Caching Video-on-Demand in Metro and Access Fog Data Centres

This paper examines the utilization of metro fog data centres and access fog datacentres with integrated solar cells and Energy Storage Devices (ESDs) to assist cloud data centres in caching Video-on-Demand content and hence, reduce the networking power consumption. A Mixed Integer Linear Programming (MILP) model is used to optimize the delivery of the content from cloud, metro fog, or access fog datacentres. The results for a range of data centre parameters show that savings by up to 38% in the transport network power consumption can be achieved when VoD is optimally served from fully renewable-powered cloud or metro fog data centres or from access fog data centres with 250 m2 solar cells. Additional 8% savings can be achieved when using ESDs of 100 kWh capacity in the access fog data centres

Fog-assisted Caching Employing Solar Renewable Energy for Delivering Video on Demand Service

This paper examines the reduction in brown power consumption of transport networks and data centres achieved by caching Video-on-Demand (VoD) contents in solar-powered fog data centers with Energy Storage Devices (ESDs). A Mixed Integer Linear Programming (MILP) model was utilized to optimize the delivery from cloud or fog data centres. The results reveal that for brown-powered cloud and fog data centres with same Power Usage Effectiveness (PUE), a saving by up to 77% in transport network power consumption can be achieved by delivering VoD demands from fog data centres. With fully renewable-powered cloud data centres and partially solar-powered fog data centres, savings of up to 26% can be achieved when considering 250 m2 solar cells. Additional saving by up to 14% can be achieved with ESDs of 50 kWh capacity

Towards Power- and Energy-Efficient Datacenters

As the Internet evolves, cloud computing is now a dominant form of computation in modern lives. Warehouse-scale computers (WSCs), or datacenters, comprising the foundation of this cloud-centric web have been able to deliver satisfactory performance to both the Internet companies and the customers. With the increased focus and popularity of the cloud, however, datacenter loads rise and grow rapidly, and Internet companies are in need of boosted computing capacity to serve such demand. Unfortunately, power and energy are often the major limiting factors prohibiting datacenter growth: it is often the case that no more servers can be added to datacenters without surpassing the capacity of the existing power infrastructure. This dissertation aims to investigate the issues of power and energy usage in a modern datacenter environment. We identify the source of power and energy inefficiency at three levels in a modern datacenter environment and provides insights and solutions to address each of these problems, aiming to prepare datacenters for critical future growth. We start at the datacenter-level and find that the peak provisioning and improper service placement in multi-level power delivery infrastructures fragment the power budget inside production datacenters, degrading the compute capacity the existing infrastructure can support. We find that the heterogeneity among datacenter workloads is key to address this issue and design systematic methods to reduce the fragmentation and improve the utilization of the power budget. This dissertation then narrow the focus to examine the energy usage of individual servers running cloud workloads. Especially, we examine the power management mechanisms employed in these servers and find that the coarse time granularity of these mechanisms is one critical factor that leads to excessive energy consumption. We propose an intelligent and low overhead solution on top of the emerging finer granularity voltage/frequency boosting circuit to effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost, improving energy efficiency without sacrificing the quality of services. The final focus of this dissertation takes a further step to investigate how using a fundamentally more efficient computing substrate, field programmable gate arrays (FPGAs), benefit datacenter power and energy efficiency. Different from other types of hardware accelerations, FPGAs can be reconfigured on-the-fly to provide fine-grain control over hardware resource allocation and presents a unique set of challenges for optimal workload scheduling and resource allocation. We aim to design a set coordinated algorithms to manage these two key factors simultaneously and fully explore the benefit of deploying FPGAs in the highly varying cloud environment.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144043/1/hsuch_1.pd

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Oil Spill Prevention and Response in the U.S. Arctic Ocean: Unexamined Risks, Unacceptable Consequences

Examines the risks, challenges, and potential impact of oil spills from Arctic oil and gas exploration and production. Recommendations include ecosystem mapping, oil spill trajectory models, blowout prevention measures, and response gap analysis

Dynamic modelling of generation capacity investment in electricity markets with high wind penetration

The ability of liberalised electricity markets to trigger investment in the generation capacity required to maintain an acceptable level of security of supply risk has been - and will continue to be - a topic of much debate. Like many capital intensive industries, generation investment suffers from long lead and construction times, lumpiness of capacity change and high uncertainty. As a result, the ‘boom-and-bust’ investment cycle phenomenon, characterised by overcapacity and low prices, followed by power shortages and high prices, is a prominent feature in the debate. Modelling the dynamics of generation investment in market environments can provide insights into the complexities involved and address the challenges of market design. Further, many governments who preside over liberalised energy markets are developing policies aimed at promoting investment in renewable generation. Of particular interest is the mix and amount of generation investment over time in response to policies promoting high penetrations of variable output renewable power such as wind. Consequently, improved methods to calculate expected output, costs and revenue of thermal generation subject to varying load and random independent thermal outages in a power system with a high wind penetration are needed. In this interdisciplinary project engineering tools are applied to an economic problem together with knowledge from numerous other disciplines. A dynamic simulation model of the aggregated Great Britain (GB) generation investment market has been developed. Investment is viewed as a negative feedback control mechanism with current and future energy prices acting as the feedback signal. Other disciplines called upon include the use of stochastic processes to address uncertainties such as future fuel prices, and economic theory to gain insights into investor behaviour. An ‘energy-only’ market setting is used where generation companies use a classical NPV approach together with the Value at Risk criterion for investment decisions. Market price mark-ups due to market power are also accounted for. The model’s ability to simulate the market trends witnessed in GB since early 2001 is scrutinised with encouraging findings reported. A reasonably good agreement of the model with reality, gives a degree of confidence in the realism of future projections. An advancement to the dynamic model to account for expected high wind penetrations is also included. Building on the initial model iteration, the short-term energy market is simulated using probabilistic production costing based on theMix of Normals distribution technique with a residual load calculation (load net of wind output). Wind speed measurement data is combined with the outputs of atmospheric models to assess the availability of the GB wind resource and its relationship with aggregate load. Simulation results for 2010-40 suggest that the GB system may experience increased generation adequacy risk during the mid to late the 2020s. In addition, many new investments are unable to recover their fixed costs. This triggered an investigation into the design of a capacity mechanism within the context of the modelling environment. In light of the ongoing GB market electricity market reform debate, two mechanisms are tested; a strategic reserve tender and a marketwide capacity market. The goal of these mechanisms is to mitigate generation adequacy risk concerns by achieving a target winter peak de-rated capacity margin