Computing and Information Systems - Theses
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ItemQoS-aware and semantic-based service coordination for multi-Cloud environments( 2013)The advantages of Cloud computing, such as cost effectiveness and ease of management, encourage companies to adapt its services. However, In a Multi-Cloud environment, the wide range of Cloud services and user specific requirements make it difficult to select the best composition of services. An automated approach is required to deal with all phases of service coordination including discovery, negotiation, selection, composition, and monitoring. To simplify the process of Cloud migration, this thesis proposes an effective architecture to provide automated QoS-aware deployment of virtual appliances on Cloud service providers. The architecture takes advantage of ontology-based discovery to semantically match user requirements to Cloud services. Then, it applies a set of negotiation, selection, and optimization strategies to pick up the best available services from the list of discovered services. Finally, this thesis shows how monitoring services have to be described, deployed (discovered and ranked), and executed to enforce accurate penalties. The key contributions of this thesis are: 1. An ontology-based Cloud service discovery is proposed that works based on modelling virtual units into Semantic Web services. This helps users to deploy their appliances on the fittest providers when providers and users are not using the same notation to describe their services and requirements. 2. A scalable methodology to create an aggregated repository of services in Web Service Modeling Ontology (WSMO) from service advertisements available in XML. 3. A negotiation strategy that acquires user preferences and provider’s resource utilization status and utilizes time-dependent tactic along with statistical methods to maximize the profit of Cloud providers while adhering to deadline constraints of users and verifying reliability of providers’ offers. The proposed negotiation strategy is tested to show how our approach helps Cloud providers to increase their profits. 4. A QoS criteria model for selection of virtual appliances and units in Cloud computing. In addition, two different selection approaches, genetic-based and Forward-checking-based backtracking (FCBB), are proposed to help users deploying net-work of appliances on Clouds based on their preferences. 5. A ranking system for Cloud service composition that let users express their preferences conveniently using high-level linguistic terms. The system utilizes evolutionary multi-objective optimization, and a fuzzy inference system to precisely capture the preferences for the ranking purpose. 6. An approach to help non-expert users with limited or no knowledge on legal and virtual appliance image format compatibility to deploy their services flawlessly. For this purpose, Cloud services are enriched with experts knowledge (from lawyers, software engineers, system administrators, etc). The knowledgebase then is used in a scalable algorithm for reasoning that identifies whether a set of Cloud service consisting of virtual appliance and units are compatible or not. 7. A semantic SLA template that can be used as a goal for discovery of necessary monitoring services. In addition, SLA dependencies are modeled using WSMO to build a knowledgebase that is exploited to eliminate the effects of SLA failure cascading on violation detection.
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ItemEnergy-efficient management of virtual machines in data centers for cloud computing( 2013)Cloud computing has revolutionized the information technology industry by enabling elastic on-demand provisioning of computing resources. The proliferation of Cloud computing has resulted in the establishment of large-scale data centers around the world containing thousands of compute nodes. However, Cloud data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. In 2010, energy consumption by data centers worldwide was estimated to be between 1.1% and 1.5% of the global electricity use and is expected to grow further. This thesis presents novel techniques, models, algorithms, and software for distributed dynamic consolidation of Virtual Machines (VMs) in Cloud data centers. The goal is to improve the utilization of computing resources and reduce energy consumption under workload independent quality of service constraints. Dynamic VM consolidation leverages fine-grained fluctuations in the application workloads and continuously reallocates VMs using live migration to minimize the number of active physical nodes. Energy consumption is reduced by dynamically deactivating and reactivating physical nodes to meet the current resource demand. The proposed approach is distributed, scalable, and efficient in managing the energy-performance trade-off. The key contributions are: - Competitive analysis of dynamic VM consolidation algorithms and proofs of the competitive ratios of optimal online deterministic algorithms for the formulated single VM migration and dynamic VM consolidation problems. - A distributed approach to energy-efficient dynamic VM consolidation and several novel heuristics following the proposed approach, which lead to a significant reduction in energy consumption with a limited performance impact, as evaluated by a simulation study using real workload traces. - An optimal offline algorithm for the host overload detection problem, as well as a novel Markov chain model that allows a derivation of an optimal randomized control policy under an explicitly specified QoS goal for any known stationary workload and a given state configuration in the online setting. - A heuristically adapted host overload detection algorithm for handling unknown non-stationary workloads. The algorithm leads to approximately 88% of the mean inter-migration time produced by the optimal offline algorithm. - An open source implementation of a software framework for distributed dynamic VM consolidation called OpenStack Neat. The framework can be applied in both further research on dynamic VM consolidation, and real OpenStack Cloud deployments to improve the utilization of resources and reduce energy consumption.
