Phoenix : a Parallel Programming Model for Accommodating Dynamically Joining Resources
ABSTRACT This paper proposes Phoenix, a programming model for writing parallel and distributed applications that accommodate dynamically joining/leaving compute resources. In the proposed model, nodes involved in an application see a large and fixed virtual node name space. They communicate via messages, whose destinations are specified by virtual node names, rather than names bound to a physical resource. We describe Phoenix API and show how it allows a transparent migration of application states, as well as dynamically joining/leaving nodes as its by-product. We also demonstrate through several application studies that Phoenix model is close enough to regular message passing, thus it is a general programming model that facilitates porting many parallel applications/algorithms to more dynamic environments. Experimental results indicate applications that have a small task migration cost can quickly take advantage of dynamically joining resources using Phoenix. Divide-and-conquer algorithms written in Phoenix achieved a good speedup with a large number of nodes across multiple LANs (120 times speedup using 169 CPUs across three LANs). We believe Phoenix provides a useful programming abstraction and platform for emerging parallel applications that must be deployed across multiple LANs and/or shared clusters having dynamically varying resource conditions.
- SourceAvailable from: Rob V. Van Nieuwpoort[Show abstract] [Hide abstract]
ABSTRACT: When parallel applications are run in large-scale distributed environments, such as grids, peer-to-peer (P2P) systems, and clouds, the set of resources used can change dynamically as machines crash, reservations end, and new resources become available. It is vital for applications to respond to these changes. Therefore, it is necessary to keep track of the available resources—a problem which is known to be notoriously difficult. In this article we argue that resource tracking must be provided as the standard functionality in the lower parts of the software stack. We propose a general solution to resource tracking: the Join–Elect–Leave (JEL) model. JEL provides unified resource tracking for parallel and distributed applications across environments. JEL is a simple yet powerful model based on notifying when resources have Joined or Left the computation. We demonstrate that JEL is suitable for resource tracking in a wide variety of programming models, ranging from the fixed resource sets traditionally used in MPI-1 to flexible grid-oriented programming models. We compare several JEL implementations, and show these to perform and scale well in several real-world scenarios involving grids, clouds and P2P systems applied concurrently, and wide-area systems with failing resources. Using JEL, we have won the first prize in a number of international distributed computing competitions. Copyright © 2010 John Wiley & Sons, Ltd.Concurrency and Computation: Practice and Experience. 01/2011; 23:17-37.
Conference Paper: Ibis: Real-world problem solving using real-world grids.[Show abstract] [Hide abstract]
ABSTRACT: Ibis is an open source software framework that drastically simplifies the process of programming and deploying large-scale parallel and distributed grid applications. Ibis supports a range of programming models that yield efficient implementations, even on distributed sets of heterogeneous resources. Also, Ibis is specifically designed to run in hostile grid environments that are inherently dynamic and faulty, and that suffer from connectivity problems. Recently, Ibis has been put to the test in two competitions organized by the IEEE Technical Committee on Scalable Computing, as part of the CCGrid 2008 and Cluster/Grid 2008 international conferences. Each of the competitions' categories focused either on the aspect of scalability, efficiency, or fault-tolerance. Our Ibis-based applications have won the first prize in all of these categories. In this paper we give an overview of Ibis, and - to exemplify its power and flexibility - we discuss our contributions to the competitions, and present an overview of our lessons learned.23rd IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2009, Rome, Italy, May 23-29, 2009; 01/2009
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ABSTRACT: MapReduce is an enabling technology in support of Cloud Computing. Hadoop which is a MapReduce implementation has been widely used in developing MapReduce applications. This paper presents HSim, a MapReduce simulator which builds on top of Hadoop. HSim models a large number of parameters that can affect the behaviors of MapReduce nodes, and thus it can be used to tune the performance of a MapReduce cluster. HSim is validated with both benchmark results and user customized MapReduce applications.Future Generation Computer Systems - FGCS. 01/2013;