Abstract Automatic modulation classification (AMC) is applied as the intermediate step between signal detection and demodulation to identify modulation schemes. AMC is a challenging task, especially in a non‐cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver. The proposed modulation classification scheme based on multi‐sensor signal fusion makes the premise that the combined signal from multiple sensors provides a more accurate description than any one of the individual signals alone. Multi‐sensor signal fusion offers increased reliability and huge processing gains in overall performance as compared with the single sensor, thus making AMC of weak signals in non‐cooperative communication environment more reliable and successful. Signal‐to‐noise ratio improvement through multi‐sensor signal fusion is studied by using second‐order and fourth‐order moments method. The classification performance based on multi‐sensor signal fusion is investigated in the additive white Gaussian noise channel as well as the flat fading channel and is evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset, and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi‐sensor signal fusion‐based AMC algorithm can greatly outperform other existing AMC methods.Copyright © 2013 John Wiley & Sons, Ltd.

Power consumption is a critically important issue for data centers. Virtual machine (VM) consolidation is fundamentally employed to improve resource utilization and power optimization in modern data centers. In general, VM consolidation is formulated as a vector bin packing problem, which is a well-known NP-hard problem. Hence, heuristic algorithms, such as single dimensional heuristics (e.g., first fit decreasing (FFD)) and dimension-aware heuristics (e.g., DotProduct), are usually deployed in practice for VM consolidation. However, all of these previous heuristic algorithms did not sufficiently explore the heterogeneity of the VMs' resource requirements. In this paper, we propose several heterogeneity aware dominant resource assistant heuristic algorithms for VM consolidation. The performance evaluations validate the effects of the proposed heterogeneity aware heuristics on VM consolidation. The proposed heuristics can achieve quite similar consolidation performance as dimension-aware heuristics with almost the same computational cost as those of the single dimensional heuristics.

Quantized Congestion Notification (QCN) has been developed for IEEE 802.1Qau to provide congestion control at the Ethernet Layer or Layer 2 in data center networks (DCNs) by the IEEE Data Center Bridging Task Group. One drawback of QCN is the rate unfairness of different flows when sharing one bottleneck link. In this paper, we propose an enhanced QCN congestion notification algorithm, called fair QCN (FQCN), to improve rate allocation fairness of multiple flows sharing one bottleneck link in DCNs. FQCN identifies congestion culprits through joint queue and per flow monitoring, feedbacks individual congestion information to each culprit through multi-casting, and ensures convergence to statistical fairness. We analyze the stability and fairness of FQCN via Lyapunov functions and evaluate the performance of FQCN through simulations in terms of the queue length stability, link throughput and rate allocations to traffic flows with different traffic dynamics under three network topologies. Simulation results confirm the rate allocation unfairness of QCN, and validate that FQCN maintains the queue length stability, successfully allocates the fair share rate to each traffic source sharing the link capacity, and enhances TCP throughput performance in the TCP Incast setting.

Data redundancy elimination (DRE), also known as data de-duplication, reduces the data amount to be transferred or stored by identifying and eliminating both intra-object and inter-object duplicated data elements. It is one of the key content delivery acceleration techniques over wide area networks (WANs) to reduce delivery latency and bandwidth consumptions by reducing the amount of data to be transferred. Deploying DRE at the end hosts maximizes the bandwidth savings and latency reductions, because the amount of content sent to the destination hosts is minimized. However, standard DRE used to identify redundant content chunks is very expensive in terms of memory and processing capability especially on resource constrained hosts. By analyzing the web application traffic traces, we find out that some types of contents have more redundant contents than others. Thus, it is possible to apply DRE selectively and opportunistically on those contents with more redundant data elements than other content types to save the memory and processing resources at the hosts. In this paper, we propose content-type based selective DRE (SDRE), which deploys DRE selectively on the contents which have the most opportunities for redundant content identification. We explore the benefits of deploying SDRE on smartphone traffic traces. The results show that SDRE can achieve almost the same bandwidth savings as that of standard DRE with less computation and smaller memory.

