Carrying IP traffic over connection-oriented networks requires the use of bandwidth on demand schemes at gateways or network interfaces. A new virtual queue occupancy, which is more accurate than the classical one, is being proposed for IP/SONET bandwidth on demand. Based on the virtual queue occupancy, two enhanced periodic approaches for lossless services, LAVQ and LAVQL, are simulated and evaluated. Simulations show that LAVQ outperforms its counterpart LAQ in terms of bandwidth utilization. By curbing the queue occupancy fluctuation, LAVQL further promotes bandwidth utilization and conceals the influence of the system latency on delay jitter as well.

The input queued (IQ) switching architecture is becoming an attractive alternative for high-speed switches owing to its scalability. In this paper, three new algorithms, referred to as the maximum credit first (MCF), enhanced MCF (EMCF), and iterative MCF (IMCF) algorithms, are introduced. Simulations show that both MCF and IMCF have similar performance as the Birkhoff-von Neumann decomposition (BVND) algorithm, which can provide cell delay bound and 100% throughput, with lower off-line computational and on-line memory complexity. Simulations also show the fairness of MCF is much better than that of BVND. Theoretic analysis shows that the EMCF algorithm has a better performance than MCF in terms of throughput and cell delay with the same complexity level as MCF. Simulation results indicate the EMCF algorithm has much lower average cell delay and delay variance as compared to the BVND algorithm.

Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, Least Mean Kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the Least Mean Square (LMS) algorithm.

The Quality of Service (QoS) requirements such as delay and cell loss ratio (CLR) are very stringent for video transmission. These constraints are difficult to meet if high network utilization is desired. Dynamic bandwidth allocation in which video traffic prediction can play an important role is thus needed. In this paper, we suggest to predict the variation of I frames instead of the actual size of I frames, and propose an algorithm that can achieve fast convergence and small prediction error, thus imposing QoS and attaining high network utilization. The performance of the scheme is studied using the renegotiated constant bit rate (RCBR) service model. The overall dynamic bandwidth allocation scheme based on our fast convergent algorithm is shown to be promising, and practically feasible for efficient transmission of real time videos.

Combined input-crosspoint buffered (CICB) packet switches have been of research interest in the last few years because of their high performance. These switches provide higher performance than input-buffered (IB) packet switches while requiring the crosspoint buffers run at the same speed as that of the input buffers in IB switches. Recently, it has been shown that CICB switches with one-cell crosspoint buffers, virtual output queues, and simple input and output arbitrations, provide 100% throughput under uniform traffic. However, it is of general interest to know the maximum throughput that a CICB switch, with no speedup, can provide under admissible traffic. This paper analyzes the throughput performance of a CICB switch beyond uniform traffic patterns and shows that a CICB switch with one-cell crosspoint buffers can provide 100% throughput under admissible traffic while using no speedup.

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations.

The use of fluid Generalized Processor Sharing (GPS) algorithm for integrated service networks has received much attention since early 1990's because of its desirable properties in terms of delay bound and service fairness. Many Packet Fair Queuing (PFQ) algorithms have been developed to approximate GPS. However, owing to the implementation complexity, it is difficult to support a large number of sessions with diverse service rates while maintaining the GPS properties. The grouping architecture has been proposed to dramatically reduce the implementation complexity. However, the grouping architecture can only support a fixed number of service rates, thus causing the problems of granularity, bandwidth fairness, utilization, and immunity of flows. In this paper, we propose a new implementation approach called dual-rate grouping, which can significantly alleviate the above problems. Compared with the grouping architecture, the proposed approach possesses better performance in terms of approximating per session-based PFQ algorithms without increasing the implementation complexity.

One purpose of Quality-of-Service (QoS) routing is to develop polynomial-time heuristic algorithms to tackle the MCP (multi-constrained-path) problem, which is NP-complete. In this paper, we introduce a new QoS routing heuristic framework, which focuses on how to increase the success ratio for finding a feasible path subject to multiple additive constraints. The key issue of this framework is to transform the single source single destination QoS routing problem to a single source multi-destination problem by expanding the destination vertex to its neighboring vertices. After that, the modified problem can be solved by existing source routing heuristic algorithms. The analysis and simulation results demonstrate that the framework can achieve a higher success ratio of finding a feasible path without increasing the computational complexity by setting the expansion operation properly.