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Cloud service for education purpose is the critical infrastructure for the smart campus. The state-of-the-art education clouds are usually developed and maintained by individual schools. The isolation nature makes these data being tampered easily, which leads to malicious tampering and information fragmentation. The blockchain is a perfect technology to address these issues. By leveraging the advantages of the public blockchain, consortium blockchain, and private blockchain, we propose EduBloud, a heterogeneous blockchain system empowered education cloud. The system showed higher reliability, lower latency, higher data throughput, and better economic efficiency than homogeneous blockchain implementations.

The creative service process is an innovative process that uses newly developed technologies to improve the service models currently used. In this paper, we proposed an IoT-based Cloud-Fog computing platform for the creative service process. The proposed Cloud-Fog computing platform is a distributed computing platform where compute, storage, and network systems of Cloud and Fog are working independently or collaboratively depending on the services performed by the platform. If a service requires resources either from Cloud or Fog, then only the designated Cloud or Fog will be involved. Otherwise, both Cloud and Fog will be collaborated to each other for the service. During the collaboration, the required data may be back and forth in between Cloud and Fog. To demonstrate the usage of the proposed platform for the creative service process, two service examples, smart classroom and city surveillance, are discussed

With the development of network technology, there are billions of devices accessing resources and services on the cloud through mobile telecommunication network. A great number of connections and data packets must be handled by the mobile network. It not only consumes the limited spectrum resources and network bandwidth, but also reduces the service quality of applications. To alleviate the problem, the concept of Mobile Edge Computing (MEC) has been proposed by European Telecommunications Standard Institute (ETSI) in 2014. MEC suggests to provide IT and cloud computing capabilities at the network edge to offer low-latency and high-bandwidth service. The architecture and the benefits of MEC have been discussed in many recent literature. But the implementation of underlying network is rarely discussed or evaluated in practice. In this paper, we present our prototype implementation of a MEC platform by developing an application-aware traffic redirection mechanism at edge network to reduce service latency and network bandwidth consumption. Our implementation is based on OAI, an open source project of 5G SoftRAN cellular system. To the best of our knowledge, it is also one of the few MEC solutions that have been built for 5G networks in practice.

IoT applications are built on top of M2M platforms which facilitate the communication infrastructure among devices and to the clouds. Because of increasing M2M communication traffic and limited edge network bandwidth, it has become a crucial problem of M2M platform to prevent network congestion and service delay. A general approach is to deploy IoT service modules in M2M platform, so that data can be pre-processed and reduced before transmitting over the networks. Moreover, the service modules often need to be deployed dynamically at various locations of M2M platform to accommodate the mobility of devices moving across access networks, and the on-demand service requirement from users. However, existing M2M platforms have limited support to deployment dynamically and automatically. Therefore, the objective of our work is to build a dynamic module deployment framework in M2M platform to manage and optimize module deployment automatically according to user service requirements. We achieved the goal by implementing a solution that integrates a OSGi-based Application Framework(Kura), with a M2M platform(OM2M). By exploiting the resource reuse method in OSGi specification, we were able to reduce the module deployment time by 50~52%. Finally, a computation efficient and near-optimal algorithm was proposed to optimize the the module placement decision in our framework.

Cloud storage services are pervasive nowadays. Many cloud storage services use distributed file systems as their backend storage systems. Some research results on file-size distribution of file systems show that file systems contain lots of small files. Therefore, this paper proposed a hybrid distributed file system based on Ceph and HDFS that can deliver satisfactory write throughputs for a cloud storage system with 80–90% small files and 10–20% large files. The experimental results show that the file allocation mechanism without RAM disk caching can improve the write throughputs of Ceph and HDFS by approximately 10% to 50%. While the one with RAM disk caching can have up to 200% write throughputs improvement.

Cloud computing is a forthcoming revolution in information technology industry because of its performance, accessibility and, low cost. It is appropriate to maximize the capacity or step up capabilities vigorously without investing in new infrastructure, nurturing new personnel or licensing new software. The federated cloud, which is the combination of more than one cloud, is the next logical step after hybrid cloud and there are many indicators that are showing more requirements for such a model. Security is the challenging issue in all cloud infrastructures such as single cloud and federated cloud. And, it is significant in distributed systems which have highly fault tolerant. One of the algorithms for this issue is Byzantine Fault Tolerant. This paper introduces a new method, that optimizes the Byzantine Fault Tolerant and decrease the latency, and detecting the number of faults.

