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The CPU---GPU communication bottleneck limits the performance improvement of GPU applications in heterogeneous GPGPU systems and usually is handled by data reuse optimization. This paper analyzes data reuse through DAG abstraction and obtains rules showing that the run-time data reuse optimization can effectively relieve the bottleneck. Based on the rules, this paper proposes a run-time optimization framework for data reuse, called R-Tracker. The R-Tracker uses locality-aware searching approach to handle reuses. It can not only low costly implement the data reuse optimization but also effectively implement the searching, the data transfers, and the GPU computation concurrently. R-Tracker relaxes the constraints that are required in compiler-based approaches and thus achieves better reuse effect. The experimental results show that R-Tracker improves the performance by 1.77---16.42 % over compiler-based approach OpenMPC and 1.40---8.39 % over CGCM in single-node execution, and 48.78---60 % over CGCM in multi-node execution.

Security is a paramount concern for various applications, such as electronic voting system as sensitive data is to be transmitted among the nodes during transmission. Recently, Wu and Lin (Inf Technol Control 42:231---237, 2013) proposed a new probabilistic signature scheme and used it to construct two universal designated verifier signature schemes. They demonstrated that their scheme was secure under the bilinear inverse Diffie---Hellman assumption. However, in this paper, we will show that their scheme cannot provide unforgeability, i.e., the adversary could forge a legal signature of any message. The analysis shows that their schemes are not suitable for practical applications. To overcome these weaknesses, we proposed three improved schemes which are provably secure in the random oracles. In summary, our contributions in this paper was listed in following three folds: (1) we have shown that Wu et al. schemes cannot provide unforgeability, i.e., the adversary could forge a legal signature of any message. (2) We have proposed three improved schemes which overcomes the drawbacks of Wu et al. scheme and finally, (3) we have implemented the real-time packet analyzer for detection of invalid signature in electronic voting system using Snort. The analysis shows that the existing scheme is not suitable for practical applications and our implemented scheme is able to detect the vulnerability in the electronic voting system. The designed scheme generated alerts at regular intervals so that administrator takes adequate measures to mitigate the intrusion presented in the system.

Database systems are widely used in many fields. As the scale of database systems increases, more firms decide to deploy the database system in a distributed environment for the sake of data integrity fault tolerance. For guaranteeing the consistency of data, database management systems adopt isolation policies. This paper discusses probabilistic models for snapshot isolation of database management system. Snapshot isolation is an effective method to enhance the consistency of database system, although it degrades the system performance where the system has large network latency such as distributed database system. Also, under the failure-prone environment, a restart scheme is considered as one countermeasure. This paper proposes probabilistic models for the dynamics of snapshot isolation of database system and exhibits the optimization of system performance with respect to updating interval of snapshot isolation within the failure-prone environment from the analytical point of view. Numerical experiments are conducted to validate the effectiveness of analytical results by using real traffic data.

Currently, most records are produced and stored digitally using various types of media storage and computer systems. Unlike physical records such as paper-based records, identifying, collecting, and analyzing digital records require technical knowledge and tools that are not found in archival institutions. As a result, archival institutions face challenges in their attempt to collect digital archives. One approach to overcome this problem is for archival institutions to use digital forensic knowledge and technologies. In this paper, we propose the Digital Archive Management System that integrates digital forensic technologies and archival information management systems to acquire, identify, analyze, and manage digital records in archival intuitions.

Securing different online e-business activities usually requires applying different cryptographic algorithms. The proxy signcryption algorithms are designed for applications such as online proxy auction or online proxy signatures on business contracts, which require a proxy agent to sign on confidential messages. This paper proposes a proxy credential forgery attack to two recent proxy signcryption schemes in the literature. Using the attack, a malicious proxy signer can create a fake proxy credential from his original credential to extend his signing power. Simple modifications to these two schemes are also provided in this paper to prevent the attack without adding too much computational complexity. In addition to the contribution of introducing a new type of attacks to signcryption schemes, the paper also points out that, while designing a secure proxy signcryption scheme, not only the unforgeability of proxy signatures is important, but also that of proxy credentials as well.

This paper presents a new unmixing-based retrieval system for remotely sensed hyperspectral imagery. The need for this kind of system is justified by the exponential growth in the volume and number of remotely sensed data sets from the surface of the Earth. This is particularly the case for hyperspectral images, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels. To deal with the high computational cost of extracting the spectral information needed to catalog new hyperspectral images in our system, we resort to efficient implementations of spectral unmixing algorithms on commodity graphics processing units (GPUs). Spectral unmixing is a very popular approach for interpreting hyperspectral data with sub-pixel precision. This paper particularly focuses on the design of the proposed framework as a web service, as well as on the efficient implementation of the system on GPUs. In addition, we present a comparison of spectral unmixing algorithms available in the system on both CPU and GPU architectures.

