Distributed algorithm

About: Distributed algorithm is a research topic. Over the lifetime, 20416 publications have been published within this topic receiving 548109 citations.

Economic models for allocating resources in computer systems

Abstract: With the advances in computer and networking technology thousands of heterogeneous com puters can be interconnected to provide a large collection of computing and communication resources These systems are used by a growing and increasingly heterogeneous set of users A macroscopic view of distributed computer systems reveals the complexity of the organi zation and management of the resources and services they provide This complexity arises from size e g no of systems no of users and heterogeneity in applications e g on line transaction processing multimedia intelligent information search and resources CPU memory bandwidth locks naming services The complexity of resource allocation is further increased by several factors First in many distributed systems the resources are in fact owned by multiple organizations Second the satisfaction of users and the performance of applications is determined by the simultane ous allocation of multiple resources A multimedia server application requires I O bandwidth to retrieve content CPU time to execute server logic and communication protocols and net working bandwidth to deliver the content to clients The performance of applications may also be altered by trading one resource for another For example the multimedia server ap plication may perform better by releasing memory and acquiring higher CPU priority This trade may result in smaller bu ers for I O and networking but improve the performance

Distributed particle filtering in agent networks: A survey, classification, and comparison

Abstract: Distributed particle filter (DPF) algorithms are sequential state estimation algorithms that are executed by a set of agents. Some or all of the agents perform local particle filtering and interact with other agents to calculate a global state estimate. DPF algorithms are attractive for large-scale, nonlinear, and non-Gaussian distributed estimation problems that often occur in applications involving agent networks (ANs). In this article, we present a survey, classification, and comparison of various DPF approaches and algorithms available to date. Our emphasis is on decentralized ANs that do not include a central processing or control unit.

Distributed multi-robot task assignment and formation control

Abstract: Distributed task assignment for multiple agents raises fundamental and novel problems in control theory and robotics. A new challenge is the development of distributed algorithms that dynamically assign tasks to multiple agents, not relying on a priori assignment information. We address this challenge using market-based coordination protocols where the agents are able to bid for task assignment with the assumption that every agent has knowledge of the maximum number of agents that any given task can accommodate. We show that our approach always achieves the desired assignment of agents to tasks after exploring at most a polynomial number of assignments, dramatically reducing the combinatorial nature of discrete assignment problems. We verify our algorithm through both simulation and experimentation on a team of non-holonomic robots performing distributed formation stabilization and group splitting and merging.

Dynamic Resource Allocation in MIMO-OFDMA Systems with Full-Duplex and Hybrid Relaying

Abstract: In this paper, we formulate a joint optimization problem for resource allocation and scheduling in full-duplex multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) relaying systems with amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. Our problem formulation takes into account heterogeneous data rate requirements for delay sensitive and non-delay sensitive users. We also consider a theoretically optimal hybrid relaying scheme as a performance benchmark, which allows a dynamic selection between AF relaying and DF relaying protocols with full-duplex and half-duplex relays. We show that under some mild conditions the optimal transmitter precoding and receiver post-processing matrices jointly diagonalize the MIMO-OFDMA relay channels for all considered relaying protocols transforming the resource allocation and scheduling problem into a scalar optimization problem. Dual decomposition is employed to solve this optimization problem and a distributed iterative resource allocation and scheduling algorithm with closed-form power and subcarrier allocation is derived. Simulation results not only illustrate that the proposed distributed algorithm converges to the optimal solution in a small number of iterations, but also demonstrate the potential performance gains achievable with full-duplex relaying protocols.

A Distributed Algorithm of Appliance Scheduling for Home Energy Management System

Abstract: Demand side management encourages the users in a smart grid to shift their electricity consumption in response to varying electricity prices. In this paper, we propose a distributed framework for the demand response based on cost minimization. Each user in the system will find an optimal start time and operating mode for the appliances in response to the varying electricity prices. We model the cost function for each user and the constraints for the appliances. We then propose an approximate greedy iterative algorithm that can be employed by each user to schedule appliances. In the proposed algorithm, each user requires only the knowledge of the price of the electricity, which depends on the aggregated load of other users, instead of the load profiles of individual users. In order for the users to coordinate with each other, we introduce a penalty term in the cost function, which penalizes large changes in the scheduling between successive iterations. Numerical simulations show that our optimization method will result in lower cost for the consumers, lower generation costs for the utility companies, lower peak load, and lower load fluctuations.