Cloud platforms often execute parallel batch applications, such as distributed machine learning (ML), that include numerous synchronization barriers. These barriers, which prevent any task from advancing beyond a specified point until all tasks have reached that point, significantly degrade application performance by reducing it to that of the slowest "straggler" task. To address the problem, researchers have proposed numerous straggler mitigation techniques, including speculatively re-executing straggler tasks and various relaxations of strict barrier semantics. While these techniques improve parallel application performance, they incur a cost in terms of the resources wasted re-executing tasks or waiting. Importantly, these costs, which are often implicit in prior work that targets dedicated resources, become explicit in the cloud, which charges for resources at fine-grained intervals. In addition, the cost difference between techniques is exacerbated in cloud platforms, since they charge substantially less for transient resources that effectively yield a probabilistic performance across a wide range. While transient resources' low list price is attractive, revocations increase the frequency and severity of stragglers, which decreases parallel job performance and increases overall execution cost. To better understand the cost of synchronization, we develop simple analytical models of different straggler mitigation techniques and compare their cost and performance on on-demand and transient resources. Our analysis shows that i) transient servers offer complex tradeoffs compared to on-demand servers, and can result in higher overall costs despite their highly discounted price due to their probabilistic performance; ii) common approaches to straggler mitigation, which is a well-studied problem, are less effective using transient servers that cause frequent and severe stragglers; and iii) a recent approach to flexible synchronization offers the best cost and performance.

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 home's 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 power's 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 classifier's 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.

Peer-to-peer networks are the most popular mechanism for the criminal acquisition and distribution of child pornography (CP). In this paper, we examine observations of peers sharing known CP on the eMule and Gnutella networks, which were collected by law enforcement using forensic tools that we developed. We characterize a year's worth of network activity and evaluate different strategies for prioritizing investigators' limited resources. The highest impact research in criminal forensics works within, and is evaluated under, the constraints and goals of investigations. We follow that principle, rather than presenting a set of isolated, exploratory characterizations of users.

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.

Support for distributed application management in large-scale networked environments remains in its early stages. Although a number of solutions exist for subtasks of application deployment, monitoring, and maintenance in distributed environments, few tools provide a unified framework for application management. Many of the existing tools address the management needs of a single type of application or service that runs in a specific environment, and these tools are not adaptable enough to be used for other applications or platforms. To this end, we present the design and implementation of Plush, a fully configurable application management infrastructure designed to meet the general requirements of several different classes of distributed applications. Plush allows developers to specifically define the flow of control needed by their computations using application building blocks. Through an extensible resource management interface, Plush supports execution in a variety of environments, including both live deployment platforms and emulated clusters. Plush also uses relaxed synchronization primitives for improving fault tolerance and liveness in failure-prone environments. To gain an understanding of how Plush manages different classes of distributed applications, we take a closer look at specific applications and evaluate how Plush provides support for each.

Deploying, running, and maintaining applications running on a distributed set of resources is a challenging task. Software developers often spend a significant amount of time dealing with the complexities associated with software configuration and management in these environments. Distributed application management systems are designed to automate the process, and to ultimately help developers cope with the common problems that arise during the design, implementation, and evaluation of distributed systems. In this talk, we highlight the key features of Plush, an application management system for PlanetLab and ModelNet, and describe how Plush simplifies peer-to-peer system visualization and evaluation.

PlanetLab is a seasoned Internet testbed for distributed applications consisting of donated nodes located at more than 350 remote locations spread across the globe. It is particularly appropriate for peer-to-peer application research due its large-scale, distributed operation, and the availability of nodes with edge characteristics. This talk describes the basic testbed offering and suggests appropriate use-cases.

Distributed applications have become a core component of the Internet's infrastructure. However, many undergraduate curriculums, especially at small colleges, do not offer courses that focus on the design and implementation of distributed systems. The courses that are offered address the theoretical aspects of system design, but often fail to provide students with the opportunity to develop and evaluate distributed applications in real-world environments. As a result, undergraduate students are not as prepared as they should be for graduate study or careers in industry. This paper describes an undergraduate course in Distributed Systems that not only studies the key design principles of distributed systems, but also has a unique emphasis on giving students hands-on access to distributed systems through the use of shared computing testbeds, such as PlanetLab and GENI, and open-source technologies, such as Xen and Hadoop. Using these platforms, students can perform large-scale, distributed experimentation even at small colleges.

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.

We describe 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 specified ranges of metric values. 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. Rather than evaluating a single implementation of SWORD, we explore a variety of architectural designs that deliver the required functionality in a scalable and highly available manner. We discuss the trade-offs of using a centralized architecture as compared to a fully decentralized design to perform wide-area resource discovery. To summarize our results, we found that a centralized architecture based on 4-node server cluster sites at network-peering facilities outperforms a decentralized DHT-based resource discovery infrastructure with respect to query latency for all but the smallest number of sites. However, although a centralized architecture shows significant promise in stable environments, we find that our decentralized implementation has acceptable performance and also benefits from the DHT's self-healing properties in more volatile environments. We evaluate the advantages and disadvantages of centralized and distributed resource discovery architectures on 1000 hosts in emulation and on approximately 200 PlanetLab nodes spread across the Internet.

Recent computing trends have shown an increase in the demand for large-scale, distributed, federated computing environments. Two of the more popular environments that have emerged are the grid and PlanetLab. At a high level, these systems are similar in many ways; both are comprised of a set of heterogeneous interconnected machines that allows secure resource sharing for a variety of different users and applications. However, at a lower level, the systems are very distinct in the sense that they were designed to solve different types of problems, and therefore have fundamental differences that make it difficult to develop and deploy applications on both platforms. As a result, application designers and researchers create software that runs on either the grid or PlanetLab, but not both. We propose to solve this problem by describing a common abstraction for both PlanetLab and grid applications. Further, we present Plush - a tool that implements the distributed application abstraction by providing a pluggable and extensible infrastructure allowing users to customize their environment for running experiments on both PlanetLab and the grid.

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 SWORD's 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 DHT's self-healing properties.

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 node's 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.