In recent years, the sport of mountaineering has become increasingly popular in many countries. Notably, during this activity, mountaineers are likely to enter into dangerous situations. In this paper, we propose a cloud-computing-based architecture to respond rapidly to an SOS emergency event requiring first aid. Our scheme constructs an aid platform based on cloud computing and, on the basis of the features of mobile devices, such as mobility and convenient communication, the injured party/patient can quickly send an SOS and summon aid. In this paper, we propose a scheme with which to accomplish the proxy authorization of the Schnorr signature from the patient. However, security is a very important issue as regards the communication. If an application suffers from a security challenge, the application is not practical. Therefore, the proposed scheme also integrates symmetric encryption technology, a message authentication code and RFC 2631 to defend against known attacks as well as performing an analysis with regards to achieving these security requirements provided by a proxy signature. Finally, we analyze the performance cost to determine whether our scheme is suitable for implementation in an emergency response and SOS system.

Finding similarities between two datasets is an important task in many research areas, particularly those of data mining, information retrieval, cloud computing, and biometrics. However, maintaining data protection and privacy while enabling similarity measurements has become a priority for data owners in recent years. In this paper, we study the design of an efficient and secure protocol to facilitate the Hamming distance computation between two semi-honest parties (a client and a server). In our protocol design, both parties are constrained to ensure that no extra information will be revealed other than the computed result (privacy is protected) and further, the output of the protocol is according to the prescribed functionality (correctness is guaranteed). In order to achieve these requirements, we utilize a multiplicative homomorphic cryptosystem and include chaff data into the computation. Two experimental results in this paper demonstrate the performance of both the client and the server.

Developing new mathematical frameworks such as distributed dynamic routing algorithms for constructing optimal incremental paths from a node to another node is an important challenge in data communication networks. These new algorithms can model network resources optimally and increase network performances. A bundle of single routes in a current communication path, which starts from a source node and ends to a destination node, can consist of several successive nodes and links. The Incremental term emphasizes that the number of routes (links and nodes) in a current path can change so that achieving more data rate and optimal efficiency in the network. In this paper, our problem is to add/omit some routes consisting of some nodes and links to/from the current unicast path dynamically and optimally. We call this problem the Optimal Dynamic Distributed Unicast Routing (ODDUR) problem and it is a NP-complete problem. This problem can be formulated as a new type of Linear Programming Problem (LPP) for finding a minimum cost multichannel unicast path, which this path will minimize end-to-end delay and bandwidth consumption along the routes from the source node to the destination node. In this paper, at first a new mathematical framework will be constructed and then this framework will propose the new optimal dynamic distributed unicast routing algorithm for solving our LPP problem. This algorithm will compute an optimal solution for our LPP based on the simplex method and postoptimality computations and will reduce computations and consumed time. Simulation results will show that our new algorithm is more efficient than other available algorithms in terms of utilization of bandwidths and data rate.

As a general purpose scalable parallel programming model for coding highly parallel applications, CUDA from NVIDIA provides several key abstractions: a hierarchy of thread blocks, shared memory, and barrier synchronization. It has proven to be rather effective at programming multithreaded many-core GPUs that scale transparently to hundreds of cores; as a result, scientists all over the industry and academia are using CUDA to dramatically expedite on production and codes. GPU-based clusters are likely to play an essential role in future cloud computing centers, because some computation-intensive applications may require GPUs as well as CPUs. In this paper, we adopted the PCI pass-through technology and set up virtual machines in a virtual environment; thus, we were able to use the NVIDIA graphics card and the CUDA high performance computing as well. In this way, the virtual machine has not only the virtual CPU but also the real GPU for computing. The performance of the virtual machine is predicted to increase dramatically. This paper measured the difference of performance between physical and virtual machines using CUDA, and investigated how virtual machines would verify CPU numbers under the influence of CUDA performance. At length, we compared CUDA performance of two open source virtualization hypervisor environments, with or without using PCI pass-through. Through experimental results, we will be able to tell which environment is most efficient in a virtual environment with CUDA.

In energy-aware systems, it is critical to discuss how to reduce the total electric power consumption of information systems. In this paper, we consider communication type applications where a server transmits a large volume of data to clients. A client first selects a server in a cluster of servers and issues a file transmission request to the server. In our previous studies, the transmission power consumption (TPC) model and the extended TPC (ETPC) model of a server to transmit files to clients are proposed. In this paper, we newly propose the transmission power consumption laxity-based (TPCLB) algorithm for a cluster of servers, which are based on the TPC and ETPC models so that the total power consumption in a cluster of servers can be reduced. We evaluate the TPCLB algorithm in terms of the total power consumption and elapse time compared with the round-robin (RR) algorithm.

This paper considers the fault-tolerant quorum-based mutual exclusion problem in a message-passing asynchronous system and determines a failure detector to solve the problem. This failure detector, which we call the modal failure detector star, and which we denote by M ?, is strictly weaker than the perfect failure detector P but strictly stronger than the eventually perfect failure detector ?P. The paper shows that at any environment, the problem is solvable with M ?. In addition, we make an analysis of our algorithm performance in terms of the number of messages and synchronization delay.

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application's owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application's workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG's specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.

