With the development of astronomical facilities, large-scale time series data observed by these facilities is being collected. Analyzing anomalies in these astronomical observations is crucial for uncovering potential celestial events and physical phenomena, thus advancing the scientific research process. However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms. To overcome the challenges, we propose AERO, a novel two-stage framework tailored for unsupervised anomaly detection in astronomical observations. In the first stage, we employ a Transformer-based encoder-decoder architecture to learn the normal temporal patterns on each variate (i.e., star) in alignment with the characteristic of variate independence. In the second stage, we enhance the graph neural network with a window-wise graph structure learning to tackle the occurrence of concurrent noise characterized by spatial and temporal randomness. In this way, AERO is not only capable of distinguishing normal temporal patterns from potential anomalies but also effectively differentiating concurrent noise, thus decreasing the number of false alarms. We conducted extensive experiments on three synthetic datasets and three real-world datasets. The results demonstrate that AERO outperforms the compared baselines. Notably, compared to the state-of-the-art model, AERO improves the F1-score by up to 8.76% and 2.63% on synthetic and real-world datasets respectively.

Finding connected components in a graph is a fundamental problem in graph analysis. In this work, we present a novel minimum-mapping based Contour algorithm to efficiently solve the connectivity problem. We prove that the Contour algorithm with two or higher order operators can identify all connected components of an undirected graph within$\mathcal{O} (\log d_{max})$iterations, with each iteration involving$\mathcal{O}(m)$work, where$d_{max}$represents the largest diameter among all components in the given graph, and$m$is the total number of edges in the graph. Importantly, each iteration is highly parallelizable, making use of the efficient minimum-mapping operator applied to all edges. To further enhance its practical performance, we optimize the Contour algorithm through asynchronous updates, early convergence checking, eliminating atomic operations, and choosing more efficient mapping operators. Our implementation of the Contour algorithm has been integrated into the open-source framework Arachne. Arachne extends Arkouda for large-scale interactive graph analytics, providing a Python API powered by the high-productivity parallel language Chapel. Experimental results on both real-world and synthetic graphs demonstrate the superior performance of our proposed Contour algorithm compared to state-of-the-art large-scale parallel algorithm FastSV and the fastest shared memory algorithm ConnectIt. On average, Contour achieves a speedup of 7.3x and 1.4x compared to FastSV and ConnectIt, respectively. All code for the Contour algorithm and the Arachne framework is publicly available on GitHub11https://githuh.comJBears-R-Us/arkouda-njit, ensuring transparency and reproducibility of our work.

Abstract Identifying anomalies, especially weak anomalies in constantly changing targets, is more difficult than in stable targets. In this article, we borrow the dynamics metrics and propose the concept of dynamics signature (DS) in multi‐dimensional feature space to efficiently distinguish the abnormal event from the normal behaviors of a variable star. The corresponding dynamics criterion is proposed to check whether a star's current state is an anomaly. Based on the proposed concept of DS, we develop a highly optimized DS algorithm that can automatically detect anomalies from millions of stars' high cadence sky survey data in real‐time. Microlensing, which is a typical anomaly in astronomical observation, is used to evaluate the proposed DS algorithm. Two datasets, parameterized sinusoidal dataset containing 262,440 light curves and real variable stars based dataset containing 462,996 light curves are used to evaluate the practical performance of the proposed DS algorithm. Experimental results show that our DS algorithm is highly accurate, sensitive to detecting weak microlensing events at very early stages, and fast enough to process 176,000 stars in less than 1 s on a commodity computer.

Detecting anomalies with high accuracy and real time from large amounts of streaming data is a challenge for many real-world applications, such as smart city, astronomical observations, and remote sensing. This article focuses on a special kind of stream, catalog stream, whose high-level catalog structure can be used to analyze the stream effectively. We first formulate the anomaly detection in catalog streams as a constrained optimization problem based on a catalog stream matrix. Then, a novel filtering-identifying based anomaly detection algorithm (FIAD) is proposed, which includes two complementary strategies, true event identifying and false alarm filtering, data-oriented general method and domain-oriented specific method together, to detect truly valuable anomalies. Furthermore, different kinds of attention windows are developed to provide corresponding data for various algorithm components. A scalable and lightweight catalog stream processing frameworkCSPFis designed to support and implement the proposed method efficiently. A prototype system is developed to evaluate the proposed algorithm. Extensive experiments are conducted on the catalog stream data sets from an operational super large field-of-view high-cadence astronomy observation. The experimental results show that the proposed method can achieve a false-positive rate as low as 0.04%, reduces the false alarms by 98.6% compared with the existing methods, and the latency to handle each catalog is 2.1 seconds (much less than the required 15 seconds). Furthermore, a total of 36 transient candidates, including seven microlensing events, 27 superflares, and two dual-superflares, are detected from 21.67 million stars (involving 1.09 million catalogs) from one observation season.