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ItemScheduling and management of data intensive application workflows in grid and cloud computing environments( 2010)Large-scale scientific experiments are being conducted in collaboration with teams that are dispersed globally. Each team shares its data and utilizes distributed resources for conducting experiments. As a result, scientific data are replicated and cached at distributed locations around the world. These data are part of application workflows, which are designed for reducing the complexity of executing and managing on distributed computing environments. In order to execute these workflows in time and cost efficient manner, a workflow management system must take into account the presence of multiple data sources in addition to distributed compute resources provided by platforms such as Grids and Clouds. Therefore, this thesis builds upon an existing workflow architecture and proposes enhanced scheduling algorithms, specifically designed for managing data intensive applications. It begins with a comprehensive survey of scheduling techniques that formed the core of Grid systems in the past. It proposes an architecture that incorporates data management components and examines its practical feasibility by executing several real world applications such as Functional Magnetic Resonance Imaging (fMRI), Evolutionary Multi-objective Optimization algorithms, and so forth, using distributed Grid and Cloud resources. It then proposes several heuristics based algorithms that take into account time and cost incurred for transferring data from multiple sources while scheduling tasks. All the heuristic proposed are based on multi-source-parallel-data-retrieval technique in contrast to retrieving data from a single best resource, as done in the past. In addition to non-linear modeling approach, the thesis explores iterative techniques, such as particle-swarm optimization, to obtain schedules quicker. In summary, this thesis makes several contributions towards the scheduling and management of data intensive application workflows. The major contributions are: (i) enhanced the abstract workflow architecture by including components that handle multisource parallel data transfers; (ii) deployed several real-world application workflows using the proposed architecture and tested the feasibility of the design on real test beds; (iii) proposed a non-linear model for scheduling workflows with an objective to minimize both execution time and execution cost; (iv) proposed static and dynamic workflow scheduling heuristic that leverages the presence of multiple data sources to minimize total execution time; (v) designed and implemented a particle-swarm-optimization based heuristic that provides feasible solutions to the workflow scheduling problem with good convergence; (vi) implemented a prototype workflow management system that consists of a portal as user-interface, a workflow engine that implements all the proposed scheduling heuristic and the real-world application workflows, and plug ins to communicate with Grid and Cloud resources.
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ItemMeta scheduling for market-oriented grid and utility computing( 2010)Grid computing enables the sharing and aggregation of autonomous IT resources to deliver them as computing utilities to end users. The management of the Grid environment is a complex task as resources are geographically distributed, heterogeneous and autonomous in nature, and their users are self-interested. In utility-oriented Grids, users define their application requirements and compete to access the most efficient and cheapest resources. Traditional resource management systems and algorithms are based on system-centric approaches which do not take into account individual requirements and interests. To this end, market-oriented scheduling is an adequate way to solve the problem. But current market-oriented systems generally, either try to maximise one user’s utility or one provider’s utility. Such approaches fail to solve the problem of contention for cheap and efficient resources which may lead to unnecessary delays in job execution and underutilisation of resources. To address these problems, this thesis proposes a market-oriented meta-scheduler called “Meta-Broker”, which not only coordinates the resource demand but also allocates the best resources to users in terms of monetary and performance costs. The thesis results demonstrate that considerable cost reduction and throughput can be gained by adopting our proposed approach. The meta-broker has a semi-decentralised architecture, where only scheduling decisions are made by the meta-broker while job submission, execution and monitoring are delegated to user and provider middleware. This thesis also investigates market-oriented meta-scheduling algorithms which aim to maximise the utility of participants. The market-oriented algorithms consider Quality of Service (QoS) requirements of multiple users to map jobs against autonomous and heterogeneous resources. This thesis also presents a novel Grid Market Exchange architecture which provides the flexibility to users in choosing their own negotiation protocol for resource trading. The key research findings and contributions of this thesis are: - The consideration of QoS requirements of all users is necessary for maximising users’ utility and utilisation of resources. The uncoordinated scheduling of applications by personalised user-brokers leads to overloading of cheap and efficient resources. - It is important to exploit the heterogeneity between different resource sites/data centers while scheduling jobs to maximise the provider’s utility. This consideration not only reduce energy cost of computing infrastructure by 33% on average, but also enhance the efficiency of resources in terms of carbon emissions. - By considering both system metrics and market parameters, we can enable more effective scheduling which maximises the utility of both users and resource providers.