Computing and Information Systems - Theses
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ItemAuto-scaling and deployment of web applications in distributed computing clouds( 2016)Cloud Computing, which allows users to acquire/release resources based on real-time demand from large data centers in a pay-as-you-go model, has attracted considerable attention from the ICT industry. Many web application providers have moved or plan to move their applications to Cloud, as it enables them to focus on their core business by freeing them from the task and the cost of managing their data center infrastructures, which are often over-provisioned or under-provisioned under a dynamic workload. Applications these days commonly serve customers from geographically dispersed regions. Therefore, to meet the stringent Quality of Service (QoS) requirements, they have to be deployed in multiple data centers close to the end customer locations. However, efficiently utilizing Cloud resources to reach high cost-efficiency, low network latency, and high availability is a challenging task for web application providers, especially when the service provider intends to deploy the application in multiple geographical distributed Cloud data centers. The problems, including how to identify satisfactory Cloud offerings, how to choose geographical locations of data centers so that the network latency is minimized, how to provision the application with minimum cost incurred, and how to guarantee high availability under failures and flash crowds, should be addressed to enable QoS-aware and cost-efficient utilization of Cloud resources. In this thesis, we investigated techniques and solutions for these questions to help application providers to efficiently manage deployment and provision of their applications in distributed computing Clouds. It extended the state-of-the-art by making the following contributions: 1. A hierarchical fuzzy inference approach for identifying satisfactory Cloud services according to individual requirements. 2. Algorithms for selection of multi-Cloud data centers and deployment of applications on them to minimize Service Level Objective (SLO) violations for web applications requiring strong consistency. 3. An auto-scaler for web applications that achieves both high availability and significant cost saving by using heterogeneous spot instances. 4. An approach that mitigates the impact of short-term application overload caused by either resource failures or flash crowds in any individual data center through geographical load balancing.
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ItemEnergy-efficient management of resources in container-based clouds( 2016)CLOUD enables access to a shared pool of virtual resources through Internet and its adoption rate is increasing because of its high availability, scalability and cost effectiveness. However, cloud data centers are one of the fastest-growing energy consumers and half of their energy consumption is wasted mostly because of inefficient allocation of the servers resources. Therefore, this thesis focuses on software level energy management techniques that are applicable to containerized cloud environments. Containerized clouds are studied as containers are increasingly gaining popularity. And containers are going to be major deployment model in cloud environments. The main objective of this thesis is to propose an architecture and algorithms to minimize the data center energy consumption while maintaining the required Quality of Service (QoS). The objective is addressed through improvements in the resource utilization both on server and virtual machine level. We investigated the two possibilities of minimizing energy consumption in a containerized cloud environment, namely the VM sizing and container consolidation. The key contributions of this thesis are as follows: 1. A taxonomy and survey of energy-efficient resource management techniques in PaaS and CaaS environments. 2. A novel architecture for virtual machine customization and task mapping in a containerized cloud environment. 3. An efficient VM sizing technique for hosting containers and investigation of the impact of workload characterization on the efficiency of the determined VM sizes. 4. A design and implementation of a simulation toolkit that enables modeling for containerized cloud environments. 5. A framework for dynamic consolidation of containers and a novel correlation-aware container consolidation algorithm. 6. A detailed comparison of energy efficiency of container consolidation algorithms with traditional virtual machine consolidation for containerized cloud environments.
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ItemResource provisioning and scheduling algorithms for scientific workflows in cloud computing environments( 2016)Scientific workflows describe a series of computations that enable the analysis of data in a structured and distributed manner. Their importance is exacerbated in todays big data era as they become a compelling mean to process and extract knowledge from the ever-growing data produced by increasingly powerful tools such as telescopes, particle accelerators, and gravitational wave detectors. Due to their large-scale nature, scheduling algorithms are key to efficiently automate their execution in distributed environments, and as a result, to facilitate and accelerate the pace of scientific progress. The emergence of the latest distributed system paradigm, cloud computing, brings with it tremendous opportunities to run workflows at low costs without the need of owning any infrastructure. In particular, Infrastructure as a Service (IaaS) clouds, offer an easily accessible, flexible, and scalable infrastructure for the deployment of these scientific applications by providing access to a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay-per-use basis. This thesis investigates novel resource provisioning and scheduling approaches for scientific workflows in IaaS clouds. They address fundamental challenges that arise from the multi-tenant, resource-abundant, and elastic resource model and are capable of fulfilling a set of quality of service requirements expressed in terms of execution time and cost. It advances the field by making the following key contributions: 1. A taxonomy and survey of the state-of-the-art scientific workflow scheduling algorithms designed exclusively for IaaS clouds. 2. A novel static scheduling algorithm that leverages Particle Swarm Optimization to generate a workflow execution and resource provisioning plan that minimizes the infrastructure cost while meeting a deadline constraint. 3. A hybrid algorithm based on a variation of the Unbounded Knapsack Problem that finds a trade-off between making static decisions to find better-quality schedules and dynamic decisions to adapt to unexpected delays. 4. A scalable algorithm that combines heuristics and two different Integer Programming models to generate schedules that minimize the execution time of the work- flow while meeting a budget constraint. 5. The implementation of a cloud resource management module and its integration to an existing Workflow Management System.