A Survey of Phase Classification Techniques for Characterizing Variable Application Behavior

Keeley Criswell, Tosiron Adegbija

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Adaptable computing is an increasingly important paradigm that specializes system resources to variable application requirements, environmental conditions, or user requirements. Adapting computing resources to variable application requirements (or application phases) is otherwise known as phase-based optimization. Phase-based optimization takes advantage of application phases, or execution intervals of an application that behave similarly, to enable effective and beneficial adaptability. In order for phase-based optimization to be effective, the phases must first be classified to determine when application phases begin and end, and ensure that system resources are accurately specialized. In this paper, we present a survey of phase classification techniques that have been proposed to exploit the advantages of adaptable computing through phase-based optimization. We focus on recent techniques and classify these techniques with respect to several factors in order to highlight their similarities and differences. We divide the techniques by their major defining characteristics-online/offline and serial/parallel. In addition, we discuss other characteristics such as prediction and detection techniques, the characteristics used for prediction, interval type, etc. We also identify gaps in the state-of-the-art and discuss future research directions to enable and fully exploit the benefits of adaptable computing.

Original languageEnglish (US)
Article number8769866
Pages (from-to)224-236
Number of pages13
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number1
StatePublished - Jan 1 2020
Externally publishedYes


  • Phase classification
  • adaptable computing
  • big data
  • dynamic optimization
  • edge computing
  • emerging applications
  • multithreaded applications
  • variable program behavior
  • workload characterization

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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