Topics in Power Usage in Network Services

The rapid advance of computing technology has created a world powered by millions of computers. Often these computers are idly consuming energy unnecessarily in spite of all the efforts of hardware manufacturers. This thesis examines proposals to determine when to power down computers without negatively impacting on the service they are used to deliver, compares and contrasts the efficiency of virtualisation with containerisation, and investigates the energy efficiency of the popular cryptocurrency Bitcoin. We begin by examining the current corpus of literature and defining the key terms we need to proceed. Then we propose a technique for improving the energy consumption of servers by moving them into a sleep state and employing a low powered device to act as a proxy in its place. After this we move on to investigate the energy efficiency of virtualisation and compare the energy efficiency of two of the most common means used to do this. Moving on from this we look at the cryptocurrency Bitcoin. We consider the energy consumption of bitcoin mining and if this compared with the value of bitcoin makes this profitable. Finally we conclude by summarising the results and findings of this thesis. This work increases our understanding of some of the challenges of energy efficient computation as well as proposing novel mechanisms to save energy

An investigation of decision support knowledge production, transfer and adoption for it outsourcing

Information Technology Outsourcing (ITO) is a widely-adopted strategy for IT governance. ITO decisions are very complicated and challenging for many organisations. During the past three decades of ITO research, numerous decision support artefacts (e.g. frameworks, models, tools) to support organisational ITO decisions have been described in academic publications. However, the scope, rigour, relevance and adoption of this research by industry practitioners had not been assessed. This study investigates the production, transfer and adoption of academic research-generated knowledge for ITO decision support through multiple perspectives of ITO researchers and practitioners (e.g. IT managers, IT consultants) to provide insights into the research problem. A mixed-methods research approach underpinned by the critical realism paradigm is employed in this study. The study comprised three phases. In Phase A, the scope of extant research for supporting ITO decisions is identified through a systematic literature review and critical assessment of the rigour and relevance of this body of research is conducted using a highly regarded research framework. One hundred and thirty three articles on IT outsourcing (including cloud sourcing) were identified as ITO decision support academic literature. These articles suggested a range of Multiple Criteria Decision Making (MCDM), optimisation and simulation methods to support different IT outsourcing decisions. The assessment of these articles raised concerns about the limited use of reference design theories, validation and naturalistic evaluation in ITO decision support academic literature. Recommendations to enhance the rigour and relevance of ITO decision support research are made in this thesis. Phase B involved interviewing and surveying academic researchers who published academic literature on ITO decision support artefacts. This phase reports researchers’ reflections on their ITO research experience and knowledge transfer activities undertaken by them. The findings indicate researchers’ motivations, knowledge transfer mechanisms, and communication/ interaction channels with industry may explain effective knowledge transfer. Impact-minded researchers were significantly more effective than publication-minded researchers in knowledge transfer. In Phase C, interviews and a survey of practitioners engaged in IT outsourcing shed light on use of academic-generated knowledge. Academic research was the least used source of decision-making knowledge among ITO practitioners. Practitioners preferred to seek advice from their peers, IT vendors and consultants to inform their ITO decision making. Two communities of users and non-users of academic research were identified in our sample of ITO practitioners, with non-users forming the majority. Six factors that may influence the use of academic research by practitioners were identified. Non-users of academic research held perceptions that academic research was not timely, required too much time to read, was far from the real world and that it was not a commonly-used knowledge source for practitioners. Also, non-users of academic research read academic research less frequently and did not perceive themselves as an audience for academic research. This study engaged two fields of research: ITO decision support and academic knowledge transfer/utilisation (including research-practice gap). ITO decision support research provide the specific context for a critical assessment of academic knowledge production, transfer and adoption. For ITO DSS, this study identified the scope, rigour and relevance of the field, and improvement opportunities. This study confirms that a research-practice gap exists in the ITO decision support field as previously suggested by some scholars. Also, this study made a significant contribution to the highly complex and contested field of research utilisation and the research-practice gap. The relationship between research and practice in terms of knowledge production, transfer and utilisation is modelled using social system theory. Multiple theories are applied through a retroductive (abductive) analysis to shed light on the root causes of the research-practice gap. This study suggests that the lack of adequate appreciation of research relevance in academic reward schemes and the academic publishing structure are the main root causes of the research-practice gap in the knowledge production side. Moreover, various institutional mechanisms exist in knowledge transfer and adoption domains that influence the knowledge adoption channels of practitioners. As a result, academic research does not become a priority source of ITO decision support knowledge for practitioners. This study suggests that to overcome the barriers to academic research adoption by practitioners, the effective structural coupling mechanism between the system of science (knowledge production domain) and organisation systems (knowledge consumption domain) needs to be identified and activated