This paper considers online energy-efficient scheduling of virtual machines (VMs) for Cloud data centers. Each request is associated with a start-time, an end-time, a processing time and a capacity demand from a Physical Machine (PM). The goal is to schedule all of the requests non-preemptively in their start-time-end-time windows, subjecting to PM capacity constraints, such that the total busy time of all used PMs is minimized (called MinTBT-ON for abbreviation). This problem is a fundamental scheduling problem for parallel jobs allocation on multiple machines; it has important applications in power-aware scheduling in cloud computing, optical network design, customer service systems, and other related areas. Offline scheduling to minimize busy time is NP-hard already in the special case where all jobs have the same processing time and can be scheduled in a fixed time interval. One best-known result for MinTBT-ON problem is a g-competitive algorithm for general instances and unit-size jobs using First-Fit algorithm where g is the total capacity of a machine. In this paper, a $(1+\frac{g-2}{k}-\frac{g-1}{k^{2}})$ -competitive algorithm, Dynamic Bipartition-First-Fit (BFF) is proposed and proved for general case, where k is the ratio of the length of the longest interval over the length of the second longest interval for k>1 and g?2. More results in general and special cases are obtained to improve the best-known bounds.

Cloud computing is ideal for image storage and processing because it provides enormously scalable storage and processing resources at low cost. One of the major drawbacks of cloud computing, however, is the lack of robust mechanisms for the users to control the privacy of the data they farm out to the clouds. In this paper, we develop an image encoding scheme that enhances the privacy of image data that is outsourced to the clouds for processing. Unlike previously proposed image encryption schemes, our encoding scheme allows different forms of pixel-level image processing to take place in the clouds while the actual image is not revealed to the cloud provider. Our encoding scheme uses a chaotic map to transform the image after it is masked with an arbitrarily chosen ambient image. We use template matching as a common image processing task to demonstrate the ability of our scheme to perform computations on privacy enhanced images. A simplified prototype of the image processing system was implemented and the experimental results are presented in this paper.

In this paper, we formulate an interesting problem of multicriteria decision-making task ranking in social networks (MCDM-TR). MCDM-TR contains two phases: (1) finding a set of decision-making experts for tasks who not only complete the decision-making of the given task but also have the minimal communication cost among them; (2) ranking the decision-made tasks according to a certain decision standard. In this paper, we focus on these two phases and propose an efficient algorithm for multicriteria decision-making tasks in social networks. A case study of academic conferences quality evaluation system (ACQES) is also studied to illustrate our strategy.

In modern world, all sciences especially engineering have insatiable demand for more power of processing. Although the use of modern micro-architectures has increased the performance of processors, this increment is only part of speeding up in responding such these demands. In fact, the need of some applications to parallel systems in large scales makes these systems more popular. Therefore, these systems are only the possible way of performing enormous computing power for applications with high performance computing. This paper comprehensively studies the topological properties of a class of n-D networks that are called HyperX from different aspects. In this paper we are going to provide a detailed description of HyperX topology in an algebraic framework with basic features (such as regularity, symmetry, etc.). The important parameters in this topology are evaluated parametrically and compared with other topologies. Having expressed this fact, we emphasize that our study is among the very few attempts reported in the literature to analyze the important parameters that can capture the performance behavior of HyperX topology. Since HyperX has many advantages of high radix switch components, it becomes a serious competitor against the other topologies and high radix networks. Hence, this study leads to finding an optimum topology for these kinds of networks.

The individuation of areas that are more likely to be impacted by new events in volcanic regions is of fundamental relevance for mitigating possible consequences, both in terms of loss of human lives and material properties. For this purpose, the lava flow hazard maps are increasingly used to evaluate, for each point of a map, the probability of being impacted by a future lava event. Typically, these maps are computed by relying on an adequate knowledge about the volcano, assessed by an accurate analysis of its past behavior, together with the explicit simulation of thousands of hypothetical events, performed by a reliable computational model. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with the SCIARA lava flow Cellular Automata model, to the process of building the lava invasion maps. Using different GPGPU devices, the paper illustrates some different implementation strategies and discusses numerical results obtained for a case study at Mt. Etna (Italy), Europe's most active volcano.

High performance computing demands constant growth in computational power and services that can be offered by modern supercomputers. It requires technological and designing advances in the multiprocessor internal structures as well as novel computing models considering the very high computing demands. One of the increasingly important requirements of computing platforms is a functionality that allows efficient managing computational resources, i.e., monitor them, restrict an access to some part of the resources, account for computational service, or ensure reliability and quality of service when some resources are broken or disabled. In this paper, we present a new model describing computational limitations for processing tasks on multiprocessor systems. The model is implemented in Hardware-Physical (H-Phy) and Overlay-Network-on-Chip (Overlay-NoC) architectures. Both architectures and the model are described and analyzed. Experimentation system is also presented, together with simulation assumptions, results of research and their study. The paper provides complete models of H-Phy and Overlay-NoC structures with an ability to restrict processing resources.

This paper proposes an efficient anonymous routing protocol for mobile ad hoc networks (MANETs). This protocol considers symmetric and asymmetric links during the wireless communication of MANETs. A MANET is one type of self-organized wireless network that can be formed by several wireless devices such as laptops, tablet PCs, and smartphones. Different wireless transmission ranges of different mobile devices lead to a special communication condition called an asymmetric link. Most research on this topic focuses on providing security and anonymity for the symmetric link without considering the asymmetric link. This paper proposes a novel distributed routing protocol beyond the symmetric and asymmetric links. This protocol guarantees the security, anonymity, and high reliability of an established route by avoiding unreliable intermediate nodes. The routes generated by the proposed protocol are shorter than previous research. The proposed protocol enhances MANET performance in assuring security and anonymity.

