Formalizing and simulating cross-layer elasticity strategies in Cloud systems
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Formalizing and simulating cross-layer elasticity strategies in Cloud systems Khaled Khebbeb1 • Nabil Hameurlain1 • Faiza Belala2 Received: 30 April 2019 / Revised: 15 January 2020 / Accepted: 29 February 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Clouds are complex systems that provide computing resources in an elastic way. Elasticity allows their adaptation to input workloads by (de)provisioning resources as the demand rises and drops. Given the numerous overlapping factors that impact their elasticity and the unpredictable nature of the workload, providing accurate action plans to manage Cloud elasticity is a particularly challenging task. In this paper, we propose a formal approach based on bigraphical reactive systems to model Cloud structures and their elastic behavior. We design cross-layer elasticity strategies which operate at application and infrastructure Cloud layers to manage the elastic adaptations. We encode the elastic behaviors in Rewriting logic, through the Maude framework, to enable their generic executability. We provide a qualitative verification of the designed behaviors’ correctness with a model-checking technique supported by the linear temporal logic. Finally, we provide a tooled simulation-based methodology, through the Queuing theory, to conduct a quantitative analysis of the designed elasticity strategies. Keywords Self-adaptation Cloud computing Cross-layer elasticity strategies Formal methods Bigraphical reactive systems Rewriting logic Maude Linear temporal logic Queuing theory
1 Introduction Cloud computing [31] is a recent paradigm that has known a great interest in both industrial and academic sectors. It consists of providing a pool of virtualized resources (servers, virtual machines, etc.) as on-demand services. These resources are offered by Cloud providers according to three fundamental service models: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). The most appealing feature that distinguishes the Cloud from other models is the elasticity property [2, 18, 21, 22]. Elasticity allows to efficiently & Khaled Khebbeb [email protected] Nabil Hameurlain [email protected] Faiza Belala [email protected] 1
LIUPPA Laboratory, University of Pau, Pau, France
2
LIRE Laboratory, Constantine 2 University, Constantine, Algeria
control resources provisioning according to workload fluctuation in a way to maintain an adequate quality of service (QoS) while minimizing operating cost [15]. Such a behavior is implemented by an elasticity controller: an entity usually based on a closed control loop which decides of the elasticity actions to be triggered to adapt to the demand [24]. In fact, managing a Cloud system’s elasticity can be particularly challenging. Elastic behaviors rely on many overlapping factors such as the available resources, current workload, the system’s state of provisioning, etc. Managi
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