Computing and Information Systems - Theses
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ItemMigration Management in Software-Defined Networking-enabled Edge and Cloud Computing Environments( 2021)Cloud and Edge Data Centers have become the backbone infrastructures of daily social and economical activities. Live migration is the cornerstone of the dynamic resource management policies for various objectives, such as application performance, networking cost, load balancing, consolidation, energy saving, user mobility, no-downtime maintenance, and disaster recovery. Live migration of VMs and containers provides a universal state-transfer standard to implement these objectives. Therefore, it is critical to manage the live migration in both computing and networking resources to guarantee the QoS, improve migration performance, and minimize migration costs and overheads. Many works have focused on the live migration mechanisms and optimization to improve the performance of individual migration. However, existing migration models in resource management neglect the resource competitions and dependencies among multiple migrations. Furthermore, performing the generated multiple live migrations in arbitrary orders can lead to service degradation. Therefore, efficient migration generation and scheduling are essential to reduce the impact of live migration overheads and improve migration performance. In addition, to prevent Quality of Service (QoS) degradations and Service Level Agreement (SLA) violations, it is necessary to respect the deadline of migration requests with various priorities and urgencies. In this thesis, we focus on network-aware multiple migration management based on Software-Defined Networking (SDN). By separating the control plane and forwarding plane, SDN provides centralized topology discovery and networking management which enables the capability of managing resource contentions in finer granularity. This thesis advances the state-of-the-art by making the following key contributions: 1. A comprehensive taxonomy and literature review on live migration management in Edge and Cloud computing environments including migration generation policies and migration planning and scheduling algorithms. 2. Empirical performance evaluation of live VM migration in SDN-enabled Clouds with respect to computing, networking, QoS, SDN traffic management, and multiple migrations. 3. A universal concurrency-aware multiple migration selector integrated with dynamic resource policy to generate scheduling-optimized migration requests. 4. SLA-aware multiple migration planning and scheduling algorithms in Cloud environments composing of a deadline-aware grouping algorithm of migrations and online scheduling to determine the migration sequence of connected VMs. 5. Efficient large-scale multiple migration algorithms in Edge environments to reduce the processing time of migration planning while maintaining multiple migration performance at scale. 6. A detailed study outlining challenges and future research directions in live migration management.
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ItemMachine Learning-based Energy and Thermal Efficient Resource Management Algorithms for Cloud Data Centres( 2021)Cloud data centres are the backbone infrastructures of modern digital society and the economy. Data centres have witnessed tremendous growth, consuming enormous energy to power IT equipment and cooling system. It is estimated that the data centres consume 2% of global electricity generated, and the cooling system alone consumes up to 50% of it. Therefore, to save significant energy and provide reliable services, workloads should be managed in both an energy and thermal efficient manner. However, existing heuristics or static rule-based resource management policies often fail to find an optimal solution due to the massive complexity and non-linear characteristics of the data centre and its workloads. In this thesis, we focus on machine learning-based resource management algorithms for energy and thermal efficiency in Cloud data centres which are proven to be efficient in capturing non-linearity between interdependent parameters. We explore how these techniques can be adapted to resource management problems to increase the energy and thermal efficiency of Cloud data centres while simultaneously satisfying application QoS requirements. In particular, we propose algorithms for workload placement, consolidation, application scheduling, and configuring efficient frequencies of resources in Cloud data centres. The proposed solutions are evaluated using various simulation toolkits and prototype systems implemented on real testbeds.
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ItemOn the economics of infrastructure as a service cloud providers: pricing, markets, and profit maximization( 2014)Cloud computing has introduced a major shift in the IT delivery model by offering computing resources for hosting applications as a utility. This helps businesses and organizations to access advanced IT facilities offered by cloud providers without the expensive up-front investments necessary to establish their own infrastructure. In this context, significant research efforts have already been made that aim to minimize costs for cloud customers; less attention however, has been given to challenges and opportunities that cloud providers face when striving for profit maximization. This thesis presents a set of novel market and economics-inspired policies, mechanisms, algorithms, and software designed to address the profit maximization problem of Infrastructure-as-a-Service (IaaS) cloud providers. Our solutions are proposed for two main types of providers: 1) those who rely solely on their own resources to serve customers and 2) those who participate in a cloud federation and benefit from resource sharing. We explore different tools and methods such as resource provisioning mechanisms, financial option markets, revenue management systems, and mechanism design methods to achieve the goal of profit maximization. Our evaluation of the proposed solutions demonstrates that IaaS cloud providers can increase their Return on Investment (ROI) while honoring Quality of Service (QoS) requirements associated with customer applications. In summary, the key contributions of this thesis towards profit maximization for IaaS cloud providers are: 1) resource provisioning policies that assist a provider in a cloud federation in deciding whether to reject, outsource or terminate spot instances to handle incoming requests; 2) a financial option-based market mechanism designed for futures trading of resources in a federated cloud environment; 3) admission control algorithms embedded within a revenue management framework that supports a joint offering of usage-based, reservation-based and demand-oriented pricing models; 4) a multi-unit, online recurrent auction mechanism for selling spare capacity of a data center that is envy-free, truthful with high probability, and generates near optimal profit for the provider; and finally 5) an implementation of the proposed auction mechanism by identifying the Spot instance pricing as a Service (SipaaS) framework and its realization in OpenStack.
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ItemSLA-based resource provisioning for management of Cloud-based Software-as-a-Service applications( 2014)The Cloud computing Software-as-a-Service (SaaS) model has changed the sales model for software providers. The SaaS model transforms the traditional license based model to a subscription model, which allows customers to access applications over the Internet without software and hardware upfront costs and provides reduced maintenance costs. However, the key for sales is still customer satisfaction which is at the heart of the selling process. To guarantee Quality of Service (QoS) for customer satisfaction therefore, the Service Level Agreement (SLA) is implemented between customers and SaaS providers, where the main objectives are profit maximization and increased market share. To achieve these objectives, there are several challenges due to the dynamic nature of the Cloud environment. Firstly, the SaaS provider utilizes shared infrastructure and various types of request loads which can lead to unpredictability in performance and availability of resources. Secondly, there is a possibility that existing customers may make changes in requirements, which can lead to resource reallocation. As such, resource allocation may cause SLA violations which could reduce the SaaS providers’ profit margin and reputation, meaning a possible loss of existing customers and potential new customers. Thirdly, SaaS providers need to attract customers with special needs and consider market competition from other providers in order to increase profit and market share. To overcome the above challenges, most proposed solutions are focused on the resource management with the aim of minimizing cost without sufficient consideration of customer needs. Therefore, to address these challenges, this thesis proposes algorithms and techniques for optimal provisioning of Cloud resources with the aim of maximizing profit and customer base by handling the dynamism associated with SLAs and heterogeneous resources. The key contributions of the thesis are: • A comprehensive survey of how SLAs are created, managed and used with case examples drawn from both academy and industry with a major emphasis on the SLA-based resource management systems. • The admission control and scheduling algorithms assist in identifying which request is more acceptable based on profitability, reducing the probability of SLA violations given the heterogeneous nature of Cloud resources. • The customer requirements driven resource provisioning algorithms can help in adapting to changes in the requirements. The proposed algorithms provide personalized attention to the customer and are also able to understand specific customer needs. • A new negotiation framework to enlarge a SaaS provider’s customer base that considers dynamism in the Cloud environment with time and market factors to make the best possible decisions for negotiation. • A prototype of the customer requirements driven SLA-based resource management system to prove the usefulness of our proposed strategies using the latest technologies.