Driven by the trends of BigData and Cloud computing, there is a growing demand for processing and analyzing data that are generated and stored across geo-distributed data centers. However, due to the limited network bandwidth between data centers and the growing data volume spread across different locations, it has become increasingly inefficient to aggregate data and to perform computations at a single data center. An approach that has been commonly used by data-intensive cluster computation systems, like Hadoop, is to distribute computations based on data locality so that data can be processed locally to reduce the network overhead and improve performance. But limited work has been done to adapt and evaluate such technique for geo-distributed data centers. In this paper, we proposed DRASH (Data-Replication Aware Scheduler), a job scheduling algorithm that enforces data locality to prevent data transfer, and exploits data replications to improve overall system performance. Our evaluation using simulations with realistic workload traces shows that DRASH can outperform other existing approaches by 16% to 60% in average job completion time, and achieve greater improvements under higher data replication factors.

The primary-copy consistency model used in Ceph cannot satisfy the low latency requirement of write operation required by users. In this paper, we propose a weak consistency model, PROAR, based on a distributed hash ring mechanism to allow clients to only commit data to the primary node and synchronize data to replication nodes asynchronously in Ceph. Based on the distributed hash ring mechanism, the low latency requirement of write operation can be met. In addition, the workload of the primary node can be reduced while that of replication nodes can be more balanced. We have evaluated the proposed scheme on a Ceph storage system with 3 storage nodes. The experimental results show that PROAR can reduce about 50% write overhead compared to that of Ceph and has a more balanced workload around all the replication nodes.

The increasing trend in IT users and their needs for computational power in cloud data centers leads to noticeable growth in physical servers. It is a challenging issue which causes the dramatic burden of power consumption and the number of Physical machines. Virtualization is remarkable method for reducing the number of physical servers with appropriate processing performance and utilization. But, it is worth saying that the fulfilling the resource utilization is still one of the significant challenging issue, especially in in data centers environment. Actually, there are some applications situated on a large single virtual machine. One way to guarantee the reasonable physical server utilization is to let the application to be split and hosted on smaller virtual machines with the sufficient computational power. Although exploiting multiple small virtual machine instead of one large virtual machine benefits appropriate physical resources utilization and reducing the number of turn on physical machine, it is sustained penalty in terms of demanding extra resources due to map the applications on new virtual machines. However, existing research have not clarified precisely the reason in terms of that the data center is sustained extra resources and computational power overhead due to splitting the original application and exploiting more smaller virtual machines provided to preserve the criteria of the original application on the large virtual machine. This paper demonstrates through mathematical modelling that the physical resource providers, which are situated in cloud data center, endure the penalty in terms of extra physical resources. The mentioned mathematical modeling in this paper will be noticeable in cloud data center energy efficiency and physical resource utilization performance.

In order to manage the Internet of things in a flexible and efficient way, this paper proposes a novel M2M framework using smart/HetNet gateways. Our approach is not only compatible to the M2M standard, but also enables the community-based coordination among gateways and devices. With smart and HetNet gateways, various types of requirements and applications can be fulfilled and handled at a local region. Accordingly, unnecessary network usage is avoided so as to reduce the traffic loads in mobile networks. We also implement a prototype to sustain an application scenario of IoT. In our prototype, the lamp is automatically switched on when a human face is detected. The demonstration shows that our system is practical and supports the subscription and notification in a community. Finally, experimental results reveal that some basic procedures can benefit from the shorter time and less uplink traffic if devices involved in an application are within the same region.

Graph computing is widely utilized today, which severely requires the ability of processing graphs of billion vertices rapidly for social network analyzing, bio-informational network analyzing and semantic processing. Therefore, graph processing play a significant role in the research and application development. Data of music and movie recommendation and LDA topics can be modeled as bipartite graph and perform the computation with graph processing engines. The most important step before graph computation is graph partitioning. Graph partitioning is a mature technology, however, most of classic graph partitioning algorithms require iterative calculation for several times, which causes high time complexity. Some algorithms with short partitioning time proposed these years, but they cannot be used in bipartite graph directly. This paper proposes a new bipartite graph partitioning algorithm, BiFennel, which effectively decreases graph processing time and network loading by reducing vertex replication factor and maintaining work balance. We implement BiFennel in a popular graph engine called PowerGraph. The performance results show that BiFennel has 29~55% improvement on communication cost and 21~49% improvement on overall runtime comparing with Aweto.

