Auctioning Vertically Integrated Online Services: Computational Approaches for Real-Time Allocation

Elastic threshold-based admission control for QoS satisfaction with reward optimization for servicing multiple priority classes in wireless networks

Optical networks are expected to provide a unified platform for a diverse set of emerging applications (three-dimensional TV, digital cinema, e-health, grid computing, etc). The service differentiation will be an essential feature of these networks. Considering the fact that users have different levels of patience for different network applications, referred to as set-up delay tolerance, it will be one of the key parameters for service differentiation. Service differentiation based on set-up delay tolerance will not only enable network users to select an appropriate service class (SC) in compliance with their requirements, but will also provide an opportunity to optimize the network resource provisioning by exploiting this information, resulting in an improvement in the overall performance. Improvement in network performance can be further enhanced by exploiting the connection holding-time awareness. However, when multiple classes of service with different set-up delay tolerances are competing for network resources, the connection requests belonging to SCs with higher set-up delay tolerance have better chances to grab the resources and leave less room for the others, resulting in degradation in the blocking performance of less patient customers. This study proposes different scheduling strategies for promoting the requests belonging to smaller set-up delay tolerance SCs, such as giving priority, reserving some fraction of available resources, and augmenting the research space by providing some extra paths. Extensive simulation results show that 1) priority in the rescheduling queue is not always sufficient for eradicating the degradation effect of high delay tolerant SCs on the provisioning rate of the most stringent SC, and 2) by utilizing the proposed strategies, resource efficiency and overall network blocking performance improve significantly in all SCs.

In wireless communications networks with multiple service classes (SCs) of calls, call admission control (CAC) schemes is an essential radio resource management (RRM) function for networks consistency. Apart from the calculation of the call blocking probability (CBP) appropriate criteria and metrics should be considered to evaluate the efficiency of various schemes. Fairness among different services is an important efficiency criterion for CAC in wireless networks with multimedia services while throughput is an efficiency criterion of general validity. In the present paper, four CAC schemes based on guard channel algorithms are proposed for multiclass services, offering priority to real time service class calls while ensuring a certain level of quality of service (QoS). The proposed schemes investigate the CAC problem in terms of ongoing users and occupied channels. Fairness among calls of different classes is evaluated and the network performance is compared with respect to input load.

Algorithms and software for proactive and adaptive detection of network/service anomalies (i.e., performance degradations) have been developed, implemented, deployed, and field-tested for transaction-oriented wide area networks (WANs). A real-time anomaly detection system called TRISTAN (transaction instantaneous anomaly notification) has been implemented, and is deployed in the commercially important AT&T transaction access services (TAS) network. TAS is a high volume, multiple

Probing is a well-known admission control technique that can achieve high utilization and per-flow quality of service in a scalable way. We have recently introduced an extension to the basic probing technique, called ε-probing, to overcome a resource stealing problem that impairs the use of probing in systems with multiple service classes. In this paper we describe an experimental system that was designed to evaluate the effectiveness of both probing and ε-probing techniques. We have developed a software module that implements the probing functionality, which can be inserted in end hosts or edge routers. Several tests were carried out to study the effect of various system parameters in the performance of the probing techniques. The results clearly show that both probing techniques are able to accurately perform admission control while achieving high utilization. Moreover, they also show that in environments with multiple service classes such as DiffServ, ε-probing can eliminate the resource stealing problem, providing an effective solution to support per flow QoS without signaling and without maintaining flow state at core routers.

This paper addresses the problem of optimal quality of service (QoS), traffic engineering (TE) and failure recovery (FR) in computer networks by introducing novel algorithms that only use source inferrable information. More precisely, optimal data rate adaptation and load balancing laws are provided which are applicable to networks where multiple paths are available and multiple classes of service (CoS) are to be provided. Different types of multiple paths are supported, including point-to-point multiple paths, point-to-multipoint multiple paths, and multicast trees. In particular, it is shown that the algorithms presented only need a minimal amount of information for optimal control, i.e., whether a path is congested or not. Hence, the control laws provided in this paper allow source inferred congestion detection without the need for explicit congestion feedback from the network. The proposed approach is applicable to utility functions of a very general form and endows the network with the important property of robustness with respect to node/link failures; i.e., upon the occurrence of such a failure, the presented control laws reroute traffic away from the inoperative node/link and converge to the optimal allocation for the ldquoreducedrdquo network. The proposed control laws set the foundation for the development of highly scalable feature-rich traffic control protocols at the IP, transport, or higher layers with provable global stability and convergence properties.

