On the Emergence of Highly Variable Distributions in the Autonomous System Topology

Recent studies have noted that vertex degree in the autonomous system (AS) graph exhibits a highly variable distribution [15, 22]. The most prominent explanatory model for this phenomenon is the Barabási-Albert (B-A) model [5, 2]. A central feature of the B-A model is preferential connectivity—meaning that the likelihood a new node in a growing graph will connect to an existing node is proportional to the existing node’s degree. In this paper we ask whether a more general explanation than the B-A model, and absent the assumption of preferential connectivity, is consistent with empirical data. We are motivated by two observations: first, AS degree and AS size are highly correlated [11]; and second, highly variable AS size can arise simply through exponential growth. We construct a model incorporating exponential growth in the size of the Internet, and in the number of ASes. We then show via analysis that such a model yields a size distribution exhibiting a power-law tail. In such a model, if an AS’s link formation is roughly proportional to its size, then AS degree will also show high variability. We instantiate such a model with empirically derived estimates of growth rates and show that the resulting degree distribution is in good agreement with that of real AS graphs.

On the Interaction between Data Aggregation and Topology Control in Wireless Sensor Networks

Wireless sensor networks are characterized by limited energy resources. To conserve energy, application-specific aggregation (fusion) of data reports from multiple sensors can be beneficial in reducing the amount of data flowing over the network. Furthermore, controlling the topology by scheduling the activity of nodes between active and sleep modes has often been used to uniformly distribute the energy consumption among all nodes by de-synchronizing their activities. We present an integrated analytical model to study the joint performance of in-network aggregation and topology control. We define performance metrics that capture the tradeoffs among delay, energy, and fidelity of the aggregation. Our results indicate that to achieve high fidelity levels under medium to high event reporting load, shorter and fatter aggregation/routing trees (toward the sink) offer the best delay-energy tradeoff as long as topology control is well coordinated with routing.

Design and Implementation of Statistical Rate Monotonic Scheduling in KURT Linux

Statistical Rate Monotonic Scheduling (SRMS) is a generalization of the classical RMS results of Liu and Layland [LL73] for periodic tasks with highly variable execution times and statistical QoS requirements. The main tenet of SRMS is that the variability in task resource requirements could be smoothed through aggregation to yield guaranteed QoS. This aggregation is done over time for a given task and across multiple tasks for a given period of time. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. SRMS feasibility test ensures that it is possible for a given periodic task set to share a given resource without violating any of the statistical QoS constraints imposed on each task in the set. The SRMS scheduling algorithm consists of two parts: a job admission controller and a scheduler. The SRMS scheduler is a simple, preemptive, fixed-priority scheduler. The SRMS job admission controller manages the QoS delivered to the various tasks through admit/reject and priority assignment decisions. In particular, it ensures the important property of task isolation, whereby tasks do not infringe on each other. In this paper we present the design and implementation of SRMS within the KURT Linux Operating System [HSPN98, SPH 98, Sri98]. KURT Linux supports conventional tasks as well as real-time tasks. It provides a mechanism for transitioning from normal Linux scheduling to a mixed scheduling of conventional and real-time tasks, and to a focused mode where only real-time tasks are scheduled. We overview the technical issues that we had to overcome in order to integrate SRMS into KURT Linux and present the API we have developed for scheduling periodic real-time tasks using SRMS.

Optimal Scheduling of Secondary Content for Aggregation in Video-on-Demand Systems

Dynamic service aggregation techniques can exploit skewed access popularity patterns to reduce the costs of building interactive VoD systems. These schemes seek to cluster and merge users into single streams by bridging the temporal skew between them, thus improving server and network utilization. Rate adaptation and secondary content insertion are two such schemes. In this paper, we present and evaluate an optimal scheduling algorithm for inserting secondary content in this scenario. The algorithm runs in polynomial time, and is optimal with respect to the total bandwidth usage over the merging interval. We present constraints on content insertion which make the overall QoS of the delivered stream acceptable, and show how our algorithm can satisfy these constraints. We report simulation results which quantify the excellent gains due to content insertion. We discuss dynamic scenarios with user arrivals and interactions, and show that content insertion reduces the channel bandwidth requirement to almost half. We also discuss differentiated service techniques, such as N-VoD and premium no-advertisement service, and show how our algorithm can support these as well.

Robust Sketching and Aggregation of Distributed Data Streams

The data streaming model provides an attractive framework for one-pass summarization of massive data sets at a single observation point. However, in an environment where multiple data streams arrive at a set of distributed observation points, sketches must be computed remotely and then must be aggregated through a hierarchy before queries may be conducted. As a result, many sketch-based methods for the single stream case do not apply directly, as either the error introduced becomes large, or because the methods assume that the streams are non-overlapping. These limitations hinder the application of these techniques to practical problems in network traffic monitoring and aggregation in sensor networks. To address this, we develop a general framework for evaluating and enabling robust computation of duplicate-sensitive aggregate functions (e.g., SUM and QUANTILE), over data produced by distributed sources. We instantiate our approach by augmenting the Count-Min and Quantile-Digest sketches to apply in this distributed setting, and analyze their performance. We conclude with experimental evaluation to validate our analysis.

Detecting Instances of Shape Classes That Exhibit Variable Structure

This paper proposes a method for detecting shapes of variable structure in images with clutter. The term "variable structure" means that some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. The particular variation of the shape structure that occurs in a given image is not known a priori. Existing computer vision methods, including deformable model methods, were not designed to detect shapes of variable structure; they may only be used to detect shapes that can be decomposed into a fixed, a priori known, number of parts. The proposed method can handle both variations in shape structure and variations in the appearance of individual shape parts. A new class of shape models is introduced, called Hidden State Shape Models, that can naturally represent shapes of variable structure. A detection algorithm is described that finds instances of such shapes in images with large amounts of clutter by finding globally optimal correspondences between image features and shape models. Experiments with real images demonstrate that our method can localize plant branches that consist of an a priori unknown number of leaves and can detect hands more accurately than a hand detector based on the chamfer distance.

Authenticated Index Structures for Aggregation Queries in Outsourced Databases

In an outsourced database system the data owner publishes information through a number of remote, untrusted servers with the goal of enabling clients to access and query the data more efficiently. As clients cannot trust servers, query authentication is an essential component in any outsourced database system. Clients should be given the capability to verify that the answers provided by the servers are correct with respect to the actual data published by the owner. While existing work provides authentication techniques for selection and projection queries, there is a lack of techniques for authenticating aggregation queries. This article introduces the first known authenticated index structures for aggregation queries. First, we design an index that features good performance characteristics for static environments, where few or no updates occur to the data. Then, we extend these ideas and propose more involved structures for the dynamic case, where the database owner is allowed to update the data arbitrarily. Our structures feature excellent average case performance for authenticating queries with multiple aggregate attributes and multiple selection predicates. We also implement working prototypes of the proposed techniques and experimentally validate the correctness of our ideas.

Variable Rate Working Memories for Phonetic Categorization and Invariant Speech Perception

Speech can be understood at widely varying production rates. A working memory is described for short-term storage of temporal lists of input items. The working memory is a cooperative-competitive neural network that automatically adjusts its integration rate, or gain, to generate a short-term memory code for a list that is independent of item presentation rate. Such an invariant working memory model is used to simulate data of Repp (1980) concerning the changes of phonetic category boundaries as a function of their presentation rate. Thus the variability of categorical boundaries can be traced to the temporal in variance of the working memory code.