As the huge growth of mobile traffic amount, conventional Radio Access Networks (RANs) suffer from high capital and operating expenditures, especially when new cellular standards are deployed. Software, and cloud RANs have been proposed, but the stringent latency requirements e.g., 1 ms transmission time interval, dictated by cellular networks is difficult to satisfy. We first present a real software RAN testbed based on an opensource LTE implementation. We also investigate the issue of quality assurance when deploying such software RANs in cloud. In particular, running software RANs in cloud leads to high latency, which may violate the latency requirements. We empirically study the problem of minimizing computational and networking latencies in lightweight container cloud. Our experiment results show the feasibility of running software RANs in real-time container cloud. More specifically, a feasible solution to host software RANs in cloud is to adopt lightweight containers with real-time kernels and fast packet processing networking.

This paper presents performance analysis for hardware Thread-Level Speculation (TLS) in the IBM Blue Gene/Q computer. Unlike traditional multi-thread programming model which uses lock to ensure the consistency of shared data, TLS is a harware mechanism to detect and resolve memory access conflicts among threads. The model shows good performance prediction, as verified by the experiments. This study helps to understand potential gains from using special purpose TLS hardware to accelerate the performance of codes that, in a strict sense, require serial processing to avoid memory conflicts. Furthermore, based on analysis and measurements of the TLS behavior and its overhead together with OpenMP comparison, a strategy is proposed to help utilize this hardware feature. The results also suggest potential improvement for the future TLS architectural designs.

With the need of data storage increases tremendously nowadays, distributed file system becomes the most important data storage system in cloud computing. In distributed file system development, there are many researchers work hard to refine the architecture to provide scalability and reliability. In our work, we propose a distributed metaserver system including metaserver scale-out, metadata replication, metaserver recovery, and metaserver management recovery mechanisms. In our experiments, the proposed system can increase the capacity of metadata and increase the reliability by fault tolerance mechanism. The overhead of read/write data is very little in the proposed system as well.

More cooperation or coordination is necessary for future UDN (Ultra-Dense Network) scenario of 5G network, of which ICIC (Inter-Cell Interference Coordination) is a promising and typical scheme. Cooperating cellular network may be thought as a special kind of distributed computing system, whose basic theory and toolsets could be adopted. CAP theory for distributed computing state that any networked shared-data system can have at most two of three desirable properties: consistency (C), high availability (A) and tolerance to network partitions (P) of the data. Since partition could not be avoided in such system, consistency or availability has to be forfeited to a certain extent. This paper extends the CAP theorem to the UDN case, and redefines the consistency, availability and partition-tolerance in the context of future cellular network architecture. Then ICIC process are interpreted with cellular CAP theorem, and the evaluation framework is developed. An improved ICIC scheme is proposed based on such framework.

On behalf of the 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2014) Organizing Committee, we are very pleased to announce that more than three hundred researchers and contributors from the world submitted their papers to share their research results and new ideas. The objective of this conference to provide a major international forum for scientists, engineers, and users to exchange and share their experiences, new ideas, and latest research results on all aspects of parallel and distributed computing systems.

For better efficiency of parallel and distributed computing, Apache Hadoop distributes the imported data randomly on data nodes. This mechanism provides some advantages for general data analysis. With the same concept Apache Sqoop separates each table into four parts and randomly distributes them on data nodes. However, there is still a database performance concern with this data placement mechanism. This paper proposes a Correlation Aware method on Sqoop (CA_Sqoop) to improve the data placement. By gathering related data as closer as it could be to reduce the data transformation cost on the network and improve the performance in terms of database usage. The CA_Sqoop also considers the table correlation and size for better data locality and query efficiency. Simulation results show that data locality of CA_Sqoop is two times better than that of original Apache Sqoop.

