11,100 research outputs found
Trade & Cap: A Customer-Managed, Market-Based System for Trading Bandwidth Allowances at a Shared Link
We propose Trade & Cap (T&C), an economics-inspired mechanism that incentivizes users to voluntarily coordinate their consumption of the bandwidth of a shared resource (e.g., a DSLAM link) so as to converge on what they perceive to be an equitable allocation, while ensuring efficient resource utilization. Under T&C, rather than acting as an arbiter, an Internet Service Provider (ISP) acts as an enforcer of what the community of rational users sharing the resource decides is a fair allocation of that resource. Our T&C mechanism proceeds in two phases. In the first, software agents acting on behalf of users engage in a strategic trading game in which each user agent selfishly chooses bandwidth slots to reserve in support of primary, interactive network usage activities. In the second phase, each user is allowed to acquire additional bandwidth slots in support of presumed open-ended need for fluid bandwidth, catering to secondary applications. The acquisition of this fluid bandwidth is subject to the remaining "buying power" of each user and by prevalent "market prices" â both of which are determined by the results of the trading phase and a desirable aggregate cap on link utilization. We present analytical results that establish the underpinnings of our T&C mechanism, including game-theoretic results pertaining to the trading phase, and pricing of fluid bandwidth allocation pertaining to the capping phase. Using real network traces, we present extensive experimental results that demonstrate the benefits of our scheme, which we also show to be practical by highlighting the salient features of an efficient implementation architecture.National Science Foundation (CCF-0820138, CSR-0720604, EFRI-0735974, CNS-0524477, and CNS-0520166); Universidad Pontificia Bolivariana and COLCIENCIASâInstituto Colombiano para el Desarrollo de la Ciencia y la TecnologĂa âFrancisco Jose Ì de Caldasâ
Weighted Max-Min Resource Allocation for Frequency Selective Channels
In this paper, we discuss the computation of weighted max-min rate allocation
using joint TDM/FDM strategies under a PSD mask constraint. We show that the
weighted max-min solution allocates the rates according to a predetermined rate
ratio defined by the weights, a fact that is very valuable for
telecommunication service providers. Furthermore, we show that the problem can
be efficiently solved using linear programming. We also discuss the resource
allocation problem in the mixed services scenario where certain users have a
required rate, while the others have flexible rate requirements. The solution
is relevant to many communication systems that are limited by a power spectral
density mask constraint such as WiMax, Wi-Fi and UWB
Statistical Multiplexing and Traffic Shaping Games for Network Slicing
Next generation wireless architectures are expected to enable slices of
shared wireless infrastructure which are customized to specific mobile
operators/services. Given infrastructure costs and the stochastic nature of
mobile services' spatial loads, it is highly desirable to achieve efficient
statistical multiplexing amongst such slices. We study a simple dynamic
resource sharing policy which allocates a 'share' of a pool of (distributed)
resources to each slice-Share Constrained Proportionally Fair (SCPF). We give a
characterization of SCPF's performance gains over static slicing and general
processor sharing. We show that higher gains are obtained when a slice's
spatial load is more 'imbalanced' than, and/or 'orthogonal' to, the aggregate
network load, and that the overall gain across slices is positive. We then
address the associated dimensioning problem. Under SCPF, traditional network
dimensioning translates to a coupled share dimensioning problem, which
characterizes the existence of a feasible share allocation given slices'
expected loads and performance requirements. We provide a solution to robust
share dimensioning for SCPF-based network slicing. Slices may wish to
unilaterally manage their users' performance via admission control which
maximizes their carried loads subject to performance requirements. We show this
can be modeled as a 'traffic shaping' game with an achievable Nash equilibrium.
Under high loads, the equilibrium is explicitly characterized, as are the gains
in the carried load under SCPF vs. static slicing. Detailed simulations of a
wireless infrastructure supporting multiple slices with heterogeneous mobile
loads show the fidelity of our models and range of validity of our high load
equilibrium analysis
Using Dedicated and Opportunistic Networks in Synergy for a Cost-effective Distributed Stream Processing Platform
This paper presents a case for exploiting the synergy of dedicated and
opportunistic network resources in a distributed hosting platform for data
stream processing applications. Our previous studies have demonstrated the
benefits of combining dedicated reliable resources with opportunistic resources
in case of high-throughput computing applications, where timely allocation of
the processing units is the primary concern. Since distributed stream
processing applications demand large volume of data transmission between the
processing sites at a consistent rate, adequate control over the network
resources is important here to assure a steady flow of processing. In this
paper, we propose a system model for the hybrid hosting platform where stream
processing servers installed at distributed sites are interconnected with a
combination of dedicated links and public Internet. Decentralized algorithms
have been developed for allocation of the two classes of network resources
among the competing tasks with an objective towards higher task throughput and
better utilization of expensive dedicated resources. Results from extensive
simulation study show that with proper management, systems exploiting the
synergy of dedicated and opportunistic resources yield considerably higher task
throughput and thus, higher return on investment over the systems solely using
expensive dedicated resources.Comment: 9 page
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