In graph analytics, a truss is a cohesive subgraph based on the number of triangles supporting each edge. It is widely used for community detection applications such as social networks and security analysis, and the performance of truss analytics highly depends on its triangle counting method. This paper proposes a novel triangle counting kernel named Minimum Search (MS). Minimum Search can select two smaller adjacency lists out of three and uses fine-grained parallelism to improve the performance of triangle counting. Then, two basic algorithms, MS-based triangle counting, and MS-based support updating are developed. Based on the novel triangle counting kernel and the two basic algorithms above, three fundamental parallel truss analytics algorithms are designed and implemented to enable different kinds of graph truss analysis. These truss algorithms include an optimized K-Truss algorithm, a Max-Truss algorithm, and a Truss Decomposition algorithm. Moreover, all proposed algorithms have been implemented in the parallel language Chapel and integrated into an open-source framework, Arkouda. Through Arkouda, data scientists can efficiently con-duct graph analysis through an easy-to-use Python interface and handle large-scale graph data in powerful back-end computing resources. Experimental results show that the proposed methods can significantly improve the performance of truss analysis on real-world graphs compared with the existing and widely adopted list intersection-based method. The implemented code is publicly available from GitHub (https://github.com/Bears-R-Us/arkouda-njit).

The transitive closure of a graph is a new graph where every vertex is directly connected to all vertices to which it had a path in the original graph. Transitive closures are useful for reachability and relationship querying. Finding the transitive closure can be computationally expensive and requires a large memory footprint as the output is typically larger than the input. Some of the original research on transitive closures assumed that graphs were dense and used dense adjacency matrices. We have since learned that many real-world networks are extremely sparse, and the existing methods do not scale. In this work, we introduce a new algorithm called Anti-section Transitive Closure (ATC) for finding the transitive closure of a graph. We present a new parallel edges operation - anti-sections - for finding new edges to reachable vertices. ATC scales to massively multi-threaded systems such as NVIDIA's GPU with tens of thousands of threads. We show that the anti-section operation shares some traits with the triangle counting intersection operation in graph analysis. Lastly, we view the transitive closure problem as a dynamic graph problem requiring edge insertions. By doing this, our memory footprint is smaller. We also show a method for creating the batches in parallel using two different techniques: dual-round and hash. Using these techniques and the Hornet dynamic graph data structure, we show our new algorithm on an NVIDIA Titan V GPU. We compare with other packages such as NetworkX, SEI-GBTL, SuiteSparse, and cuSparse.

Material screening entails a large number of electronic structure simulations. Traditionally, these simulation runs are treated separately as solving independent Kohn-Sham (KS) equations. In this paper, we formulate material screening as an inter-job scheduling problem for solving a system of KS equations, and in doing so allowing one to explore different scheduling methods that use the results of some equations to expedite the solution of others. We propose the concept of sharing iterative simulation and employ several optimization methods to initialize a simulation run using the distribution of particles from similar jobs as the initial condition. More specifically, we propose two similarity metrics, one qualitative and the other quantitative, to predict the simulation runtime of a material screen job based on its similarity to other jobs. Accordingly, we present two inter-job scheduling algorithms that make use the qualitative and quantitative similarity information. We conducted extensive experiments on the Sunway TaihuLight supercomputer for a practical material screening problem to evaluate the performance of the two scheduling algorithms using the proposed similarity metrics. We show that the total time required to run the large number of material screening jobs can be significantly reduced, and the algorithms are robust even with moderate inaccurate prediction on the simulation runtime. The quantitative algorithm achieves better results than the qualitative algorithm using more accurate prediction and thus achieving more significant runtime reduction.

