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.
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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 01/2013; DOI:10.1016/j.future.2011.05.007 · 2.64 Impact Factor