5876633
Analysis of Communities of Interest in Data Networks
Communities of interest (COI) have been applied in a variety of environments ranging from characterizing the online buying behavior of individuals to detecting fraud in telephone networks. The common thread among these applications is that the historical COI of an individual can be used to predict future behavior as well as the behavior of other members of the COI. It would clearly be beneficial if COIs can be used in the same manner to characterize and predict the behavior of hosts within a data network. In this paper, we introduce a methodology for evaluating various aspects of COIs of hosts within an IP network. In the context of this study, we broadly define a COI as a collection of interacting hosts. We apply our methodology using data collected from a large enterprise network over a eleven week period. First, we study the distributions and stability of the size of COIs. Second, we evaluate multiple heuristics to determine a stable core set of COIs and determine the stability of these sets over time. Third, we evaluate how much of the communication is not captured by these core COI sets.
1578205
Broadcast on demand: efficient and timely dissemination of data in mobile environments
The demand for efficient scalable and cost effective mobile information access systems is rapidly growing. Radiofrequency broadcast plays a major role in mobile computing, and there is a need to provide service models for broadcasting information according to mobile users' needs. The authors present a model called broadcast on demand (BoD), which provides timely broadcasts according to requests from users. Compared to static broadcast, this approach has a different emphasis: it is based on a demand driven framework, aimed at satisfying the temporal constraints of the requests, and uses scheduling techniques at the server side to utilize the limited bandwidth dynamically and efficiently. Several broadcast transmission scheduling policies for BoD are examined. The study indicates that EDF-based policies combined with batching of requests achieve good performance. The results show that BoD is successful in satisfying the temporal constraints of the requests and is a viable service model for wireless broadcast stations.
6303606
Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification
The ability to provide different Quality of Service (QoS) guarantees to traffic from different applications is a highly desired feature for many IP network operators, particularly for enterprise networks. Although various mechanisms exist for providing QoS in the network, QoS is yet to be widely deployed. We believe that a key factor holding back widespread QoS adoption is the absence of suitable methodologies/processes for appropriately mapping the traffic from different applications to different QoS classes. This is a challenging task, because many enterprise network operators who are interested in QoS do not know all the applications running on their network, and furthermore, over recent years port-based application classification has become problematic. We argue that measurement based automated Class of Service (CoS) mapping is an important practical problem that needs to be studied.
 In this paper we describe the requirements and associated challenges, and outline a solution framework for measurement based classification of traffic for QoS based on statistical application signatures. In our approach the signatures are chosen in such as way as to make them insensitive to the particular application layer protocol, but rather to determine the way in which an application is used -- for instance is it used interactively, or for bulk-data transport. The resulting application signature can then be used to derive the network layer signatures required to determine the CoS class for individual IP datagrams. Our evaluations using traffic traces from a variety of network locations, demonstrate the feasibility and potential of the approach.
2290480
Optimal multicast smoothing of streaming video over an internetwork
A number of applications such as internet video broadcasts, corporate telecasts, distance learning etc. require transmission of streaming video to multiple simultaneous users across an internetwork. The high bandwidth requirements coupled with the multi-timescale burstiness of compressed video make it a challenging problem to provision network resources for transmitting streaming multimedia. For such applications to become affordable and ubiquitous, it is necessary to develop scalable techniques which can efficiently deliver streaming video to multiple heterogeneous clients across a heterogeneous internetwork. We propose using multicasting of smoothed video and differential caching of the video at intermediate nodes in the distribution tree, as techniques for reducing the network bandwidth requirements of such dissemination. We formulate the multicast smoothing problem, and develop an algorithm for computing the set of optimally smoothed transmission schedules for the tree (such that the transmission schedule along each link in the tree has the lowest peak rate and rate variability for any feasible transmission schedule for that link) given a buffer allocation to the different nodes in the tree. We also develop an algorithm to compute the minimum total buffer allocation to the entire tree and the corresponding allocation to each node, such that feasible transmission is possible to all the clients, when the tree has heterogeneous rate constraints. MPEG-2 trace-driven performance evaluations indicate that there are substantial benefits from multicast smoothing and differential caching. For example, the optimal multicast smoothing can reduce the total transmission bandwidth requirements in the distribution tree by more than a factor of 3 as compared to multicasting the unsmoothed stream.
1681173
Optimal proxy cache allocation for efficient streaming media distribution
We address the problem of efficiently streaming a set of heterogeneous videos from a remote server through a proxy to multiple asynchronous clients so that they can experience playback with low startup delays. We determine the optimal proxy prefix cache allocation to the videos that minimizes the aggregate network bandwidth cost. We integrate proxy caching with traditional server-based reactive transmission schemes such as hatching, patching and stream merging to develop a set of proxy-assisted delivery schemes. We quantitatively explore the impact of the choice of transmission scheme, cache allocation policy, proxy cache size, and availability of unicast versus multicast capability, on the resulting transmission cost. Our evaluations show that even a relatively small prefix cache (10%-20% of the video repository) is sufficient to realize substantial savings in transmission cost. We find that carefully designed proxy-assisted reactive transmission schemes can produce significant cost savings even in a predominantly unicast environment such as the Internet.
