Power Management for Cloud-Scale Data Centers

Recent years have seen the rapid growth of large and geographically distributed data centers deployed by Internet service operators to support various services such as cloud computing. Consequently, high electricity bills, as well as negative environmental implications (e.g., CO2 emission and global warming) come along. In this thesis, we first propose a novel electricity bill capping algorithm that not only minimizes the electricity cost, but also enforces a cost budget on the monthly bill for cloud-scale data centers that impact the power markets. Our solution first explicitly models the impacts of the power demands induced by cloud-scale data centers on electricity prices and the power consumption of cooling and networking in the minimization of electricity bill. In the second step, if the electricity cost exceeds a desired monthly budget due to unexpectedly high workloads, our solution guarantees the quality of service for premium customers and trades off the request throughput of ordinary customers. We formulate electricity bill capping as two related constrained optimization problems and propose efficient algorithms based on mixed integer programming. We then propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained I optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP)

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter

A Miniscule Survey on Blockchain Scalability

With the rise of cryptocurrency and NFTs in the past decade, blockchain technology has been an area of increasing interest to both industry and academic experts. In this paper, we discuss the feasibility of such systems through the lens of scalability. We also briefly dive into the security issues of such systems, as well as some applications, including healthcare, supply chain, and government applications

Legal infrastructure and urban networks for just and democratic smart cities

This article positions itself within the urban law and policy scholarship as a contribution to the creation of a subsection of this body of law, the urban law of services and assets. It shows that in three kind of urban infrastructure and networks (i.e. transport, energy, digital) there is growing attention towards a new general legal principle of urban law, the principle of tech justice which can be the center pillar of a more comprehensive legal infrastructure, the internet of humans. This legal infrastructure is necessary if public authorities want to design and shape just and democratic smart cities. Concepts like the Internet of Things, Internet of Everything and Internet of People suggest that objects, devices, and people will be increasingly inter-connected through digital infrastructure able to generate a growing gathering of data. At the same time, the literature on smart city and sharing city celebrate them as urban policy visions that by relying heavily on new technologies bear the promise of efficient and thriving cities. When addressing the impact of technological innovations, law and policy scholarship has either focused on questions related to privacy, discrimination, security, or issues related to the production and use of big data, digital public services, egovernment. Little attention has been paid to the disruptive impact of technological development on urban governance and city inhabitants\u2019 rights of equal access, participation, management and even ownership, in order to understand whether and how technology can also enhance the protection of human rights and social justice in the city

A SIMULATION MODEL FOR MAPPING CARBON DIOXIDE EMISSIONS TO DEVELOP A GREEN LOGISTICS SYSTEM

The aim of this thesis is to develop a simulation tool that helps companies to track and re-evaluate the environmental impacts on their logistics systems. A discrete event based simulation model is used and developed in this thesis and it provides a simple and visible solution to map the carbon emission footprints and calculate carbon emission values throughout an outbound logistics distribution network. The total carbon emission level in a simplified logistics distribution system is primarily determined by the total transport distance and different emission factors which categorized by many other important parameters such as load factor, empty trip rate, batch size, vehicle type and fuel consumption rate and so on. By visualizing carbon emission footprints and understanding how the carbon emission values in different transport paths are accumulated in the whole distribution system, the developed simulation model helps supply chain and logistics planners to investigate their current logistics systems and identify improvement areas in their systems to lead a better and greener logistics design. Website-based simulation software is also purposed as future research recommendation for realistic industry use.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

An Overview of Carbon Footprint Mitigation Strategies. Machine Learning for Societal Improvement, Modernization, and Progress

Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in each of these areas. The chapter highlights how various machine learning algorithms are used to increase the use of renewable energy, efficient transportation, and waste management systems to reduce the carbon footprint. The authors summarize the findings from the current research literature and conclude by providing a few future directions

Advanced Control and Optimization for Future Grid with Energy Storage Devices

In the future grid environment, more sustainable resources will be increasing steadily. Their inherent unpredictable and intermittent characteristics will inevitably cause adverse impacts on the system static, dynamic and economic performance simultaneously. In this context, energy storage (ES) devices have been receiving growing attention because of their significant falling prices. Therefore, how to utilize these ES to help alleviate the problem of renewable energy (RE) sources integration has become more and more attractive. In my thesis, I will try to resolve some of the related problems from several perspectives. First of all, a comprehensive Future Australian transmission network simulation platform is constructed in the software DIgSILENT. Then in-depth research has been done on the aspect of frequency controller design. Based on mathematical reasoning, an advanced robust H∞ Load Frequency Controller (LFC) is developed, which can be used to assist the power system to maintain a stable frequency when accommodating more renewables. Afterwards, I develop a power system sensitivity analysis based-Enhanced Optimal Distributed Consensus Algorithm (EODCA). In the following study, a Modified Consensus Alternating Direction Method of Multipliers (MC-ADMM) is proposed, with this approach it can be verified that the convergence speed is notably accelerated even for complex large dimensional systems. Overall, in the Master thesis, I successfully provide several novel and practical solutions, algorithms and methodologies in regards to tackling both the frequency, voltage and the power flow issues in a future grid with the assistance of energy storage devices. The scientific control and optimal dispatch of these facilities could provide us with a promising approach to mitigate the potential threats that the intermittent renewables posed on the power system in the following decades