The selection of a topology is essential to the performance of interconnection networks, so designing a new, cost-effective topology is very significant. 2D mesh is one of the most popular topologies. However, the diameter and average distance of a 2D mesh are large enough to greatly influence the performance of the network. This paper presents a novel topology called TM, which combines the advantages of both a 2D torus and a 2D mesh. For an n×n network, the total number of links in a TM is the same as that in a mesh, while the diameter of a TM is extremely close to that of a torus. Besides, the average distance of a TM is at the middle of that of a torus and that of a mesh. To prevent deadlocks in TMs, a virtual network partitioning scheme is adopted into the TM network. Moreover, both of the deterministic and fully-adaptive routing techniques in TMs are proposed in this paper. Compared to mesh, the TM network provides average distance and diameter reduction, which contributes to the performance enhancement. Sufficient simulation results are presented to show the effectiveness of the TM network, and the new routing schemes proposed for it, by comparing with the mesh network. Compared to the torus, which requires at least 3 virtual channels to support fully-adaptive routing, the TM network can support fully-adaptive routing with only 2 virtual channels. Seen from the experimental results, in most cases, the performance of TM is worse than the torus, while in some cases, the performance of TM is comparable to torus or even better than the torus.

To improve the performance of information retrieval in Delay Tolerant Networks (DTN), multiple replicas of information are often deployed. In this paper, we mainly study dynamic information, which evolves with time. For simplicity, we use the information IM as an example. We use the discrete time model, and the time is divided into many slots. In each slot, a new version of the information is created. We say that one node's age is k if it receives a replica of the information IM k?1 slots ago. Due to the constraint of the buffer or other factors, we cannot deploy too many replicas for a specific message. The goal of this paper is to study efficient ways for distributing the limited replicas to maximize the availability of the information. In particular, we consider two policies, which are source-control policy and destination-control policy, respectively. In the first one, if the source node encounters with one node whose age is k, it will forward a new version of the information to this node with probability p(k). In the second policy, if one node's age is k, it discards the replica with probability p(k). We prove that the optimal value of p(k) in both cases conforms to the threshold form. Simulations based on both synthetic and real motion traces show the accuracy of our model. Numerical results show that the destination-control policy is better.

In recent years, High Performance Computing (HPC) systems have been shifting from expensive massively parallel architectures to clusters of commodity PCs to take advantage of cost and performance benefits. Fault tolerance in such systems is a growing concern for long-running applications. In this paper, we briefly review the failure rates of HPC systems and also survey the fault tolerance approaches for HPC systems and issues with these approaches. Rollback-recovery techniques which are most often used for long-running applications on HPC clusters are discussed because they are widely used for long-running applications on HPC systems. Specifically, the feature requirements of rollback-recovery are discussed and a taxonomy is developed for over twenty popular checkpoint/restart solutions. The intent of this paper is to aid researchers in the domain as well as to facilitate development of new checkpointing solutions.

The cloud architecture is usually composed of several XaaS layers--including Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). The paper studies efficient resource allocation to optimize objectives of cloud users, IaaS provider and SaaS provider in cloud computing. The paper proposes the composition of different layers in the cloud, such as IaaS and SaaS, and its joint optimization for efficient resource allocation. The efficient resource allocation optimization problem is conducted by subproblems. The proposed cloud resource allocation optimization algorithm is achieved through an iterative algorithm. The experiments are conducted to compare the performance of proposed joint optimization algorithm for efficient resource allocation with other related works.

In this paper, we introduce contention locality in Transactional Memory (TM) which describes the likelihood that a previously aborted transaction conflicts again in the future. We find that conflicts are highly predictable in TMs and we propose two optimization techniques based on contention locality: The first optimization technique is Speculative Contention Avoidance (SCA). SCA dynamically controls the number of concurrently executing transactions and serializes those transactions that are likely to conflict. As such, SCA reduces contention in TMs and improves performance. The second optimization technique is Adaptive Validation (AV). We show that there is no single validation policy that works well across all applications. AV adjusts validation based on applications' behavior and improves performance of TMs. In this paper, SCA and AV are evaluated using Transactional Locking II (TL2) and Stamp v0.9.10 benchmark suite. The evaluation reveals that SCA and AV are effective and improve performance significantly.

Recent research has shown that field programmable gate arrays (FPGAs) have a large potential for accelerating demanding applications, such as high performance digital signal process applications with low-volume market. The loss of generality in the architecture is one disadvantage of using FPGAs, however, the reconfigurability of FPGAs allow reprogramming for other applications. Therefore, a uniform FPGA-based architecture, an efficient programming model, and a simple mapping method are paramount for the wide acceptance of FPGA technology. This paper presents MASALA, a dynamically reconfigurable FPGA-based accelerator for parallel programs written in thread-intensive and explicit memory management (TEMM) programming models. Our system uses a TEMM programming model to parallelize demanding applications, including application decomposition into separate thread blocks and compute and data load/store decoupling. Hardware engines are included into MASALA using partial dynamic reconfiguration modules, each of which encapsulates a thread process engine that implements the hardware's thread functionality. A data dispatching scheme is also included in MASALA to enable the explicit communication of multiple memory hierarchies such as interhardware engines, host processors, and hardware engines. Finally, this paper illustrates a multi-FPGA prototype system of the presented architecture: MASALA-SX. A large synthetic aperture radar image formatting experiment shows that MASALA's architecture facilitates the construction of a TEMM program accelerator by providing greater performance and less power consumption than current CPU platforms, without sacrificing programmability, flexibility, and scalability.

