7334644
Analyzing peer-to-peer traffic across large networks
The use of peer-to-peer (P2P) applications is growing dramatically, particularly for sharing large video/audio files and software. In this paper, we analyze P2P traffic by measuring flow-level information collected at multiple border routers across a large ISP network, and report our investigation of three popular P2P systems-FastTrack, Gnutella, and Direct-Connect. We characterize the P2P traffic observed at a single ISP and its impact on the underlying network. We observe very skewed distribution in the traffic across the network at different levels of spatial aggregation (IP, prefix, AS). All three P2P systems exhibit significant dynamics at short time scale and particularly at the IP address level. Still, the fraction of P2P traffic contributed by each prefix is more stable than the corresponding distribution of either Web traffic or overall traffic. The high volume and good stability properties of P2P traffic suggests that the P2P workload is a good candidate for being managed via application-specific layer-3 traffic engineering in an ISP's network.
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.
16333487
Playback restart in interactive streaming video applications
Low latency is crucial in networked multimedia applications such as on-demand streaming audio and video. In this paper we consider the problem of restarting or resuming playback following an interactive operation such as fast forward, rewind, or indexed jump in stored on-demand video. We present two approaches to restart playback after an interactive operation, and develop algorithms to compute the latencies incurred in restarting playback. Using long MPEG-1 traces, with an optimal smoothing technique to transmit data for regular playback, we demonstrate that the latencies incurred under our approaches are very small. We next examine the latencies incurred when restarting playback in a video server under two policies for sharing its bandwidth: (a) one in which only regular playback bandwidth is available to restart playback and (b) one in which additional free server bandwidth (if any) is accessible. We find playback restart latencies to be similarly low under both policies, suggesting that the simpler approach (a) is sufficient.
2225573
Integrated scheduling of multimedia and hard real-time tasks
An integrated platform which is capable of meeting the requirements of both traditional real-time control processing and multimedia processing has enormous potential for accommodating various kinds of new applications. However, except for the simplest of situations, few, if any, research or commercial systems successfully provide architectural and OS mechanisms which can efficiently support both hard real-time computation and multimedia soft real-time computation. The authors propose a multimedia server execution on multiprocessor real-time operating systems to provide different classes of guarantee to support both types of processing. The multimedia server supports multiple periodic multimedia streams with a capability for graceful QoS degradation during system overload. They (i) develop several multimedia server scheduling algorithms, (ii) evaluate the performance of these algorithms, and (iii) discuss realistic system implementation issues on the SGI IRIX/REACT/PRO operating system.
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.
17022940
Using Windows NT for Real-Time Applications: Experimental Observations and Recommendations
Windows NT was not designed as a real time operating system, but market forces and the acceptance of NT in industrial applications have generated a need for achieving real time functionality with NT. As its use for real time applications proliferates, based on an experimental evaluation of NT, we quantitatively characterize the obstacles placed by NT. As a result of these observations, we provide a set of recommendations for users to consider while building real time applications on NT. These are validated by the use of NT for a prototype application involving real time control that includes multimedia information processing. The results of the above study should provide system designers with guidelines, as well as insight, into the design of an architecture based on NT for supporting applications with components having real time constraints.
13052705
Learning to Coordinate without Sharing Information
Researchers in the field of Distributed Artificial Intelligence (DAI) have been developing efficient mechanisms to coordinate the activities of multiple autonomous agents. The need for coordination arises because agents have to share resources and expertise required to achieve their goals. Previous work in the area includes using sophisticated information exchange protocols, investigating heuristics for negotiation, and developing formal models of possibilities of conflict and cooperation among agent interests. In order to handle the changing requirements of continuous and dynamic environments, we propose learning as a means to provide additional possibilities for effective coordination. We use reinforcement learning techniques on a block pushing problem to show that agents can learn complimentary policies to follow a desired path without any knowledge about each other. We theoretically analyze and experimentally verify the effects of learning rate on system convergence, and demonstrate benefits of using learned coordination knowledge on similar problems. Reinforcement learning based coordination can be achieved in both cooperative and non-cooperative domains, and in domains with noisy communication channels and other stochastic characteristics that present a formidable challenge to using other coordination schemes.
1384565
Parallel Sorting in Two-Dimensional VLSI Models of Computation
The gradual refinement of a general approach to two-dimensional sorting, the shear-sort algorithm, to more sophisticated and specialized sorting algorithms on mesh-connected computers is described. The analysis of the shear-sort algorithm gives rise to a novel perspective of two-dimensional sorting, which seems to be a very powerful tool for developing efficient algorithms. The same methods can be extended for sorting in higher dimensions, for example, in the three-dimensional mesh. The concept of clean and dirty rows can be modified to clean and dirty planes (or hyperplanes for dimensions greater than three). Although only two schemes (purely recursive and iterative) are explicitly described, the reader may construct his own algorithm using similar technique and slight modifications. Designing an O(n) algorithm for sorting on a mesh becomes much simpler using the techniques developed. >
5984823
The evolution and stability of cooperative traits
Recent works in multi-agent systems have identified agent behaviors that can develop and sustain mutually beneficial cooperative relationships with like-minded agents and can resist exploitation from selfish agents. Researchers have proposed the use of a probabilistic reciprocity scheme that uses summary information from past interactions to decide whether or not to honor a request for help from another agent. This behavior has been found to be close to optimal in homogeneous groups and outperform exploiters in mixed groups. A major shortcoming of these experiments, however, is that the composition of the group in term of agent behaviors is fixed. We believe that real-life rational agents, on the contrary, will change their behaviors based on observed performances of different behavioral traits with the goal of maximizing performance. In this paper, we present results from experiments on two distinct domains with population groups whose behavioral composition changes based on the performance of the agents. Based on the experimental results, we identify ecological niches for variants of exploitative selfish agents and robust reciprocative agents.
41882641
Using limited information to enhance group stability
The performance of individual agents in a group depends critically on the quality of information available to them about local and global goals and resources. In general, it is assumed that the more accurate and comprehensive the available information, the better is the expected performance of the individual and the group. This conclusion can be challenged in a number of scenarios. We investigate the use of limited information by agents in choosing betwen one of several different options, and conclude that if agents are kept ignorant about, or they deliberately ignore, any number of options, the group can converge faster to a stable and optimal configuration. We present a probabilistic analysis that sheds light on the observed phenomenon of quicker system convergence with less global information. This analysis suggests a desirable adaptive behavior on the part of individual agents. Experiments with agents following these adaptive behavior exhibits faster convergence. We also demonstrate how a couple of coalition formation schemes can improve the rate of convergence. A variable coalition formation mechanism is found to be more effective than a static one.
2206839
Multi-Dimensional Bid Improvement Algorithm for Simultaneous Auctions
Bidding for multi-items in simultaneous auctions raises challenging problems. In multi-auction settings, the determination of optimal bids by potential buyers requires combinatorial calculations. While an optimal bidding strategy is known when bidding in sequential auctions, only suboptimal strategies are available when bidding for items being sold in simultaneous auctions. We investigate a multi-dimensional bid improvement scheme, motivated by optimization techniques, to derive optimal bids for item bundles in simultaneous auctions. Given a vector of initial bids, the proposed scheme systematically improves bids for each item. Such multi-dimensional improvements result in locally optimal bid vectors. Globally optimal bid vectors are guaranteed in the limit for infinite restarts. For ease of presentation we use two-item scenarios to explain the working of the algorithm. Experimental results show polynomial complexity of variants of this algorithm under different types of bidder valuations for item bundles.