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Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation

Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation

ABSTRACT :

Stochastic network optimization problems entail finding resource allocation policies that are optimum on an average but must be designed in an online fashion. Such problems are ubiquitous in communication networks, where resources such as energy and bandwidth are divided among nodes to satisfy certain longterm objectives. This paper proposes an asynchronous incremental dual decent resource allocation algorithm that utilizes delayed stochastic gradients for carrying out its updates. The proposed algorithm is well-suited to heterogeneous networks as it allows the computationally-challenged or energy-starved nodes to, at times, postpone the updates. The asymptotic analysis of the proposed algorithm is carried out, establishing dual convergence under both, constant and diminishing step sizes. It is also shown that with constant step size, the proposed resource allocation policy is asymptotically near-optimal. An application involving multicell coordinated beamforming is detailed, demonstrating the usefulness of the proposed algorithm.

EXISTING SYSTEM :

Key requirements for heterogeneous network protocols include scalability, robustness, and tolerance to delays and packet losses. Towards this end, a number of distributed algorithms have been proposed in the literature. By eliminating the need for a fusion center, the distributed algorithms operate with reduced communication overhead, and render the network resilient to single-point failures.

heterogeneous networks, such delays are often unavoidable, arising due to poor channel conditions, traffic congestion, or limited processing power at certain nodes. This paper proposes a distributed asynchronous stochastic resource allocation algorithm that tolerates such delays. The next subsection outlines the main contributions of this paper.

PROPOSED SYSTEM :

The proposed algorithm is well-suited to heterogeneous networks as it allows the computationally-challenged or energy-starved nodes to, at times, postpone the updates.

The asymptotic analysis of the proposed algorithm is carried out, establishing dual convergence under both, constant and diminishing step sizes.

It is also shown that with constant step size, the proposed resource allocation policy is asymptotically near-optimal. An application involving multicell coordinated beamforming is detailed, demonstrating the usefulness of the proposed algorithm.

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