High-performance processors give opportunities and challenges for development of real-time and embedded applications. New advances in hardware introduce new questions as alternatives to enable multiple applications to share a single processor and memory, so that the high-performance hardware that contains millions of transistors can be fully utilized, as also the way to keep system dependable and stable by making applications stay in spatial and temporal isolation inside same system. It is introduced in this paper XtratuM, a real-time hypervisor designed and implemented based on the concept of a partitioned system, by enabling partitions to execute simultaneously in spatial and temporal isolation without interfering with each other, but sharing the same hardware. Still in this paper, we provide a brief introduction on partitioned systems and its significance, also presenting the prototype implementation of XtratuM on PowerPC architecture including essential parts: hypercalls, timer, interrupt, and memory management implementations. Benchmark applications have been carried out to illustrate that the model implemented by XtratuM is suitable to offer the capability of spatial and temporal isolation under real-time requirements.

Forecasting volatility is an important issue in financial econometric analysis. This paper aims to seek a computationally feasible approach for predicting large scale conditional volatility and covariance of financial time series. In the case of multi-variant time series, the volatility is represented by a Conditional Covariance Matrix (CCM). Traditional models for predicting CCM such as GARCH models are incapable of dealing with high-dimensional cases as there are O(N 2) parameters to be estimated in the case of N-variant asset return, and it is difficult to accelerate the computation of estimating these parameters by utilizing modern multi-core architecture. These GARCH models also have difficulties in modeling non-linear properties. The widely used Restricted Boltzmann Machine (RBM) is an energy-based stochastic recurrent neural network and its extended model, Conditional RBM (CRBM), has shown its capability in modeling high-dimensional time series. In this paper, we first propose a CRBM-based approach to forecast CCM and show how to capture the long memory properties in volatility, and then we implement the proposed model on GPU by using CUDA and CUBLAS. Experiment results indicate that the proposed CRBM-based model obtains better forecasting accuracy for low-dimensional volatility and it also shows great potential in modeling for large-scale cases compared with traditional GARCH models.

This paper presents an overview of our experiments in integrating modern software engineering tools and techniques with the process of developing parallel applications for distributed memory architectures. The main goal was to determine the methods that have the potential of reducing the complexities associated with explicit parallelization. We experimented with template metaprogramming, aspect-oriented programming, program transformation engine, and a domain-specific language called Hi-PaL. The pros and cons of using each technique for explicit parallelization are presented in this paper. Our experiments demonstrate that through a combination of modern software engineering tools and techniques, the effort involved in explicit parallelization can be reduced by 90 % without any significant loss in performance.

In this paper, we present several methods of secure outsourcing of numerical and scientific computations. Current outsourcing techniques are inspired by the numerous problems in computational mathematics, where a solution is obtained in the form of an approximation. Examples of such problems can be found in the fields of economics, military, petroleum industry, and in other areas. Many of today's scientific and numerical problems require large computational resources; therefore, they can only be solved on supercomputers or by using the capabilities of the largest computing systems, such as grid technology, cloud, etc. We believe that it is imperative to improve the mathematical framework to enable secure outsourcing. Therefore, the main goal of this paper is to present different methods of finding approximate solutions to some equations solved by an external computer. To accomplish this, we chose certain classes of algebraic and differential equations because, in most cases, modern computing problems are reduced to solving such systems of equations (differential equations, linear programming, etc.). As an important application example we are presenting a specific applied problem related to geological exploration.

The gait recognition is to recognize an individual based on the characteristics extracted from the gait image sequence. There are many researches for the gait recognition which use diverse kinds of information such as shape of gait silhouette, motion variation caused by walking, and so on. In general, shape information is more useful for recognition. However, shape information is influenced by a variety of factors, which degrade the recognition performance. Moreover, the information used in most of those studies might be able to be extracted after all of one or more sequences of the gait cycle are known. And it is also hard to discriminate the gait cycle from given gait sequences exactly by the online approach. In regard to these difficulties, we propose a novel gait recognition method based on the multilinear tensor analysis. To recognize the cyclic characteristic of gait without an exact division for the gait cycle, this paper's propose is the method to form the accumulated silhouette and then describes those as the tensor. For the accumulated silhouette proposed by this paper, the image sequence of one gait cycle is divided into four sections in the training phase. However, discrimination for the gait cycle in the training phase is not directly related to the recognition phase, thus the online approach is possible. We first form the accumulated silhouettes for every individual using gait silhouettes within each section. And then, we represent these accumulated silhouettes as the tensor. Using a multilinear tensor analysis, we compute the core tensor which governs the interaction between factors organizing the original tensor, and then compose the basis to recognize the individual in the online recognition framework. Finally, we recognize the individual using the computation of similarity based on the Euclidean distance, which is more suitable to our method. We verify the superiority of the proposed approach via experiments with real gait sequences.