This paper presents a preliminary performance model for hardware Thread-Level Speculation (TLS) in the IBM Blue Gene/Q computer. The model analyzes the TLS behavior and its overhead. We model the scenario when there are 0, 1 and 2 conflicts. The model shows good performance prediction and is verified with experiments. This study helps to understand potential gains from using special purpose TLS hardware to accelerate the performance of codes that, in a strict sense, require serial processing to avoid memory conflicts.

This paper introduces a prototype of Taiwan UniCloud, a community-driven hybrid cloud platform for academics in Taiwan. The goal is to leverage resources in multiple clouds among different organizations. Each self-managing cloud can join the UniCloud platform to share its resources and simultaneously benefit from other clouds with scale-out capabilities. Accordingly, resources are elastic and sharable with each other such as to afford unexpected resource demands to each cloud. The proposed platform provides a web portal to operate each cloud via a uniform user interface. The construction of virtual clusters with multi-core VMs is supplied for parallel and distributed processing models. An object-based storage system is also delivered to federate different storage providers. This paper not only presents the architectural design of Taiwan UniCloud, but also evaluates the performance to demonstrate the possibility of current implementation. Experimental results show the feasibility of the proposed platform as well as the benefit from the cloud federation.

Big data refers to data that is so large that it exceeds the processing capabilities of traditional systems. Big data can be awkward to work and the storage, processing and analysis of big data can be problematic. MapReduce is a recent programming model that can handle big data. MapReduce achieves this by distributing the storage and processing of data amongst a large number of computers (nodes). However, this means the time required to process a MapReduce job is dependent on whichever node is last to complete a task. This problem is exacerbated by heterogeneous environments. In this paper we propose a method to improve MapReduce execution in heterogeneous environments. This is done by dynamically partitioning data during the Map phase and by using virtual machine mapping in the Reduce phase in order to maximize resource utilization.

With the ability to provide on-demand service and to reduce the IT cost, cloud computing becomes more and more popular recently. Virtualization is one of the important technologies in cloud computing, whose main idea is to provide abstractions of the physical resources. However, such abstraction can cause performance degradation, especially for I/O virtualization, which is usually the performance bottleneck in cloud computing. InfiniBand is a network system that provides very low latency (less than 5us) and very high bandwidth (multiple Gbps). Due to its excellent performance, InfiniBand is commonly used in high performance computing (HPC) area. In this paper, we propose Virt-IB for InfiniBand virtualization on Kernel-based Virtual Machine (KVM). The main components of Virt-IB are VM IB library and Virt-IB driver. Our design processes InfiniBand APIs directly in guest VM and communicates with InfiniBand device indirectly to perform the real operations. VM IB library provides API interface and user-level InfiniBand driver. Virt-IB driver provides a channel for VM IB library to write commands into InfiniBand device indirectly. Evaluation results show that our current work is better than network virtualization and it can achieve about 50% performance of native InfiniBand.

As the scale of datacenter continues to grow, it is hard to keep servers homogenous, with the same hardware and performance characteristics. Today's datacenters commonly operates on several generations of servers from multiple vendors, and mix both high-end and low-end devices together to deliver service quality requirement with lowest cost. However, the heterogenous environment also complicates the management of the datacenters, especially in terms of resource allocation. In this paper, we focus on the resource allocation of a tightly unified block-level storage with SSD and HDD. We conduct experiments to quantify the performance of difference access patterns on each type storage devices. Then formulate our resource allocation problem into a ILP (Integer Linear Programming), and proposed data migration algorithms based on the observations. We evaluate our solution by implementing a heterogenous storage consist of HDD, SDD and iSCSI HDD, and show the data access response time can be reduced by 27%.

Multiple sequence alignments with constrains has become an important problem in the computational biology. The concept of constrained sequence alignment is proposed to incorporate the biologist's domain knowledge into sequence alignments such that the user-specified residues/segments are aligned together in the alignment results. Over the past decade, a series of constrained multiple sequence alignment tools were proposed in the literature. GPU-REMuSiC is a newest tool with the regular expression constrains and uses the graphics processing units (GPUs) with CUDA. GPU-REMuSiC can achieve 29× speedups for overall computation time by the experimental results. However, the execution environment of GPU-REMuSiC need to build, and it's a threshold for biologists to set up. Therefore, we design an intuitive friendly user interface for the potential cloud server with GPUs. Use the user interface through network, we can send the input data to remote server without cumbersome setting in local host. Finally, we can receive the alignment results from the remote cloud server with GPUs.

