Reliable and energy efficient resource provisioning in cloud computing systems

Cloud Computing has revolutionized the Information Technology sector by giving computing a perspective of service. The services of cloud computing can be accessed by users not knowing about the underlying system with easy-to-use portals. To provide such an abstract view, cloud computing systems have to perform many complex operations besides managing a large underlying infrastructure. Such complex operations confront service providers with many challenges such as security, sustainability, reliability, energy consumption and resource management. Among all the challenges, reliability and energy consumption are two key challenges focused on in this thesis because of their conflicting nature. Current solutions either focused on reliability techniques or energy efficiency methods. But it has been observed that mechanisms providing reliability in cloud computing systems can deteriorate the energy consumption. Adding backup resources and running replicated systems provide strong fault tolerance but also increase energy consumption. Reducing energy consumption by running resources on low power scaling levels or by reducing the number of active but idle sitting resources such as backup resources reduces the system reliability. This creates a critical trade-off between these two metrics that are investigated in this thesis. To address this problem, this thesis presents novel resource management policies which target the provisioning of best resources in terms of reliability and energy efficiency and allocate them to suitable virtual machines. A mathematical framework showing interplay between reliability and energy consumption is also proposed in this thesis. A formal method to calculate the finishing time of tasks running in a cloud computing environment impacted with independent and correlated failures is also provided. The proposed policies adopted various fault tolerance mechanisms while satisfying the constraints such as task deadlines and utility values. This thesis also provides a novel failure-aware VM consolidation method, which takes the failure characteristics of resources into consideration before performing VM consolidation. All the proposed resource management methods are evaluated by using real failure traces collected from various distributed computing sites. In order to perform the evaluation, a cloud computing framework, 'ReliableCloudSim' capable of simulating failure-prone cloud computing systems is developed. The key research findings and contributions of this thesis are: 1. If the emphasis is given only to energy optimization without considering reliability in a failure prone cloud computing environment, the results can be contrary to the intuitive expectations. Rather than reducing energy consumption, a system ends up consuming more energy due to the energy losses incurred because of failure overheads. 2. While performing VM consolidation in a failure prone cloud computing environment, a significant improvement in terms of energy efficiency and reliability can be achieved by considering failure characteristics of physical resources. 3. By considering correlated occurrence of failures during resource provisioning and VM allocation, the service downtime or interruption is reduced significantly by 34% in comparison to the environments with the assumption of independent occurrence of failures. Moreover, measured by our mathematical model, the ratio of reliability and energy consumption is improved by 14%

Cloud and HPC Headway for Next-Generation Management of Projects and Technologies

In the last decade, cloud computing has changed dramatically. More providers and administration contributions have entered the market, and cloud infrastructure, once limited to single-provider data centers, is expanding. This article discusses the shifting cloud foundation and the benefits of decentralizing computing from data centers. These patterns necessitate novel cloud computing architectures. These models may affect linking people and devices, data-intensive computing, the service space, and self-learning frameworks. Finally, we compiled a list of issues to consider while assessing modern cloud frameworks. Architectural and urban design projects breach scale and predictability constraints and seek enhanced competency, maintainability, energy performance, and cost-efficiency. Simulation and large-scale information processing drive this cycle. Advances in calculations and computer power help address the complex elements of a coordinated whole-structure framework. Adaptability is a barrier to the configuration, control, and development of whole-system frameworks. This position paper proposes several solutions for semi-or fully automated projects, such as short-plan boundary space exploration, large-scope high-accuracy simulation, and integrated multidisciplinary development. These computer-intensive operations were previously only accessible to the exam network. Once empowered by cloud computing and high-performance computing, these methods can stimulate intelligent plan measures, leading to enhanced results and shorter development times