Asynchronous transfer mode networks with parallel links and multiple service classes

Cognitive technology enables licensed users (primary users, PUs) to trade the surplus spectrum and to transfer temporarily spectrum usage right to the unlicensed users (secondary users, SUs) to get some reward. The rented spectrum is used to establish secondary network. However, the rented spectrum size influences the quality of service (QoS) for the PU and the gained rewards. Therefore, the PU needs a resource management scheme that helps it to allocate optimally a given amount of the offered spectrum among multiple service classes and to adapt to changes in the network conditions. The PU should support different classes of SUs that pay different prices for their usage of spectrum. We propose a novel approach to maximize a PU reward and to maintain QoS for the PUs and for the different classes of SUs. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed to derive resource adaptations to changing network conditions, so that PUs' profit can continuously be maximized. Available spectrum is managed by the PU that executes the optimal control policy, which is extracted using RL. Performance evaluation of the proposed RL solution shows that the scheme is able to adapt to different conditions and to guarantee the required QoS for PUs and to maintain the QoS for a multiple classes of SUs, while maximizing PUs profits. The results have shown that cognitive mesh network can support additional SUs traffic while still ensuring PUs QoS. In our model, PUs exchange channels based on the spectrum demand and traffic load. The solution is extended to the case in which there are multiple PUs in the network where a new distributed algorithm is proposed to dynamically manage spectrum allocation among PUs.

Capacity planning for IaaS cloud providers offering multiple service classes

Stochastic data-driven optimization for multi-class dynamic pricing and capacity allocation in the passenger railroad transportation

Service composition is a key enabling technology in service-oriented computing for developing versatile applications by integrating various existing interoperable services. Although actively studied, most existing works on service composition neglect the existence of complementarity among candidate services within a service class, so-called internal complementarity. In fact, complementary candidate services within a service class can be composed to form a composite candidate service which may yield better service utility than that provided by any existing candidate service within that service class. This work focuses on web service composition where internal complementarity is more likely to happen. Specifically, we aim at addressing the problem of QoS-aware web service selection with internal complementarity (WSS-IC). We first transform this problem into a multi-choice multi-dimensional knapsack problem (MMKP) and prove such a transformation per se has non-polynomial time complexity in the worse case.Then, we perform complexity analysis to demonstrate that existing approaches to MMKPs are not computationally feasible to resolve QoS-aware WSS-IC. This fact motivates us to propose an iteratively improving framework for deriving the solution iteration by iteration while taking into account both solution structure and QoS constraints. At each iteration, the current solution gets improved by solving a disjunctively constrained knapsack problem. To verify the effectiveness of the proposed framework, two heuristic approaches are implemented under this framework. Experimental results demonstrate that our approaches outperform the compared methods in terms of both solution quality and computation time.

A model for medium-term operations planning in an intermodal rail-truck service

We present two UMTS site selecting algorithms. The algorithm takes into account propagation predictions as well as the traffic distribution in the target area, considers multiple service classes, transmit power constraints and quality requirements, both in uplink and downlink. For a traffic distribution sample, the proposed algorithms (Heuristic Backtracking Algorithm (HBA) and Genetic Algorithm (GA)) find the minimum number of Nodes B needed to provide the demanded capacity or a low number of nodes according to heuristic criteria.

Pricing is an effective tool to control congestion and achieve quality of service (QoS) provisioning for multiple differentiated levels of service. In this paper, we consider the problem of pricing for congestion control in the case of a network of nodes under multiple service classes. Our work draws upon D. Garg, et al. (2005) and R. Tandra, et al. (2004) in various ways. We use the Tirupati pricing scheme in conjunction with the stochastic approximation based adaptive pricing methodology for queue control (proposed in D. Garg, et al. (2005)) for minimizing network congestion. However, unlike the methodology of D. Garg, et al. (2005) where pricing for entire routes is directly considered, we consider prices for individual link-service grade tuples. Further, we adapt the methodology proposed in R. Tandra, et al. (2004) for a single-node scenario to the case of a network of nodes, for evaluating performance in terms of price, revenue rate and disutility. We obtain considerable performance improvements using our approach over that in D. Garg, et al. (2005). In particular, our approach exhibits a throughput improvement in the range of 54 to 80 percent in all cases studied (over all routes) while exhibiting a lower packet delay in the range of 26 to 38 percent over the scheme in D. Garg, et al. (2005).

ACKS (ACK skipping) is a mechanism to enable QoS support in standard IEEE 802.11 WLANs working in infrastructure configuration. The paper presents an analytical model to estimate ACKS performance under saturation conditions, when multiple service classes are considered. Based on this model, the paper proposes an algorithm that optimally configures ACKS networks to provide QoS guarantees for DiffServ-like configurations, that is, for three classes of stations characterized by different QoS requirements. The effectiveness of the proposed algorithm is proved by simulation in different network scenarios.