36453760
Integrated approach to buffer management and query optimization
40618061
Transaction Recovery in Distributed DB/DC Systems: A Progressive Approach
2808600
The Maximum Factor Queue Length Batching Scheme for Video-on-Demand Systems
In a video-on-demand environment, batching of video requests is often used to reduce I/O demand and improve throughput. Since viewers may defect if they experience long waits, a good video scheduling policy needs to consider not only the batch size but also the viewer defection probabilities and wait times. Two conventional scheduling policies for batching are the first-come-first-served (FCFS) policy, which schedules the video with the longest waiting request, and the maximum queue length-(MQL) policy, which selects the video with the maximum number of waiting requests. Neither of these policies leads to entirely satisfactory results. MQL tends to be too aggressive in scheduling popular videos by considering only the queue length to maximize batch size, while FCFS has the opposite effect by completely ignoring the queue length and focusing on arrival time to reduce defection. In this paper, we introduce the notion of factored queue length and propose a batching policy that schedules the video with the maximum factored queue length. We refer to this as the MFQL policy. The factored queue length is obtained by weighting each video queue length with a factor which is biased against the more popular videos. An optimization problem is formulated to solve for the best weighting factors for the various videos. We also consider MFQL implementation issues. A simulation is developed to compare the proposed MFQL variants with FCFS and MQL. Our study shows that MFQL yields excellent empirical results in terms of standard performance measures such as average latency time, defection rates, and fairness.
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.
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>>
26676459
Progressive Transaction Recovery in Distributed DB/DC Systems
The demand for on-line transaction processing has grown rapidly in recent years. To meet the transaction demand, several DB (database management) and DC (data communication management) subsystems can be coupled together to form a distributed DB/DC system. A key problem is to provide these distributed systems with effective means to recover transactions upon failure while paying little performance penalty during normal processing. Also, there should be minimal interference of fault-free components, during the recovery of failed component. By decentralizing recovery management, and using transaction level structural information to eliminate costly lower level handshaking protocols, proposed progressive transaction recovery protocols seek to solve the problem. A queueing model for evaluating the transaction response time during normal processing for the progressive and pessimistic protocols is developed and solved, via simulation. The progressive recovery protocols are shown to reduce normal processing overhead and lead to performance improvement over the pessimistic protocol.
3748150
Mesh blending
A new method for smoothly connecting different patches on triangle meshes with arbitrary connectivity, called mesh blending, is presented. A major feature of mesh blending is to move vertices of the blending region to a virtual blending surface by choosing an appropriate parameterization of those vertices. Once blending is completed, the parameterization optimization is performed to perfect the final meshes. Combining mesh blending with multiresolution techniques, an effective blending technique for meshes is obtained. Our method has several advantages: (1) the user can intuitively control the blending result using different blending radii, (2) the shape of cross-section curves can be adjusted to flexibly design complex models, and (3) the resulting mesh has the same connectivity as the original mesh. In this paper, some examples about smoothing, sharpening, and mesh editing show the efficiency of the method.
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.
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.