Over the past decade, the development of Internet technology has led to more awareness on the powerfulness of Peer-to-Peer (P2P) overlay network. How to efficiently establish or maintain overlay networks in large-scale environments always are important issues. Besides, the ways of improving routing efficiency also attract much attention. This study proposes a replica mechanism based on the Arrangement Graph-based Overlay (AGO), and enhances the joining procedures of the AGO. Enhanced AGO reduces system overhead by reducing large number of polling messages of the joining process. In addition, replica mechanism is also integrated into the enhanced AGO to buttress the efficiency of the searching algorithm. Experimental results of the enhanced AGO have demonstrated that efficient routing as well as less bandwidth consumption during communication can be realized.

Communication-aware task mapping algorithms, which map parallel tasks onto processing nodes according to the communication patterns of applications, are essential to reduce the communication time in modern high performance computing. In this paper, we design algorithms specifically for interconnected multicore systems, whose architectural property, namely small number of cores per node, large number of nodes, and large performance gap between the communication within a multicore and among multicores, had brought new challenges and opportunities to the mapping problem. Let k be the number of cores per multicore and n be the number of tasks. We consider the practical case that k « n for k = 2, 4, and 6. The designed algorithms are optimal for the mapping measurement, called Maximum Interconnective Message Size (MIMS), and of time complexity merely O(mlogm) for m communication pairs. Thus, they are highly scalable for large applications. We had experimented the algorithms on the IBM Blue Gene/P system for two synthetic benchmarks and two applications. The results show good communication performance improvement.

As the computational power of Graphics Processing Unit (GPU) increases, data transmission becomes the major performance bottleneck. In this study, we investigate two techniques, data streaming and data compression, to reduce the communication cost on GPU. Data streaming enables overlap of communication and computation, whereas data compression reduces the data size transferred among different memory spaces. Although both techniques increase computation cost, overall performance can still be enhanced by reducing communication cost. We demonstrate the effectiveness of the two techniques via two case studies: radix sort and 3-star, a deployment algorithm in wireless sensor networks. For radix sort, a new algorithm, which mixes MSD and LSD algorithms and employs data streaming, is presented. Its performance is 25% faster than the fastest GPU radix sort implementation currently available in the public domain. For the 3-star algorithm, the speed increases several hundreds of times faster than that obtained by the CPU code. The data streaming and data compression, which is a hybrid CPU-GPU algorithm, provide an additional 54% performance improvement to the GPU implementation. Data compression not only reduces communication cost, but also improves the computation time, by which further performance enhancement can be achieved.

With the consideration of scalability and robustness, distributed computing systems such as grids and clouds may exploit the P2P approach to enhance their performance. However, conventional techniques in P2P systems cannot be applied directly into grid systems due to restricted sort of queries for desired resources. In this paper, we consider a fully decentralized resource discovery service based on an unstructured overlay, where the major challenge is to locate desired resources without the global knowledge about sharing resource information. Consequently, more nodes involved in the resource discovery scheme may incur higher network overhead. To achieve an efficient resource discovery, this paper aims to alleviate the network traffic among unstructured information systems. Relying on the information of resource attributes and characteristics, we propose the direction-aware resource discovery scheme to improve the overall performance. Experimental results illustrate that the proposed approach is efficient and scalable compared with conventional approaches.

A full system emulator, such as QEMU, can provide a versatile virtual platform for software development. However, most current system simulators do not have sufficient support for multi-processor emulations to effectively utilize the underlying parallelism presented by today's multi-core processors. In this paper, we focus on parallelizing a system emulator and implement a prototype parallel emulator based on the widely used QEMU. Using this parallel QEMU, emulating an ARM11MPCore platform on a quad-core Intel i7 machine with the SPLASH-2 benchmarks, we have achieved 3.8x speedup over the original QEMU design. We have also evaluated and compared the performance impact of two different parallelization strategies, one with minimum sharing among emulated CPU, and one with maximum sharing.