With the rapid growth of data center networks in providing a myriad of services and applications to store, access, and process data, the power consumption of data center networks has become critically important and must be efficiently managed. In this paper, we discuss the power optimization of data center networks in a hierarchical perspective. We establish a two-level power optimization model to reduce the power consumption of data center networks by switching off network switches and links while still guaranteeing full connectivity and maximum link utilization. After having shown that the addressed problem falls in the class of capacity constraint multi-commodity flow problems, we design several simple heuristic algorithms to solve the problem. Our simulations show that hierarchical energy optimization can effectively save power consumption in data center networks.

Transmission Control Protocol (TCP) slow start degrades TCP performance under conditions of long-distance and high end-to-end latency, i.e., inherent characteristics of wide area networks (WANs). In this paper, we propose a new TCP slow start algorithm for WANs, called Adaptive Fast Start (AFStart), which incorporates an inline available bandwidth measurement over TCP technique into TCP slow start to set the slow start threshold adaptively and adjusts the congestion window intelligently. The performance of AFStart is evaluated through simulations using the dumb-bell topology and parking-lot topology by applying AFStart to Fast TCP. The simulation results show that AFStart can ramp up the congestion window from its initial value to the slow start threshold more quickly and smoothly than standard slow start, and AFStart achieves higher network link utilization and TCP throughput during the slow start than Fast TCP.

Automatic modulation classification (AMC) is deployed, as the intermediate step between signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver; the problem will be more challenging in the multipath fading channel. The proposed AMC method based on optimal decision fusion by using wireless sensor networks provides a more accurate classification result than any one of the individual signal alone. Wireless sensor networks offer increased reliability and optimal decision fusion provides huge gains in overall classification performance as compared to that of the single sensor. Thus, optimal decision fusion based AMC by using wireless sensor networks greatly enhances classification performance of weak signals in non-cooperative communication environment. Classification performances of optimal decision fusion based AMC by using wireless sensor networks in the multipath fading channel are investigated and evaluated in terms of correct classification probability. Through Monte Carlo simulations, we demonstrate that the proposed AMC algorithm can greatly outperform that of single sensor in multipath fading channel.

Automatic blind modulation classification (MC) is deployed, as the intermediate step between signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver. The proposed MC scheme based on multi-sensor signal fusion makes the premise that the combined signal from multiple sensors provides a more accurate description than any one of the individual signal alone. Multi-sensor signal fusion offers increased reliability and huge gains in overall performance as compared to the single sensor one, thus making automatic modulation classification (AMC) of weak signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-sensor signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-sensor signal fusion, Q-PSK signal SNR estimations using the M2M4 method after multi-sensor signal fusion with 10 sensors versus SNR are also presented. Finally, classification performances based on multi-sensor signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-sensor signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.

TCP Incast, also known as TCP throughput collapse, is a term used to describe a link capacity under-utilization phenomenon in certain many-to-one communication patterns, typically in many datacenter applications. The main root cause of TCP Incast analyzed by prior works is attributed to packet drops at the congestion switch that result in TCP timeout. Congestion control algorithms have been developed to reduce or eliminate packet drops at the congestion switch. In this paper, the performance of Quantized Congestion Notification (QCN) with respect to the TCP incast problem during data access from clustered servers in datacenters are investigated. QCN can effectively control link rates very rapidly in a datacenter environment. However, it performs poorly when TCP Incast is observed. To explain this low link utilization, we examine the rate fluctuation of different flows within one synchronous reading request, and find that the poor performance of TCP throughput with QCN is due to the rate unfairness of different flows. Therefore, an enhanced QCN congestion control algorithm, called fair Quantized Congestion Notification (FQCN), is proposed to improve fairness of multiple flows sharing one bottleneck link. We evaluate the performance of FQCN as compared to that of QCN in terms of fairness and convergence with four simultaneous and eight staggered source flows. As compared to QCN, fairness is improved greatly and the queue length at the bottleneck link converges to the equilibrium queue length very fast. The effects of FQCN to TCP throughput collapse are also investigated. Simulation results show that FQCN significantly enhances TCP throughput performance in a TCP Incast setup.