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Low-Overhead Development of Scalable Resource-Efficient Software Systems

Low-Overhead Development of Scalable Resource-Efficient Software Systems
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Author(s): Wei-Chih Huang (Imperial College London, UK) and William J. Knottenbelt (Imperial College London, UK)
Copyright: 2014
Pages: 25
Source title: Handbook of Research on Emerging Advancements and Technologies in Software Engineering
Source Author(s)/Editor(s): Imran Ghani (Universiti Teknologi Malaysia, Malaysia), Wan Mohd Nasir Wan Kadir (Universiti Teknologi Malaysia, Malaysia) and Mohammad Nazir Ahmad (Universiti Teknologi Malaysia, Malaysia)
DOI: 10.4018/978-1-4666-6026-7.ch005

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Abstract

As the variety of execution environments and application contexts increases exponentially, modern software is often repeatedly refactored to meet ever-changing non-functional requirements. Although programmer effort can be reduced through the use of standardised libraries, software adjustment for scalability, reliability, and performance remains a time-consuming and manual job that requires high levels of expertise. Previous research has proposed three broad classes of techniques to overcome these difficulties in specific application domains: probabilistic techniques, out of core storage, and parallelism. However, due to limited cross-pollination of knowledge between domains, the same or very similar techniques have been reinvented all over again, and the application of techniques still requires manual effort. This chapter introduces the vision of self-adaptive scalable resource-efficient software that is able to reconfigure itself with little other than programmer-specified Service-Level Objectives and a description of the resource constraints of the current execution environment. The approach is designed to be low-overhead from the programmer's perspective – indeed a naïve implementation should suffice. To illustrate the vision, the authors have implemented in C++ a prototype library of self-adaptive containers, which dynamically adjust themselves to meet non-functional requirements at run time and which automatically deploy mitigating techniques when resource limits are reached. The authors describe the architecture of the library and the functionality of each component, as well as the process of self-adaptation. They explore the potential of the library in the context of a case study, which shows that the library can allow a naïve program to accept large-scale input and become resource-aware with very little programmer overhead.

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