Journal of Interconnection Networks

The conditional diagnosability of interconnection networks has been studied in a number of ad-hoc methods resulting in various conditional diagnosability results. In this paper, we utilize these existing results to give an unified approach in studying this problem. Following this approach, we derive the exact value of the conditional diagnosability for a number of interconnection networks including Cayley graphs generated by 2-trees (which generalize alternating group graphs), arrangement graphs (which generalize star graphs and alternating group graphs), hyper Petersen networks, and dual-cube like networks (which generalize dual-cubes.)

Physiological sensors have been used to detect different stress levels in order to improve human health and well-being. When analyzing these sensor data, sensor features are generated in the experiment and a subset of the features are selected and then combined using a host of informatics techniques (machine learning, data mining, or information fusion). Our previous work studied feature selection using correlation and diversity as well as feature combination using five methods C4.5, Naïve Bayes, Linear Discriminant Function, Support Vector Machine, and k-Nearest Neighbors. In this paper, we use combinatorial fusion, based on performance criterion (CF-P) and cognitive diversity (CF-CD), to combine those multiple sensor features. Our results showed that: (a) sensor feature combination method is distinctly much better than CF-CD and other algorithms, and (b) CF-CD is as good as other five feature combination methods, and is better in most of the cases.

With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.

The computer industry is moving towards two extremes: extremely high-performance high-throughput cloud computing, and low-power mobile computing. Cloud computing, while providing high performance, is very costly. Google and Microsoft Bing spend billions of dollars each year to maintain their server farms, mainly due to the high power bills. On the other hand, mobile computing is under a very tight energy budget, but yet the end users demand ever increasing performance on these devices. This trend indicates that conventional architectures are not able to deliver high-performance and low power consumption at the same time, and we need a new architecture model to address the needs of both extremes. In this paper, we thus introduce our Extremely Heterogeneous Architecture (EHA) project: EHA is a novel architecture that incorporates both general-purpose and specialized cores on the same chip. The general-purpose cores take care of generic control and computation. On the other hand, the specialized cores, including GPU, hard accelerators (ASIC accelerators), and soft accelerators (FPGAs), are designed for accelerating frequently used or heavy weight applications. When acceleration is not needed, the specialized cores are turned off to reduce power consumption. We demonstrate that EHA is able to improve performance through acceleration, and at the same time reduce power consumption. Since EHA is a heterogeneous architecture, it is suitable for accelerating heterogeneous workloads on the same chip. For example, data centers and clouds provide many services, including media streaming, searching, indexing, scientific computations. The ultimate goal of the EHA project is two-fold: first, to design a chip that is able to run different cloud services on it, and through this design, we would be able to greatly reduce the cost, both recurring and non-recurring, of data centers\clouds; second, to design a light-weight EHA that runs on mobile devices, providing end users with improved experience even under tight battery budget constraints.

The chromatic number of a graph G, denoted χ(G) is the minimum number of colors needed to color vertices of G so that no two adjacent vertices share the same color. A functigraph over a given graph is obtained as follows: Let G' be a disjoint copy of a given G and f be a function f : V(G) → V(G'). The functigraph over G, denoted by C(G, f), is the graph with V(C(G, f)) = V(G) ∪ V(G') and E(C(G, f)) = E(G) ∪ E(G') ∪ {uv : u ∈ V(G), v ∈ V(G'), v = f(u)}. Recently, Chen et al. proved that . In this paper, we first provide sufficient conditions on functions f to reach the lower bound for any graph. We then study the attainability of the chromatic numbers of functigraphs. Finally, we extend the definition of a functigraph in different ways and then investigate the bounds of chromatic numbers of such graphs.

In this paper, a load balancing mechanism based on cell breathing was proposed for the hybrid EPON-WiMAX network. The hybrid EPON-WiMAX network integrates the emerging Ethernet Passive Optical Network (EPON) and Worldwide Interoperability for Microwave Access (WiMAX) technologies to support high bandwidth, broadband access, mobility, and Quality of Service (QoS) guarantee. Load balancing is required due to uneven distribution of traffic among different WiMAX base stations (BS) as well as mobility of WiMAX Subscriber Station (SS). Cell breathing is a well-known load balancing technique adopted in wireless networks. By changing the transmission powers of the base stations, the cell breathing technique is able to adjust the traffic, both uplink and downlink, load of base stations. In the integrated EPON-WiMAX network, transmission powers of WiMAX base stations can be optimally allocated at the EPON optical networking unit (ONU). In this paper, we formulate the cell breathing-based load balancing problem into a linear programming problem. Our goal is to find the best power adjustment that maximizes system throughput. To alleviate the time complexity of solving the linear programming problem, we propose a Heaviest Load First Algorithm (HLFA) to obtain the near optimal solution. The solution obtained by the HLFA algorithm will not waste any transmission power to make the entire system energy-efficiency. We demonstrate the performance of HLFA via extensive simulations. The simulation results show that the HLFA can provide the best solution to achieve load balancing and enhance the system throughput as compared to existing solutions. Furthermore, HLFA maintains good performance even when the system has multiple overloaded ONU-BSs.

Energy efficiency is an important issue of wireless sensor networks. In-network data aggregation is a data collection technique that improves energy efficiency and alleviates congestive routing traffic by reducing data forwarding in wireless sensor networks. Ant-colony aggregation is a distributed algorithm that provides an intrinsic way of exploring the search space to optimize settings for optimal data aggregation. This paper aims to refine the heuristic function and the aggregation node selection method to maximize energy efficiency and to extend network lifetime. Two proposed algorithms are shown to yield longer maximum lifetime than the conventional algorithm with the same hop-count delay. One of the proposed algorithms is shown to have improved scalability than the conventional algorithm.

The handoff rate is identified in a hybrid channel allocation algorithm of a wireless and mobile network. In this network model, both new calls and handoff calls to a cell have possibilities to borrow channels from the control center if all allocated channels to the cell are used up. In addition, there is a buffer with finite size reserved exclusively for handoff calls in a cell when they arrived but are not be able to borrow channels from the control center. In this paper, we derived a novel and theoretical expression of the handoff rate in the network first. This analytical expression for handoff rate is a general form which covers several special cases discussed in the literature. Then, an efficient iterative algorithm to calculate this handoff rate is proposed also. By using this algorithm, the handoff rate and the blocking probability of both new calls and handoff calls will be successfully derived at the same time. Furthermore, our numerical analysis validates the algorithm and the corresponding theoretic expression.