Distributed algorithm

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

Distributed Population Dynamics: Optimization and Control Applications

Abstract: Population dynamics have been widely used in the design of learning and control systems for networked engineering applications, where the information dependency among elements of the network has become a relevant issue. Classic population dynamics (e.g., replicator, logit choice, Smith, and projection) require full information to evolve to the solution (Nash equilibrium). The main reason is that classic population dynamics are deduced by assuming well-mixed populations, which limits the applications where this theory can be implemented. In this paper, we extend the concept of population dynamics for nonwell-mixed populations in order to deal with distributed information structures that are characterized by noncomplete graphs. Although the distributed population dynamics proposed in this paper use partial information, they preserve similar characteristics and properties of their classic counterpart. Specifically, we prove mass conservation and convergence to Nash equilibrium. To illustrate the performance of the proposed dynamics, we show some applications in the solution of optimization problems, classic games, and the design of distributed controllers.

Process synchronization: design and performance evaluation of distributed algorithms

Abstract: The author presents a simple solution for the committee coordination problem, which encompasses the synchronization and exclusion problems associated with implementing multiway rendezvous, and shows how it can be implemented to develop a family of algorithms. The algorithms use message counts to solve the synchronization problem, and they solve the exclusion problem by using a circulating token or by using auxiliary resources as in the solutions for the dining or drinking philosophers' problems. Results of a simulation study of the performance of the algorithms are presented. The experiments measured the response time and message complexity of each algorithm as a function of variations in the model parameters, including network topology and level of conflict in the system. The results show that the response time for algorithms proposed is significantly better than for existing algorithms, whereas the message complexity is considerably worse. >

A Mathematical Framework for Designing a Low-Loss, Low-Delay Internet

Abstract: We survey some recent results on modeling, analysis and design of congestion control schemes for the Internet. Using tools from convex optimization and control theory, we show that congestion controllers can be viewed as distributed algorithms for achieving fair resource allocation among competing sources. We illustrate the use of simple mathematical models to analyze the behavior of currently deployed Internet congestion control protocols as well as to design new protocols for networks with large capacities, delays and general topology. These new protocols are designed to nearly eliminate loss and queueing delay in the Internet, yet achieving high utilization and any desired fairness.

Temperature aware workload management in geo-distributed datacenters

Abstract: Datacenters consume an enormous amount of energy with significant financial and environmental costs. For geo-distributed datacenters, a workload management approach that routes user requests to locations with cheaper and cleaner electricity has been shown to be promising lately. We consider two key aspects that have not been explored in this approach. First, through empirical studies, we find that the energy efficiency of the cooling system depends directly on the ambient temperature, which exhibits a significant degree of geographical diversity. Temperature diversity can be used by workload management to reduce the overall cooling energy overhead. Second, energy consumption comes from not only interactive workloads driven by user requests, but also delay tolerant batch workloads that run at the back-end. The elastic nature of batch workloads can be exploited to further reduce the energy cost. In this work, we propose to make workload management for geo-distributed datacenters temperature aware. We formulate the problem as a joint optimization of request routing for interactive workloads and capacity allocation for batch workloads. We develop a distributed algorithm based on an m-block alternating direction method of multipliers (ADMM) algorithm that extends the classical 2-block algorithm. We prove the convergence and rate of convergence results under general assumptions. Trace-driven simulations demonstrate that our approach is able to provide 5%--20% overall cost savings for geo-distributed datacenters.

Performance of a Distributed Stochastic Approximation Algorithm

Abstract: In this paper, a distributed stochastic approximation algorithm is studied. Applications of such algorithms include decentralized estimation, optimization, control or computing. The algorithm consists in two steps: a local step, where each node in a network updates a local estimate using a stochastic approximation algorithm with decreasing step size, and a gossip step, where a node computes a local weighted average between its estimates and those of its neighbors. Convergence of the estimates toward a consensus is established under weak assumptions. The approach relies on two main ingredients: the existence of a Lyapunov function for the mean field in the agreement subspace, and a contraction property of the random matrices of weights in the subspace orthogonal to the agreement subspace. A second-order analysis of the algorithm is also performed under the form of a central limit Theorem. The Polyak-averaged version of the algorithm is also considered.