Power Modeling and Resource Optimization in Virtualized Environments

The provisioning of on-demand cloud services has revolutionized the IT industry. This emerging paradigm has drastically increased the growth of data centers (DCs) worldwide. Consequently, this rising number of DCs is contributing to a large amount of world total power consumption. This has directed the attention of researchers and service providers to investigate a power-aware solution for the deployment and management of these systems and networks. However, these solutions could be bene\ufb01cial only if derived from a precisely estimated power consumption at run-time. Accuracy in power estimation is a challenge in virtualized environments due to the lack of certainty of actual resources consumed by virtualized entities and of their impact on applications\u2019 performance. The heterogeneous cloud, composed of multi-tenancy architecture, has also raised several management challenges for both service providers and their clients. Task scheduling and resource allocation in such a system are considered as an NP-hard problem. The inappropriate allocation of resources causes the under-utilization of servers, hence reducing throughput and energy e\ufb03ciency. In this context, the cloud framework needs an e\ufb00ective management solution to maximize the use of available resources and capacity, and also to reduce the impact of their carbon footprint on the environment with reduced power consumption. This thesis addresses the issues of power measurement and resource utilization in virtualized environments as two primary objectives. At \ufb01rst, a survey on prior work of server power modeling and methods in virtualization architectures is carried out. This helps investigate the key challenges that elude the precision of power estimation when dealing with virtualized entities. A di\ufb00erent systematic approach is then presented to improve the prediction accuracy in these networks, considering the resource abstraction at di\ufb00erent architectural levels. Resource usage monitoring at the host and guest helps in identifying the di\ufb00erence in performance between the two. Using virtual Performance Monitoring Counters (vPMCs) at a guest level provides detailed information that helps in improving the prediction accuracy and can be further used for resource optimization, consolidation and load balancing. Later, the research also targets the critical issue of optimal resource utilization in cloud computing. This study seeks a generic, robust but simple approach to deal with resource allocation in cloud computing and networking. The inappropriate scheduling in the cloud causes under- and over- utilization of resources which in turn increases the power consumption and also degrades the system performance. This work \ufb01rst addresses some of the major challenges related to task scheduling in heterogeneous systems. After a critical analysis of existing approaches, this thesis presents a rather simple scheduling scheme based on the combination of heuristic solutions. Improved resource utilization with reduced processing time can be achieved using the proposed energy-e\ufb03cient scheduling algorithm

Partitioning workflow applications over federated clouds to meet non-functional requirements

PhD ThesisWith cloud computing, users can acquire computer resources when they need them on a pay-as-you-go business model. Because of this, many applications are now being deployed in the cloud, and there are many di erent cloud providers worldwide. Importantly, all these various infrastructure providers o er services with di erent levels of quality. For example, cloud data centres are governed by the privacy and security policies of the country where the centre is located, while many organisations have created their own internal \private cloud" to meet security needs. With all this varieties and uncertainties, application developers who decide to host their system in the cloud face the issue of which cloud to choose to get the best operational conditions in terms of price, reliability and security. And the decision becomes even more complicated if their application consists of a number of distributed components, each with slightly di erent requirements. Rather than trying to identify the single best cloud for an application, this thesis considers an alternative approach, that is, combining di erent clouds to meet users' non-functional requirements. Cloud federation o ers the ability to distribute a single application across two or more clouds, so that the application can bene t from the advantages of each one of them. The key challenge for this approach is how to nd the distribution (or deployment) of application components, which can yield the greatest bene ts. In this thesis, we tackle this problem and propose a set of algorithms, and a framework, to partition a work ow-based application over federated clouds in order to exploit the strengths of each cloud. The speci c goal is to split a distributed application structured as a work ow such that the security and reliability requirements of each component are met, whilst the overall cost of execution is minimised. To achieve this, we propose and evaluate a cloud broker for partitioning a work ow application over federated clouds. The broker integrates with the e-Science Central cloud platform to automatically deploy a work ow over public and private clouds. We developed a deployment planning algorithm to partition a large work ow appli- - i - cation across federated clouds so as to meet security requirements and minimise the monetary cost. A more generic framework is then proposed to model, quantify and guide the partitioning and deployment of work ows over federated clouds. This framework considers the situation where changes in cloud availability (including cloud failure) arise during work ow execution

