21,450 research outputs found
On the Flow-level Dynamics of a Packet-switched Network
The packet is the fundamental unit of transportation in modern communication
networks such as the Internet. Physical layer scheduling decisions are made at
the level of packets, and packet-level models with exogenous arrival processes
have long been employed to study network performance, as well as design
scheduling policies that more efficiently utilize network resources. On the
other hand, a user of the network is more concerned with end-to-end bandwidth,
which is allocated through congestion control policies such as TCP.
Utility-based flow-level models have played an important role in understanding
congestion control protocols. In summary, these two classes of models have
provided separate insights for flow-level and packet-level dynamics of a
network
Large-Scale Distributed Coalition Formation
The CyberCraft project is an effort to construct a large scale Distributed Multi-Agent System (DMAS) to provide autonomous Cyberspace defense and mission assurance for the DoD. It employs a small but flexible agent structure that is dynamically reconfigurable to accommodate new tasks and policies. This document describes research into developing protocols and algorithms to ensure continued mission execution in a system of one million or more agents, focusing on protocols for coalition formation and Command and Control. It begins by building large-scale routing algorithms for a Hierarchical Peer to Peer structured overlay network, called Resource-Clustered Chord (RC-Chord). RC-Chord introduces the ability to efficiently locate agents by resources that agents possess. Combined with a task model defined for CyberCraft, this technology feeds into an algorithm that constructs task coalitions in a large-scale DMAS. Experiments reveal the flexibility and effectiveness of these concepts for achieving maximum work throughput in a simulated CyberCraft environment
Operating-system support for distributed multimedia
Multimedia applications place new demands upon processors, networks and operating systems. While some network designers, through ATM for example, have considered revolutionary approaches to supporting multimedia, the same cannot be said for operating systems designers. Most work is evolutionary in nature, attempting to identify additional features that can be added to existing systems to support multimedia. Here we describe the Pegasus project's attempt to build an integrated hardware and operating system environment from\ud
the ground up specifically targeted towards multimedia
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An Overview of the Isochronets Architecture for High Speed Networks
This paper overviews a novel switching architecture for high-speed networks: Isochronets. Isochronets time-divide network bandwidth among routing trees. Traffic moves down a routing tree to the root during its time band. Network functions such as routing and flow control are entirely governed by band timers and require no processing of frame headers bits. Frame motions need not be delayed for switch processing, allowing Isochronets to scale over a large spectrum of transmission speeds and support all-optical implementations. The network functions as a media-access layer that can support multiple framing protocols simultaneously, handled by higher layers at the periphery. Internetworking is reduced to a simple media-layer bridging. Isochronets provide flexible quality of service control and multicasting through allocation of bands to routing trees. They can be tuned to span a spectrum of performance behaviors outperforming both circuit or packet switching
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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