Stephen Lee

SpotCheck: designing a derivative IaaS cloud on the spot market

Infrastructure-as-a-Service (IaaS) cloud platforms rent resources, in the form of virtual machines (VMs), under a variety of contract terms that offer different levels of risk and cost. For example, users may acquire VMs in the spot market that are often cheap but entail significant risk, since their price varies over time based on market supply and demand and they may terminate at any time if the price rises too high. Currently, users must manage all the risks associated with using spot servers. As a result, conventional wisdom holds that spot servers are only appropriate for delay-tolerant batch applications. In this paper, we propose a derivative cloud platform, called SpotCheck, that transparently manages the risks associated with using spot servers for users. SpotCheck provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports all types of applications, including interactive ones. SpotChecks design combines the use of nested VMs with live bounded-time migration and novel server pool management policies to maximize availability, while balancing risk and cost. We implement SpotCheck on Amazons EC2 and show that it i) provides nested VMs to users that are 99.9989% available, ii) achieves nearly 5x cost savings compared to using equivalent types of on-demand VMs, and iii) eliminates any risk of losing VM state.

Integrating Energy Storage in Electricity Distribution Networks

Electricity generation combined with its transmission and distribution form the majority of an electric utilitys recurring operating costs. These costs are determined, not only by the aggregate energy generated, but also by the maximum instantaneous peak power demand required over time. Prior work proposes using energy storage devices to reduce these costs by periodically releasing energy to lower the electric grids peak demand. However, prior work generally considers only a single storage technology employed at a single level of the electric grids hierarchy. In this paper, we examine the efficacy of employing different combinations of storage technologies at different levels of the grids distribution hierarchy. We present an optimization framework for modeling the primary characteristics that dictate the lifetime cost of many prominent energy storage technologies. Our framework captures the important tradeoffs in placing different technologies at different levels of the distribution hierarchy with the goal of minimizing a utilitys operating costs. We evaluate our framework using real smart meter data from 5000 customers of a local electric utility. We show that by employing hybrid storage technologies at multiple levels of the distribution hierarchy, utilities can reduce their daily operating costs due to distributing electricity by up to 12%.

SunShade: enabling software-defined solar-powered systems

The electric grid was not designed to support the large-scale penetration of intermittent solar generation. As a result, current policies place hard caps on the solar capacity that may connect to the grid. Unfortunately, users are increasingly hitting these caps, which is restricting the natural growth of solar power. To address the problem, we propose Software-defined Solar-powered (SDS) systems that dynamically regulate the amount of solar power that flows into the grid. To enable SDS systems, this paper introduces fundamental mechanisms for programmatically controlling the size of solar flows, including mechanisms to both enforce an absolute limit on solar output and a new class of Weighted Power Point Tracking (WPPT) algorithms that enforce a relative limit on solar output as a fraction of its maximum power point (MPP). We implement an SDS prototype, called SunShade, and evaluate tradeoffs in the accuracy and fidelity of these mechanisms to enforce limits on solar flows. For example, we quantify the effects of variable conditions, such as clouds, passersby, and other shading, on the fidelity of a search-based WPPT algorithm, which must periodically deviate from its cap to discover changes in the MPP that affect the caps accuracy.

Cost Minimization Using Renewable Cooling and Thermal Energy Storage in CDNs

Content delivery networks (CDNs) employ hundreds of data centers that are distributed across various geographical locations. These data centers consume a significant amount of energy to power and cool their servers. This paper investigates the joint effectiveness of using two new cooling technologies - open air cooling (OAC) and thermal energy storage (TES) - in CDNs to reduce their dependence on traditional chiller-based cooling and minimize its energy costs. Our Lyapunov-based online algorithm optimally distributes workload to data centers leveraging price and weather variations. We conduct a trace based simulation using weather data from NOAA and workload data from a global CDN. Our results show that CDNs can achieve at least 64% and 98% cooling energy savings during summer and winter respectively. Further, CDNs can significantly reduce their cooling energy footprint by switching to renewable open air cooling. We also empirically evaluate our approach and show that it performs optimally.

Distributed Rate Control for Smart Solar Arrays

Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback on increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid, and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. Our evaluation on a city-scale dataset shows that our approach enables 2.6x more solar penetration, while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30x faster convergence. Finally, we implement our distributed algorithm on a Raspberry Pi-class processor to demonstrate its feasibility on grid-tied solar inverters with limited processing capability.

