Jyrki Joutsensalo

Comparison and analysis of the revenue-based adaptive queuing models

This paper presents several adaptive resource sharing models that use a revenue criterion to allocate bandwidth in an optimal way. The models ensure QoS requirements of data flows and, at the same time, maximize the total revenue by adjusting parameters of the underlying schedulers. Besides, the adaptive models eliminate the need to find the optimal static weight values because they are calculated dynamically. The simulation consists of several cases that analyse the models and the way they provide the required QoS guarantees. The simulation reveals that the installation of the adaptive model increases the total revenue and ensures the QoS requirements for all service classes. The paper also presents how the adaptive models can be integrated with the IntServ and DiffServ QoS frameworks.

Revenue-maximization-based adaptive WFQ

In the future Internet, different applications such as Voice over IP (VoIP) and Video-on-Demand (VoD) arise with different Quality of Service (QoS) parameters including e.g. guaranteed bandwidth, delay jitter, and latency. Different kinds of service classes (e.g. gold, silver, bronze) arise. The customers of different classes pay different prices to the service provider, who must share resources in a plausible way. In a router, packets are queued using a multi-queue system, where each queue corresponds to one service class. In this paper, an adaptive Weighted Fair Queue based algorithm for traffic allocation is presented and studied. The weights in gradient type WFQ algorithm are adapted using revenue as a target function.

Revenue-Based adaptive deficit round robin

This paper presents an adaptive resource allocation model that is based on the DRR queuing policy. The model ensures QoS requirements and tries to maximize a service providers revenue by manipulating quantum values of the DRR scheduler. To calculate quantum values, it is proposed to use the revenue criterion that controls the allocation of free resources. The simulation considers a single node with the implemented model that serves several service classes with different QoS requirements and traffic characteristics. It is shown that the total revenue can be increased due to the allocation of unused resources to more expensive service classes. At the same time, bandwidth and delay guarantees are provided. Furthermore, the adaptive model eliminates the need to find the optimal static quantum values because they are calculated dynamically.

Gradient scheduling algorithm for fair delay guarantee in logarithmic pricing scenario

In this paper we propose a packet scheduling scheme for ensuring delay as a Quality of Service (QoS) requirement. For customers, fair service is given while optimizing revenue of the network service provider. Gradient type algorithm for updating the weights of a packet scheduler is derived from a revenue-based optimization problem in the logarithmic pricing scenario. Algorithm is simple to implement. We compared algorithm with optimal brute-force method. The weight updating procedure is independent on the assumption of the connections statistical behavior, and therefore it is robust against erroneous estimates of statistics.

Providing QOS in 3G-WLAN environment with RSVP and DIFFSERV

Here we present the end-to-end QoS mechanism in 3G-multiaccess network environment. As multi-access wireless WLAN and wired xDSL wideband multi-access technologies has emerge and become more popular a need for interoperability with different technologies and domains has become necessity. There is also a need for end-to-end QoS management. We show a scenario where the UE-GGSN connection is covered by RSVP and RAN network part uses partial over dimensioning and real-time controlled ATM queuing. DiffServ covers WLAN-Core QoS and radio interface between WLAN AP and WLAN UE uses IEEEs 802.11e. Our interest is to find out how well 3G traffic classes can survive in different traffic conditions in the end-to-end case.

Revenue-Aware Resource Allocation in the Future Multi-Service IP Networks

In the future IP networks, a wide range of different service classes must be supported in a network node and different classes of customers will pay different prices for their used node resources based on their Service-Level-Agreements. In this paper, we link the resource allocation issue with pricing strategies and explore the problem of maximizing the revenue of service providers in a network node by optimally allocating a given amount of node resources among multiple service classes. Under the linear pricing strategy, the optimal resource allocation scheme is derived for the case that no firm Quality-of-Service (QoS) guarantees are required for all service classes, which can achieve the maximum revenue in a network node; moreover, the suboptimal allocation scheme is proposed for the case that all classes have their firm QoS (mean delay) requirements, which can satisfy those required QoS guarantees while still being able to achieve very high revenue close to the analytic maximum one.

The simulation and analysis of the revenue critierion based adaptive WFQ

This paper presents the simulation and analysis of the adaptive resource allocation model, which was proposed and theoretically considered in our previous works. It relies upon the Weighted Fair Queueing (WFQ) service policy and uses the revenue criterion to adjust weights. The purpose of the proposed model is to maximize a providers revenue and, at the same time, ensure the required Quality-of-Service (QoS) for end-users. Our previous works provided the theoretical evaluation of the proposed model and considered the single-node case only. This paper presents more realistic network scenario, which includes a set of clients and several intermediate switching nodes with the proposed model. The adaptive and non-adaptive approaches to the WFQ are considered in terms of obtained revenue and state of queues at intermediate nodes. It is shown that the adaptive approach can improve the total revenue obtained by a provider when compared to the non-adaptive approach.

Network channel allocation and revenue maximization

This paper introduces a model that can be used to share link capacity among customers under different kind of traffic conditions. This model is suitable for different kind of networks like the 4G networks (fast wireless access to wired network) to support connections of given duration that requires a certain quality of service. We study different types of network traffic mixed in a same communication link. A single link is considered as a bottleneck and the goal is to find customer traffic profiles that maximizes the revenue of the link. Presented allocation system accepts every calls and there is not absolute blocking, but the offered data rate/user depends on the network load. Data arrival rate depends on the current link utilization, users payment (selected CoS class) and delay. The arrival rate is (i) increasing with respect to the offered data rate, (ii) decreasing with respect to the price, (iii) decreasing with respect to the network load, and (iv) decreasing with respect to the delay. As an example, explicit formula obeying these conditions is given and analyzed.

Statistical bandwidth allocation for multiservice networks

Multiservice networks will carry different kinds of applications in the near future. Bandwidth requirements change rapidly, and the network resource management will play an important role to guarantee the use of the limited resources in the most efficient way. We approach the channel capacity allocation problem by developing an SLA (Service Level Agreement) based channel allocation method. In our model, the channel may be wired or wireless, so this method can be adapted in multi-technique networks. The algorithm allocates resources to several different service classes via several different capacity routes. Service provider perfroms optimization by allocating data rate in such a way that the satisfactory of the customers as well as the revenue is maximized.