Given the complicated label hierarchy, hierarchical text classification (HTC) has emerged as a challenging subtask in the realm of multi-label text classification. Existing methods enhance the quality of text representations by contrastive learning, but this supervised contrastive learning is designed for single-label setting and has two main limitations. On one hand, sample pairs with completely identical labels which should be treated as positive pairs are ignored. On the other hand, a simple pair is deemed as an absolutely positive or negative pair, which lacks consideration about the situation where sample pairs share some labels while having labels unique to each sample. Therefore, we propose a method combining multi-label contrastive learning with KNN (MLCL-KNN) for HTC. The proposed multi-label contrastive learning method can make text representations of sample pairs having more shared labels closer and separate those with no labels in common. During inference, we employ KNN to retrieve several neighbor samples and regard their labels as additional prediction, which is interpolated into the model output to further improve the performance of MLCL-KNN. Compared with the strongest baseline, MLCL-KNN achieves average improvements of 0.31%, 0.76%, 0.83%, and 0.43% on Micro-F1, Macro-F1, accuracy, and HiF respectively, which demonstrates its effectiveness.

In computer vision, the joint development of the algorithm and computing dimensions cannot be separated. Models and algorithms are constantly evolving, while hardware designs must adapt to new or updated algorithms. Reconfigurable devices are recognized as important platforms for computer vision applications because of their reconfigurability. There are two typical design approaches: customized and overlay design. However, existing work is unable to achieve both efficient performance and scalability to adapt to a wide range of models. To address both considerations, we propose a design framework based on reconfigurable devices to provide unified support for computer vision models. It provides software-programmable modules while leaving unit design space for problem-specific algorithms. Based on the proposed framework, we design a model mapping method and a hardware architecture with two processor arrays to enable dynamic and static reconfiguration, thereby relieving redesign pressure. In addition, resource consumption and efficiency can be balanced by adjusting the hyperparameter. In experiments on CNN, vision Transformer, and vision MLP models, our work’s throughput is improved by 18.8x–33.6x and 1.4x–2.0x compared to CPU and GPU. Compared to others on the same platform, accelerators based on our framework can better balance resource consumption and efficiency.

The secure authenticated key establishment between nodes in Wireless Sensor Networks (WSNs) has not been fully solved in the existing schemes. It’s a good idea to apply the Diffie-Hellman protocol to address it perfectly, but the existing authenticated Diffie-Hellman (ADH) protocols are not perfect because their authentication are partial or delayed. In this paper, we first present a concept of full authentication and propose a new fully authenticated Diffie-Hellman (FADH) prototype with light-certificate-based authentication. And then based on the theory of elliptic curve cryptography, we construct the TinyADH (Tiny Authenticated Diffie-Hellman) protocol with applying the FADH in WSNs. Compared with the existing similar solutions, TinyADH has lower communication overload, is easier to implement into existing standards, and more secure under equivalent computational complexity. The experimental results show that using this scheme for a successful key agreement between two nodes averagely takes about 54 seconds on TelosB. Moreover, the simulation results indicate that repeated key agreement can improve the secure connectivity rate. However, considering the cost performance ratio, it is advisable to take 2 runs of the negotiation.

The relatively high cost of record deserialization is increasingly becoming the bottleneck of column-based storage systems in tree-structured applications [58]. Due to record transformation in the storage layer, unnecessary processing costs derived from fields and rows irrelevant to queries may be very heavy in nested schemas, significantly wasting the computational resources in large-scale analytical workloads. This leads to the question of how to reduce both the deserialization and IO costs of queries with highly selective filters following arbitrary paths in a nested schema. We present CORES (Column-Oriented Regeneration Embedding Scheme) to push highly selective filters down into column-based storage engines, where each filter consists of several filtering conditions on a field. By applying highly selective filters in the storage layer, we demonstrate that both the deserialization and IO costs could be significantly reduced. We show how to introduce fine-grained composition on filtering results. We generalize this technique by two pair-wise operations, rollup and drilldown, such that a series of conjunctive filters can effectively deliver their payloads in nested schema. The proposed methods are implemented on an open-source platform. For practical purposes, we highlight how to build a column storage engine and how to drive a query efficiently based on a cost model. We apply this design to the nested relational model especially when hierarchical entities are frequently required by ad hoc queries. The experiments, including a real workload and the modified TPCH benchmark, demonstrate that CORES improves the performance by 0.7×--26.9× compared to state-of-the-art platforms in scan-intensive workloads.