Internet-of-Things (IoT) environment has a dynamic nature with high risks of confidentiality, integrity, and availability violations. The loss of information, denial of access, information leakage, collusion, technical failures, and data security breaches are difficult to predict and anticipate in advance. These types of nonstationarity are one of the main issues in the design of the reliable IoT infrastructure capable of mitigating their consequences. It is not sufficient to propose solutions for a given scenario, but mechanisms to adapt the current solution to changes in the environment. In this article, we present a multicloud storage architecture called WA-MRC-RRNS that combines the weighted access scheme, threshold secret sharing, and redundant residue number system with multiple failure detection/recovery mechanisms and homomorphic ciphers. We provide a theoretical analysis of the probability of information loss, data redundancy, speed of encoding/decoding, and show how to dynamically configure parameters to cope with different objective preferences, workloads, and cloud properties. We propose a multiobjective optimization mechanism to adjust redundancy, encryption-decryption speed, and data loss probability. Comprehensive experimental analysis with real data shows that our approach provides a secure way to mitigate the uncertainty of the use of untrusted and not reliable IoT infrastructure.

We present the design of and a demonstration for SciDetector, a system of scientific research for online analysis. SciDetector can efficiently online analyze scientific events on large-scale newly arriving data. In modern scientific research, especially astronomy survey, with the development of the observation infrastructure, Short-Timescale and Large Field-of-view (STLF) sky survey has been the major topic. However, existing scientific system focus on offline analysis of long-term historical data, not real-time analysis of large-scale scientific data. Using real data, we demonstrate how SciDetector processes the data and generate the scientific product.

Honeypots are designed to trap the attacker with the purpose of investigating its malicious behavior. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean a significant improvement in sensibility, countermeasure, and stealth, are necessary to tackle the problem. In this paper, we propose a novel honeypot architecture termed HoneyDOC to support all-round honeypot design and implementation. Our HoneyDOC architecture clearly identifies three essential independent and collaborative modules, Decoy, Captor, and Orchestrator. Based on the efficient architecture, a software-defined networking-enabled honeypot system is designed, which supplies a high programmability for technically sustaining the features for capturing high-quality data. A proof-of-concept system is implemented to validate its feasibility and effectiveness. The experimental results show the benefits by using the proposed architecture compared with the previous honeypot solutions.

Astronomy is well recognized as big data driven science. As the novel observation infrastructures are developed, the sky survey cycles have been shortened from a few days to a few seconds, causing data processing pressure to shift from offline to online. However, existing scientific databases focus on offline analysis of long-term historical data, not real-time and low latency analysis of large-scale newly arriving data.In this paper, a cloud based method is proposed to efficiently analyze scientific events on large-scale newly arriving data. The solution is implemented as a highly efficient system, namely Aserv. A set of compact data store and index structures are proposed to describe the proposed scientific events and a typical analysis pattern is formulized as a set of query operations. Domain aware filter, accuracy aware data partition, highly efficient index and frequently used statistical data designs are four key methods to optimize the performance of Aserv. Experimental results under the typical cloud environment show that the presented optimization mechanism can meet the low latency demand for both large data insertion and scientific event analysis. Aserv can insert 3.5 million rows of data within 3 seconds and perform the heaviest query on 6.7 billion rows of data also within 3 seconds. Furthermore, a performance model is given to help Aserv choose the right cloud resource setup to meet the guaranteed real-time performance requirement.

Directed networks find many applications in computer science, social science and biomedicine, among others. In this paper we propose a new graph mining algorithm that is capable of locating all frequent induced subgraphs in a given set of directed networks. We present an incremental coding scheme f or representing the canonical form of a graph, study its properties, and develop new techniques for pattern generation suitable for directed networks. We prove that our algorithm is complete, meaning that no qualified pattern is missed by the algorithm. Furthermore, our algorithm is correct in the sense that all patterns found by the algorithm are frequent induced subgraphs in the given networks. Experimental results based on synthetic data and gene regulatory networks show the good performance of our algorithm, and its application in network inference.

Honeypots are designed to investigate malicious behaviour. Each type of homogeneous honeypot system has its own characteristics in respect of specific security functionality, and also suffers functional drawbacks that restrict its application scenario. In practical scenarios, therefore, security researchers always need to apply heterogeneous honeypots to cope with different attacks. However, there is a lack of general tools or platforms that can support versatile honeynet deployment in order to investigate the malicious behavior. In this study, the authors propose a versatile virtual honeynet management tool to address this problem. It is a flexible tool that offers security researchers the versatility to deploy various types of honeypots. It can also generate and manage the virtual honeynet through a dynamic configuration approach adapting to the mutable network environment. The experimental results demonstrate that this tool is effective to perform automated honeynet deployment toward a variety of heterogeneous honeypots.

