Many energy optimizations require fine-grained, load-level energy data collected in real time, most typically by a plug-level energy meter. Online load tracking is the problem of monitoring an individual electrical load’s energy usage in software by analyzing the building’s aggregate smart meter data. Load tracking differs from the well-studied problem of load disaggregation in that it emphasizes per-load accuracy and efficient, online operation rather than accurate disaggregation of every building load via offline analysis. In essence, tracking a particular load creates a virtual power meter for it, which mimics having a networked-connected power meter attached to the load, but notably does not require tracking every other load as well. We propose PowerPlay , a model-driven system for performing accurate, high-performance online load tracking. Our results from applying the system to real-world energy data demonstrate that PowerPlay (i) enables efficient online tracking on low-power embedded platforms, (ii) scales to thousands of loads (across many buildings) on server platforms, and (iii) improves per-load accuracy by more than a factor of two compared to a state-of-the-art load disaggregation algorithm. Our results point to the potential of replacing physical energy meters by “virtual” power meters using a system like PowerPlay.

The Internet of Things (IoT) is growing rapidly, with increasingly sophisticated networking, sensing, and actuation functions embedded into everyday devices. One important IoT application is managing a building's energy usage by monitoring and controlling its electrical devices. Many existing IoT-enabled devices operate through low-cost and convenient power line networks, using protocols such as X10 and Insteon for communication. However, as these technologies have traditionally targeted low-bandwidth device control, they are often not readily suited to higher bandwidth uses such as continuous energy monitoring. In this paper, we consider the challenge of leveraging existing low-bandwidth power line communication networks for energy monitoring, and present several techniques that enable reliable and high-resolution monitoring in such networks. As a case study, we consider the popular Insteon protocol and show that intelligent polling and event detection methods can reduce the bandwidth requirements and undetected power events in a realworld Insteon network by 50% or more versus naive methods. Our techniques have been employed in a real IoT-enabled smart home, which has collected much of the data publicly released in the UMass Smart* energy dataset.

Smart meter deployments are spurring renewed interest in analysis techniques for electricity usage data. However, an important prerequisite for data analysis is characterizing and modeling how electrical loads use power. While prior work has made significant progress in deriving insights from electricity data, one issue that limits accuracy is the use of general and often simplistic load models. Prior models often associate a fixed power level with an “on” state and either no power, or some minimal amount, with an “off” state. This paper's goal is to develop a new methodology for modeling electric loads that is both simple and accurate. Our approach is empirical in nature: we monitor a wide variety of common loads to distill a small number of common usage characteristics, which we then leverage to construct accurate load-specific models. We show that our models are significantly more accurate than binary on-off models, decreasing the root mean square error by as much as 8× for representative loads. Finally, we demonstrate three novel applications that use our empirical load models to analyze and derive insights from smart meter data, including i) generating device-accurate synthetic traces of building electricity usage; ii) filtering out loads that generate rapid and random power variations in smart meter data; and iii) detecting the presence of specific load models in time-series power data.

Cloud-based data management platforms often employ multitenant databases, where service providers achieve economies of scale by consolidating multiple tenants on shared servers. In such database systems, a key functionality for service providers is database migration, which is useful for dynamic provisioning, load balancing, and system maintenance. Practical migration solutions have several requirements, including high availability, low performance overhead, and self-management. We present Slacker, an end-to-end database migration system at the middleware level satisfying these requirements. Slacker leverages off-the-shelf hot backup tools to achieve live migration with effectively zero down-time. Additionally, Slacker minimizes the performance impact of migrations on both the migrating tenant and collocated tenants by leveraging 'migration slack', or resources that can be used for migration without excessively impacting query latency. We apply a PID controller to this problem, allowing Slacker to automatically detect and exploit migration slack in real time. Using our prototype, we demonstrate that Slacker effectively controls interference during migrations, maintaining latency within 10% of a given latency target, while still performing migrations rapidly and efficiently.

Flattening household electricity demand reduces generation costs, since costs are disproportionately affected by peak demands. While the vast majority of household electrical loads are interactive and have little scheduling flexibility (TVs, microwaves, etc.), a substantial fraction of home energy use derives from background loads with some, albeit limited, flexibility. Examples of such devices include A/Cs, refrigerators, and dehumidifiers. In this paper, we study the extent to which a home is able to transparently flatten its electricity demand by scheduling only background loads with such flexibility. We propose a Least Slack First (LSF) scheduling algorithm for household loads, inspired by the well-known Earliest Deadline First algorithm. We then integrate the algorithm into Smart-Cap, a system we have built for monitoring and controlling electric loads in homes. To evaluate LSF, we collected power data at outlets, panels, and switches from a real home for 82 days. We use this data to drive simulations, as well as experiment with a real testbed implementation that uses similar background loads as our home. Our results indicate that LSF is most useful during peak usage periods that exhibit “peaky” behavior, where power deviates frequently and significantly from the average. For example, LSF decreases the average deviation from the mean power by over 20% across all 4-hour periods where the deviation is at least 400 watts.

Reducing the energy footprint of data centers continues to receive significant attention due to both its financial and environmental impact. There are numerous methods that limit the impact of both factors, such as expanding the use of renewable energy or participating in automated demand-response programs. To take advantage of these methods, servers and applications must gracefully handle intermittent constraints in their power supply. In this paper, we propose blinking---metered transitions between a high-power active state and a low-power inactive state---as the primary abstraction for conforming to intermittent power constraints. We design Blink, an application-independent hardware-software platform for developing and evaluating blinking applications, and define multiple types of blinking policies. We then use Blink to design BlinkCache, a blinking version of memcached, to demonstrate the effect of blinking on an example application. Our results show that a load-proportional blinking policy combines the advantages of both activation and synchronous blinking for realistic Zipf-like popularity distributions and wind/solar power signals by achieving near optimal hit rates (within 15% of an activation policy), while also providing fairer access to the cache (within 2% of a syn- chronous policy) for equally popular objects.

Cloud computing platforms enable users to rent computing and storage resources on-demand to run their networked applications and employ virtualization to multiplex virtual servers belonging to different customers on a shared set of servers. In this paper, we empirically evaluate the efficacy of cloud platforms for running latency-sensitive multimedia applications. Since multiple virtual machines running disparate applications from independent users may share a physical server, our study focuses on whether dynamically varying background load from such applications can interfere with the performance seen by latency-sensitive tasks. We first conduct a series of experiments on Amazon's EC2 system to quantify the CPU, disk, and network jitter and throughput fluctuations seen over a period of several days. We then turn to a laboratory-based cloud and systematically introduce different levels of background load and study the ability to isolate applications under different settings of the underlying resource control mechanisms. We use a combination of micro-benchmarks and two real-world applications--the Doom 3 game server and Apple's Darwin Streaming Server--for our experimental evaluation. Our results reveal that the jitter and the throughput seen by a latency-sensitive application can indeed degrade due to background load from other virtual machines. The degree of interference varies from resource to resource and is the most pronounced for disk-bound latency-sensitive tasks, which can degrade by nearly 75% under sustained background load. We also find that careful configuration of the resource control mechanisms within the virtualization layer can mitigate, but not eliminate, this interference.