6084623
Proxy prefix caching for multimedia streams
High latency and loss rates in the Internet make it difficult to stream audio and video without introducing a large playback delay. To address these problems, we propose a prefix caching technique whereby a proxy stores the initial frames of popular clips. Upon receiving a request for the stream, the proxy initiates transmission to the client and simultaneously requests the remaining frames from the server. In addition to hiding the delay, throughput, and loss effects of a weaker service model between the server and the proxy, this novel yet simple prefix caching technique aids the proxy in performing workahead smoothing into the client playback buffer. By transmitting large frames in advance of each burst, workahead smoothing substantially reduces the peak and variability of the network resource requirements along the path from the proxy to the client. We describe how to construct a smooth transmission schedule, based on the size of the prefix, smoothing, and playback buffers, without increasing client playback delay. Experiments with MPEG traces show how a few megabytes of buffer space at the proxy can substantially reduce the bandwidth requirements of variable-bit-rate video. Drawing on these results, we present guidelines for allocating buffer space for each stream, and how to effectively share buffer and bandwidth resources among multiple clients and streams.
207161230
FeedMe: a collaborative alert filtering system
As the number of alerts generated by collaborative applications grows, users receive more unwanted alerts. FeedMe is a general alert management system based on XML feed protocols such as RSS and ATOM. In addition to traditional rule-based alert filtering, FeedMe uses techniques from machine-learning to infer alert preferences based on user feedback. In this paper, we present and evaluate a new collaborative naïve Bayes filtering algorithm. Using FeedMe, we collected alert ratings from 33 users over 29 days. We used the data to design and verify the accuracy of the filtering algorithm and provide insights into alert prediction.
656475
Evaluating concurrent reinforcement learners
Assumptions underlying the convergence proofs of reinforcement learning (RL) algorithms like Q-learning are violated when multiple interacting agents adapt their strategies online as a result of learning. Empirical investigations in several domains, however, have produced encouraging results. We evaluate the convergence behavior of concurrent reinforcement learning agents using game matrices as studied by Claus and Boutilier (1998). Variants of simple RL algorithms are evaluated for convergence under increasing number of agents per group, scale up of game matrix size, delayed feedback and game matrix characteristics. Our results show surprising departures from that observed by Claus and Boutilier, particular for larger problem sizes.
6087385
Effect of joining decisions on peer clusters
Super-peer networks have been proposed to address the issue of search latency and scalability in traditional peer-to-peer (P2P) networks. In a super-peer network, instead of having a fully distributed systems of peer nodes with similar or comparable capabilities, some nodes that possess considerable computing power and resources are designated as super-peers. Each super-peer acts as a server for multiple client peers under it. This hierarchical structure of a super-peer network improves the performance of a super-peer network over traditional P2P networks by handling most search queries between the few super-peer nodes, thereby reducing overall network traffic and improving search latency. In this paper, we address the problem of mutual selection by super-peers and client peers. In particular, we evaluate alternative decision functions used by super-peers to allow new client peers to join the cluster of clients under it. We experiment with peers with known resources and demands. By formally representing and reasoning with capability and query distributions, we develop peer-selection functions that either promote concentration or diversification of capabilities within a cluster. We evaluate the effectiveness of these different selection functions for different environments where peer capabilities are aligned or are independent of their queries. We offer insight and analysis on the effects on inter and intra-peer bandwidth consumption which will allow a super-peer to adopt appropriate peer-selection functions given their expectations about the environment.
929830
Parallel searching in generalized Monge arrays with applications
This paper investigates the parallel time and processor complexities of several searching problems involving Monge and Monge-composite arrays. We present array-searching algorithms for concurrent-readconcurrent-write (CRCW) PRAMS, concurrent-readexclusive-write (CREW) PRAMS, hypercubes, cubeconnected-cycles, and shuffle-exchange networks. All these algorithms run in optimal time, and their processor-time products are all within an O(lg n) factor of the worst-case sequential bounds. Several applications of these algorithms are also given. Two applications improve previous results substantially, and the others provide novel parallel algorithms for problems not previously considered.
17321203
On a Simple, Practical, Optimal, Output-Sensitive Randomized Planar Convex Hull Algorithm
In this paper we present a truly practical and provably optimalO(nlogh) time output-sensitive algorithm for the planar convex hull problem. The basic algorithm is similar to the algorithm presented by2, where the median-finding step is replaced by an approximate median. We analyze two such schemes and show that for both methods, the algorithm runs in expectedO(nlogh) time. We further show that the probability of deviation from expected running time approaches 0 rapidly with increasing values ofnandhfor any input. Our experiments suggest that this algorithm is a practical alternative to the worst-caseO(nlogn) algorithms such as Graham's and is especially faster for small output-sizes. Our approach bears some resemblance to a recent algorithm of13, but our analysis is substantially different.