Search

In this paper, we propose a novel protection scheme for N-qubit Greenberger–Horne–Zeilinger (GHZ) states against amplitude damping noise, using unilateral operations. The key innovation lies in the implementation of local operations on a single qubit, irrespective of the total number of qubits, thereby significantly reducing the operational complexity and resource requirements compared to existing methods. The scheme involves a unilateral rotation to transform the GHZ state into a less vulnerable configuration before exposing it to noise, followed by a weak measurement and another unilateral rotation to recover the initial state. We provide a comprehensive mathematical analysis of the proposed unilateral protection scheme, deriving expressions for both fidelity and success probability. The practical feasibility of our scheme is demonstrated through simulations using the Qiskit SDK, in which we provide the corresponding quantum circuit for performing the partial weak measurement used in our scheme. Comparative analysis with pioneer protection schemes reveals a substantial enhancement in success probability and fidelity for both GHZ and generalized GHZ states, emphasizing the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Kubernetes makes it easier to automate deployment and scale containerized applications to achieve a near-native performance. However, there is still a lack of systematic performance studies on how Spark applications perform on Kubernetes. In this paper, we first propose a model to capture the execution behavior of tasks, stages, and jobs, and present an implementation of a prototype system based on the model. The system is then used to collect and analyze various types of performance and system metrics, such as execution time and CPU utilization. Second, with the use of various Spark applications, we evaluate the performance of Spark on Kubernetes by comparing it with its baseline, i.e., Spark on bare metal. Based on the comparison and leveraging the system, we locate what stages suffer from the performance loss of these applications on Kubernetes, and then reveal the root causes of the loss by analyzing their work-flows, execution time and costs of system resources. Through extensive measurements, we find that Spark on Kubernetes falls behind its baseline in the range of - 2.9 % to 83.9%. There are several root causes of the performance loss and benefits of Spark on Kubernetes. First, data locality deterioration by pods is a crucial root cause of the loss. To address the problem, we propose an approach to schedule tasks by taking both data locality and the utilization of executors into account. Experiments show that this approach increases the performance of Spark on Kubernetes by up to 32.2%. Second, the lower CPU usages of executors are another root cause of the performance loss, even if they have an equivalent CPU configuration on both Kubernetes and bare metal. In contrast, with the same memory configuration, executors use more memory on Kubernetes than on bare metal, contributing to the performance benefit of Spark on Kubernetes in some stages. Our research efforts in this paper benefit developers and researchers when they make valuable decisions on deploying Spark applications on Kubernetes for a better performance. [ABSTRACT FROM AUTHOR]

In the realm of multiprocessor systems, the evaluation of interconnection network reliability holds utmost significance, both in terms of design and maintenance. The intricate nature of these systems calls for a systematic assessment of reliability metrics, among which, two metrics emerge as vital: connectivity and diagnosability. The R g -conditional connectivity is the minimum number of processors whose deletion will disconnect the multiprocessor system and every processor has at least g fault-free neighbors. The R g -conditional diagnosability is a novel generalized conditional diagnosability, which is the maximum number of faulty processors that can be identified under the condition that every processor has no less than g fault-free neighbors. In this paper, we first investigate the R g -conditional connectivity of generalized exchanged X-cubes G E X (s , t) and present the lower (upper) bounds of the R g -conditional diagnosability of G E X (s , t) under the PMC model. Applying our results, the R g -conditional connectivity and the lower (upper) bounds of R g -conditional diagnosability of generalized exchanged hypercubes, generalized exchanged crossed cubes, and locally generalized exchanged twisted cubes under the PMC model are determined. Our comparative analysis highlights the superiority of R g -conditional diagnosability, showcasing its effectiveness in guiding reliability studies across a diverse set of networks. [ABSTRACT FROM AUTHOR]

In this paper, we focus on various compression and classification algorithms for three different paralinguistic signal classification tasks. These tasks are quite difficult for humans because the sound information from such signals is difficult to distinguish. Therefore, when machine learning techniques are applied to analyze paralinguistic signals, several different aspects of speech-related information, such as prosody, energy, and cepstral information, are usually considered for feature extraction. However, when the size of the training corpus is not sufficiently large, it is extremely difficult to directly apply machine learning to classify such signals due to their high feature dimensions; this problem is also known as the curse of dimensionality. This paper proposes to address this limitation by means of feature compression. First, we present experimental results obtained by using various compression algorithms to compress signals to eliminate redundancy of the signal features. We observe that compared with the original features, the compressed signal features still provide a comparable ability to distinguish the signals, especially when using a fully connected neural network classifier. Second, we calculate the output distribution of the F1-score for each emotion in the speech emotion recognition problem and show that the fully connected neural network classifier performs more stably than other classical methods. [ABSTRACT FROM AUTHOR]