Spatial channel models are often proposed for modeling the angular aspects of mobile radio channel in picocell, microcell, and macrocellular environments. These models are validated through comparison with available measurement results. The comparisons are usually based on the fitness of their pdfs of angle of arrival to the histogram of occurrences of the signals over an angular span, given in the measurement data. This paper presents a comparison of the notable scattering models with various spatial channel measurements. The paper suggests criteria for the comparative analysis of the previously proposed spatial channel models and measurements on the basis of their fading statistics. Quantitative analysis of the considered models and the field measurements is also presented using multipath shape factors i.e. angle spread, the angular constriction and direction of maximum fading. Based on the obtained shape factors, fading statistics like level crossing rates, average fade duration, auto-covariance and coherence distance are evaluated. Effect of increasing Doppler spread on the level crossing rates and average fade duration is also elaborated in detail.

Layout methods for dense and sparse data are often seen as two separate problems with their own particular techniques. However, they are based on the same basic concepts. This paper studies how to integrate automatic data-layout and partition techniques for both dense and sparse data structures. In particular, we show how to include support for sparse matrices or graphs in Hitmap, a library for hierarchical tiling and automatic mapping of arrays. The paper shows that it is possible to offer a unique interface to work with both dense and sparse data structures. Thus, the programmer can use a single and homogeneous programming style, reducing the development effort and simplifying the use of sparse data structures in parallel computations. Our experimental evaluation shows that this integration of techniques can be effectively done without compromising performance.

Modern microprocessors achieve high application performance at an acceptable level of power dissipation. Reorder buffer is used for out-of-order instructions to be committed in-order. The reorder buffer plays a key role in modern microprocessors because performance improvement techniques highly rely on aggressive speculation to feed wider issue, out-of-order, and deep pipelines. In terms of power to performance trade-off, reorder buffer is particularly important. This is because enlarging the reorder buffer size achieves high performance but naive scaling of the conventional reorder buffer architecture can severely increase the complexity and power consumption. In this paper, we propose low-power reorder buffer techniques for contemporary microprocessors. First, the separated reorder buffer reduces power dissipation by deferred allocation and early release. The deferred allocation delays the SROB allocation of instructions until all their data dependencies are resolved. Then, the instructions are executed in program order and they are released faster from the SROB. The result of the instruction is written into rename buffers immediately after the execution completes. Then, the result values in the rename buffer are written into the architectural register file at the commit state. The proposed approaches in this paper provide higher resource utilization and low power consumption.

This paper is concerned with data provisioning services (information search, retrieval, storage, etc.) dealing with a large and heterogeneous information repository. Increasingly, this class of services is being hosted and delivered through Cloud infrastructures. Although such systems are becoming popular, existing resource management methods (e.g. load-balancing techniques) do not consider workload patterns nor do they perform well when subjected to non-uniformly distributed datasets. If these problems can be solved, this class of services can be made to operate in more a scalable, efficient, and reliable manner. The main contribution of this paper is a approach that combines proprietary cloud-based load balancing techniques and density-based partitioning for efficient range query processing across relational database-as-a-service in cloud computing environments. The study is conducted over a real-world data provisioning service that manages a large historical news database from Thomson Reuters. The proposed approach has been implemented and tested as a multi-tier web application suite consisting of load-balancing, application, and database layers. We have validated our approach by conducting a set of rigorous performance evaluation experiments using the Amazon EC2 infrastructure. The results prove that augmenting a cloud-based load-balancing service (e.g. Amazon Elastic Load Balancer) with workload characterization intelligence (density and distribution of data; composition of queries) offers significant benefits with regards to the overall system's performance (i.e. query latency and database service throughput).

This paper aims to advance the management and delivery of services in large, heterogeneous, uncertain, and evolving cloud computing environments. The goal is important because such systems are becoming increasingly popular, yet existing service management methods do not scale well, and nor do they perform well under highly unpredictable conditions. If these problems can be solved, then Information Technology (IT) services can be made to operate in more scalable and reliable manner. In this paper, we present a peer-to-peer approach for managing services in large scale, dynamic, and evolving cloud computing environments. The system components such as virtualized services, computing servers, storage, and databases self-organize themselves using a peer-to-peer networking overlay. Inter-networking system components through peer-to-peer routing and information dissemination structure is essential to avoid the problems of management bottleneck and single point of failure that is predominantly associated with traditional centralized and hierarchical distributed (grids/clouds) system design approaches. We have validated our approach by conducting a set of rigorous performance evaluation study using the Amazon EC2 cloud computing environment. The results prove that managing services based on peer-to-peer routing and information dissemination structure is feasible and offers significant performance benefits as regards to overall system reliability, scalability, and self-management.

Cloud computing is a revolution in the information technology industry. It allows computing services provided as utilities. The traditional cloud services include Software as a Service, Platform as a Service, Hardware/Infrastructure as a Service, and Database as a Service. In this paper, we introduce the notion of Ontology as a Service (OaaS), whereby the ontology tailoring process is a service in the cloud. This is particularly relevant as we are moving toward Cloud 2.0--multi-cloud providers to provide an interoperable service to customers. To illustrate OaaS, in this paper we propose sub-ontology extraction and merging, whereby multiple sub-ontologies are extracted from various source ontologies, and then these extracted sub-ontologies are merged to form a complete ontology to be used by the user. We use the Minimum extraction method to facilitate this. A walkthrough case study using the UMLS meta-thesaurus ontology is elaborated, and its performance in the cloud is also discussed.