With the wide deployment of cloud computing in many business enterprises as well as science and engineering domains, high quality security services are increasingly critical for processing workflow applications with sensitive intermediate data. Unfortunately, most existing worklfow scheduling approaches disregard the security requirements of the intermediate data produced by workflows, and overlook the performance impact of encryption time of intermediate data on the start of subsequent workflow tasks. Furthermore, the idle time slots on resources, resulting from data dependencies among workflow tasks, have not been adequately exploited to mitigate the impact of data encryption time on workflows' makespans and monetary cost. To address these issues, this paper presents a novel task-scheduling framework for security sensitive workflows with three novel features. First, we provide comprehensive theoretical analyses on how selectively duplicating a task's predecessor tasks is helpful for preventing both the data transmission time and encryption time from delaying task's start time. Then, we define workflow tasks' latest finish time, and prove that tasks can be completed before tasks' latest finish time by using cheapest resources to reduce monetary cost without delaying tasks' successors' start time and workflows' makespans. Based on these analyses, we devise a novel scheduling approach with selective tasks duplication, named SOLID, incorporating two important phases: 1) task scheduling with selectively duplicating predecessor tasks to idle time slots on resources; and 2) intermediate data encrypting by effectively exploiting tasks' laxity time. We evaluate our solution approach through rigorous performance evaluation study using both randomly generated workflows and some real-world workflow traces. Our results show that the proposed SOLID approach prevails over existing algorithms in terms of makespan, monetary costs and resource efficiency.

PPMLR-MHD is a new magnetohydrodynamics (MHD) model used to simulate the interactions of the solar wind with the magnetosphere, which has been proved to be the key element of the space weather cause-and-effect chain process from the Sun to Earth. Compared to existing MHD methods, PPMLR-MHD achieves the advantage of high order spatial accuracy and low numerical dissipation. However, the accuracy comes at a cost. On one hand, this method requires more intensive computation. On the other hand, more boundary data is subject to be transferred during the process of simulation. In this work, we present a parallel hybrid solution of the PPMLR-MHD model implemented using the computing capabilities of both CPUs and GPUs. We demonstrate that our optimized implementation alleviates the data transfer overhead by using GPU Direct technology and can scale up to 151 processes and achieve significant performance gains by distributing the workload among the CPUs and GPUs on Titan at Oak Ridge National Laboratory. The performance results show that our implementation is fast enough to carry out highly accurate MHD simulations in real time.

With the help of Internet, Massive Open Online Courses (MOOC) are recognized as a new path to learn courses via the web instead of in the traditional classrooms. MOOC can break many limits such as distance, time, participants, on the traditional courses. At the same time, it brings some new issues, such as high drop out ratio. Nowadays increasing MOOC courses are available and even more common people are involved into this kind of new learning procedure. How to evaluate the learning behaviors of MOOC is still an open problem. We propose an efficient algorithm to cluster the MOOC learning events into many closely related sets and name such set as LES (Learning Events Set) to model one basic learning procedure on MOOC. The quality of LES is highly dependent on the maximum time period Tmax between two LESes. We systematically investigate this problem and propose an efficient method to set the value of Tmax. Our method has been employed into one MOOC platform, XuetangX and the experimental results demonstrate that our method can really work.

Serving as cache disks, flash-based solid-state drives (SSDs) can significantly boost the performance of read-intensive applications. However, frequent data updating, the necessary condition for classical replacement algorithms (e.g., LRU, MQ, LIRS, and ARC) to achieve a high hit rate, makes SSDs wear out quickly. To address this problem, we propose a new approach—write-efficient caching (WEC)—to greatly improve the write durability of SSD cache. WEC is conducive to reducing the total number of writes issued to SSDs while achieving high hit rates. WEC takes two steps to improve write durability and performance of SSD cache. First, WEC discovers write-efficient data, which tend to be active for a long time period and to be frequently accessed. Second, WEC keeps the write-efficient data in SSDs long enough to avoid excessive number of unnecessary updates. Our findings based on a wide range of popular real-world traces show that write-efficient data does exist in a wide range of popular read-intensive applications. Our experimental results indicate that compared with the classical algorithms, WEC judiciously improves the mean hits of each written block by approximately two orders of magnitude while exhibiting similar or even higher hit rates.

