A new approach to service provisioning in ATM networks

The authors formulate and solve a problem of allocating resources among competing services differentiated by user traffic characteristics and maximum end-to-end delay. The solution leads to an alternative approach to service provisioning in an ATM network, in which the network offers directly for rent its bandwidth and buffers and users purchase freely resources to meet their desired quality. Users make their decisions based on their own traffic parameters and delay requirements and the network sets prices for those resources. The procedure is iterative in that the network periodically adjusts prices based on monitored user demand, and is decentralized in that only local information is needed for individual users to determine resource requests. The authors derive the network's adjustment scheme and the users' decision rule and establish their optimality. Since the approach does not require the network to know user traffic and delay parameters, it does not require traffic policing on the part of the network

On the Burstiness of Distributed Machine Learning Traffic

Traffic from distributed training of machine learning (ML) models makes up a large and growing fraction of the traffic mix in enterprise data centers. While work on distributed ML abounds, the network traffic generated by distributed ML has received little attention. Using measurements on a testbed network, we investigate the traffic characteristics generated by the training of the ResNet-50 neural network with an emphasis on studying its short-term burstiness. For the latter we propose metrics that quantify traffic burstiness at different time scales. Our analysis reveals that distributed ML traffic exhibits a very high degree of burstiness on short time scales, exceeding a 60:1 peak-to-mean ratio on time intervals as long as 5~ms. We observe that training software orchestrates transmissions in such a way that burst transmissions from different sources within the same application do not result in congestion and packet losses. An extrapolation of the measurement data to multiple applications underscores the challenges of distributed ML traffic for congestion and flow control algorithms

Bandwidth Allocation By Pricing In ATM Networks

Admission control and bandwidth allocation are important issues in telecommunications networks, especially when there are random fluctuating demands for service and variations in the service rates. In the emerging broadband communications environment these services are likely to be offered via an ATM network. In order to make ATM future safe, methods for controlling the network should not be based on the characteristics of present services. We propose one bandwidth allocation method which has this property . Our proposed approach is based on pricing bandwidth to reflect network utilization, with users competing for resources according to their individual bandwidth valuations. The prices may be components of an actual tariff or they may be used as control signals, as in a private network. Simulation results show the improvement possible with our scheme versus a leaky bucket method in terms of cell loss probability, and confirm that a small queue with pricing can be efficient to multiplex heterogeneous sources

Analyzing coalitions in wireless heterogeneous networks and their economic aspects

The massive investment that is essential to implement a large area wireless network is one of the significant roadblocks that stops service providers from offering more affordable data services. The fact that the fast evolution of wireless technologies requires frequent updates of hardware and software also leads to higher capital expenditure and operation costs for the providers and subsequently to more expensive data plans for the end users. The implementation of suboptimal pricing schemes in today’s wireless market, which does not consider service level agreements and forces users to pay for both network connectivity and data transfer, is another reason to decrease the overall satisfaction of subscribers. In view of these issues our objective in this thesis is to study the proper pricing methods based on the reality of current market as well as to consider alternative options that can reduce the service costs of wireless providers are our objectives. We study the volume-based pricing which is the dominant method in cellular networks nowadays. We derive the optimal data plan parameters such as the data volume cap, price, and data rate. Considering the cost-reduction possibilities, we prove that a coalition of providers in which they can serve users of each other is a valid alternative that reduces the implementation costs of network expansion. We build our analysis based on the cooperation between heterogeneous providers and we consider the heterogeneity in both technology and service aspects. We avoid the models which consider a coalition of all providers since it forms a monopoly and is prohibited by regulatory entities. Hence, we study models of coalitional structures that include several sets of providers. In this way, users have the option to select their data plan based on the service offered by a coalitional set of providers that can have different technologies in their access network. Concerning the service-oriented heterogeneous networks, we track the directions of payments from the content providers (CP) to the service providers (SP) and finally to the end users and try to modify it based on social fairness. To do so, we analyze several content types based on subscriber usage patterns and we find the ones that can be offered with a different pricing method without causing profit loss to CP or SP. Our goal is to set a coalitional framework between CP and SP that can lead to a free unlimited access to particular content types. We show that such agreements, if set correctly, can increase the profit of CP and SP. Throughout this thesis, the analytical models are verified with numerical examples that are designed to simulate the real world scenarios