Program control language: a programming language for adaptive distributed applications

  • Authors:

  • Brian Ensink;Joel Stanley;Vikram Adve

  • Affiliations:

  • Computer Science Department, University of Illinois at Urbana-Champaign, 1304 W. Springfield Avenue, Urbana, IL;Computer Science Department, University of Illinois at Urbana-Champaign, 1304 W. Springfield Avenue, Urbana, IL;Computer Science Department, University of Illinois at Urbana-Champaign, 1304 W. Springfield Avenue, Urbana, IL

  • Venue:
  • Journal of Parallel and Distributed Computing
  • Year:
  • 2003

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Abstract

Many distributed applications must meet stringent performance requirements even when the performance characteristics of the underlying systems and networks vary significantly at runtime. Runtime adaptation can be used to tolerate such changes, but sophisticated adaptive distributed programs can be extremely challenging to design, implement and debug. This paper proposes a language called Program Control Language (PCL) that provides a novel means of specifying adaptations in distributed applications. PCL is based on an abstract, global representation of a distributed program (a static task graph), which enables a programmer to reason about and to describe a wide range of application-specific adaptation strategies at a high level, using a few key mechanisms. PCL provides simple high-level syntax for local and remote adaptation operations, local and remote performance monitoring (and aggregation), and for performing adaptations synchronously or asynchronously with respect to the execution of the application process initiating the adaptation. The global task graph representation enables remote performance metrics and adaptation operations to be specified in simple global terms by any process, and the compiler and runtime system automatically perform the communication and synchronization required for the remote operations. The paper describes the conceptual adaptation framework, the PCL language, and our implementation of the PCL compiler and runtime system. The paper uses three adaptative applications examples to illustrate the capabilities and benefits of PCL, and to show experimentally that the performance overheads of using PCL for implementing an adaptive application are negligible.