Computing and Information Systems - Theses
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ItemBudget-constrained Workflow Applications Scheduling in Workflow-as-a-Service Cloud Computing Environments( 2020)The adoption of workflow, an inter-connected tasks and data processing application model, in the scientific community has led to the acceleration of scientific discovery. The workflow facilitates the execution of complex scientific applications that involves a vast amount of data. These workflows are large-scale applications and require massive computational infrastructures. Therefore, deploying them in distributed systems, such as cloud computing environments, is a necessity to acquire a reasonable amount of processing time. With the increasing demand for scientific workflows execution and the rising trends of cloud computing environments, there is a potential market to provide a computational service for executing scientific workflows in the clouds. Hence, the term Workflow-as-a-Service (WaaS) emerges along with the rising of the Everything-as-a-Service concept. This WaaS concept escalates the functionality of a conventional workflow management system (WMS) to serve a more significant number of users in a utility service model. In this case, the platform, which is called the WaaS platform, must be able to handle multiple workflows scheduling and resource provisioning in cloud computing environments in contrast to its single workflow management of traditional WMS. This thesis investigates the novel approaches for budget-constrained multiple workflows resource provisioning and scheduling in the context of the WaaS platform. They address the challenges in managing multiple workflows execution that not only comes from the users' perspective, which includes the heterogeneity of workloads, quality of services, and software requirements, but also problems that arise from the cloud environments as the underlying computational infrastructure. The latter aspect brings up the issues of the heterogeneity of resources, performance variability, and uncertainties in the form of overhead delays of resource provisioning and network-related activities. It pushes a boundary in the area by making the following contributions: - A taxonomy and survey of the state-of-the-art multiple workflows scheduling in multi-tenant distributed computing systems. - A budget distribution strategy to assign tasks' budgets based on the heterogeneous type of VMs in cloud computing environments. - A budget-constrained resource provisioning and scheduling algorithm for multiple workflows that aims to minimize workflows' makespan while meeting the budget. - An online and incremental learning approach to predict task runtime that considers the performance variability of cloud computing environments. - The implementation of multiple workflows scheduling algorithm and its integration to extend the existing WMS for the development of WaaS platform.
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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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ItemCost-efficient resource provisioning for large-scale graph processing systems in cloud computing environments( 2018)A large amount of data that is being generated on Internet every day is in the form of graphs. Many services and applications namely as social networks, Internet of Things (IoT), mobile applications, business applications, etc. in which every data entity can be considered as a vertex and the relationships between entities shape the edges of a graph, are in this category. Since 2010, exclusive large-scale graph processing frameworks are being developed to overcome the inefficiency of traditional processing solutions such as MapReduce. However, most frameworks are designed to be employed on high performance computing (HPC) clusters which are only available to whom can afford such infrastructure. Cloud computing is a new computing paradigm that offers unprecedented features such as scalability, elasticity and pay-as-you-go billing model and is accessible to everyone. Nevertheless, the advantages that cloud computing can bring to the architecture of large-scale graph processing systems are less studied. Resource provisioning and management is a critical part of any processing system in cloud environments. To provide the optimized amount of resources for a particular operation, several factors such as monetary cost, throughput, scalability, network performance, etc. can be taken into consideration. In this thesis, we investigate and propose novel solutions and algorithms for cost-efficient resource provisioning for large-scale graph processing systems. The outcome is a series of research works that increase the performance of such processing by making it aware of the operating environment while decreasing the dollar cost significantly. We have particularly made the following contributions: 1. We introduced iGiraph, a cost-efficient framework for processing large-scale graphs on public clouds. iGiraph also provides a new graph algorithm categorization and processes the graph accordingly. 2. To demonstrate the impact of network on the processing in cloud environment, we developed two network-aware algorithms that utilize network factors such as traffic, bandwidth and also the computation power. 3. We developed an auto-scaling technique to take advantage of resource heterogeneity on clouds. 4. We introduced a large-scale graph processing service for clouds where we consider the service level agreement (SLA) requirements in the operations. The service can handle multiple processing requests by its new prioritization and provisioning approach.
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ItemUnderstanding how cloud computing enables business model innovation in start-up companies( 2017)Start-up companies contribute significantly to the national economies of many countries but their failure rate is notably high. Successful start-ups typically depend on innovative business models to be competitive and maintain profitability. This thesis explores how the new technologies of cloud computing might enable start-ups to create and maintain competitive advantage. A conceptual framework called Cloud-Enabled Business Model Innovation (CEBMI) is presented that identifies three research questions concerning how cloud computing might enable business model innovation, what form this innovation takes, and how the innovation leads to competitive advantage. These questions were then investigated through three empirical studies involving six case studies with start-ups and two qualitative studies involving interviews with 11 business consultants and three cloud service providers. The detailed findings are presented as a set of key propositions that offer answers to the research questions, and together sketch a view of how CEBMI might enable start-ups to achieve competitive advantage.
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ItemIntegrated provisioning of compute and network resources in Software-Defined Cloud Data Centers( 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.
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ItemEnergy and carbon-efficient resource management in geographically distributed cloud data centers( 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.