In this paper, we study a coalitional game approach to resource allocation in a multi-channel cooperative cognitive radio network with multiple primary users (PUs) and secondary users (SUs). We propose to form the grand coalition by grouping all PUs and SUs in a set, where each PU can lease its spectrum to all SUs in a time-division manner while the SUs in return help the data transmission of PUs by relaying. The grand coalition is shown to be stable in the considered scenario by justifying that the solution concept of the coalitional game (the core) is nonempty. Also, optimal relay strategies of SUs, to relay or to transmit its own data, are obtained so that the sum rate of all PUs and SUs is maximized. Finally, we demonstrate in simulation the benefits of the grand coalition comparing to the case of direct transmission only and other forms of coalitions as well.

With the continuous innovation of advanced Internet technology, Peer-to-Peer (P2P) system emerges as an important information-sharing system for the widespread exchange of resources and information among thousands of users. In this study, we applied properties of arrangement graphs to design a newly structured overlay system, named as Arrangement Graph-based Overlay (AGO). In such an overlay, the IDs between the two adjacent nodes differ only one digit and thus, the joining and leaving processes are easy while keeping the low maintenance cost. Furthermore, the searching performance in the AGO system is efficient, adaptive and scalable. Analyses of experimental results show that the establishment of the system and the node searching could achieve better performance than those obtained in the Chord system.

Communication-aware task mapping algorithms, which map parallel tasks onto processing nodes according to the communication patterns of applications, are essential to reduce the communication time in modern high performance computing. In this paper, we design algorithms specifically for interconnected multicore systems, whose architectural property, namely small number of cores per node, large number of nodes, and large performance gap between the communication within a multicore and among multicores, had brought new challenges and opportunities to the mapping problem. Let k be the number of cores per multicore and n be the number of tasks. We consider the practical case that k is much smaller than n, for k = 2, 4, and 6. The designed algorithms are optimal for the mapping measurement, called Maximum Interconnective Message Size (MIMS), and of time complexity merely O(mlogm) for m communication pairs. Thus, they are highly scalable for large applications. We had experimented the algorithms on the IBM Blue Gene/P system for two synthetic benchmarks and two applications. The results show good communication performance improvement.

The ubiquity of many-core architectures brings challenges in making scalable application software, changing dramatically from the way applications are traditionally developed. Optimization of programs for many-core platforms is a multifaceted problem, where system and architectural factors should be taken into consideration. In this paper, we attack the problem on the aspect of programming paradigm. We propose a hybrid producer-write plus consumer-read shared-memory programming paradigm for implementation of a master-worker video decoder on the TILE64 many-core platform. To evaluate the scalability and performance benefits of different programing paradigms, a Motion JPEG decoder is parallelized using master-worker structure and implemented with combinations of consumer-read programming and producer-write programming. Experimental results show that the proposed implementation obtained competitive performance speedup, scaling well with number of available cores and up to 4 times performance improvement over other implementations on the decoding of a 1080P video.

This paper proposes a new hardware accelerator to speed up the performance of vector graphics applications on complex embedded systems. The resulting hardware accelerator is synthesized on a field-programmable gate array (FPGA) and integrated with software components. The paper also introduces a hardware/software co-verification environment which provides in-system at-speed functional verification and performance evaluation to verify the hardware/software integrated architecture. The experimental results demonstrate that the integrated hardware accelerator is fifty times faster than a compiler-optimized software component and it enables vector graphics applications to run nearly two times faster.

In this paper, we investigate efficient algorithms and implementations using GPU plus CPU to solve the rectangle intersection problem on a plane. The problem is to report all intersecting pairs of iso-oriented rectangles, whose parallelization on GPUs poses two major computational challenges: data partition and the massive output. The algorithm we presented is called PRI-GC, Parallel Rectangle Intersection algorithm on GPU+CPU, which consists of two phases: mapping and intersection-checking. In the mapping phase, rectangles are hashed into different subspaces (called cells) to reduce the unnecessary intersection checking for far-apart rectangles. In the intersection-checking phase, pairs of rectangles within the same cell are examined in parallel, and the intersecting pairs of rectangles are reported. Several optimization techniques, including rectangles re-ordering, output data compressing/encoding, and the execution overlapping of GPU and CPU, are applied to enhance the performance. We had evaluated the performance of PRI-GC and the result shows over 30x speedup against two well-implemented sequential algorithms on single CPU. The effectiveness of each optimization technique for this problem was evaluated as well. Several parameters, including different degrees of rectangle coverage, different block sizes, and different cell sizes, were also experimented to explore their influences on the performance of PRI-GC.