Qiang Chen, Wenjie Li, Yu Lei, Xule Liu, Yanxiang He. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Wearable devices are becoming increasingly important in our daily lives. Energy harvesting instead of battery is a better power source for these wearable devices due to many advantages. However, harvested energy is often unstable and program execution will be frequently interrupted. Non-volatile processors demonstrate promising advantages to back up volatile state before the system energy is depleted. But Non-volatile processors require additional memory for backing up, thus introducing non-negligible overhead in terms of energy, runtime as well as chip area. In this work, we target at non-volatile register reduction for energy harvesting based wearable devices. This paper proposes to stack trimming the memory footprint via a novel compiler directed method. The evaluation results deliver on average 28.6% reduction of non-volatile register files for backing up stack area, with ultra low runtime overhead.

Spin-transfer torque RAM (STT-RAM) has been proposed to build on-chip caches because of its attractive features such as high storage density and ultra low leakage power. However, long write latency and high write energy are the two challenges for STT-RAM. Recently, researchers propose to improve the write performance of STT-RAM by relaxing its non-volatility property. To avoid data losses resulting from volatility, refresh schemes have been proposed. However, refresh operations consume additional overhead. In this paper, we propose to significantly reduce the number of refresh operations through re-arranging program data layout at compilation time. An N-refresh scheme is also proposed to further reduce the number of refreshes. Experimental results show that, on average, the proposed methods can reduce the number of refresh operations by 84.2 percent, and reduce the dynamic energy consumption by 38.0 percent for volatile STT-RAM caches while incurring only 4.1 percent performance degradation.

Phase Change Memory (PCM) is a promising DRAM replacement in embedded systems due to its attractive characteristics such as extremely low leakage power, high storage density and good scalability. However, PCM's low endurance constrains its practical applications. In this paper, we propose a wear leveling aware dynamic stack to extend PCM's lifetime when it is adopted in embedded systems as main memory. Through a dynamic stack, the memory space is circularly allocated to stack frames, and thus an even usage of PCM memory is achieved. The experimental results show that the proposed method can significantly reduce the write variation on PCM cells and enhance the lifetime of PCM memory.

Hybrid caches consisting of static RAM (SRAM) and spin-torque transfer (STT)-RAM have been proposed recently for energy efficiency. To explore the advantages of hybrid cache, most of the management strategies for hybrid caches employ migration-based techniques to dynamically move write-intensive data from STT-RAM to SRAM. These techniques involve additional access operations, and thus lead to extra overheads. In this paper, we propose two compilation-based approaches to improve the energy efficiency and performance of STT-RAM-based hybrid cache by reducing the migration overheads. The first approach, migration-aware data layout, is proposed to reduce the migrations by rearranging the data layout. The second approach, migration-aware cache locking, is proposed to reduce the migrations by locking migration-intensive memory blocks into SRAM part of hybrid cache. Furthermore, experiments show that these two methods can be combined to reduce more migrations. The reduction of migration overheads can improve the energy efficiency and performance of STT-RAM-based hybrid cache. Experimental results show that, combining these two methods, on average, the number of write operations on STT-RAM is reduced by 17.6%, the number of migrations is reduced by 38.9%, the total dynamic energy is reduced by 15.6%, and the total access latency is reduced by 13.8%.

Spin-Transfer Torque RAM (STT-RAM) has been proposed to build on-chip caches because of its attractive features: high storage density and negligible leakage power. Recently, researchers propose to improve the write performance of STT-RAM by relaxing its non-volatility property. To avoid data loss resulting from volatility, refresh schemes are proposed. However, refresh operations consume additional energy. In this paper, we propose to reduce the number of refresh operations through re-arranging program data layout at compilation time. An N-refresh scheme is also proposed. Experimental results show that, on average, the proposedmethods can reduce the number of refresh operations by 73.3%, and reduce the dynamic energy consumption by 27.6%.

Micro-Controller Units (MCUs) are widely adopted ubiquitous computing devices. Due to tight cost and energy constraints, MCUs often integrate very limited internal RAM memory on top of Flash storage, which exposes Flash to heavy write traffic and results in short system lifetime. Architecting emerging Phase Change Memory (PCM) is a promising approach for MCUs due to its fast read speed and long write endurance.