We consider a new tree mining problem that aims to discover restrictedly embedded subtree patterns from a set of rooted labeled unordered trees. We study the properties of a canonical form of unordered trees, and develop new Apriori-based techniques to generate all candidate subtrees level by level through two efficient rightmost expansion operations: 1) pairwise joining and 2) leg attachment. Next, we show that restrictedly embedded subtree detection can be achieved by calculating the restricted edit distance between a candidate subtree and a data tree. These techniques are then integrated into an efficient algorithm, named frequent restrictedly embedded subtree miner (FRESTM), to solve the tree mining problem at hand. The correctness of the FRESTM algorithm is proved and the time and space complexities of the algorithm are discussed. Experimental results on synthetic and real-world data demonstrate the effectiveness of the proposed approach.

Large-scale network simulation imposes extremely high computing demand. While parallel processing techniques allows network simulation to scale up and benefit from contemporary high-end computing platforms, multi-resolutional modeling techniques, which differentiate network traffic representations in network models, can substantially reduce the computational requirement. In this paper, we present a novel method for offloading computationally intensive bulk traffic calculations to the background onto GPU, while leaving CPU to simulate detailed network transactions in the foreground. We present a hybrid traffic model that combines the foreground packet-oriented discrete-event simulation on CPU with the background fluid-based numerical calculations on GPU. In particular, we present several optimizations to efficiently integrate packet and fluid flows in simulation with overlapping computations on CPU and GPU. These optimizations exploit the lookahead inherent to the fluid equations, and take advantage of batch runs with fix-up computation and on-demand prefetching to reduce the frequency of interactions between CPU and GPU. Experiments show that our GPU-assisted hybrid traffic model can achieve substantial performance improvement over the CPU-only approach, while still maintaining good accuracy.

High response quality is critical for many best-effort interactive services, and at the same time, reducing energy consumption can directly reduce the operational cost of service providers. In this paper, we study the quality-energy tradeoff for such services by using a composite performance metric that captures their relative importance in practice: Service providers usually grant top priority to quality guarantee and explore energy saving secondly. We consider scheduling on multicore systems with core-level DVFS support and a power budget. Our solution consists of two steps. First, we employ an equal sharing principle for both job and power distribution. Specifically, we present a "Cumulative Round-Robin" policy to distribute the jobs onto the cores, and a "Water-Filling" policy to distribute the power dynamically among the cores. Second, we exploit the concave quality function of many best-effort applications, and develop Online-QE, a myopic optimal online algorithm for scheduling jobs on a single-core system. Combining the two steps together, we present a heuristic online algorithm, called DES (Dynamic Equal Sharing), for scheduling best-effort interactive services on multicore systems. The simulation results based on a web search engine application show that DES takes advantage of the core-level DVFS architecture and exploits the concave quality function of best-effort applications to achieve high service quality with low energy consumption.

Energy conservation and emission reduction is an increasingly prominent and global issue in green computing. Among the various components of a streaming media server, the storage system is the biggest power consumer. In this paper, a Three-State Disk Model (3SDM) is proposed to conserve energy for streaming media servers without losing quality. According to the load threshold, the disks are dynamically divided into three states: overload, normal and standby. With the requests arriving and departing, the disk state transition among these three states. The purpose of 3SDM is to skew the load among the disks to achieve high quality and energy efficiency for streaming media applications. The load of disks in overload state will move to disks in normal state to improve the quality of service (QoS) level. The load of disks in normal state will be packed together to switch some disks into standby state to save energy. The key problem here is to identify the blocks that need migrating among disks. A sliding window replacement (SWR) algorithm is developed for this purpose, which calculates the block weight based on the request frequency falling within the window of a block. Employing a validated simulator, this paper evaluates the SWR algorithm for conventional disks based on the proposed 3SDM model. The results show that this scheme is able to yield energy efficient streaming media servers.

Reducing energy consumption has been an important design issue for large-scale streaming media storage systems. Existing energy conservation techniques are inadequate to achieve high energy efficiency for streaming media computing environments due to high data migration overhead. To address this problem, we propose in this paper a new energy-efficient method called Explicit Energy Saving Disk Cooling or EESDC. EESDC significantly reduces data migration overhead because of two reasons. First, a set of disks referred to Explicit Energy Saving Disks (EESD) is explicitly fixed according to temporal system load. Second, all the migrated data in EESDC directly contribute on extending the idle time of EESD to conserve more energy efficiently. Therefore, the EESDC method is conducive to saving more energy by quickly achieving energy-efficient data layouts without unnecessary data migrations. We implement EESDC in a simulated disk system, which is validated against a prototype system powered by our EESDC. Our experimental results using both real-world traces and synthetic traces show that EESDC can save up to 28.13-29.33 percent energy consumption for typical streaming media traces. Energy efficiency of streaming media storage systems can be improved by 3.3-6.0 times when EESDC is coupled.

