Low-Overhead Development of Scalable Resource-Efficient Software Systems

Low-Overhead Development of Scalable Resource-Efficient Software Systems

Wei-Chih Huang (Imperial College London, UK) and William J. Knottenbelt (Imperial College London, UK)
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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1. Introduction

Modern software engineers are faced with an explosion in the number of execution environments in which their applications might execute (e.g. smartphone, tablet, laptop, server, etc.). In each of these potential execution environments, each class of application is subject to different resource constraints and may also be subject to different Quality of Service (QoS) requirements. Consider Figure 1, which presents the importance of three common QoS parameters (performance, memory efficiency, and reliability) in different application contexts and execution environments. For example, when a game is operated on a game console, the game’s performance should be at a very high level to meet players’ expectations, possibly leading to the use of more memory space and higher electric power consumption. By contrast, if the game is executed on a smartphone, lower performance may be tolerated to save battery power and to use less memory space. Similarly, if a web browser runs on a smartphone, due to high usage frequency of the web browser and limited memory space of the smartphone, high performance with low memory consumption is expected. But when the web browser is executed on a server or a game console, the demand of high performance with low memory consumption is not required because these two platforms can provide sufficient memory space and are not frequently used to surf the internet.

Figure 1.

The importance of QoS requirements on different application contexts and execution environments

It is a major challenge to write software capable of maintaining QoS in every possible execution environment and application context, especially in the face of bursty and/or high-intensity workloads that may frequently stretch or exceed resource limitations. To avoid unacceptable degradations in the quality of user experience, it is necessary to implement mechanisms for scalability, robustness and intelligent resource exploitation. Even if sound software engineering principles are applied to maximise software reuse, there are major barriers to the application of traditional software development techniques in light of these challenges. Specifically, significant manual reimplementation and refactoring must be carried out for each execution environment, and substantial levels of programmer expertise is necessary.

To address this situation, we propose a self-adaptive framework for “intelligent” software which adapts at run-time to the resource constraints of its present execution environment, as well as automatically scaling up to handle large input sizes, all the while respecting non-functional Quality of Service requirements. Ideally, the method of developing such software should be as close to that of developing ordinary software as possible, in order to reduce required levels of expertise and programmer effort. Our framework focuses on containers, whose underlying data structures differ in performance, memory consumption, and reliability, as they are critical to QoS requirements. Dynamic and automatic selection of underlying data structures can enable software to change its resource usage at run time, which releases programmers from reimplementing software.

The present chapter is motivated by the observation of the reinvention of similar techniques for scalability, robustness and intelligent resource exploitation across different application domains. Table 1 shows a real example, which demonstrates that in order to deal with large-scale input, five different application domains have adopted the techniques of out-of-core storage, probabilistic data structures, and parallelism. The first application domain, explicit state-space exploration, which is based on a breadth-first search core, is the key primary step in either the model checking or performance analysis of concurrent systems. The major issue regarding this domain is the explosion of states, which results in shortage of primary memory. Through the use of these techniques, the supported capability has been improved from ~ 105 states to ~ 1010 states (Bingham et al., 2010).

Key Terms in this Chapter

Out-of-Core Algorithm: An algorithm which exploits external storage in order to support large data volumes that cannot be supported by primary memory.

Self-Adaptive System: A system which can automatically reconfigure itself in response to changes in its environment.

Resource-Aware System: A system which has the ability to monitor its resource usage and to dynamically manage resources according to user-specified constraints.

Quality of Service: An objective characterisation of the performance levels delivered to users by a system or service. Ideally, a system should maintain QoS at or above some minimum level.

Container: An data structure that holds a set of other objects. In many standardised libraries, container classes feature member functions to manipulate and access the held objects.

Standard Template Library: A C++ software library that provides commonly-used containers and algorithms to simplify software design.

Service Level Objective: A measurable QoS-related target, defined jointly by service providers and customers.

Probabilistic Data Structure: A data structure which exploits randomness to boost its efficiency, for example skip lists and Bloom filters. In the case of Bloom filters, the results of certain operations may be incorrect with a small probability.

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