Hybrid caches consisting of both STT-RAM and SRAM have been proposed recently for energy efficiency. To explore the advantages of hybrid cache, most work on hybrid caches employs migration based strategies to dynamically move write-intensive data from STT-RAM to SRAM. Migrations require additional read and write operations for data movement and may lead to significant overheads. To address this issue, this paper proposes a Migration-Aware Compilation (MAC) approach to improve the energy efficiency and performance of STT-RAM based hybrid cache. By re-arranging data layout, the data access pattern in memory blocks is changed such that the number of migrations is reduced without any hardware modification. The reduction of migration overheads in turn improves energy efficiency and performance. The experimental results show that with the proposed approach, on average, the number of write operations on STT-RAM is reduced by 13.4%, the number of migrations is reduced by 16.1%, the total dynamic energy is reduced by 8.5%, and the total latency is reduced by 12.1%.

As technology scales down, energy consumption is becoming a big problem for traditional SRAM-based cache hierarchies. The emerging Spin-Torque Transfer RAM (STT-RAM) is a promising replacement for large on-chip cache due to its ultra low leakage power and high storage density. However, write operations on STT-RAM suffer from considerably higher energy consumption and longer latency than SRAM. Hybrid cache consisting of both SRAM and STT-RAM has been proposed recently for both performance and energy efficiency. Most management strategies for hybrid caches employ migration-based techniques to dynamically move write-intensive data from STT-RAM to SRAM. These techniques lead to extra overheads. In this paper, we propose a compiler-assisted approach, preferred caching, to significantly reduce the migration overhead by giving migration-intensive memory blocks the preference for the SRAM part of the hybrid cache. Furthermore, a data assignment technique is proposed to improve the efficiency of preferred caching. The reduction of migration overhead can in turn improve the performance and energy efficiency of STT-RAM based hybrid cache. The experimental results show that, with the proposed techniques, on average, the number of migrations is reduced by 21.3%, the total latency is reduced by 8.0% and the total dynamic energy is reduced by 10.8%.

Compared to html page, the mobile web page has different features. The mobile web page is arranged through some important tags such as;N;, which are called Gathering Node in this paper. Based on these Gathering Nodes, we proposed a Block Gathering Based Page Segmentation algorithm (BGBPS). The experiment shows that BGBPS algorithm has great effect on semantic segmentation of mobile web page.

Spatial Audio Coding (SAC) is an emerging technology with a distinguishing feature of delivering good even excellent audio quality at monotonic or stereo bitrate of conventional perceptual transform coders. By a systematic exploitation of spatial hearing, Binaural Cue Coding illustrates the power and potentials of SAC in the future for intelligent multimedia services. MPEG Surround, receiving cumulative efforts from industry and academy, strives to build a SAC system with great versatility and high quality. The initial test results of MPEG Surround show its performance advantage over conventional state-of-the-art coders in a wide range of coding configurations.

This paper propose a method for sentence ordering in multi-doucument summarization task, which combine support vector machine (SVM) and Grey Model(GM). Firstly, the method train the SVM with sentences of source documents and predict sentences sequence of summary as primary dataset. Secondly, using Grey Model to process the primary dataset, and achieve the final sequence of summary sentences. Experiments on 100 summaries showed this method provide a much higher precision than probabilistic model in sentence ordering task.

Recently, control and coordination of a set of autonomous mobile robots has been paid a lot of attentions, because the cooperation of simple robots offers several advantages, such as redundancy and flexibility, and allows performing hard tasks that could be impossible for one single robot. There are a lot of interesting applications of multiple robots, such as satellite exploration and surveillance missions. So far, there are many papers working on the coordination of mobile robots. The characteristic of simplicity of mobile robots brings potential wide applications; however this characteristic also lead to crash with higher probability during cooperation, especially in harsh environment. Surprisingly, only few researches consider the fault tolerance of mobile robots, especially for dynamic coordination application - robot flocking. In this paper, we summarize the existed flocking algorithms and discuss their characteristics. Then we briefly described our fault tolerant flocking algorithms in different models. Finally we proposed the potential future research directions for dynamic flocking of a group of mobile robots. In all, this work can provide a good reference for the researchers working on dynamic cooperation of agents in distributed system.

We address the problem of unsupervised ensemble ranking in this paper. Traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. Beyond the aforementioned combine-then-rank and rank-then-combine approaches, we propose a novel rank-learn-combine ranking framework, called Interactive Ranking (iRANK), which allows two base rankers to "teach" each other before combination during the ranking process by providing their own ranking results as feedback to the others so as to boost the ranking performance. This mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. We apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the DUC 2005 data set. The results are encouraging with consistent and promising improvements.