Data centers now play an important role in modern IT infrastructures. Although much research effort has been made in the field of green data center computing, performance metrics for green data centers have been left ignored. This paper is devoted to categorization of green computing performance metrics in data centers, such as basic metrics like power metrics, thermal metrics and extended performance metrics i.e. multiple data center indicators. Based on a taxonomy of performance metrics, this paper summarizes features of currently available metrics and presents insights for the study on green data center computing.

Interconnection among VoIP networks over the Internet is being an important trend. Therefore, distributed and self-organized VoIP networks are emerging as the times require. Because of VoIP's cheapness and simplicity, many institutes forecast that it emerges as next spam entryway, like e-mail service. Especially, interconnected VoIP networks are self-organized and lack centralized authority. In this paper, we propose a novel scheme, called ADVS (Anti-Distributed Voice Spam), against SPIT (SPam over Internet Telephony) on the distributed and self-organized VoIP networks. ADVS presents a proper reputation model to evaluate end-user's past behavior and accumulate other users' referrals for detecting and filtering spam calls. In the VoIP network, ADVS acts as a middleware of VoIP proxy and constructs a DHT-based P2P network over call proxies and part of P2P-VoIP client to interlink each other and share end-user's reputation. At the end of the paper, we verify AVDS through building simulation test bed. The results of experiment show that ADVS could detect spam calls accurately and stably.

We have recently introduced the Extended OTIS-n-Cube to overcome the weakness of some limitations found in the well-known OTIS-n-Cube such as the degree and the diameter. This paper investigates the topological properties of the new interconnection network by proposing an extensive study on some attractive topological properties of the extended OTIS-n-Cube interconnection network. Inspired by the attractive features of the new network, such as regular degree, small diameter, and semantic structure, we present a theoretical study on some topological properties of the Extended OTIS-n-Cube including routing paths and embedded cycles. Furthermore, the paper presents a performance evaluation on the topology by comparing it with the OTIS-n-Cube. Results prove the superiority of the new topology especially in minimizing routing distances.

Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization solutions, which increase the cost and considerably limit the location-based applications. The Internet of Things (IOT) integrates many technologies, such as Internet, Zigbee, Bluetooth, infrared, WiFi, GPRS, 3G, etc, which can enable different ways to obtain the location information of various objects. Location-based service is a primary service of the IOT, while localization accuracy is a key issue. In this paper, a higher accuracy localization scheme is proposed which can effectively satisfy diverse requirements for many indoor and outdoor location services. The proposed scheme composes of two phases: 1) partition phase, in which the target region is split into small grids; 2) localization refinement phase, in which a higher accuracy of localization can be obtained by applying an algorithm designed in the paper. A trial system is set up to verify correctness of the proposed scheme and furthermore to illustrate its feasibility and availability. The experimental results show that the proposed scheme can improve the localization accuracy., To appear in Journal of Supercomputing. arXiv admin note: substantial text overlap with arXiv:1011.3097

Dynamic memory allocation is an important feature of modern programming systems. However, the cost of memory allocation in massively parallel execution environments such as CUDA has been too high for many types of kernels. This paper presents XMalloc, a high-throughput memory allocation mechanism that dramatically magnifies the allocation throughput of an underlying memory allocator. XMalloc embodies two key techniques: allocation coalescing and buffering using efficient queues. This paper describes these two techniques and presents our implementation of XMalloc as a memory allocator library. The library is designed to be called from kernels executed by massive numbers of threads. Our experimental results based on the NVIDIA G480 GPU show that XMalloc magnifies the allocation throughput of the underlying memory allocator by a factor of 48.

Machine Learning as network attack detection is one of the popular methods researched. Signature based network attack detection is no longer convinced the efficiency in the diversified intrusions (Limmer and Dressler in 17th ACM Conference on Computer and Communication Security, 2010). Moreover, as the various Zero-day attacks, non notified attacks cannot be detected (Wu and Banzhaf in Appl Soft Comput 10(1):1---35, 2010). This paper suggests an effective update method of data set on Machine Learning to detect non notified attacks. In addition, this paper compares and verifies the effects of Machine Learning Detection with updated data set to the former methods.

As an IEEE 802.11-based mobile computing system has been established as the base structure of high-speed wireless network, interest in mobility and security of mobile terminal has increased. To reinforce security, 802.1x and 802.11i using EAP were used in standardized instrument. But it was found to be unsuitable for real time multimedia service because of the time delay. In this paper, we suggest Fast and Secure Handover (FSH) scheme which minimizes time delay in handover authentication process and prevents MITM (Man in the Middle) attack. This scheme carries out re-association process which is necessary for high-speed handover using Inter Access Point Protocol (IAPP) and Old_MSK. To make existing 802.1x-based user certification procedure suitable for high-speed handover, the terminal and pre-handover-accessed Old_AP make Rough_AP to prevent MITM. To do this, Old_AP uses the Old_MSK-used encrypted method which was used to encrypt MAC information of the mobile terminal and Old_AP. Hereby, FSH has been developed to become high-speed handover which has the 802.1x-supported security level and the skill of preventing MITM. In this paper, by simulation (NS-2), we confirmed the superiority in streaming service such as decreased handover time delay and VoIP.