Hadoop is a state-of-the-art industry's de facto tool for the computation of Big Data. Native fault tolerance procedure in Hadoop is dilatory and leads us towards performance degradation. Moreover, it is failed to completely consider the computational overhead and storage cost. On the other hand, the dynamic nature of MapReduce and complexity are also important parameters that affect the response time of the job. To achieve all this, it is essential to have a foolproof failure handling technique. In this paper, we have performed an analysis of notable fault tolerance techniques to see the impact of using different performance metrics under variable dataset with variable fault injections. The critical result shows that response timewise, the byzantine technique has a performance priority over the retrying and checkpointing technique in regards to killing one node failure. In addition, throughput wise, task-level byzantine fault tolerance technique once again had high priority as compared to checkpointing and retrying in terms of network disconnect failure. All in all, this comparative study highlights the strengths and weaknesses of different fault-tolerant techniques and is essential in determining the best technique in a given environment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cloud computing has become a new computing paradigm that has huge potentials in enterprise and business. Green cloud computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. To maximize utilization and minimize total cost of the cloud computing infrastructure and running applications, resources need to be managed properly and virtual machines shall allocate proper host nodes to perform the computation. In this paper, we propose performance analysis based resource allocation scheme for the efficient allocation of virtual machines on the cloud infrastructure. We experimented the proposed resource allocation algorithm using CloudSim and its performance is compared with two other existing models. [ABSTRACT FROM AUTHOR]

Green computing is the study and practice of efficiently using computers resources. The main purpose of green computing is to achieve an algorithmic efficiency by designing resource-efficient, accurate and energy-efficient algorithms. It is important to achieve the algorithmic efficiency in handling time-series data. One of the main tasks in handling time-series data is to find subsequence matches similar to a given query sequence. The state-of-the-art methods to find subsequence matches in time-series data produce many false alarms by filtering points through comparing only one query window with its corresponding data window. In this paper, we propose a subsequence matching method for green computing, which is called the Efficient Duality-based Subsequence Matching (simply, E-Dual Match). E-Dual Match handles all possible query windows for determining candidates. Hence, E-Dual Match not only reduces the false alarms, and improves the performance compared to Dual Match, but also does so by considering the main requirements of the green computing. In other words, E-Dual Match efficiently uses limited computer resources, accurate and energy-efficient. Experiment results show that E-Dual Match reduces the number of candidates by up to 4.90 times over Dual Match, and improves the subsequence matching time by up to 2.35 times over Dual Match. We also show that E-Dual Match reduces the number of data page accesses by up to 3.04 times over Dual Match. [ABSTRACT FROM AUTHOR]

Gang scheduling (GS) is an efficient scheduling approach for distributed systems. The CompactMatrix is the second phase in GS and plays a main role in accelerating the scheduling of the larger gangs. In this paper, a novel method is proposed to compact the scheduling matrix by using two new ideas. By using simulation, the impact of the proposed ideas was compared to the basic CompactMatrix algorithm, either individually or simultaneously. The results show that the average response time in the Improved CompactMatrix Algorithm (ICMA), compared with the basic CompactMatrix, is reduced about 2 to 20, 0 to 10, and −5 to 5 percent by selecting the migration overhead values equal to 5, 10, and 15 percent of the time-slices lengths, respectively. As can be seen, ICMA will show better results in smaller migration overheads. [ABSTRACT FROM AUTHOR]

This paper addresses the resolution of the transport (advection-diffusion) equation in 3D by the use of the finite difference method as a discretization technique. In particular, our focus is the comparison of different time schemes (implicit, explicit and predictor-corrector) from the viewpoint of the numerical accuracy and the performance scalability in the use of massively parallel platforms. More in detail, each scheme is implemented in two parallel codes (C+CUDA and C+OpenMP), and in a sequential one (in C). We evaluated the performance of each implementation using several sizes of grid, and also, we measured the precision of these schemes for different discretization levels. The obtained results show that the GPU-based implementations offer significant speed-ups as compared to the CPU counterpart. Moreover, the explicit methods offer a much better performance scalability on this kind of massively parallel devices. This situation reveals that explicit methods can outperform implicit ones even if the accuracy is considered at least in contexts of non stiff problems. [ABSTRACT FROM AUTHOR]

In peer-to-peer (P2P) systems, peers often must interact with unknown or unfamiliar peers without the benefit of trusted third parties or authorities to mediate the interactions. Trust management through reputation mechanism to facilitate such interactions is recognized as an important element of P2P systems. It is, however, faced by the problems of how to stimulate reputation information sharing and honest recommendation elicitation. This paper presents an incentive compatible reputation mechanism for P2P systems. It has two unique features: (1) a recommender's trustworthiness and level of confidence about the recommendation is considered for a more accurate calculation of reputations and fair evaluation of recommendations. (2) Incentive for participation and honest recommendation is implemented through a fair differential service mechanism. It relies on peer's level of participation and on the recommendation credibility. Theoretic analysis and simulation show that the reputation mechanism we propose can help peers effectively detect dishonest recommendations in a variety of scenarios where more complex malicious strategies are introduced. Moreover, it can also stimulate peers to send sufficiently honest recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can elicit the most honest (helpful) recommendations and thus suffer the least number of wrong trust decisions. [ABSTRACT FROM AUTHOR]

