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ItemIntegrated provisioning of compute and network resources in Software-Defined Cloud Data CentersSon, Jungmin ( 2018)Software-Defined Networking (SDN) has opened up new opportunities in networking technology with its decoupled concept of the control plane from the packet forwarding hardware, which enabled the network to be programmable and configurable dynamically through the centralized controller. Cloud computing has been empowered with the adoption of SDN for infrastructure management in a data center where dynamic controllability is indispensable in order to provide elastic services. The integrated provisioning of compute and network resources enabled by SDN is essential in clouds to enforce reasonable Service Level Agreements (SLAs) stating the Quality of Service (QoS) while saving energy consumption and resource wastage. This thesis presents the joint compute and network resource provisioning in SDN-enabled cloud data center for QoS fulfillment and energy efficiency. It focuses on the techniques for allocating virtual machines and networks on physical hosts and switches considering SLA, QoS, and energy efficiency aspects. The thesis advances the state-of-the-art with the following key contributions: 1. A taxonomy and survey of the current research on SDN-enabled cloud computing, including the state-of-the-art joint resource provisioning methods and system architectures. 2. A modeling and simulation environment for SDN-enabled cloud data centers abstracting functionalities and behaviors of virtual and physical resources. 3. A novel dynamic overbooking algorithm for energy efficiency and SLA enforcement with the migration of virtual machines and network flows. 4. A QoS-aware computing and networking resource allocation algorithm based on the application priority to fulfill different QoS requirements. 5. A prototype system of the integrated control platform for joint management of cloud and network resources simultaneously based on OpenStack and OpenDaylight.
ItemEnergy and carbon-efficient resource management in geographically distributed cloud data centersKhosravi, Atefeh ( 2017)Cloud computing provides on-demand access to computing resources for users across the world. It offers services on a pay-as-you-go model through data center sites that are scattered across diverse geographies. However, cloud data centers consume huge amount of electricity and leave high amount of carbon footprint in the ecosystem. This makes data centers responsible for 2% of the global CO2 emission, the same as the aviation industry. Therefore, having energy and carbon-efficient techniques for distributed cloud data centers is inevitable. Cloud providers while efficiently allocating computing resources to users, should also meet their required quality of service. The main objective of this thesis is to address the problem of energy and carbon efficient resource management in geographically distributed cloud data centers. It focuses on the techniques for VM placement, investigates the parameters with largest effect on the energy and carbon cost, migration of VMs between data center sites to harvest renewable energy sources, and prediction of renewable energy to maximize its usage. The key contributions of this thesis are as follows: (1) A VM placement algorithm to optimally select the data center and server to reduce energy consumption and carbon footprint with considering energy and carbon related parameters. (2) A dynamic method for the initial placement of VMs in geographically distributed cloud data centers that simultaneously considers energy and carbon cost and maximizes renewable energy utilization at each data center to minimize the total cost. (3) Variations of VM placement methods, which explore the effects of different parameters in minimizing energy and carbon cost for a cloud computing environment. (4) The optimal offline algorithm and two online algorithms, which exploit available renewable energy levels across distributed data center sites for VM migration to minimize total energy cost and maximize renewable energy usage. (5) A prediction model for renewable energy availability at data center sites to incorporate into online VM migration algorithm and maximize renewable energy usage.