Jeannie R. Albrecht

Bullet: high bandwidth data dissemination using an overlay mesh

In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes self-organize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burden on the underlying network. In this paper, we target high-bandwidth data distribution from a single source to a large number of receivers. Applications include large-file transfers and real-time multimedia streaming. For these applications, we argue that an overlay mesh, rather than a tree, can deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to self-organize into a high bandwidth overlay mesh. We construct Bullet around the insight that data should be distributed in a disjoint manner to strategic points in the network. Individual Bullet receivers are then responsible for locating and retrieving the data from multiple points in parallel.Key contributions of this work include: i) an algorithm that sends data to different points in the overlay such that any data object is equally likely to appear at any node, ii) a scalable and decentralized algorithm that allows nodes to locate and recover missing data items, and iii) a complete implementation and evaluation of Bullet running across the Internet and in a large-scale emulation environment reveals up to a factor two bandwidth improvements under a variety of circumstances. In addition, we find that, relative to tree-based solutions, Bullet reduces the need to perform expensive bandwidth probing. In a tree, it is critical that a nodes parent delivers a high rate of application data to each child. In Bullet however, nodes simultaneously receive data from multiple sources in parallel, making it less important to locate any single source capable of sustaining a high transmission rate.

Design and implementation trade-offs for wide-area resource discovery

This paper describes the design and implementation of SWORD, a scalable resource discovery service for wide-area distributed systems. In contrast to previous systems, SWORD allows users to describe desired resources as a topology of interconnected groups with required intragroup, intergroup, and per-node characteristics, along with the utility that the application derives from various ranges of values of those characteristics. This design gives users the flexibility to find geographically distributed resources for applications that are sensitive to both node and network characteristics, and allows the system to rank acceptable configurations based on their quality for that application. We explore a variety of architectures to deliver SWORDs functionality in a scalable and highly-available manner. A 1000-node ModelNet evaluation using a workload of measurements collected from PlanetLab shows that an architecture based on 4-node server cluster sites at network peering facilities outperforms a decentralized DHT-based resource discovery infrastructure for all but the smallest number of sites. While such a centralized architecture shows significant promise, we find that our decentralized implementation, both in emulation and running continuously on over 200 PlanetLab nodes, performs well while benefiting from the DHTs self-healing properties.

Design and implementation tradeoffs for wide-area resource discovery

This paper describes the design and implementation of SWORD, a scalable resource discovery service for wide-area distributed systems. In contrast to previous systems, SWORD allows users to describe desired resources as a topology of interconnected groups with required intragroup, intergroup, and per-node characteristics, along with the utility that the application derives from various ranges of values of those characteristics. This design gives users the flexibility to find geographically distributed resources for applications that are sensitive to both node and network characteristics, and allows the system to rank acceptable configurations based on their quality for that application. We explore a variety of architectures to deliver SWORDs functionality in a scalable and highly-available manner. A 1000-node ModelNet evaluation using a workload of measurements collected from PlanetLab shows that an architecture based on 4-node server cluster sites at network peering facilities outperforms a decentralized DHT-based resource discovery infrastructure for all but the smallest number of sites. While such a centralized architecture shows significant promise, we find that our decentralized implementation, both in emulation and running continuously on over 200 PlanetLab nodes, performs well while benefiting from the DHTs self-healing properties.

SmartCap: Flattening peak electricity demand in smart homes

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.

PlanetLab application management using plush

Support for application deployment and monitoring in large-scale distributed systems such as PlanetLab remains in its early stages. While a number of solutions exist for specific subtasks of deployment and monitoring, these tools suffer from a lack of integration. Most tools were developed specifically to deploy and manage a particular service or application on a single platform and were not designed to be general enough to support different environments. In this paper, we consider three different classes of PlanetLab applications to distill a set of requirements for a general application-control infrastructure. We then discuss initial experiences and lessons learned during the development and PlanetLab deployment of Plush, a tool designed to manage applications running over large-scale distributed systems.