Information hiding is an important research issue in digital life. In this paper, we propose a two-stage data hiding method with high capacity and good visual quality based on image interpolation and histogram modification techniques. At the first stage, we first generate a high-quality cover image using the developed enhanced neighbor mean interpolation and then take the difference values from input and cover pixels as a carrier to embed secret data. In this stage, our proposed scheme raises the image quality a lot due to the ENMI method. At the second stage, a histogram modification method is applied on the difference image to further increase the embedding capacity and preserve the image quality without distortion. Experimental results indicate that the proposed method have better PSNR value of stego-image with improving 43 % on the average when compared the past key-studies. [ABSTRACT FROM AUTHOR]

Two user authentication schemes for multi-server environments have been proposed by Tsai and Wang et al., respectively. However, there are some flaws existing in both schemes. Therefore, a new scheme for improving these drawbacks is proposed in this paper. The proposed scheme has the following benefits: (1) it complies with all the requirements for multi-server environments; (2) it can withstand all the well-known attacks at the present time; (3) it is equipped with a more secure key agreement procedure; and (4) it is quite efficient in terms of the cost of computation and transmission. In addition, the analysis and comparisons show that the proposed scheme outperforms the other related schemes in various aspects. [ABSTRACT FROM AUTHOR]

Among different traffic features, the urban traffic has received a lot of attention due to the ongoing traffic congestion as a result of increased car usage, population growth, and changes in population density. In urban networks, the vehicles flow differs when compared with highways flow because of the freeway's low speed limit but mostly because of the traffic lights control. In this paper, a real-time hardware implemented bio-inspired model for traffic lights control is presented. The proposed model arrives from Cellular Automata (CAs), which have been proven very flexible and powerful computational traffic models, in that they are able to capture all previously mentioned basic phenomena that occur in traffic flows. The resulting CA model was hardware implemented on FPGA to take full advantage of the inherent parallelism of the CAs and to support the function of an advanced electronic system able to provide real-time adaptive control of traffic lights designed to consider traffic conditions for the whole intersections. The analytical results, obtained by application of the aforementioned FPGA CA processor are found in excellent agreement with the numerical simulations. [ABSTRACT FROM AUTHOR]

Cellular Automata ( CA) are discrete and parallel computational models useful for simulating dynamic systems that evolve on the basis on local interactions. Some natural events, such as some types of landslides, fall into this type of phenomena and lend themselves well to be simulated with this approach. This paper describes the latest version of the SCIDDICA CA family models, specifically developed to simulate debris-flows type landslides. The latest model of the family, named SCIDDICA-SS, inherits all the features of its predecessor, SCIDDICA-SS, with the addition of a particular strategy to manage momentum. The introduction of the latter permits a better approximation of inertial effects that characterize some rapid debris flows. First simulations attempts of real landslides with SCIDDICA-SS have produced quite satisfactory results, comparable with the previous model. [ABSTRACT FROM AUTHOR]

Multi-stage Interconnection Networks (MINs) are designed to achieve fault-tolerance and collision solving by providing a set of disjoint paths. Ching-Wen Chen and Chung-Ping Chung had proposed a fault-tolerant network called Combining Switches Multi-stage Interconnection Network (CSMIN) and an inaccurate algorithm that provided two correct disjoint paths only for some source-destination pairs. This paper provides a more comprehensive and accurate algorithm that always generate correct routing-tags for two disjoint paths for every source-destination pair in the CSMIN. The 1-fault tolerant CSMIN causes the two disjoint paths to have regular distances at each stage. Moreover, our algorithm backtracks a packet to the previous stage and takes the other disjoint path in the event of a fault or a collision in the network. Furthermore, to eliminate the backtracking penalties of CSMIN, we propose a new design called Fault-tolerant Fully-Chained Combining Switches Multi-stage Interconnection Network (FCSMIN). It has similar characteristics of 1-fault tolerance and two disjoint paths between any source-destination pair, but it can tolerate only one link or switch fault at each stage without backtracking. Our simulation and comparative analysis result shows that FCSMIN has added advantages of destination-tag routing, lower hardware costs, strong reroutability, lower preprocessing overhead, and higher fault-tolerance power in comparison to CSMIN. [ABSTRACT FROM AUTHOR]