High Level Architecture (HLA) is a distributed simulation architecture which is applied to many simulation environments. In most of these environments, the simulation entities (called the federate in HLA) communicate with each other by interconnected local area networks (LANs). Due to the communications among federates which are in the same LAN have shorter latency and higher bandwidth, this paper proposes a message publish/subscribe scheme of HLA based on structured peer-to-peer overlay according to the principle of locality, by increasing the size of affordable federates and the size of workload by using the same hardware environment. Moreover, the run-time infrastructure (RTI) is implemented in accordance with HLA for performance evaluation. Experimental results show that the proposed scheme improves the performance of simulations.

Multiple sequence alignment is a scientific tool to assist the study of DNA homology, phylogeny determinations, and conserved motifs identification. Various heuristic MSA methods have been presented to obtain the resulting alignment for multiple sequences. Although these alignment tools are able to align protein, DNA, and RNA sequences successfully, they are not such successful in aligning coding region sequences because the resulting alignments maybe not consistent with practical observations. Therefore, we propose a method, CORAL-M, a heuristic coding regions alignment method for multiple genome sequences, especially for coding regions. CORAL-M adopts a probabilistic filtration model and the local optimal solution to align genome sequences (codon to codon with the wobble mask rule) by the sliding windows and, thus, obtains the near-optimal alignment in linear time. In the experimental results, CORAL-M can be used to find the potential function sites by aligning viral strains of Poliovirus 1–3, Enterovirus 71, and Coxsackievirus 16.

In this paper, we propose a tiling-based wireless sensor network (WSN) deployment approach based on the polygon model for sensor nodes with directional sensing areas. In the tiling-based deployment approach, a hexagon tile is first generated from the polygon that represents the sensing area of a given directional sensor. Then, a tiling process is applied to place tiles to the deployment area. Both sensing coverage holes surrounding the boundaries and the obstacles are considered under the proposed approach. To evaluate the proposed deployment approach, we compare its performance with the strip-based deployment pattern approach, which is under the sector model, in terms of the sensing coverage rate and the usage of sensor nodes. The simulation results show that the sensing coverage rate of the proposed deployment approach is higher than that of the strip-based deployment pattern approach for different types of sensor nodes on deployment areas with/without obstacles.

Although medical technologies developed in the twenty-first century have successfully increased man's life span, the pressure of modern life has consequently brought many modern civilization diseases and chronic illness. When all these problems are tackled by hospitals, they will consume considerable amount of medical resources. Alternatively, providing health care services at home is an important issue for improving personal health and save hospital resources. In this paper, we present an ongoing project that designs and implements a pervasive health service infrastructure based on the grid system which is integrated with the P2P's resource sharing mechanism, to provide the personal health service. The personal health status is recorded, monitored, and even mined in/from the proposed pervasive health service system for preventive medicine. Additionally, wireless sensor equipments for mobile personal health services are also integrated into the pervasive health service system, in order to construct a situation-aware, context-aware and environment-aware mobile-health-service platform.

Data distribution management (DDM) aims to reduce the transmission of irrelevant data between High Level Architecture (HLA) compliant simulators by taking their interesting regions into account (i.e. region matching). In a large-scale simulation, computation intensive region matching would have a direct impact on the simulation performance. To deal with the high computation cost of region matching, the whole process of region matching is offloaded to graphical processing units (GPUs) based on Computer Unified Device Architecture (CUDA). Two approaches are proposed to perform region matching in parallel. Several metrics, including different numbers of regions, different sizes of regions and different distributions of regions, are used in the experimental tests. The experimental results indicate that the performance of region matching on a GPU can be improved more than one or two orders of magnitude in comparison with that on a CPU.

