Journal of Interconnection Networks

In Broadband Wireless Access (BWA) networks, provisioning of Quality of Service (QoS) is absolutely essential for real time and non real time services. Congestion in these networks can lead to deterioration in QoS experienced by the network users. Congestion avoidance is rarely considered to ensure the QoS of wireless connections. Efforts are mainly aimed at reducing the overload once it has occurred. To avoid congestion at the base station in WiMAX networks, the WiMAX Fair Intelligent Congestion Control (WFICC) was proposed to ensure the traffic is scheduled in such a way that the base station output buffer operates around a target operating point without violating the QoS requirements of connections. The aim of this paper is to investigate WFICC thoroughly and evaluate its performance in terms of throughput, delay and jitter for different Classes of Services (CoSs) under various parameter settings of the algorithm.

A detailed and comprehensive simulation study on various settings of parameters is performed in ns-2. The results show that WFICC performs excellently in allocating resources fairly among Class of Services (CoSs) and also preserves their QoS requirements. Furthermore, WFICC is robust and easily adapted to various traffic conditions.

By means of virtualization, computing and storage resources are effectively multiplexed by different applications in cloud data centers. However, there lacks useful approaches to share the internal network resource of cloud data centers. Invalid network sharing not only degrade the performance of applications, but also affect the efficiency of data center operation. To guarantee network performance of applications and provide fine-grained service differentiation, in this paper, we propose a differentiated bandwidth guarantee scheme for data center networks. Utility functions are constructed according to the throughput and delay sensitive characteristics of different applications. Aiming to maximize the utility of all applications, the problem is formulated as a multi-objective optimization problem. We solve this problem using a heuristic algorithm: the elitist Non-Dominated Sorted Genetic Algorithm-II(NSGA-II), and we make a multi-attribute decision to refine the solutions. Extensive simulations are conducted to show that our scheme provides minimum band-width guarantees and achieves more fine-grained service differentiation than existing approaches. The simulation also verifies that the proposed mechanism is suitable for arbitrary data center architectures.

This paper presents a two-stage approach for multi-topic contrastive viewpoint summarization on opinionated texts. In the first stage, we model the opinionated texts with TAM and get the topic and aspect attribute of each sentence. In the second stage, we successively use the basic LexRank, Comparative LexRank, Topic-sensitive tf*idf LexRank, Topic-sensitive tf*idf & Comparative LexRank, and Biased & Comparative LexRank to evaluate the centrality of each sentence, based on which the summary is finally generated. Experimental results show that the best summary comes from the proposed Topic-sensitive tf*idf LexRank and Topic-sensitive tf*idf & Comparative LexRank.

Circulant network has been used for decades in the design of computer and telecommunication networks due to optimal fault-tolerance and routing capabilities. Further, it has been used in VLSI design and distributed computation.

Hexagonal chains are of great importance of theoretical chemistry because they are the natural graph representations of benzenoid hydrocarbons, a great deal of investigations in mathematical chemistry has been developed to hexagonal chains. Hexagonal chains are exclusively constructed by hexagons of length one. Phenylenes are a class of chemical compounds in which carbon atoms form 6 and 4 membered cycles.

Graph embedding has been known as a powerful tool for implementation of parallel algorithms or simulation of different interconnection networks. An embedding f of a guest graph G into a host graph H is a bijection on the vertices such that each edge of G is mapped into a path of H. The wirelength (layout) of this embedding is defined to be the sum of the length of the paths corresponding to the edges of G.

In this paper we obtain the minimum wirelength of embedding circulant networks into linear hexagonal chains and linear phenylenes. Further we discuss the embedding of faulty circulant networks into linear hexagonal chains and linear phenylenes.

Synchronous Data Flow (SDF) is a useful computational model in image processing, computer vision, and DSP. Previously, throughput and buffer requirement analyses have been studied for SDFs. In this paper, we address energy-aware scheduling for acyclic SDFs on multiprocessors. The multiprocessor considered here has the capability of Dynamic Voltage and Frequency Scaling (DVFS), which allows processors to operate at different power/energy levels to reduce the energy consumption. An acyclic SDF graph can first be transformed to an equivalent homogeneous SDF graph and then to a Directed Acyclic Graph (DAG) for one iteration of it. We propose pipeline scheduling to address two closely related problems. The first problem is minimizing energy consumption per iteration of the acyclic SDF given a throughput constraint; the second problem is maximizing throughput given an energy consumption constraint per iteration of the acyclic SDF. Since the space of valid total orders of the transformed DAG is exponential, and finding the optimal order is no easy task, we first derive a valid total order, which can be achieved via various strategies, based on the DAG. Given the derived order, we design two dynamic programming algorithms, which produce optimal pipeline scheduling (including pipeline stage partitioning and the frequency setting for each stage), for the two problems, respectively. We also compare the performances of using various strategies to derive a valid total order. Analyses, experiments, and simulations demonstrate the strength of our proposed pipeline scheduling and dynamic programming algorithms.