In contrast to traditional brute-force attacks, RoQ (Reduction of Quality) attacks are periodic, stealthy, yet potent, which exploit the vulnerability of adaptation mechanisms to undermine certain services. As the application-level peer-to-peer (p2p) protocols depend on a recovery-adjustment process to maintain global consistency of routing information when peers join and leave the systems, we propose a novel breed of RoQ attacks in structured p2p systems: (1) We induce a general model for RoQ attacks, and then derive in structured p2p networks a new attack form that RoQ attackers periodically create concurrent failure through manipulation of massive nodes, degrading the system performance repeatedly. (2) We explore the impacts of RoQ attacks on Chord with detailed analysis and theoretical estimations, and confirm them by simulation results on p2psim, including successful lookup ratio and lookup latency. Moreover, we also discuss the detection and defense against such attacks and the improvements of protocols for attack tolerance.

Interprocessor communication is an important factor in determining the performance scalability of parallel systems. The communication requirements of a parallel application can be quantified to understand its communication pattern and communication pattern similarities among applications can be determined. This is essential for the efficient mapping of applications on parallel systems and leads to better interprocessor communication implementation among others. This paper proposes a methodology to compare the communication pattern of distributed-memory programs. Communication correlation coefficient quantifies the degree of similarity between two applications based on the communication metrics selected to characterize the applications. To capture the network topology requirements, we extract the communication graph of each applications and quantities this similarity. We apply this methodology to four applications in the NAS parallel benchmark suite and evaluate the communication patterns by studying the effects of varying problem size and the number of logical processes (LPs).

For massive data transmission in Data Grid supporting radio and wireless, a set of novel hierarchical multicast algorithms is proposed to attain higher efficiency of data transfers. The newly-proposed algorithms first form different clusters, second calculate the space weight vector W' and the data quantity weight vector W'' in very cluster. Then the algorithms try to find a new vector W composed by linear combination of the two old ones W' and W". The space factor and data factor game and balance, and the point of game and balance is ¿i¿j=0m-1wi, j' = ßi¿j=0m-1wi, j" a built a binary simple equation, and we sought linear parameters and generate a least weight path tree, namely multicast tree, which is then constructed by using the newly-proposed algorithms to implement the inter-cluster routing. Extended simulation results indicate that the new algorithms are more suitable for Data Grid.

Abstract Sentence ranking is the issue of most concern in document summarization today. While traditional feature‐based approaches evaluate sentence significance and rank the sentences relying on the features that are particularly designed to characterize the different aspects of the individual sentences, the newly emerging graph‐based ranking algorithms (such as the PageRank‐like algorithms) recursively compute sentence significance using the global information in a text graph that links sentences together. In general, the existing PageRank‐like algorithms can model well the phenomena that a sentence is important if it is linked by many other important sentences. Or they are capable of modeling the mutual reinforcement among the sentences in the text graph. However, when dealing with multidocument summarization these algorithms often assemble a set of documents into one large file. The document dimension is totally ignored. In this article we present a framework to model the two‐level mutual reinforcement among sentences as well as documents. Under this framework we design and develop a novel ranking algorithm such that the document reinforcement is taken into account in the process of sentence ranking. The convergence issue is examined. We also explore an interesting and important property of the proposed algorithm. When evaluated on the DUC 2005 and 2006 query‐oriented multidocument summarization datasets, significant results are achieved.

Web services can be composed to build domain-specific application and solution. The standards of several Web services composition (WSC) are proposed, for example, WS-BPEL and WS-CDL. Moreover, there is a great demand for the performance optimization of WSC recently. However, WS-BPEL lacks formal semantics, so it is very difficult to verify WSC and evaluate the performance of WSC. Therefore, considering such situation and long-running transaction (LRT) in WS-BPEL, in this paper, we adopt general stochastic high-level Petri net (GSHLPN) as basic formal description tool of WSC based on WS-BPEL and model the context of LRT. Our approach can provide a more real environment for evaluating and optimizing the performance of WSC based on WS-BPEL.

