Forecast-Based Interference: Modelling Multicore Interference from Observable Factors

David Jack Griffin; Benjamin Michael Jean-Rene Lesage; Iain John Bate; Frank Soboczenski; Robert Ian Davis

doi:10.1145/3139258.3139275

Forecast-Based Interference: Modelling Multicore Interference from Observable Factors

David Jack Griffin, Benjamin Michael Jean-Rene Lesage, Iain John Bate, Frank Soboczenski, Robert Ian Davis

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

While there is significant interest in the use of COTS multicore platforms for Real-time Systems, there has been very little in terms of practical methods to calculate the interference multiplier (i.e. the increase in execution time due to interference) between tasks on such systems. COTS multicore platforms present two distinct challenges: firstly, the variable interference between tasks competing for shared resources such as cache, and secondly the complexity of the hardware mechanisms and policies used, which may result in a system which is very difficult if not impossible to analyse; assuming that the exact details of the hardware are even disclosed! This paper proposes a new technique, Forecast-Based Interference analysis, which mitigates both of these issues by combining measurement-based techniques with statistical techniques and forecast modelling to enable the prediction of an interference multiplier for a given set of tasks, in an automated and reliable manner. The combination of execution times and interference multipliers can be used both in the design, e.g. for specifying timing watchdogs, and analysis, e.g. verifying schedulability.

Original language	English
Title of host publication	International Conference on Real-Time Networks and Systems
Publisher	ACM
Pages	198-207
ISBN (Print)	9781450352864
DOIs	https://doi.org/10.1145/3139258.3139275
Publication status	Published - 2017

Bibliographical note

© 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

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10.1145/3139258.3139275

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© 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details
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Cite this

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title = "Forecast-Based Interference: Modelling Multicore Interference from Observable Factors",

abstract = "While there is significant interest in the use of COTS multicore platforms for Real-time Systems, there has been very little in terms of practical methods to calculate the interference multiplier (i.e. the increase in execution time due to interference) between tasks on such systems. COTS multicore platforms present two distinct challenges: firstly, the variable interference between tasks competing for shared resources such as cache, and secondly the complexity of the hardware mechanisms and policies used, which may result in a system which is very difficult if not impossible to analyse; assuming that the exact details of the hardware are even disclosed! This paper proposes a new technique, Forecast-Based Interference analysis, which mitigates both of these issues by combining measurement-based techniques with statistical techniques and forecast modelling to enable the prediction of an interference multiplier for a given set of tasks, in an automated and reliable manner. The combination of execution times and interference multipliers can be used both in the design, e.g. for specifying timing watchdogs, and analysis, e.g. verifying schedulability.",

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AU - Davis, Robert Ian

N1 - © 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

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N2 - While there is significant interest in the use of COTS multicore platforms for Real-time Systems, there has been very little in terms of practical methods to calculate the interference multiplier (i.e. the increase in execution time due to interference) between tasks on such systems. COTS multicore platforms present two distinct challenges: firstly, the variable interference between tasks competing for shared resources such as cache, and secondly the complexity of the hardware mechanisms and policies used, which may result in a system which is very difficult if not impossible to analyse; assuming that the exact details of the hardware are even disclosed! This paper proposes a new technique, Forecast-Based Interference analysis, which mitigates both of these issues by combining measurement-based techniques with statistical techniques and forecast modelling to enable the prediction of an interference multiplier for a given set of tasks, in an automated and reliable manner. The combination of execution times and interference multipliers can be used both in the design, e.g. for specifying timing watchdogs, and analysis, e.g. verifying schedulability.

AB - While there is significant interest in the use of COTS multicore platforms for Real-time Systems, there has been very little in terms of practical methods to calculate the interference multiplier (i.e. the increase in execution time due to interference) between tasks on such systems. COTS multicore platforms present two distinct challenges: firstly, the variable interference between tasks competing for shared resources such as cache, and secondly the complexity of the hardware mechanisms and policies used, which may result in a system which is very difficult if not impossible to analyse; assuming that the exact details of the hardware are even disclosed! This paper proposes a new technique, Forecast-Based Interference analysis, which mitigates both of these issues by combining measurement-based techniques with statistical techniques and forecast modelling to enable the prediction of an interference multiplier for a given set of tasks, in an automated and reliable manner. The combination of execution times and interference multipliers can be used both in the design, e.g. for specifying timing watchdogs, and analysis, e.g. verifying schedulability.

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