16849208
The state of the art in locally distributed Web-server systems
The overall increase in traffic on the World Wide Web is augmenting user-perceived response times from popular Web sites, especially in conjunction with special events. System platforms that do not replicate information content cannot provide the needed scalability to handle large traffic volumes and to match rapid and dramatic changes in the number of clients. The need to improve the performance of Web-based services has produced a variety of novel content delivery architectures. This article will focus on Web system architectures that consist of multiple server nodes distributed on a local area, with one or more mechanisms to spread client requests among the nodes. After years of continual proposals of new system solutions, routing mechanisms, and policies (the first dated back to 1994 when the NCSA Web site had to face the first million of requests per day), many problems concerning multiple server architectures for Web sites have been solved. Other issues remain to be addressed, especially at the network application layer, but the main techniques and methodologies for building scalable Web content delivery architectures placed in a single location are settled now. This article classifies and describes main mechanisms to split the traffic load among the server nodes, discussing both the alternative architectures and the load sharing policies. To this purpose, it focuses on architectures, internal routing mechanisms, and dispatching request algorithms for designing and implementing scalable Web-server systems under the control of one content provider. It identifies also some of the open research issues associated with the use of distributed systems for highly accessed Web sites.
40618061
Transaction Recovery in Distributed DB/DC Systems: A Progressive Approach
5368193
Progressive modeling
Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and training time. If the accuracy of the produced model is too low, all the computing resources are misspent. In this paper we propose a progressive modeling framework. In progressive modeling, the learning algorithm estimates online both the accuracy of the final model and remaining training time. If the estimated accuracy is far below expectation, the user can terminate training prior to completion without wasting further resources. If the user chooses to complete the learning process, progressive modeling will compute a model with expected accuracy in expected time. We describe one implementation of progressive modeling using ensemble of classifiers.
36453760
Integrated approach to buffer management and query optimization
206711990
Performance comparison of active-sender and active-receiver policies for distributed caching
The authors propose a distributed caching approach to off-loading data access requests from overloaded data servers in a distributed system to nodes that are idle or less busy. Helping out the busy servers on data accesses, the idle or less busy nodes are called mutual servers. Frequently accessed data are cached in the main memory of mutual servers in addition to server and client local caches. The authors evaluate several data propagation strategies among data servers and mutual servers. Simulation results show that the active-sender passive-receiver policy is the method of choice in most cases. Active-sender policies are best able to exploit the main memory of other idle nodes in the expected normal condition where some nodes are overloaded and others are less loaded. All active policies perform far better than the policy without distributed caching.<<ETX>>
1929844
Outlier detection for high dimensional data
The outlier detection problem has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. Most such applications are high dimensional domains in which the data can contain hundreds of dimensions. Many recent algorithms use concepts of proximity in order to find outliers based on their relationship to the rest of the data. However, in high dimensional space, the data is sparse and the notion of proximity fails to retain its meaningfulness. In fact, the sparsity of high dimensional data implies that every point is an almost equally good outlier from the perspective of proximity-based definitions. Consequently, for high dimensional data, the notion of finding meaningful outliers becomes substantially more complex and non-obvious. In this paper, we discuss new techniques for outlier detection which find the outliers by studying the behavior of projections from the data set.
16133419
Executing Hardware as Parallel Software for Picoblaze Networks
Multi-processor architectures have gained interest recently because of their ability to exploit programmable silicon parallelism at acceptable power-efficiency figures. Despite the potential benefit they offer over single-processor architectures, it is unresolved how one can write compact and efficient programs for multiple parallel cores. In this paper, we propose the use of a synchronous hardware description language to program a network of small PicoBlaze processors. The partitioning of a multiprocessor program over multiple cores is straightforward because the input specification is fully parallel. A systematic transformation process converts the parallel input specification into concurrent PicoBlaze programs. We demonstrate the mapping of a cryptographic design (AES) onto four PicoBlaze processors, showing almost linear speedup over an equivalent single-core design
7510592
A matching pursuit technique for computing the simplest normal forms of vector fields
This paper presents a matching pursuit technique for computing the simplest normal forms of vector fields. First a simple, explicit recursive formula is derived for general differential equations, which reduces computation to the minimum. Then a matching pursuit technique is introduced and applied to the Takens-Bogdanov dynamical singularity. It is shown that unlike other methods for computing normal forms, the technique using matching pursuit does not need any algebraic constraints which are required for the existence of the simplest normal form. The efficient method and matching pursuit technique, which have been implemented using Maple, can be "automatically" executed on various computer systems. A number of examples are presented to demonstrate the advantages of the technique.
16104127
Bilateral Filter for Meshes Using New Predictor
A new predictor of bilateral filter for smoothing meshes is presented. It prevents shrinkage of corners. A major feature of mesh smoothing is to move every vertex along the direction determined by the mean curvature normal with speed defined by the predictor. It prevents unnatural deformation for irregular meshes. In order to remove thenormal noise, we use adaptive Gaussian filter to smooth triangle normals.