1. |
- Ali-Eldin, Ahmed, 1985-
(författare)
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Capacity Scaling for Elastic Compute Clouds
- 2013
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- AbstractCloud computing is a computing model that allows better management, higher utiliza-tion and reduced operating costs for datacenters while providing on demand resourceprovisioning for different customers. Data centers are often enormous in size andcomplexity. In order to fully realize the cloud computing model, efficient cloud man-agement software systems that can deal with the datacenter size and complexity needto be designed and built.This thesis studies automated cloud elasticity management, one of the main andcrucial datacenter management capabilities. Elasticity can be defined as the abilityof cloud infrastructures to rapidly change the amount of resources allocated to anapplication in the cloud according to its demand. This work introduces algorithms,techniques and tools that a cloud provider can use to automate dynamic resource pro-visioning allowing the provider to better manage the datacenter resources. We designtwo automated elasticity algorithms for cloud infrastructures that predict the futureload for an application running on the cloud. It is assumed that a request is either ser-viced or dropped after one time unit, that all requests are homogeneous and that it takesone time unit to add or remove resources. We discuss the different design approachesfor elasticity controllers and evaluate our algorithms using real workload traces. Wecompare the performance of our algorithms with a state-of-the-art controller. We ex-tend on the design of the best performing controller out of our two controllers anddrop the assumptions made during the first design. The controller is evaluated with aset of different real workloads.All controllers are designed using certain assumptions on the underlying systemmodel and operating conditions. This limits a controller’s performance if the modelor operating conditions change. With this as a starting point, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components,an analyzer and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.
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2. |
- Li, Wubin, 1983-
(författare)
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Virtual Machine Placement in Cloud Environments
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With the emergence of cloud computing, computing resources (i.e., networks, servers, storage, applications, and services) are provisioned as metered on-demand services over networks, and can be rapidly allocated and released with minimal management effort. In the cloud computing paradigm, the virtual machine is one of the most commonly used resource carriers in which business services are encapsulated. Virtual machine placement optimization, i.e., finding optimal placement schemes for virtual machines, and reconfigurations according to the changes of environments, become challenging issues.The primary contribution of this licentiate thesis is the development and evaluation of our combinatorial optimization approaches to virtual machine placement in cloud environments. We present modeling for dynamic cloud scheduling via migration of virtual machines in multi-cloud environments, and virtual machine placement for predictable and time-constrained peak loads in single-cloud environments. The studied problems are encoded in a mathematical modeling language and solved using a linear programming solver. In addition to scientific publications, this work also contributes in the form of software tools (in EU-funded project OPTIMIS) that demonstrate the feasibility and characteristics of the approaches presented.
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