Computing and Information Systems - Theses
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ItemBrownout-oriented and energy efficient management of cloud data centers( 2018)Cloud computing paradigm supports dynamic provisioning of resources for delivering computing for applications as utility services as a pay-as-you-go basis. However, the energy consumption of cloud data centers has become a major concern as a typical data center can consume as much energy as 25,000 households. The dominant energy efficient approaches, like Dynamic Voltage Frequency Scaling and VM consolidation, cannot function well when the whole data center is overloaded. Therefore, a novel paradigm called brownout has been proposed, which can dynamically activate/deactivate the optional parts of the application system. Brownout has successfully shown it can avoid overloads due to changes in the workload and achieve better load balancing and energy saving effects. In this thesis, we propose brownout-based approaches to address energy efficiency and cost-aware problem, and to facilitate resource management in cloud data centers. They are able to reduce data center energy consumption while ensuring Service Level Agreement defined by service providers. Specifically, the thesis advances the state-of-art by making the following key contributions: 1) An approach for scheduling cloud application components with brownout. The approach models the brownout enabled system by considering application components, which are either mandatory or optional. It also contains brownout-based algorithm to determine when to use brownout and how much utilization can be reduced. 2) A resource scheduling algorithm based on brownout and approximate Markov Decision Process approach. The approach considers the trade-offs between saved energy and the discount that is given to the user if components or microservices are deactivated. 3) A framework that enables brownout paradigm to manage the container-based environment, and provides fine-grained control on containers, which also contains several scheduling policies for managing containers to achieve power saving and QoS constraints. 4) The design and development of a software prototype based on Docker Swarm to reduce energy consumption while ensuring QoS in Clouds, and evaluations of different container scheduling policies under real testbeds to help service provider deploying services in a more energy-efficient manner while ensuring QoS constraint. 5) A perspective model for multi-level resource scheduling and a self-adaptive approach for interactive workloads and batch workloads to ensure their QoS by considering the renewable energy at Melbourne based on support vector machine. The proposed approach is evaluated under our developed prototype system.
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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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ItemAn energy and spectrum efficient distributed scheduling scheme for Wireless Mesh Networks( 2013)The success of Wireless Mesh Network (WMN) applications depend on the effective energy efficiency, spectrum reuse, scalability, and robustness of scheduling schemes. However, to the best of our knowledge the available schedulers fail to address these requirements simultaneously. This thesis proposes an autonomous, scalable, and deployable scheduler for WMNs with energy efficient transceiver activation and efficient spectrum reuse. Our goals are: (i) to conserve energy for longer sustainability, (ii) to effectively reuse the radio spectrum for higher throughput, lower delay, lower packet loss, and fairness, and (iii) to ensure that the proposed solution serves common WMN applications. Our research identified three major approaches in scheduling, eight key attributes, and detailed the evolution of wireless standards for distributed schedulers. Among the solutions, pseudo random access (PRA) is expected to have the strengths of randomness for scalability and robustness, and determinism for energy efficiency and spectrum reuse. However, literature on the IEEE 802.16s election based transmission timing (EBTT) scheme - the only known standardized PRA solution - is limited in scope. We use a combination of simulations, modelling, and analysis in our research. Since the existing simulators did not support our ambitious range of investigations, we developed our own simulator which we called Election Based Pseudo Random Access (EBPRA) simulator. Moreover, we introduced two types of synthetic mesh networks as a way to decompose the complexities of WMN topologies and systematically study their effects. The benchmarking study on the EBTT against a centralised cyclic access (CCA) scheme revealed less than 50% spectrum reuse, 75% low fairness measure, and more significantly, an energy wastage of up to 90% in reception with collisions in transmissions in the EBTT. Hence we propose an enhanced pseudo random access (EPRA) scheme to mitigate the issues. The EPRA does not introduce additional overheads and can be deployed on IEEE 802.16 nodes with minor firmware modifications. Simulations on the EPRA show significant improvements in the energy efficiency where collisions are eliminated and the reception is near 100% efficient. Moreover, the spectrum reuse and fairness measures also improved. These results validated the findings of the analytical models that we derived. Finally we propose two alternative solutions to handle user data packets, namely: EPRA based single scheduler (EPRA-SS), and EPRA based dual scheduler (EPRA-DS). Since satisfying requirements of voice services means requirements for data service are met, we concentrated our investigation with voice. Through extensive simulations and multidimensional data analysis, we identified the supported ranges of network densities, traffic intensities, and buffer allocations to satisfy per hop delay and packet drop conditions. Hence, we demonstrated for the first time that near 100% energy efficiency should be possible with a distributed scheduler when our EPRA scheme is used. In addition, we have also shown improvements in spectrum reuse for better throughput, shorter delays, and better fairness. Finally, EPRA based schemes have been demonstrated as effective schedulers for user data traffic over WMN deployment scenarios fulfilling our research objectives.