As cloud computing increasingly enters the commercial domain, the resource management issues are becoming a major concern. The dynamics and complexity of cloud environments pose some challenges in managing the resources to ensure the quality of service (QoS) continuously met under fluctuating workloads. In this paper, we focus on the advance resource reservation management issues for the cloud environments. The architecture of virtualization-based resource reservation is first described and then the formulation of an optimization problem concerning the reservation acceptance decision is presented. To solve this problem, an adaptive QoS-aware reservation management approach is proposed, which calculates the reservation acceptance gain to make the final decision. Performance evaluation results of simulation experiments demonstrate that by using the approach we designed, the QoS of both types of applications could be guaranteed and thus the total revenue of the resource provider could stably keep rising. Detailed analysis shows that proper choices can be made to deal with resource reservation requests in a QoS-aware manner.

Service-oriented architecture and cloud computing have become the prevalent computing paradigm. In this paradigm, computing resources can be accessed like other utility services available in today's society. In the meantime, robotics applications are joining the trend. More and more robot applications are shifting from manufacture to non-manufacture and service industries. However, for the on-demand supply of the large-scale heterogeneous robots, It is still a problem have not yet been studied, including the fundamental management and efficiency issues in using of these resources. In this paper, we design a framework of "Robot Cloud Center" (RCC) following the general cloud computing paradigm to address the current limitations in capacity and versatility of robotic applications. In this framework, a robot can be provided as a service just like a public utility service so that everyone can access the powerful robotic services easily, efficiently, and cheaply. Based on a given scenario, a robot scheduling algorithm in RCC is proposed to take advantage of the heterogeneous robot resources to meet the end user's requirement with the minimum cost.

To explore chip-level parallelism, the PSC (Parallel Shared Cache) model is provided in this paper to describe high performance shared cache of Chip Multi-Processors (CMP). Then for a specific application, parallel sorting, a cache-conscious parallel algorithm, PMCC (Partition-Merge based Cache-Conscious) is designed based on the PSC model. The PMCC algorithm consists of two steps: the partition-based in-cache sorting and merge-based k-way merge sorting. In the first stage, PMCC first divides the input dataset into multiple blocks so that each block can fit into the shared L2 cache, and then employs multiple cores to perform parallel cache sorting to generate sorted blocks. In the second stage, PMCC first selects an optimized parameter k which can not only improve the parallelism but also reduce the cache missing rate, then performs a k-way merge sorting to merge all the sorted blocks. The I/O complexity of the in-cache sorting step and k-way merge step are analyzed in detail. The simulation results show that the PSC based PMCC algorithm can out-performance the latest PEM based cache-conscious algorithm and the scalability of PMCC is also discussed. The low I/O complexity, high parallelism and the high scalability of PMCC can take advantage of CMP to improve its performance significantly and deal with large scale problem efficiently.

The task scheduling model and algorithm is very important to achieve high performance for message passing programs. The SPG (subtask precedence graph) model abstracts a task as a set of communication and computation sections so it can explore the dependence among subtasks precisely. The TSSF (task section based scheduling framework ) is designed to show how to generate subtasks and how to schedule subtasks on to different processors. Based on the SPG model and the TSSF Framework, two TSD(task section duplication based) algorithms, SMU(searching-marking-unmarking) and Scalable SMU are described in detail to show how to get multiple parallel executing paths based on task section duplication. Compared with four typical traditional task scheduling algorithms, the simulation results show that our algorithms outperform other algorithms significantly.