Current trends in HPC (high performance computing) suggest that clusters will soon consist with hundreds, if not thousands, processors and the size of current scientific problems becomes much larger than before. Many researchers have predicted that the communication among these processors has dominated the execution time of the scientific parallel applications. Users will need well understanding on communication patterns among scientific parallel applications and their similarities so that users benefit not only from cost saving on constructing the running environment for these applications but also from obtaining better performance. In this paper, we address the communication pattern matching, and focus on point-to-point communication, which is primarily utilized (over 90% all MPI (message passing interface) calls) in most MPI codes and has much more impact on the communication performance than collective communication does. In this work, our contribution is that we propose a new approach MPACP (matching of parallel application communication patterns) to automate the analysis of the similarity between two parallel applications and provide a reliable report which will help users or developers understand the similarity among communication patterns of parallel applications. Furthermore, experimental results demonstrate the effective performance of our scheme in terms of the automatic matching of parallel application communication patterns.

In recently years, web service composition becomes a new approach to overcome many difficult problems confronted by B2B e-commerce, inter-organization workflow management, enterprise application integration etc. Due to the uncertainty of the Internet and various web services, the performance of the composed web service can not be ensured. How to model and predict the performance of the composed web service is a difficult problem in the web service composition. A novel simulation model that can model and simulate time and non-time performance characters, called STPM+, is presented in this paper. Based on Petri net, the STPM+ model can simulate and predict multiple performance characters, such as the cost, the reliability and the reputation of the composed web service etc. To examine the validation of the STMP+ model, a visual performance evaluation tool, called VisualWSCPE, has been implemented. Besides, some simulation experiments have been fulfilled based on VisualWSCPE. The experiment results demonstrate the feasibility and efficiency of the STPM+ model.

For attained high data multicast efficiency for the P2P Database system, the paper proposes a set of novel multicast algorithms. In contrast with the current algorithms, the new algorithms firstly divide the group members into different clusters in terms of static delay distance, then find the central node in the clusters, cal-culate the space weight of every node, search the data quantity of every node, and find the maximal data quantity node. After obtaining the spatial weight vector and the data quantity weight vector, the algorithms try to find a new vector composed by linear combination of the spatial and data weight vectors. Then the algo-rithms build binary simple equations between them, seek linear modulus and generate a least weight path tree, namely multicast tree. The multicast tree is then constructed by using the new algorithms. Extended simulation results indicate that these new algorithms are more suitable for P2P Database compared with other well-known existing multicast solutions.

Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.

The number of operations and states of a plan in multi-agent system is finite, and the number of coloured Petri nets element also is finite. Moreover, the action of plan is similar to the transition of coloured Petri nets and the state or the resource of plan is similar to the place of coloured Petri nets. Therefore plan can be modelled effectively by coloured Petri nets. Consequently, the action, the state and the interaction of plan are modelled by coloured Petri nets detailedly. Furthermore, the specification, validity and simulation of plan can be described by the theoretical techniques of Coloured Petri nets.

The detection of failures is a fundamental issue for fault tolerance in distributed systems. Recently, many people have come to realize that failure detection ought to be provided as some form of generic service, similar to IP address lookup. However, this has not been successful so far; one of the reasons being the fact that classical failure detectors were not designed to satisfy several application requirements simultaneously. More specifically, traditional implementations of failure detectors are often tuned for running over local networks and fail to address some important problems found in wide-area distributed systems with a large number of monitored components. In this paper, we study the quality of service (QoS) of failure detectors. We first present a novel failure detector scheme combined with control theory that can help in solving or optimizing some problems. Furthermore, this paper discusses the design and analysis of implementing a scalable failure detection service for such large wide-area distributed systems considering dynamically adjusting the heartbeat streams, so that it satisfies the bottleneck router requirements. We further show how the online failure detector control algorithm can be used to design a controller, analyze the theoretical aspects of the proposed algorithm and verify its agreement. Simulation results show the efficiency of our scheme in terms of high utilization of the bottleneck link, fast response and good stability of the bottleneck router buffer occupancy as well as of the controlled sending rates. In conclusion, the new failure detector algorithm provides a better QoS.

With the increase of multicast data applications, research interests have focused on the design of congestion control schemes for multicast communications. This paper describes a novel control-theoretic multicast congestion control scheme, which is based on the distributed self-tuning proportional plus integrative (SPI) controller. The control parameters can be designed to ensure the stability of the control loop in terms of source rate. The distributed explicit rate SPI overcomes the vulnerability that suffers from the heterogeneous multicast receivers. It is suggested that the congestion controller is located at the multipoint-to-multipoint multicast source to regulate the transmission rate. We further analyze the theoretical aspects of the proposed algorithm, and show how the control mechanism can be used to design a controller to support multicast transmissions. Simulation results demonstrate the efficiency of the proposed scheme in terms of system stability and fast response of the buffer occupancy, as well as controlled sending rates, low packet loss, and high scalability