The Software as a Service (SaaS) methodology is a key paradigm of Cloud computing. In this paper, we focus on an interesting topic--to dynamically host services on existing production Grid infrastructures. In general, production Grids normally employ a Job-Submission-Execution (JSE) model with rigid access interfaces. In this paper, we implement the Cyberaide onServe, a lightweight middleware with a virtual appliance. The Cyberaide onServe implements the SaaS model on production Grids by translating the SaaS model to the JSE model. The Cyberaide onServe can be deployed on demand in a virtual appliance, host users' software as a Web service, accept Web service invocations; finally, the Cyberaide onServe can execute them on production Grids. We have deployed the Cyberaide onServe on the TeraGrid and the test results show that the Cyberaide onServe can provide SaaS functionalities with a good performance.

The increased complexity and scale of high performance computing and future extreme-scale systems have made resilience a key issue, since it is expected that future systems will have various faults during critical operations. It is also expected that current solutions for resiliency, mainly counting on checkpointing in hardware and applications, will become infeasible because of unacceptable recovery time for checkpointing and restarting. In this paper, we present innovative concepts for anomaly detection and identification, analyzing the duration of pattern transition sequences of an execution window. We use a three-dimensional array of features to capture spatial and temporal variability to be used by an anomaly analysis system to immediately generate an alert and identify the source of faults when an abnormal behavior pattern is captured, indicating some kind of software or hardware failure. The main contributions of this paper include the innovative analysis methodology and feature selection to detect and identify anomalous behavior. Evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9% with no occurrences of false alarms for a wide range of scenarios, and accuracy rate of 100% with short root cause analysis time.

Data centers now play an important role in modern IT infrastructures. Related research shows that the energy consumption for data center cooling systems has recently increased significantly. There is also strong evidence to show that high temperatures in a data center will lead to higher hardware failure rates, and thus an increase in maintenance costs. This paper devotes itself in the field of thermal aware workload placement for data centers. In this paper, we propose an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then two thermal aware task scheduling algorithms, TASA and TASA-B, are presented which aim to reduce temperatures and cooling system power consumption in a data center. A simulation study is carried out to evaluate the performance of the proposed algorithms. Simulation results show that our algorithms can significantly reduce temperatures in data centers by introducing endurable decline in system performance.

Recently, General Purpose Graphical Processing Units (GP-GPUs) have been identified as an intriguing technology to accelerate numerous data-parallel algorithms. Several GPU architectures and programming models are beginning to emerge and establish their niche in the High-Performance Computing (HPC) community. New massively parallel architectures such as the Nvidia’s Fermi and AMD/ATi’s Radeon pack tremendous computing power in their large number of multiprocessors. Their performance is unleashed using one of the two GP-GPU programming models: Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL). Both of them offer constructs and features that have direct bearing on the application runtime performance. In this paper, we compare the two GP-GPU architectures and the two programming models using a two-level character recognition network. The two-level network is developed using four different Spiking Neural Network (SNN) models, each with different ratios of computation-to-communication requirements. To compare the architectures, we have chosen the two extremes of the SNN models for implementation of the aforementioned two-level network. An architectural performance comparison of the SNN application running on Nvidia’s Fermi and AMD/ATi’s Radeon is done using the OpenCL programming model exhausting all of the optimization strategies plausible for the two architectures. To compare the programming models, we implement the two-level network on Nvidia’s Tesla C2050 based on the Fermi architecture. We present a hierarchy of implementations, where we successively add optimization techniques associated with the two programming models. We then compare the two programming models at these different levels of implementation and also present the effect of the network size (problem size) on the performance. We report significant application speed-up, as high as 1095× for the most computation intensive SNN neuron model, against a serial implementation on the Intel Core 2 Quad host. A comprehensive study presented in this paper establishes connections between programming models, architectures and applications.

The recent development in Cloud computing has enabled the realization of delivering computing as an utility. Many industries such as Amazon and Google have started offering Cloud services on a "pay as you go" basis. These advances have led to the evolution of the market infrastructure in the form of a Market Exchange (ME) that facilitates the trading between consumers and Cloud providers. Such market environment eases the trading process by aggregating IT services from a variety of sources, and allows consumers to easily select them. In this paper, we propose a light weight and platform independent ME framework called "Mandi", which allows consumers and providers to trade computing resources according to their requirements. The novelty of Mandi is that it not only gives its users the flexibility in terms of negotiation protocol, but also allows the simultaneous coexistence of multiple trading negotiations. In this paper, we first present the requirements that motivated our design and discuss how these facilitate the trading of compute resources using multiple market models (also called negotiation protocols). Finally, we evaluate the performance of the first prototype of "Mandi" in terms of its scalability.

Scientific datasets are often stored on distributed archival storage systems, because geographically distributed sensor devices store the datasets in their local machines and also because the size of scientific datasets demands large amount of disk space. Multidimensional indexing techniques have been shown to greatly improve range query performance into large scientific datasets. In this paper, we discuss several ways of distributing a multidimensional index in order to speed up access to large distributed scientific datasets. This paper compares the designs, challenges, and problems for distributed multidimensional indexing schemes, and provides a comprehensive performance study of distributed indexing to provide guidelines to choose a distributed multidimensional index for a specific data analysis application.