Xiaolin Chang

A stable queue-based adaptive controller for improving AQM performance

Active queue management (AQM) mechanisms are designed to provide better support for end-to-end congestion control mechanisms of transmission control protocol (TCP) in TCP/IP networks. This paper introduces a stable queue-based adaptive proportional-integral (Q-SAPI) controller for AQM and presents an implementation. The starting points of our approach are the recently developed fluid-flow modeling and control theoretic interpretation of the TCP/AQM dynamics, and the recently developed fixed-gain proportional-integral (PI) controller for AQM. Q-SAPI aims to improve the transient performance of the fixed-gain PI controller while maintaining its steady-state performance over a wide range of uncertainties in round-trip time (RTT) and the number of active TCP flows. The robustness of Q-SAPI is studied in detail, which provides guidelines for selecting control parameters. Through extensive simulations, we demonstrate the ability of Q-SAPI in controlling queue length in both transient and steady states. Q-SAPI achieves this by adapting the controller gains according to the queue length.

Reducing power consumption in embedding virtual infrastructures

Network virtualization is considered to be not only an enabler to overcome the inflexibility of the current Internet infrastructure but also an enabler to achieve an energy-efficient Future Internet. Virtual network embedding (VNE) is a critical issue in network virtualization technology. This paper explores a joint power-aware node and link resource allocation approach to handle the VNE problem with the objective of minimizing energy consumption. We first present a generalized power consumption model of embedding a VN. Then we formulate the problem as a mixed integer program and propose embedding algorithms. Simulation results demonstrate that the proposed algorithms perform better than the existing algorithms in terms of the power consumption in the overprovisioned scenarios.

VQ-RED: An efficient virtual queue management approach to improve fairness in infrastructure WLAN

In this paper, we consider two fairness problems (downlink/uplink fairness and fairness among flows in the same direction) that arise in the infrastructure WLAN. We propose a virtual queue management approach, named VQ-RED to address the fairness problems. We demonstrate the effectiveness of our approach by conducting a series of simulations. The results show that compared with standard DCF, VQRED not only greatly improves the fairness, but also reduces packet delays

A robust nonlinear PI controller for improving AQM performance

In this paper a simple robust proportional-integral (R-PI) controller is proposed for active queue management (AQM). We assume that TCP/AQM dynamics can be described by the linearized TCP/AQM model (C. V. Hollot et al., April 2001). R-PI aims to address the tradeoff between responsiveness and stability and the tradeoff between responsiveness and high link utilization over a large range of structured and unstructured uncertainties. This controller achieves these goals by varying its control parameters according to the system state. We show that the closed-loop system is asymptotically stable as long as the control parameters are time-invariant and varying in a range. Extensive simulation results demonstrate the robust ability of R-PI compared with some other AQM mechanisms in the literature.

Green cloud virtual network provisioning based ant colony optimization

Network virtualization is being regarded as a promising technology to create an ecosystem for cloud computing applications. One critical issue in network virtualization technology is power-efficient virtual network embedding (PE-VNE), which deals with the physical resource allocation to virtual nodes and links of a virtual network while minimizing the energy consumption in the cloud data center. When the node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VN embedding problem is NP-hard, even in the offline case. This paper aims to investigate the ability of the Ant-Colony-Optimization (ACO) technique in handling PE-VNE problem. We propose an ACO-based heuristic PE-VNE algorithm, called E-ACO. E-ACO minimizes the energy consumption by considering the embedding power consumption in the node mapping phase and by making an implicit coordination between the node and link mapping phases. Extensive simulations are conducted to evaluate the performance of the proposed algorithm and investigate different energy-aware link embedding algorithms on the ability of E-ACO.

Embedding Virtual Infrastructure Based on Genetic Algorithm

The virtual network embedding (VNE) problem deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in the offline case. The genetic algorithm (GA) is an excellent approach to solving complex problems in optimization with difficult constraints. This paper explores applying GA to handle the VNE problem. We propose two GA-based VNE algorithms and evaluate them by comparing with the existing state-of-the-art VNE algorithms, including PSO-based VNE approaches. Extensive simulation results validate the capability of the proposed GA-based VNE algorithms in terms of the InP long-term revenue and the VN embedding cost.

Analysis of Interrupt Coalescing Schemes for Receive-Livelock Problem in Gigabit Ethernet Network Hosts

Interrupt coalescing (IC) technique has been used in general-purpose operating systems to mitigate receive livelock (RL) problem in gigabit Ethernet network hosts. Schemes for dynamically tuning the interrupt coalescing behavior of a communication interface based on traffic load or system state have been proposed. However, all the existing IC schemes are designed using heuristics. In this paper we present an analytical model for the IC technique and carry out a detailed study of existing IC schemes in terms of their performance characteristics including system goodput, CPU consumption and latency. We validate our analysis through measurement-based experiments.

Towards Robust Green Virtual Cloud Data Center Provisioning

Cloud data center (CDC) network virtualization is being regarded as a promising technology to provide performance guarantee for cloud computing applications. One critical issue in CDC network virtualization technology is virtual data center (VDC) embedding, which deals with the CDC physical resource allocation to virtual nodes (virtual switches and virtual servers) and virtual links of a VDC. When node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VDC embedding problem is NP-hard, even in the offline case. Node heterogeneity and CDC network scale bring challenges to the VDC embedding. This paper aims to embed a VDC in a robust and green way. We propose two effective, computation-efficient and energy-efficient embedding algorithms. Extensive simulations under various network scales and topologies are carried out to compare the proposed algorithms with the existing VDC embedding algorithms in terms of the VDC acceptance ratio, the long-term revenue of the cloud service provider (CSP), the CDC’s long-term energy consumption in light-load CDCs, and in terms of CSPs long-term revenue in heavy-load CDCs.

Performance evaluation of artificial intelligence algorithms for virtual network embedding

Network virtualization is not only regarded as a promising technology to create an ecosystem for cloud computing applications, but also considered a promising technology for the future Internet. One of the most important issues in network virtualization is the virtual network embedding (VNE) problem, which deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in an offline situation. Some Artificial Intelligence (AI) techniques have been applied to the VNE algorithm design and displayed their abilities. This paper aims to compare the computational effectiveness and efficiency of different AI techniques for handling the cost-aware VNE problem. We first propose two kinds of VNE algorithms, based on Ant Colony Optimization and genetic algorithm. Then we carry out extensive simulations to compare the proposed VNE algorithms with the existing AI-based VNE algorithms in terms of the VN Acceptance Ratio, the long-term revenue of the service provider, and the VN embedding cost.

A Queue-based Adaptive Polling Scheme to Improve System Performance in Gigabit Ethernet Networks

Gigabit Ethernet is now finding wider deployment in computer networks. The conventional operating system suffers from the receive livelock problem in Gigabit Ethernet networks. The device hybrid (interrupt + polling) scheme has been widely used to overcome this problem in current operating systems such as GNU/Linux and FreeBSD. However, controlling the polling time without regard to the system state can degrade the ability of a hybrid scheme in some situations. This paper focuses on the system performance of the operating systems that employ the device hybrid scheme in kernel space. A queue-based adaptive polling (QAPolling) scheme is introduced that: (1) significantly improves system goodput and reduces packet loss over a wide range of computer hardware configurations and traffic conditions, (2) is scalable and easily deployed. The key idea behind QAPolling is to adjust the polling time adaptively according to the information of the application receiving queues, which are in kernel space and change with the system state, instead of the packet arrival rate. We validate our design through experimental results in Gigabit Ethernet networks.