High-bandwidth data dissemination for large-scale distributed systems

This article focuses on the multireceiver data dissemination problem. Initially, IP multicast formed the basis for efficiently supporting such distribution. More recently, overlay networks have emerged to support point-to-multipoint communication. Both techniques focus on constructing trees rooted at the source to distribute content among all interested receivers. We argue, however, that trees have two fundamental limitations for data dissemination. First, since all data comes from a single parent, participants must often continuously probe in search of a parent with an acceptable level of bandwidth. Second, due to packet losses and failures, available bandwidth is monotonically decreasing down the tree. To address these limitations, we present Bullet, a data dissemination mesh that takes advantage of the computational and storage capabilities of end hosts to create a distribution structure where a node receives data in parallel from multiple peers. For the mesh to deliver improved bandwidth and reliability, we need to solve several key problems: (i) disseminating disjoint data over the mesh, (ii) locating missing content, (iii) finding who to peer with (peering strategy), (iv) retrieving data at the right rate from all peers (flow control), and (v) recovering from failures and adapting to dynamically changing network conditions. Additionally, the system should be self-adjusting and should have few user-adjustable parameter settings. We describe our approach to addressing all of these problems in a working, deployed system across the Internet. Bullet outperforms state-of-the-art systems, including BitTorrent, by 25-70% and exhibits strong performance and reliability in a range of deployment settings. In addition, we find that, relative to tree-based solutions, Bullet reduces the need to perform expensive bandwidth probing.

Live Debugging of Distributed Systems

Debugging distributed systems is challenging. Although incremental debugging during development finds some bugs, developers are rarely able to fully test their systems under realistic operating conditions prior to deployment. While deploying a system exposes it to realistic conditions, debugging requires the developer to: (i) detect a bug, (ii) gather the system state necessary for diagnosis, and (iii) sift through the gathered state to determine a root cause. In this paper, we present MaceODB, a tool to assist programmers with debugging deployed distributed systems. Programmers define a set of runtime properties for their system, which MaceODB checks for violations during execution. Once MaceODB detects a violation, it provides the programmer with the information to determine its root cause. We have been able to diagnose several non-trivial bugs in existing mature distributed systems using MaceODB; we discuss two of these bugs in this paper. Benchmarks indicate that the approach has low overhead and is suitable for in situ debugging of deployed systems.

Exploiting home automation protocols for load monitoring in smart buildings

Monitoring and controlling electrical loads is crucial for demand-side energy management in smart grids. Home automation (HA) protocols, such as X10 and Insteon, have provided programmatic load control for many years, and are being widely deployed in early smart grid field trials. While HA protocols include basic monitoring functions, extreme bandwidth limitations (<180bps) have prevented their use in load monitoring. In this paper, we highlight challenges in designing AutoMeter, a system for exploiting HA for accurate load monitoring at scale. We quantify Insteons limitations to query device status---once every 10 seconds to achieve less than 5% loss rate---and then evaluate techniques to disaggregate coarse HA data from fine-grained building-wide power data. In particular, our techniques learn switched load power using on-off-dim events, and tag fine-grained building-wide power data using readings from plug meters every 5 minutes.

Preventing Occupancy Detection From Smart Meters

Utilities are rapidly deploying smart meters that measure electricity usage in real-time. Unfortunately, smart meters indirectly leak sensitive information about a homes occupancy, which is easy to detect because it highly correlates with simple statistical metrics, such as powers mean, variance, and range. To prevent occupancy detection, we propose using the thermal energy storage of electric water heaters already present in many homes. In essence, our approach, which we call combined heat and privacy (CHPr), modulates a water heaters power usage to make it look like someone is always home. We design a CHPr-enabled water heater that regulates its energy usage to thwart a variety of occupancy detection attacks without violating its objective-to provide hot water on demand-and evaluate it in simulation using real data. Our results show that a standard 50-gal CHPr-enabled water heater prevents a wide range of state-of-the-art occupancy detection attacks.

Combined heat and privacy: Preventing occupancy detection from smart meters

Electric utilities are rapidly deploying smart meters that record and transmit electricity usage in real-time. As prior research shows, smart meter data indirectly leaks sensitive, and potentially valuable, information about a homes activities. An important example of the sensitive information smart meters reveal is occupancy-whether or not someone is home and when. As prior work also shows, occupancy is surprisingly easy to detect, since it highly correlates with simple statistical metrics, such as powers mean, variance, and range. Unfortunately, prior research that uses chemical energy storage, e.g., batteries, to prevent appliance power signature detection is prohibitively expensive when applied to occupancy detection. To address this problem, we propose preventing occupancy detection using the thermal energy storage of large elastic heating loads already present in many homes, such as electric water and space heaters. In essence, our approach, which we call Combined Heat and Privacy (CHPr), controls the power usage of these large loads to make it look like someone is always home. We design a CHPr-enabled water heater that regulates its energy usage to mask occupancy without violating its objective, e.g., to provide hot water on demand, and evaluate it in simulation and using a prototype. Our results show that a 50-gallon CHPr-enabled water heater decreases the Matthews Correlation Coefficient (a standard measure of a binary classifiers performance) of a threshold-based occupancy detection attack in a representative home by 10x (from 0.44 to 0.045), effectively preventing occupancy detection at no extra cost.