Abstract
The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. The existing methods suffer from issues like: (1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; (2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and (3) the lack of considering uncertainty aspects while designing auto-scaling solutions. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches.
Highlights
The pool of virtually unlimited on-demand computational resources, provided by cloud providers (CPs), and many attractive features of cloud computing, such as pay-as-yougo pricing and on-the-fly re-adjustment of hired computational resources, is a perfect match to host web applications that are subject to fluctuating workload conditions [1, 2]
The key objective of implementing cloud elasticity is to improve the utilisation of computational resources whilst maintaining the desired performance of the system and reducing its operational cost
This paper investigates the horizontal elasticity problem from the service providers (SPs) perspective and proposes biologically-inspired auto-scaling solutions
Summary
The pool of virtually unlimited on-demand computational resources, provided by cloud providers (CPs), and many attractive features of cloud computing, such as pay-as-yougo pricing and on-the-fly re-adjustment of hired computational resources (elasticity), is a perfect match to host web applications that are subject to fluctuating workload conditions [1, 2]. The existing research literature on cloud elasticity differs in various aspects, e.g. triggering behaviour (Reactive/ Predictive/Hybrid), scope (CPs/SPs perspective), dependency on metrics (CPU utilisation/Response time, etc.), and the implementation technique (Control Theory/ Machine learning/Rule-based, etc.). Despite such differences most of the existing methods can generally be grouped into Fixed or Adaptive categories based on their design and working mechanism to analyse their pros and cons as a whole [25]
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