Wireless sensor networks play a key role in monitoring remote or inhospitable physical environments. One of the most important constraints is the energy efficiency problem. Power conservation and power management must be taken into account at all levels of the sensor networks system hierarchy. DPM (dynamic power management) technology has been widely used in sensor networks. In this paper, we propose a new energy-efficient DPM, which is a modified sleep state policy developed by Simunic and Chdrakasan (2001) and combined with optimal geographical density control (OGDC) by Zhang and Hou (2004) to keep a minimal number of sensor nodes in the active mode in wireless sensor networks. Implementing dynamic power management with considering the battery status, probability of event generation and OGDC will reduce the energy consumption and prolong the whole lifetime of the sensor networks

One important characteristic of wireless sensor networks is energy stringency. Constructing a connected dominating set (CDS) has been widely used as a topology control strategy to reduce the network communication overhead. In the paper, a novel energy efficient distributed connected dominating set algorithm based on coordinated reconstruction mechanism is presented to further prolong the network lifetime and balance energy consumption. The algorithm is with O(n) time complexity and O(n) message complexity. The simulation results show that our algorithm outperforms several existing algorithms in terms of network lifetime and CDS performance.

Evolutionary testing (ET) is an efficient technique of automated test case generation. ET uses a kind of metaheuristic search technique, genetic algorithm (GA), to convert the task of test case generation into an optimal problem. The configuration strategies of GA have notable influences upon the performance of ET. In this paper, represent a dynamic self-adaptation strategy for evolutionary structural testing. It monitors evolution process dynamically, detects the symptom of prematurity by analyzing the population, and adjusts the mutation possibility to recover the diversity of the population. The empirical results show that the strategy can greatly improve the performance of the ET in many cases. Besides, some valuable advices are provided for the configuration strategies of ET by the empirical study.

Reasonable analysis and corrective measurement for the testing results of Web applications can effectively judge the effect and efficiency of the testing. Therefore, based on the previous work, we propose a new method for testing results analysis and comparison, which uses the semantic label and XML description technique to realize the information separation between data and display in the Web pages, so as to directly compare the testing results and the expected results. Furthermore, combined with the realities, we determine the metric indexes of Web application testing, so as to provide the criterions and guidelines for the evaluations of the Web applications and their testing processes. And we introduce the feedback control mechanism into the development and evolvement of Web applications, so as to further improve the system quality.

This paper presents a run-time scheduling policy to map tasks to resources in grid computing systems based on Multi-Agent System (MAS). This policy schedules tasks in run-time and avoids the waste of resources. A simulation result is presented to prove the availability of this policy.

In this paper, we propose a novel active queue management (AQM) scheme based on the Random Early Detection (RED) of the loss ratio and the total sending rate control, called LRC-RED, to regulate the queue length with small variation and to achieve high utilization with small packet loss. This scheme measures the latest packet loss ratio, and uses it and the total sending rate as complements to queue length in order to dynamically adjust packet drop probability. Further, we also provide the design rules for this scheme based on the well-known TCP control model. On the basis of the design rules, we develop a simple, scalable and systematic rule for tuning the control parameters which can be adaptive to dynamic network conditions. Through ns 2 simulations, we show the faster response time and better robustness of the proposed LRC-RED as compared with the Loss Ratio based RED (LRED) [5] algorithm.

In this paper, we investigate the issues of QoS multicast routing in ad hoc wireless networks. Due to limited bandwidth of a wireless node, a QoS multicast call could often be blocked if there does not exist a single multicast tree that has the requested bandwidth, even though there is enough bandwidth in the system to support the call. In this paper we propose a multicast routing scheme by using multiple paths or multiple trees to meet the bandwidth requirement of a call. Three multicast routing strategies are studied, SPT (shortest path tree) based multiple-paths (SPTM), least cost tree based multiple-paths (LCTM) and multiple least cost trees (MLCT). The final routing tree(s) can meet the user's QoS requirements such that the delay from the source node to the furthest destination node shall not exceed the bound and the aggregate bandwidth of the paths or trees shall meet the bandwidth requirement of the call. Extensive simulations have been conducted to evaluate the performance. The simulation results show that the new scheme has three major advantages: 1) it greatly reduces the system blockings; 2) multicast routing is in a fully distributed fashion; 3) the proposed routing protocol follows the format of existing on-demand multicast routing protocols for ad hoc networks, which makes it easy to be incorporated into the existing on-demand routing protocols