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ItemAuto-scaling and deployment of web applications in distributed computing clouds( 2016)Cloud Computing, which allows users to acquire/release resources based on real-time demand from large data centers in a pay-as-you-go model, has attracted considerable attention from the ICT industry. Many web application providers have moved or plan to move their applications to Cloud, as it enables them to focus on their core business by freeing them from the task and the cost of managing their data center infrastructures, which are often over-provisioned or under-provisioned under a dynamic workload. Applications these days commonly serve customers from geographically dispersed regions. Therefore, to meet the stringent Quality of Service (QoS) requirements, they have to be deployed in multiple data centers close to the end customer locations. However, efficiently utilizing Cloud resources to reach high cost-efficiency, low network latency, and high availability is a challenging task for web application providers, especially when the service provider intends to deploy the application in multiple geographical distributed Cloud data centers. The problems, including how to identify satisfactory Cloud offerings, how to choose geographical locations of data centers so that the network latency is minimized, how to provision the application with minimum cost incurred, and how to guarantee high availability under failures and flash crowds, should be addressed to enable QoS-aware and cost-efficient utilization of Cloud resources. In this thesis, we investigated techniques and solutions for these questions to help application providers to efficiently manage deployment and provision of their applications in distributed computing Clouds. It extended the state-of-the-art by making the following contributions: 1. A hierarchical fuzzy inference approach for identifying satisfactory Cloud services according to individual requirements. 2. Algorithms for selection of multi-Cloud data centers and deployment of applications on them to minimize Service Level Objective (SLO) violations for web applications requiring strong consistency. 3. An auto-scaler for web applications that achieves both high availability and significant cost saving by using heterogeneous spot instances. 4. An approach that mitigates the impact of short-term application overload caused by either resource failures or flash crowds in any individual data center through geographical load balancing.
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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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ItemResource provisioning and scheduling algorithms for scientific workflows in cloud computing environments( 2016)Scientific workflows describe a series of computations that enable the analysis of data in a structured and distributed manner. Their importance is exacerbated in todays big data era as they become a compelling mean to process and extract knowledge from the ever-growing data produced by increasingly powerful tools such as telescopes, particle accelerators, and gravitational wave detectors. Due to their large-scale nature, scheduling algorithms are key to efficiently automate their execution in distributed environments, and as a result, to facilitate and accelerate the pace of scientific progress. The emergence of the latest distributed system paradigm, cloud computing, brings with it tremendous opportunities to run workflows at low costs without the need of owning any infrastructure. In particular, Infrastructure as a Service (IaaS) clouds, offer an easily accessible, flexible, and scalable infrastructure for the deployment of these scientific applications by providing access to a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay-per-use basis. This thesis investigates novel resource provisioning and scheduling approaches for scientific workflows in IaaS clouds. They address fundamental challenges that arise from the multi-tenant, resource-abundant, and elastic resource model and are capable of fulfilling a set of quality of service requirements expressed in terms of execution time and cost. It advances the field by making the following key contributions: 1. A taxonomy and survey of the state-of-the-art scientific workflow scheduling algorithms designed exclusively for IaaS clouds. 2. A novel static scheduling algorithm that leverages Particle Swarm Optimization to generate a workflow execution and resource provisioning plan that minimizes the infrastructure cost while meeting a deadline constraint. 3. A hybrid algorithm based on a variation of the Unbounded Knapsack Problem that finds a trade-off between making static decisions to find better-quality schedules and dynamic decisions to adapt to unexpected delays. 4. A scalable algorithm that combines heuristics and two different Integer Programming models to generate schedules that minimize the execution time of the work- flow while meeting a budget constraint. 5. The implementation of a cloud resource management module and its integration to an existing Workflow Management System.
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ItemService value in business-to-business cloud computing( 2014)This thesis is concerned with determining and measuring the components of service value in the business-to-business cloud computing context. Although service value measurement and its perceptions have been identified as key issues for researchers and practitioners, theoretical and empirical studies have experienced great challenges in measuring perceptions of service value in numerous business contexts. The thesis first determines the components of service value and then measures the service value perceptions of users in a business-to-business context of cloud computing. In this thesis, I: • undertook qualitative in-depth interviews (N=21) of managers who are responsible for deciding on the adoption and maintenance of cloud computing services. Two key findings of the interviews are that the four components of an established service value model in a business-to-consumer setting are appropriate in a business-to-business context of cloud computing and found evidence that an additional component, which we call cloud service governance, applies and does not fit the existing four components; • conducted a survey (N=328) of cloud computing practitioners to demonstrate that the findings from the qualitative in-depth interviews are generalisable to a number of industry sectors and across geographical locations; • assessed the measurement models, comprising both reflective and formative, and structural model by using partial least squares structural equation modeling, and provided evidence of specifying Service Value as a formative second-order hierarchical latent variable by using a sequential latent variable score method; • demonstrated that Service Equity is not a statistically significant component of service value in the first-order model, Service Quality is consistently significant for both first-order model and second-order, formative model, and the additional construct called Cloud Service Governance is significant; and, • for the first time, fully tested a reliable service value instrument for use by the customers of cloud computing, and aiming to engage cloud service providers in order to enhance customer satisfaction and increase repurchase intentions.