Software performance self-adaptation through efficient model predictive control

Incerto, Emilio; Tribastone, Mirco; Trubiani, Catia

doi:10.1109/ase.2017.8115660

Cited by 23 publications

(13 citation statements)

References 58 publications

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“…The second reason is more likely. Planning [186], [202], [224], [294], testing [103], fault tolerance [33], and uncertainty [102], [182] all highlight common research trends seen before in the timelines, as is model-predictive control [136]. Mobile systems most likely also share the same similarities with overlap as networking and robotics to IoT and IaaS.…”

Section: -2020 -The Last Decade -Second Halfmentioning

confidence: 75%

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

Wong¹,

Wagner²

2021

Preprint

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Championed by IBM's vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system -a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals despite changes in its environment. Despite a number of literature reviews on specific aspects of SASs ranging from their requirements to quality attributes, we lack a systematic understanding of the current state of the art. This paper contributes a systematic literature review into self-adaptive systems using the dblp computer science bibliography as a database. We filtered the records systematically in successive steps to arrive at 293 relevant papers. Each paper was critically analyzed and categorized into an attribute matrix. This matrix consisted of five categories, with each category having multiple attributes. The attributes of each paper, along with the summary of its contents formed the basis of the literature review that spanned 30 years . We characterize the maturation process of the research area from theoretical papers over practical implementations to more holistic and generic approaches, frameworks, and exemplars, applied to areas such as networking, web services, and robotics, with much of the recent work focusing on IoT and IaaS.

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Section: -2020 -The Last Decade -Second Halfmentioning

confidence: 75%

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

Wong¹,

Wagner²

2021

Preprint

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“…3.1.1 nodeJS. nodeJS 3 is a widely-used JavaScript execution environment (78k stars on Github) [9,10,21]. Inputs I.…”

Section: Selecting the Subject Systemsmentioning

confidence: 99%

The interplay of compile-time and run-time options for performance prediction

Lesoil¹,

Acher²,

Tërnava³

et al. 2021

Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A

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Many software projects are configurable through compile-time options (e.g., using ./configure) and also through run-time options (e.g., command-line parameters, fed to the software at execution time). Several works have shown how to predict the effect of run-time options on performance. However it is yet to be studied how these prediction models behave when the software is built with different compile-time options. For instance, is the best run-time configuration always the best w.r.t. the chosen compilation options? In this paper, we investigate the effect of compile-time options on the performance distributions of 4 software systems. There are cases where the compiler layer effect is linear which is an opportunity to generalize performance models or to tune and measure runtime performance at lower cost. We also prove there can exist an interplay by exhibiting a case where compile-time options significantly alter the performance distributions of a configurable system. CCS CONCEPTS• Software and its engineering → Software product lines; Software product lines; Software performance; • Computing methodologies → Machine learning.

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“…Steps 1 & 2 -Software Systems. We consider 8 software systems, opensource and well-known in various fields, that the literature already studied: gcc [69], ImageMagick [83], lingeling [34], nodeJS [36], poppler [55], SQLite [85], x264 [39] and xz [91]. We choose these systems because they handle different types of input data, allowing us to draw as general conclusions as possible.…”

Section: Data Collectionmentioning

confidence: 99%

The Interaction between Inputs and Configurations fed to Software Systems: an Empirical Study

Lesoil,

Acher,

Blouin

et al. 2021

Preprint

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Widely used software systems such as video encoders are by necessity highly configurable, with hundreds or even thousands of options to choose from. Their users often have a hard time finding suitable values for these options (i.e., finding a proper configuration of the software system) to meet their goals for the tasks at hand, e.g., compress a video down to a certain size. One dimension of the problem is of course that performance depends on the input data: e.g., a video as input to an encoder like x264 or a file system fed to a tool like xz . To achieve good performance, users should therefore take into account both dimensions of (1) software variability and (2) input data. In this problem-statement paper, we conduct a large study over 8 configurable systems that quantifies the existing interactions between input data and configurations of software systems. The results exhibit that (1) inputs fed to software systems interact with their configuration options in non monotonous ways, significantly impacting their performance properties (2) tuning a software system for its input data makes it possible to multiply its performance by up to ten (3) input variability can jeopardize the relevance of performance predictive models for a field deployment.

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Software performance self-adaptation through efficient model predictive control

Cited by 23 publications

References 58 publications

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

The interplay of compile-time and run-time options for performance prediction

The Interaction between Inputs and Configurations fed to Software Systems: an Empirical Study

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