The large L2 cache's access latency, which is mainly caused by wire delay, is a critical problem to improve the performance of CMP (Chip Multi-Processor) in NUCA (Non-Uniform Cache Architecture). A CMP L2 cache accessing performance model is provided first to analyze and evaluate the L2 access efficiency in this paper. The total L2 cache access latency problem is formalized as an optimal problem and the lower bound of L2 cache access latency is given based on this model. A novel PBM (Prediction based L2 cache data Migration) algorithm, which employs the sequential prediction technology to identify the data to be accessed in the near future, is designed to migrate the data to be accessed toward their users in early and this method can enable the cores to perform their accesses to the L2 cache in close banks. The analysis results show that this active data migration algorithm can take advantage of the principle of locality to reduce the data access latency much more than the traditional lazy data migration policy. To evaluate the theoretic analysis results, the HMTT toolkit is used to capture the complete memory trace of the SPEC 2000 benchmark running on an SMP computer. The memory trace shows that our prediction technology can work well and at the same time, an L2 cache access simulator is developed to deal with the memory trace data. The simulation experiments show that both the shorter block transfer distance and the lower average access latency can be achieved in the PBM policy. The average block transfer distance can be reduced by up to 16.9%, and the average L2 access latency can be reduced by up to 8.4%.

Isolated Distributed Stream Servers (IDSS) is the main form of video-on-demand (VOD) service architecture in industrial community nowadays. Contrast to the previous work on load sharing in distributed VOD system which all follows the idea of having high-speed inner network among service nodes, in this paper, we firstly focus on load sharing algorithm under IDSS architecture and introduce global stream distribution optimization, future user arrival rate estimation and more disk storage redundancy rate to make the effect of load sharing more satisfactory. Moreover the influence of video redundancy rate and some other parameters in our algorithm is detected through simulation. Preliminary experiment results suggest that our algorithm outperforms existing load sharing algorithms under IDSS architecture and sometimes even works better than existing ones with inner network.

Abstract The Grid is a new type of resource sharing infrastructure. Due to software and hardware limitations, the service that a certain Grid can offer is finite, and so is the number of users it can accommodate. If the number of users is too small, much of the planned resources would be wasted. On the other hand, excessive loading due to too many users could substantially reduce the benefit enjoyed by each user and also the efficiency of the Grid service. Therefore, there are two main problems for Grid design. (1) How many users should the Grid serve so that each user can receive the maximum benefit? (2) To a certain group of users, how much resources should be invested so that the construction and maintenance of the Grid become viable? Based on the economic theory of clubs, this paper gives a quantitative analysis of the quasi‐optimal number of users and amount of each resource by regarding Grid services and resources as club goods. Based on our assumptions on the system model, we deduce two preliminary results and verify them by experiments using GridFTP. These two results allow the users to run randomized algorithms to achieve better system performance. Copyright © 2006 John Wiley & Sons, Ltd.

This paper firstly concisely introduces the research background of simulation grid, then combined with authors' ongoing project on simulation grid, the phased research achievements of simulation grid project are introduced in detail, including completed simulation grid prototype named Cosim-Grid 0.1v, parts of solved key technologies and some typical application demonstration systems of simulation grid. It is shown from the primary practice that the simulation grid developed by authors has the following new features: (1) An architecture of service oriented simulation grid, based on HLA (high level architecture), PLM (product lifecycle management) and grid/Web service, is proposed. It overcomes shortcomings of HLA on dynamical share, autonomy, fault tolerant, capability of collaboration and security mechanism. (2) A simulation grid prototype named Cosim-Grid 0.1v has been developed, which has independent copyright and is suitable for simulation application. It consists of simulation grid portal, simulation application oriented service middleware, grid middleware GOS and the simulation grid resources including various encapsulated simulation model services. (3) It extends simulation application pattern and implements new simulation method based on Internet and grid. Finally, the conclusion and some further works are given.

Virtual Interface Architecture (VIA) established a communication model with low latency and high bandwidth, and defined the standard of user-level high-performance communication specification in cluster systems. This paper analyzes the current development, principle and implementations of VIA, and presents user-level high-performance communication software, MyVIA, based on Myrinet, which is comfortable with VIA specification. The paper first describes the design principle and framework of MyVIA, then proposes new technologies of MyVIA including User TLB, continued host physical memory and varied NIC buffer, the pipelining communication based on resource and DMA chain, and physical descriptor ring. Experimental results of performance comparisons and analysis are presented; the one-way bandwidth of MyVIA for a 4 KB message is 250 MB/s, and the lowest one-way latency is 8.46 /spl mu/s, which shows that the performance of MyVIA surpassed that of other implementations of VIA.

The paperreveals some amazing results: the cluster is making so rapid a progress at anexponential speed, and the efficiency of clusters are settled so successfully tobe around 60%, despite most public speakers still speak of it in the 10-30%range. Moreover, the paper shows some important statistics data for clusters,along with the key features of current top clusters. These results come from anextensive analysis of the TOP500 and Clusters @ TOP500database.