Distributed and Dynamic Resource Allocation for Delay Sensitive Network Services

Abstract

In this paper, we present a distributed algorithm to dynamically allocate the available resources of a service-oriented network to delay sensitive network services. We use a utilitybased framework to differentiate services based on both their relative profitability and quality-of-service requirements. Our performance metric is the end-to-end delay that a service class experiences in the network. We use network calculus to obtain a deterministic upper bound of this delay and we incorporate this information into our optimization problem formulation. We leverage a moving average control scheme to capture traffic shifts in real time, which makes our solution to react adaptively to traffic dynamics. Finally, we evaluate our system using real traces of instant messaging service traffic.

A distributed algorithm for resource allocation for delay sensitive services

“…Note that this happens only when there is a significant change in traffic characteristics; this is indicated by an out-of-control signal which is triggered by our Exponentially Weighted Moving Average (EWMA) control scheme when the traffic intensity of a particular class changes. More information about the MBORA system is provided in our earlier work [2], [3].…”

“…Specifically, we use three different traces of instant messaging traffic that were captured at the internet gateway of a large enterprise (see [3]). The incidence matrix for this scenario is: …”

“…Traffic characterization includes: peak rate R i = 1 (normalized to C) and burst size σ i = 10 × L i , where L i is the maximum message size (200 bytes, see [3]). …”

“…Anticipating large and distributed future SON deployments, it is desirable to investigate the scalability of distributed resource allocation algorithm, such as the one presented in [3]. While certainly being attractive from an analytical point of view, gradient descent algorithms that are based on dual decomposition, coupled objective functions, etc., can have high implementation overheads that limit their scalability in real systems.…”

“…We combine ideas from economics, optimization theory, and traffic analysis to extend our existing Measurement Based Optimal Resource Allocation (MBORA) framework, presented in [2], and further developed in [3], that directly addresses the dynamic and distributed resource allocation problem in service-oriented networks. Our problem formulation considers services with different priorities and quality-of-service requirements based on predefined service level agreements on end-to-end delay.…”