau, Francis C. M. Gao, Daqi East China Univ Sci & Technol Shanghai 200237 Peoples R China Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783642024689

Content distribution networks (CDNs) increasingly have been used to reduce the response times experienced by Internet users through placing surrogates close to the clients. This paper presents an object replacement approach based on an evolutionary game generalized particle model (G-GPM). We first propose a problem model for CDNs. The CDN model is then fit into a gravitational field. The origin servers and surrogates are regarded as two kinds of particles which are located in two force-fields. The cache allocation problem is thus transformed into the kinematics and dynamics of the particles in the annular and the round force-fields. The G-GPM approach is unique in four aspects: 1) direct viewing of individual and overall optimization;2) parallel computing (lower time complexity);3) multi-objective solution;and 4) being able to deal with some social interactions behaviors.

关键词： Content Delivery Networks (CDN) cache resource allocation evolutionary game generalized particle model (G-GPM) placement algorithm distributed and parallel algorithm

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A Nature-Inspired algorithm for Intelligent Optimization of Network Resources

A Nature-Inspired Algorithm for Intelligent Optimization of ...

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11th IEEE Singapore International Conference on Communication Systems

作者： Feng, Xiang Lau, Francis C. M. Shuai, Dianxun East China Univ Sci & Technol Dept Comp Sci Shanghai 200237 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781424424238

In complex computer networks having the characteristic of social dynamics, bandwidth allocation is a fundamental problem where bandwidth has to be reserved for connections in advance. This paper presents the theory and approach of the economic generalized particle model (EGPM) for intelligent allocation of network bandwidth. This approach transforms the complicated network bandwidth allocation problem into efficient, parallel allocation of network bandwidth. This approach is an important extension and further development of the generalized particle model (GPM) [1]. EGPM emphasizes the use of pricing as the network control mechanism. For the pricing, it makes use of the tatonnement process in economics. EGPM arises from GPM but can overcome some of GPM's deficiencies for the network bandwidth allocation problem.

关键词： Intelligent bandwidth allocation economic generalized particle model (EGPM) price and demands dynamic modulation distributed and parallel algorithm dynamical process

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A New Bio-inspired Approach to the Traveling Salesman Problem

A New Bio-inspired Approach to the Traveling Salesman Proble...

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1st International Conference on Complex Sciences: Theory and Applications (Complex 2009)

作者： Feng, Xiang Lau, Francis C. M. Gao, Daqi East China Univ Sci & Technol Shanghai 200237 Peoples R China Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783642024689

The host-seeking behavior of mosquitoes is very interesting. In this paper, we propose a novel mosquito host-seeking algorithm (MHSA) as a new branch of biology-inspired algorithms for solving TSP problems. The MHSA is inspired by the host-seeking behavior of mosquitoes. We present the mathematical model, the algorithm, the motivation, and the biological model. The MHSA can work out the theoretical optimum solution, which is important and exciting, and we give the theoretical foundation and present experiment results that verify this fact.

关键词： Bio-inspired algorithm traveling salesman problem (TSP) mosquito host-seeking algorithm (MHSA) distributed and parallel algorithm

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