Bounded Delay Scheduling with Packet Dependencies

A common situation occurring when dealing with multimedia traffic is having large data frames fragmented into smaller IP packets, and having these packets sent independently through the network. For real-time multimedia traffic, dropping even few packets of a frame may render the entire frame useless. Such traffic is usually modeled as having {\em inter-packet dependencies}. We study the problem of scheduling traffic with such dependencies, where each packet has a deadline by which it should arrive at its destination. Such deadlines are common for real-time multimedia applications, and are derived from stringent delay constraints posed by the application. The figure of merit in such environments is maximizing the system's {\em goodput}, namely, the number of frames successfully delivered. We study online algorithms for the problem of maximizing goodput of delay-bounded traffic with inter-packet dependencies, and use competitive analysis to evaluate their performance. We present competitive algorithms for the problem, as well as matching lower bounds that are tight up to a constant factor. We further present the results of a simulation study which further validates our algorithmic approach and shows that insights arising from our analysis are indeed manifested in practice

FITing-Tree: A Data-aware Index Structure

Index structures are one of the most important tools that DBAs leverage to improve the performance of analytics and transactional workloads. However, building several indexes over large datasets can often become prohibitive and consume valuable system resources. In fact, a recent study showed that indexes created as part of the TPC-C benchmark can account for 55% of the total memory available in a modern DBMS. This overhead consumes valuable and expensive main memory, and limits the amount of space available to store new data or process existing data. In this paper, we present FITing-Tree, a novel form of a learned index which uses piece-wise linear functions with a bounded error specified at construction time. This error knob provides a tunable parameter that allows a DBA to FIT an index to a dataset and workload by being able to balance lookup performance and space consumption. To navigate this tradeoff, we provide a cost model that helps determine an appropriate error parameter given either (1) a lookup latency requirement (e.g., 500ns) or (2) a storage budget (e.g., 100MB). Using a variety of real-world datasets, we show that our index is able to provide performance that is comparable to full index structures while reducing the storage footprint by orders of magnitude.Comment: 18 page

Confusion and Conflict in Assessing the Physical Activity Status of Middle-Aged Men

BACKGROUND: Physical activity (including exercise) is prescribed for health and there are various recommendations that can be used to gauge physical activity status. The objective of the current study was to determine whether twelve commonly-used physical activity recommendations similarly classified middle-aged men as sufficiently active for general health. METHODS AND FINDINGS: We examined the commonality in the classification of physical activity status between twelve variations of physical activity recommendations for general health in ninety men aged 45-64 years. Physical activity was assessed using synchronised accelerometry and heart rate. Using different guidelines but the same raw data, the proportion of men defined as active ranged from to 11% to 98% for individual recommendations (median 73%, IQR 30% to 87%). There was very poor absolute agreement between the recommendations, with an intraclass correlation coefficient (A,1) of 0.24 (95% CI, 0.15 to 0.34). Only 8% of men met all 12 recommendations and would therefore be unanimously classified as active and only one man failed to meet every recommendation and would therefore be unanimously classified as not sufficiently active. The wide variability in physical activity classification was explained by ostensibly subtle differences between the 12 recommendations for thresholds related to activity volume (time or energy), distribution (e.g., number of days of the week), moderate intensity cut-point (e.g., 3 vs. 4 metabolic equivalents or METs), and duration (including bout length). CONCLUSIONS: Physical activity status varies enormously depending on the physical activity recommendation that is applied and even ostensibly small differences have a major impact. Approximately nine out of every ten men in the present study could be variably described as either active or not sufficiently active. Either the effective dose or prescription that underlies each physical activity recommendation is different or each recommendation is seeking the same prescriptive outcome but with variable success

SHEAR: A Highly Available and Flexible Network Architecture Marrying Distributed and Logically Centralized Control Planes

This paper presents SHEAR, a highly available hybrid network architecture which marries distributed legacy protocols with Software-Defined Networking (SDN) technology. SHEAR is based on a small deployment of Openflow switches which serve as “observability points”: SHEAR leverages legacy distributed control plane protocols to detect and localize failures, but outsources the actual failover logic to the logically centralized SHEAR controller, which can make faster and more informed routing decisions. Moreover, the Openflow switches are used to logically decompose the legacy network into loopfree components, enabling a simple and flexible traffic-engineering. The deployment problem solved by SHEAR can be seen as a new variant of a network tomography problem, and may be of independent interest. Our simulations show that in enterprise networks, between 2 to 10 % Openflow switches are sufficient to implement SHEAR. We also report on our prototype implementation which detects a failure and reroutes traffic in less than.3 sec-onds in our testbed—much faster than what is achieved by the less flexible and distributed legacy protocols. More generally, SHEAR demonstrates that in con-trast to common belief, operating a hybrid software-defined network can be simple, and given its benefits, a partial Openflow deployment may even be a long-term solution

When Optimal is Just Not Good Enough: Learning Fast Informative Action Cost Partitionings ∗

Several recent heuristics for domain independent planning adopt some action cost partitioning scheme to derive admissible heuristic estimates. Given a state, two methods for obtaining an action cost partitioning have been proposed: optimal cost partitioning, which results in the best possible heuristic estimate for that state, but requires a substantial computational effort, and ad-hoc (uniform) cost partitioning, which is much faster, but is usually less informative. These two methods represent almost opposite points in the tradeoff between heuristic accuracy and heuristic computation time. One compromise that has been proposed between these two is using an optimal cost partitioning for the initial state to evaluate all states. In this paper, we propose a novel method for deriving a fast, informative cost-partitioning scheme, that is based on computing optimal action cost partitionings for a small set of states, and using these to derive heuristic estimates for all states. Our method provides greater control over the accuracy/computation-time tradeoff, which, as our empirical evaluation shows, can result in better performance

Selective sampling for nearest neighbor classifiers

Abstract. Most existing inductive learning algorithms work under the assumption that their training examples are already tagged. There are domains, however, where the tagging procedure requires significant computation resources or manual labor. In such cases, it may be beneficial for the learner to be active, intelligently selecting the examples for labeling with the goal of reducing the labeling cost. In this paper we present LSS- a lookahead algorithm for selective sampling of examples for nearest neighbor classifiers. The algorithm is looking for the example with the highest utility, taking its effect on the resulting classifier into account. Computing the expected utility of an example requires estimating the probability of its possible labels. We propose to use the random field model for this estimation. The LSS algorithm was evaluated empirically on seven real and artificial data sets, and its performance was compared to other selective sampling algorithms. The experiments show that the proposed algorithm outperforms other methods in terms of average error rate and stability

Selective Sampling for Nearest Neighbor Classifiers

In the passive, traditional, approach to learning, the information available to the learner is a set of classified examples, which are randomly drawn from the instance space. In many applications, however, the initial classification of the training set is a costly process, and an intelligently selection of training examples from unlabeled data is done by an active learner. This paper proposes a lookahead algorithm for example selection and addresses the problem of active learning in the context of nearest neighbor classifiers. The proposed approach relies on using a random field model for the example labeling, which implies a dynamic change of the label estimates during the sampling process. The proposed selective sampling algorithm was evaluated empirically on artificial and real data sets. The experiments show that the proposed method outperforms other methods in most cases. Introduction In many real-world domains it is expensive to label a large number of examples for training, and..