11,136 research outputs found
Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness
This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner
NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming
In recent years, there has been an increasing demand for ubiquitous streaming
like applications in data networks. In this paper, we concentrate on NUM-based
rate allocation for streaming applications with the so-called S-curve utility
functions. Due to non-concavity of such utility functions, the underlying NUM
problem would be non-convex for which dual methods might become quite useless.
To tackle the non-convex problem, using elementary techniques we make the
utility of the network concave, however this results in reverse-convex
constraints which make the problem non-convex. To deal with such a transformed
NUM, we leverage Sequential Convex Programming (SCP) approach to approximate
the non-convex problem by a series of convex ones. Based on this approach, we
propose a distributed rate allocation algorithm and demonstrate that under mild
conditions, it converges to a locally optimal solution of the original NUM.
Numerical results validate the effectiveness, in terms of tractable convergence
of the proposed rate allocation algorithm.Comment: 6 pages, conference submissio
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