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.
5908082
Request Redirection Algorithms for Distributed Web Systems
Replication of information among multiple servers is necessary to support high request rates to popular Web sites. We consider systems that maintain one interface to users, even it they consist of multiple nodes with visible IP addresses that are distributed among different networks. In these systems, first-level dispatching is achieved through the Domain Name System (DNS) during the address lookup phase. Distributed Web systems can use a request redirection mechanism as second-level dispatching because the DNS routing scheme has limited control on offered load. Redirection is always executed by the servers, but there are many alternatives that are worth investigating. We explore the combination of DNS dispatching with redirection schemes that use centralized or distributed control on the basis of global or local state information. In fully distributed schemes, DNS dispatching is carried out by simple algorithms because load sharing is taken by some redirection mechanisms that each server activates autonomously. On the other hand, in fully centralized schemes, redirection is used as a tool to enforce decisions taken by the same centralized entity that provides the first-level dispatching. We also investigate hybrid strategies. We conclude that distributed algorithms are preferable over their centralized counterpart because they provide stable performance, take content-aware dispatching decisions, limit the percentage of redirected requests, and their implementation is much simpler than that required by centralized schemes.
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.
37300277
Data placement and buffer management for concurrent mergesorts with parallel prefetching
Various data placement policies are studied for the merge phase of concurrent mergesorts using parallel prefetching, where initial sorted runs (input) of a merge and its final sorted run (output) are stored on multiple disks but each run resides only on a single disk. Since the merge phase involves only sequential references, parallel prefetching can be attractive an reducing the average response time for concurrent merges. However, without careful buffer control, severe thrashing may develop under certain run placement policies, reducing the benefits of prefetching. The authors examine through detailed simulations three different run placement policies. The results show that even though buffer thrashing can be almost avoided by placing the output run of a job on the same disk with at least one of its input runs, this thrashing-avoiding run placement policy can be substantially outperformed by other policies that use buffer thrashing control. With buffer thrashing avoidance, the best performance as achieved by a run placement policy that uses a proper subset of disks dedicated for writing the output runs while the rest of the disks are used for prefetching the input runs in parallel.<<ETX>>
40618061
Transaction Recovery in Distributed DB/DC Systems: A Progressive Approach
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.
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.
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