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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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.