Distributed algorithm

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

Algorithms for Mutual Exclusion

Abstract: The problem of mutual exclusion - or of defining fundamental operations so that it is possible to resolve conflicts resulting from several concurrent processes sharing the resources of a computer system - has emerged over the last 20 years as a prime example of the difficulties associated with parallel or distributed programming. The implementation of a mutual exclusion mechanism, therefore, is a very real phenomenon that faces every designer of operating systems as well as applications programmers who use services provided by computer systems built around several processing units, or linked by a network. This book presents a remarkable survey of a vast field of concrete and highly complex research on algorithms for parallel or distributed control. Since parallelism makes it difficult to understand the behavior or to analyze the properties of algorithms that can solve these problems, all of the algorithms have been rewritten in a single language and restructured so that they are easy to understand and compare. The book systematically stresses the principles guiding their design, provides arguments to prove their validity and gives quantitative data allowing their assessment. Contents: Preface. The Nature of Control Problems in Parallel Processing. The Mutual Exclusion Problem in a Centralized Framework: Software Solutions. The Mutual Exclusion Problem in a Centralized Framework: Hardware Solutions. The Mutual Exclusion Problem in a Distributed Framework: Solutions Based on State Variables. The Mutual Exclusion Problem in a Distributed Framework: Solutions Based on Message Communication. Two Further Control Problems. M. Raynal is a professor, Department Informatique, IRISA-Universite deRennes 1, France. Algorithms for Mutual Exclusion is included in the Scientific Computation Series, edited by Dennis Gannon.

Scheduling distributed applications: the SimGrid simulation framework

Abstract: Since the advent of distributed computer systems an active field of research has been the investigation of scheduling strategies for parallel applications. The common approach is to employ scheduling heuristics that approximate an optimal schedule. Unfortunately, it is often impossible to obtain analytical results to compare the efficacy of these heuristics. One possibility is to conducts large numbers of back-to-back experiments on real platforms. While this is possible on tightly-coupled platforms, it is infeasible on modern distributed platforms (i.e. Grids) as it is labor-intensive and does not enable repeatable results. The solution is to resort to simulations. Simulations not only enables repeatable results but also make it possible to explore wide ranges of platform and application scenarios. In this paper we present the SimGrid framework which enables the simulation of distributed applications in distributed computing environments for the specific purpose of developing and evaluating scheduling algorithms. This paper focuses on SimGrid v2, which greatly improves on the first version of the software with more realistic network models and topologies. SimGrid v2 also enables the simulation of distributed scheduling agents, which has become critical for current scheduling research in large-scale platforms. After describing and validating these features, we present a case study by which we demonstrate the usefulness of SimGrid for conducting scheduling research.

Occupy the cloud: distributed computing for the 99%

Abstract: Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. While distributed computation frameworks have moved beyond a simple map-reduce model, many users are still left to struggle with complex cluster management and configuration tools, even for running simple embarrassingly parallel jobs. We argue that stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity. Furthermore, using our prototype implementation, PyWren, we show that this model is general enough to implement a number of distributed computing models, such as BSP, efficiently. Extrapolating from recent trends in network bandwidth and the advent of disaggregated storage, we suggest that stateless functions are a natural fit for data processing in future computing environments.

On network correlated data gathering

Abstract: We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a Slepian-Wolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the Slepian-Wolf setting, we derive a closed form solution and an efficient distributed approximation algorithm with a good performance. For the explicit communication case, we prove that building an optimal data gathering tree is NP-complete and we propose various distributed approximation algorithms.

Supporting service differentiation in wireless packet networks using distributed control

Abstract: This paper investigates differentiated services in wireless packet networks using a fully distributed approach that supports service differentiation, radio monitoring, and admission control. While our proposal is generally applicable to distributed wireless access schemes, we design, implement, and evaluate our framework within the context of existing wireless technology. Service differentiation is based on the IEEE 802.11 distributed coordination function (DCF) originally designed to support best-effort data services. We analyze the delay experienced by a mobile host implementing the IEEE 802.11 DCF and derive a closed-form formula. We then extend the DCF to provide service differentiation for delay-sensitive and best-effort traffic based on the results from the analysis. Two distributed estimation algorithms are proposed. These algorithms are evaluated using simulation, analysis, and experimentation. A virtual MAC (VMAC) algorithm passively monitors the radio channel and estimates locally achievable service levels. The VMAC estimates key MAC level statistics related to service quality such as delay, delay variation, packet collision, and packet loss. We show the efficiency of the VMAC algorithm through simulation and consider significantly overlapping cells and highly bursty traffic mixes. In addition, we implement and evaluate the VMAC in an experimental differentiated services wireless testbed. A virtual source (VS) algorithm utilizes the VMAC to estimate application-level service quality. The VS allows application parameters to be tuned in response to dynamic channel conditions based on "virtual delay curves." We demonstrate through simulation that when these distributed victual algorithms are applied to the admission control of the radio channel then a globally stable state can be maintained without the need for complex centralized radio resource management.