Enabling Distributed Energy Storage by Incentivizing Small Load Shifts

Reducing peak demands and achieving a high penetration of renewable energy sources are important goals in achieving a smarter grid. To reduce peak demand, utilities are introducing variable rate electricity prices to incentivize consumers to manually shift their demand to low-price periods. Consumers may also use energy storage to automatically shift their demand by storing energy during low-price periods for use during high-price periods. Unfortunately, variable rate pricing provides only a weak incentive for distributed energy storage and does not promote its adoption at large scales. In this article, we present the storage adoption dilemma to capture the problems with incentivizing energy storage using variable rate prices. To address the problem, we propose a simple pricing scheme, called flat-power pricing, which incentivizes consumers to shift small amounts of load to flatten their demand rather than shift as much of their power usage as possible to low-price, off-peak periods. We show that compared to variable rate pricing, flat-power pricing (i) reduces consumers’ upfront capital costs, as it requires significantly less storage capacity per consumer; (ii) increases energy storage’s return on investment, as it mitigates free riding and maintains the incentive to use energy storage at large scales; and (iii) uses aggregate storage capacity within 31% of an optimal centralized approach. In addition, unlike variable rate pricing, we also show that flat-power pricing incentivizes the scheduling of elastic background loads, such as air conditioners and heaters, to reduce peak demand. We evaluate our approach using real smart meter data from 14,000 homes in a small town.

Shared solar-powered EV charging stations: Feasibility and benefits

Electric vehicles (EV) are growing in popularity as a credible alternative to gas-powered vehicles. These vehicles require their batteries to be “fueled up” for operation. While EV charging has traditionally been grid-based, use of solar powered chargers has emerged as an interesting opportunity. These chargers provide clean electricity to electric-powered cars that are themselves pollution free resulting in positive environmental effects. In this paper, we design a solar-powered EV charging station in a parking lot of a car-share service. In such a car-share service rental pick up and drop off times are known. We formulate a Linear Programming approach to charge EVs that maximize the utilization of solar energy while maintaining similar battery levels for all cars. We evaluate the performance of our algorithm on a real-world and synthetically derived datasets to show that it fairly distributes the available electric charge among candidate EVs across seasons with variable demand profiles. Further, we reduce the disparity in the battery charge levels by 60% compared to best effort charging policy. Moreover, we show that 80th percentile of EVs have at least 75% battery level at the end of their charging session. Finally, we demonstrate the feasibility of our charging station and show that a solar installation proportional to the size of a parking lot adequately apportions available solar energy generated to the EVs serviced.

How to cool internet-scale distributed networks on the cheap

Internet-scale Distributed Networks (IDNs) are large distributed systems that comprise hundreds of thousands of servers located in hundreds of data centers around the world. A canonical example of an IDN is a content delivery network (CDN) that delivers content to users from a large global deployment of servers around the world. IDNs consume significant amounts of energy to power their deployed server infrastructure, and nearly as much energy to cool that infrastructure. We study the potential benefits of using two new cooling technologies---open air cooling (OAC) and thermal energy storage (TES)---to reduce the energy usage as well as the operational and capital costs incurred by an IDN for cooling. We develop novel algorithms to incorporate both technologies into the IDN architecture and empirically evaluate their efficacy using extensive work load traces from Akamais global CDN and global weather data from NOAA. Our results show that both technologies hold great promise for the future sustainability of Internet-scale distributed networks. Our algorithm for power management of TES is provably near-optimal, is the first to incorporate storage efficiency, and is broadly applicable to other storage devices such as batteries.

WattHome: A Data-driven Approach for Energy Efficiency Analytics at City-scale

Buildings consume over 40% of the total energy in modern societies and improving their energy efficiency can significantly reduce our energy footprint. In this paper, we present WattHome, a data-driven approach to identify the least energy efficient buildings from a large population of buildings in a city or a region. Unlike previous approaches such as least squares that use point estimates, WattHome uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the parameter distribution of a building. Further, it compares them with similar homes in a given population using widely available datasets. WattHome also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in different settings. Moreover, we present results from a case study from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults respectively.

vSolar: Virtualizing Community Solar and Storage for Energy Sharing

Since many residential locations are unsuitable for solar deployments due to space constraints, community-owned solar arrays with energy storage that are collectively shared by a group of homes have emerged as a solution. However, such a group-owned system does not allow individual control over how the electricity generation from the solar array and energy stored in the battery is used for optimizing a homes electricity bill. To overcome this limitation, we propose vSolar, a technique that virtualizes community solar and battery arrays such that each virtual system can be independently controlled, regardless of others. Further, we present mechanisms and algorithms that allow homes with surplus energy to lend to homes with deficit energy.