Data races widely exist in concurrent programs and the harmful races have caused severe failures. To detect the harmful races, previous tools verify all the races, identifying the harmful ones. However, efficiency is affected when there are a large number of races needed to be verified. The multicore technology trend worsens this problem. Unlike previous work, to detect the harmful races, we try to improve the efficiency through parallel verification. We use imprecise race detection to find the races, including benign races and harmful races. The races are divided into many parts, and each part is sent to one machine for verification. On each machine, the races are verified dynamically, identifying the harmful races that would lead to program failures. To our knowledge, this is the first work that parallelizes race verification to improve the efficiency. We have experimented on a number of real-world concurrent programs and all the known harmful races in known benchmarks are detected. Additionally, our tool could scale well as the number of machines increases, and the speedup can also be increased linearly with the number of machines. Comparing with many previous tools, our work imposes lower runtime overhead. [ABSTRACT FROM AUTHOR]

Datacenter servers are stepping into an era marked by powerful multi-/many-core processors. Severe problems such as I/O contentions in those large-scale platforms pose an unprecedented challenge. Prior studies primarily considered I/O bandwidth as a major performance bottleneck. However, our work reveals that in many cases the fundamental cause of I/O contentions is the inefficiency of OS schedulers. Particularly, the modern system is not aware of this fact and thus suffers from poor I/O performance, especially for datacenter servers. Based on our findings, we propose a new software-based scheduling approach, WiseThrottling, to reduce I/O contention. WiseThrottling performs asynchronous and self-adjustment scheduling for concurrent tasks. We evaluate our approach across a wide range of C/OpenMP/MapReduce workloads on a 64-core server in Dawning Cluster datacenter. The experimental results exhibit that WiseThrottling is effective for reducing the I/O bottleneck and it can improve the overall system performance by up to 207 %. [ABSTRACT FROM AUTHOR]

Cluster analysis-as a technique for grouping a set of objects into similar clusters-is an integral part of data analysis and has received wide interest among data mining specialists. The parallel wavelet-based clustering algorithm using discrete wavelet transforms has been shown to extract the approximation component of the input data on which objects of the clusters are detected based on the object connectivity property. However, this algorithm suffers from inefficient I/O operations and performance degradation due to redundant data processing. We address these issues to improve the parallel algorithm's efficiency and extend the algorithm further by investigating two merging techniques (both merge-table and priority-queue based approaches), and apply them on three-dimensional data. In this study, we compare two parallel WaveCluster algorithms and a parallel K-means algorithm to evaluate the implemented algorithms' effectiveness. [ABSTRACT FROM AUTHOR]

Heterogeneous analytical models are valuable tools that facilitate optimal application tuning via runtime prediction; however, they require several man-hours of effort to understand and employ for meaningful performance prediction. Consequently, developers face the challenge of selecting adequate performance models that best fit their design goals and level of system knowledge. In this research, we present a classification that enables users to select a set of easy-to-use and reliable analytical models for quality performance prediction. These models, which target the general-purpose graphical processing unit (GPGPU)-based systems, are categorized into two primary analytical classes: subjective-analytical and objective-analytical. The subjective-analytical models predict the computation and communication components of an application by describing the system using minimum qualitative relations among the system parameters; whereas the objective-analytical models predict these components by measuring pertinent hardware events using micro-benchmarks. We categorize, enhance, and characterize the existing analytical models for GPGPU computations, network-level, and inter-connect communications to facilitate fast and reliable application performance prediction. We also explore a suitable combination of the aforementioned analytical classes, the hybrid approach, for high-quality performance prediction and report prediction accuracy up to 95 % for several tested GPGPU cluster configurations. The research aims to ultimately provide a collection of easy-to-select analytical models that promote straightforward and accurate performance prediction prior to large-scale implementation. [ABSTRACT FROM AUTHOR]

To effectively evaluate and analyze R&D performance, it is necessary to measure the relative importance of performance analysis factors and quantitative analysis methods that consider the objectivity and relevance of detail factors that constitute performance evaluation. This study suggests a framework for R&D performance evaluations by computing weights through an AHP (Analytical Hierarchy Process) expert survey and by applying a Bayesian Network approach whereby, through which, giving objectivity and allowing inference analyses. This framework can be used as a performance analysis indicator, which uses input and output performance factors in order to perform quantitative analysis for projects. We can quantitatively define the satisfactory level of each project and each performance analysis factor by assigning probability values. It is possible to analyze the relationship between project evaluation results (qualitative evaluation) and performance analysis indicator (quantitative performance). This performance analysis framework can infer posteriori probability using the prior probability and the likelihood function of each performance factor. In addition, by inferring the relationships among performance factors, it allows performing probability analyses on the successful and unsuccessful factors, which can provide further feedback. In conclusion, the framework would improve the national R&D program in terms of financial investment efficiency by aligning budget allocation and performance evaluation. [ABSTRACT FROM AUTHOR]