Optimising the usage of cloud resources for execution bag-of-tasks applications

Cloud computing has been widely adopted by many organisations, due to its flexibility in resource provisioning and on-demand pricing models. Entire clusters of machines can now be dynamically provisioned to meet the computational demands of users. By moving operations to the cloud, users hope to reduce the costs of building and maintaining a computational cluster without sacrificing the quality of service. However, cloud computing has presented challenges in scheduling and managing the usage of resources, which users of more traditional resource pooling models, such as grid and clusters, have never encountered before. Firstly, the costs associated with resource usage changes dynamically, and is based on the type and duration of resources used; this prevents users from greedily acquiring as many resources as possible due to the associated costs. Secondly, the cloud computing marketplace offers an assortment of on-demand resources with a wide range of performance capabilities. Given the variety of resources, this makes it difficult for users to construct a cluster which is suitable for their applications. As a result, it is challenging for users to ensure the desired quality of service while running applications on the cloud. The research in this thesis focuses on optimising the usage of cloud computing resources. We propose approaches for scheduling the execution of applications on to the cloud, such that the desired performance is met whilst the incurred monetary cost is minimised. Furthermore, this thesis presents a set of mechanisms which manages the execution at runtime, in order to detect and handle unexpected events with undesirable consequences, such as the violation of quality of service, or cost overheads. Using both simulated and real world experiments, we validate the feasibility of the proposed research by executing applications on the cloud with low costs without sacrificing performance. The key result is that it is possible to optimise the usage of cloud resources for user applications by using the research reported in this thesis

Optimising the usage of cloud resources for execution bag-of-tasks applications

Cloud computing has been widely adopted by many organisations, due to its flexibility in resource provisioning and on-demand pricing models. Entire clusters of machines can now be dynamically provisioned to meet the computational demands of users. By moving operations to the cloud, users hope to reduce the costs of building and maintaining a computational cluster without sacrificing the quality of service. However, cloud computing has presented challenges in scheduling and managing the usage of resources, which users of more traditional resource pooling models, such as grid and clusters, have never encountered before. Firstly, the costs associated with resource usage changes dynamically, and is based on the type and duration of resources used; this prevents users from greedily acquiring as many resources as possible due to the associated costs. Secondly, the cloud computing marketplace offers an assortment of on-demand resources with a wide range of performance capabilities. Given the variety of resources, this makes it difficult for users to construct a cluster which is suitable for their applications. As a result, it is challenging for users to ensure the desired quality of service while running applications on the cloud. The research in this thesis focuses on optimising the usage of cloud computing resources. We propose approaches for scheduling the execution of applications on to the cloud, such that the desired performance is met whilst the incurred monetary cost is minimised. Furthermore, this thesis presents a set of mechanisms which manages the execution at runtime, in order to detect and handle unexpected events with undesirable consequences, such as the violation of quality of service, or cost overheads. Using both simulated and real world experiments, we validate the feasibility of the proposed research by executing applications on the cloud with low costs without sacrificing performance. The key result is that it is possible to optimise the usage of cloud resources for user applications by using the research reported in this thesis

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Hadoop performance modeling and job optimization for big data analytics

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonBig data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.Abdul Wali Khan University Marda

Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics