Modeling and evaluation of rule-based elasticity for cloud-based applications

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Copyright: Suleiman, Basem
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Abstract
Cloud computing has evolved to become a dominant computing paradigm in which computing resources such as software, platform and infrastructure are provisioned and consumed as services. The Infrastructure as a Service (IaaS) cloud model has particularly attracted many web-based businesses to run their business applications. This is primarily due to elasticity characteristic which encompasses dynamic provisioning and de-provisioning of computing resources, e.g., servers, storage and network, on-demand through internet self-service interfaces. In this model, resources usage are billed based on usage per unit of time. Different application classes, specifically internet-based business (or e-business) applications, can highly benefit from IaaS elasticity. Such applications are of high business value and subject to variable workload patterns and volumes due to its exposure to the web. Realizing elasticity benefits of IaaS for e-business applications primarily relies on achieving application Service Level Objectives (SLOs) through efficient use of computing resources. These SLOs are often specified in Service Level Agreements (SLAs) agreed between businesses and their customers. This is of paramount importance for both IaaS cloud providers and consumers, but it has been faced with many challenges. First, most IaaS providers support elasticity through auto-scaling rules which are mainly based on thresholds. Choosing appropriate values for these thresholds, however, is not a trivial task for the cloud consumer as it requires exhaustive empirical testing with applications workload and performance and cost metrics. Second, IaaS elasticity is primarily driven by resource-based metrics such CPU utilization. However, elasticity that is based on application SLA metrics are also crucial for cloud consumers. Third, while resource and application metrics are readily available, it is still a challenge to fashion resource provisioning rules that perform well in terms of important performance and cost. In this thesis, we address these challenges through three main contributions. First, we propose novel analytical models that capture core elasticity thresholds and emulate how IaaS elasticity works. The proposed models also approximate key metrics for evaluating performance of elasticity rules including CPU utilization, applications response time and server usage cost. We also develop algorithms that decide when and how to scale-out and scale-in based on CPU utilization and other thresholds, and estimate servers cost incurred by scaling actions. We validate our models and algorithms using Matlab simulation and equivalent experiments in Amazon EC2 cloud using an e-commerce 3-tier web application. Second, we propose an architecture and a method for deriving IaaS elasticity based on application SLA metrics. We present algorithms that monitor response time SLA satisfaction and decide when to scale-out and scale-in application servers in an IaaS cloud environment. Third, we extensively evaluate the two common types of elasticity rules with different CPU utilization and response time SLA thresholds.In addition, we carry out trade-off analysis of the performance of both elasticity approaches in terms of key metrics including response time SLA satisfaction, CPU utilization and servers usage cost. We carry out our evaluation of performance of both elasticity approaches using the same e-commerce 3-tier web application running on Amazon EC2 cloud.
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Author(s)
Suleiman, Basem
Supervisor(s)
Venugopal, Srikumar
Jeffery, Ross
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Publication Year
2015
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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download public version.pdf 3.2 MB Adobe Portable Document Format
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