P2P live streaming networks recently become an emerging research topic. In such multimedia steaming networks, autonomous users cooperate with each other to provide a distributed, scalable, and cost-efficient transmission environment. However, since each user is self-interested such that they want to receive high-quality video with minimum upload bandwidth, full cooperation and stable playback quality can not be guaranteed. This paper proposes the design of a two-phase, contribution-aware mechanism for P2P live streaming networks. We first propose our two-layered system architecture and the problem statements. Then, we propose a contribution-aware mechanism for identifying the stable peers. We also propose the method concept of stimulating peer cooperation in such a hybrid push-pull P2P streaming platform.

Leakage power becomes a key challenge and occupies an increasing portion of the total power consumption in nano-scale circuit design. There are many novel cache designs to reduce the leakage power based on the characteristics of programs. One of them is Asymmetric SRAM that can reduce leakage power on cache while storing bit "0". In this paper, we propose two algorithms, value-position-switch algorithm and code-bit-switch algorithm, to make the JPEG image bias on bit "0" based on Asymmetric SRAM. The value-position-switch algorithm and code-bit-switch algorithm can reduce the amount of bit "1" in Huffman coded data up to 7.33% and 25.20%, respectively. The overheads of instruction count, cycle count and power consumption for these two algorithms are negligible (

Profile-profile alignment is an important technique in the computational biology filed. Several profile–profile alignment methods have been proposed to improve the sensitivity and the alignment quality compared with other sequence–sequence and profile–sequence methods. An increasing number of studies indicated that the three-way alignment may provide additional information or more accurate alignment result than the pair-wise alignment does. Therefore, we propose the dynamic programming based three-profile alignment method, TPA, at first to align three profiles simultaneously. The time and space complexities of TPA are O(n^3) and O(n^2), respectively. To reduce the complexities of TPA, we further develop the parallel version of TPA, PTPA, which achieves O(n^3/p) time and O(n^2/p) space complexities, where p is the number of the processor. In the case study I, the result presented that PTPA can find more conserve candidates than those by the profile-profile alignment method (CLUSTALW). In the case study II, we applied the PTPA to the Feature Amplified Voting Algorithm (FAVA) to analysis the Amidohydrolase superfamily. Several amino acid residues those were known to be related to the function or the structure of mammalian imidase are identified by PTPA-FAVA.

In a heterogeneous grid environment, there are two major factors which would severely affect overall system performance: speed heterogeneity and resource fragmentation. Moreover, the relative effect of these two factors changes with different workload and resource conditions. Processor allocation methods have to deal with this issue. However, most existing allocation methods focus on one of these two factors. This paper first analyzes the relative strength of different existing methods. Based on the analysis, we propose an intelligent processor allocation method which considers both the speed heterogeneity and resource fragmentation effects. Extensive simulation studies have been conducted to show that the proposed method can effectively deliver better performance under most resource and workload conditions.

Recently, the resource sharing systems apply the P2P technique to provide scalable multi-attribute range queries. However, due to the heterogeneity of resources and the variation of sharing policies in different providers, current P2P-based resource discovery systems may suffer the load imbalance problem in a large scale distributed system. In this paper, we propose a self-adaptive resource index and discovery system (SARIDS) to achieve load balancing. SARIDS adopts a two-tier architecture based on the structured P2P overlay. The intra-overlay is constructed by normal peers with the same attribute via the locality preserving hash function; and, the inter-overlay is constructed by super-peers with classified attributes in different intra-overlays. SARIDS supports not only the multi-attribute range queries but also the self-adaptive mechanisms for load balancing in the intra-overlay and among the intra-overlays. The simulation results show that SARIDS is scalable and efficient for load balancing even in the non-uniform peer range environment.

The multicast operation is a useful operation in parallel applications. With the hardware supported multicast of the InfiniBand Architecture (IBA), we propose a multicast scheme for mxn mesh InfiniBand networks based on the XY routing scheme. The basic concept of the proposed multicast scheme is to find the union sets of the output ports of switches that are in the paths between the source node and each destination node in a multicast group. We have implemented the proposed multicast scheme on a 2-D mesh InfiniBand network simulator. Several multicast cases with different message size and different traffic workload are simulated. The simulation results show that the proposed multicast scheme outperforms their corresponding unicast scheme for all simulated cases. The larger the message size, the number of multicast source nodes, and the size of the multicast group, the better speedup can be expected from the proposed multicast scheme.