2701585
“Data in your face”: push technology in perspective
Push technology has recently generated a tremendous amount of media attention, commercial activity, and contro versy. The wide range of opinions on push is understandable given that it represents a major departure from the way distributed information systems have traditionally been b Adding to the noise, however, is confusion about the basic principles of push and where it fits in to the world of data delivery. For example, many discussions on the topic blur the distinction between push and broadcast. We argue that this confusion stems from two fundamental causes: First, push is just one dimension of a larger design space of data delivery mechanisms. Second, networked information systems can employ different data delivery options between different s ets of information producers and consumers. In this short paper we characterize the design space for dissemination-based i nformation systems and applications, and show how current “push” solutions fit into this space. We then use this framework highlight how the implementation of current Internetbased push solutions differs from the appearance that they present to users.
2033914
Data Staging for On-Demand Broadcast
:A>@<b_DCGAdPBEG>R<:c\A8:Q@L]J.A8<0H(L[MOz:CTL0PBc:zbP[<bc4EGADCG ? >@_DA8EHGbP[HP[CTA>@<::ADCTAD<.HTQRJz:CTL0PBcb_DPBEwHw_DPBbP[zbQRA bP0EqPBc\A>Uc\ADPBCGAdP$c:P]HFP z:CGL.PBc:_8PBEwHP[<P]HGHGCFPB_ HG>@?BA4c:P]HFP c\A8QR>@?BA8CGJ P[QRHGA8CG<kP]HG>@?BA2MSLBCQ@PBCG0A&_Q@>RA8<.H  LB:a:QUP]HT>RL0<bE8`v:ADCTAbP0Evz ADAD<Ew>@B<:>Rk_8P[<.HvyL0CGZ HGL]P[CFc:EOc\A8?BA8QRL0:>R<b$LB<\QR>@<:Az:CTL0P0c:_DP0EKHET_F:Adc\a:QR >@<:P[Q@BLBCT>RHG:E MSLBCEGJ\EKHTADE :ADCTA!P[Q@Q c:P]HFP >RHGADEOPBCGAyCGAdPBc\>@QRJPY?]PB>RQUP[z:Q@A>@<¡HG:AEGADCT?BADCd¢ E6P[>@< ADLBCTJB` :A8EGA£EwHGabc:>RAdE(>@B<bLBCTA HG:Ac:P]HFP2P[<: P[0ADAD<.H>UETEwa:AdEOHTbP]HP[CT>@EGA:A8<HG:A c:P[HTP HTL£z A z:CTL0P0c:_DP0EKH£akEKH&z A L0z\HTPB>R<bA8c MSCGL0¤EGA8_DLB<bcbP[CTJ EwHGLBCFP[0A LBC,CTADL[HTA Q@L\_DP]HT>RL0<bE8`¥<¦HT:>@E4bPBkA8C8V  A§:CTLB L0EGA HG:CTADA_LB:Q@ADA8<0HFP[CTJ ̈EGLBQ@a\HG>@LB<kE HGL EGab_F ̈©Ba[«a ¬­«aD®B ̄±°.®_L0<b_A8CG<kED2§L0: LBCGHGa:<:>UEw HG>U_ ET_F:A8c:a:QR>@<:kVEGADCT?BA8C2_DP0_F:>R<bbVP[<bc bCGADMSAHT_F: >@<:b`&bA8EGAHGA8_Fb<:>@3.a:AdE$A ́\:Q@LB>RH:>@<.HTE$:CTL]? >@c:A8c z J,HG:AET_F:A8c\abQR>@<:,P[Q@BLBCT>RHG:,`$μ¶c:AHTPB>RQ@A8c kA8Cw MSLBCTP[<b_DA AD?]P[Q@abP]HT>RL0< akEw>@<:P[<·¥K ̧o1 a:QRHG>U_DPBEwHw zbP0EwAdc HGAdEKHTzkAdc EG:L]vE$HTbP]HHG:AdEwA4c:P]HFP EwHTPBB>@<: HGAd_F:<:>U3.a:A8Ev_8P[< c\CFP[P]HT>@_8P[Q@QRJA8<:bPB<b_A HT:A$kA8Cw MSLBCTP[<b_DA L[M£P Q@PBCG0AEG_8P[Q@A,LB<\c\ADPB<bcz:CTL0P0c  _DP0EKHvEGJ\EKHTAD,`
7889988
Crash recovery in client-server EXODUS
In this paper, we address the correctness and performance issues that arise when implementing logging and crash recovery in a page-server environment. The issues result from two characteristics of page-server systems: 1) the fact that data is modified and cached in client database buffers that are not accessible by the server, and 2) the performance and cost trade-offs that are inherent in a client-server environment. We describe a recovery system that we have implemented for the client-server version of the EXODUS storage manager. The implementation supports efficient buffer management policies, allows flexibility in the interaction between clients and the server, and reduces the server load by generating log records at clients. We also present a preliminary performance analysis of the implementation.
2815522
Efficient Filtering of XML Documents for Selective Dissemination of Information
Information Dissemination applications are gaining increasing popularity due to dramatic improvements in communications bandwidth and ubiquity. The sheer volume of data available necessitates the use of selective approaches to dissemination in order to avoid overwhelming users with unnecessaryinformation. Existing mechanisms for selective dissemination typically rely on simple keyword matching or “bag of words” information retrieval techniques. The advent of XML as a standard for information exchangeand the development of query languages for XML data enables the development of more sophisticated filtering mechanisms that take structure information into account. We have developed several index organizations and search algorithms for performing efficient filtering of XML documents for large-scale information dissemination systems. In this paper we describe these techniques and examine their performance across a range of document, workload, and scale scenarios.
1006062
The design of an acquisitional query processor for sensor networks
We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.
2567437
Dynamic Query Operator Scheduling for Wide-Area Remote Access
Distributed databases operating over wide-area networks such as the Internet, must deal with the unpredictable nature of the performance of communication. The response times of accessing remote sources can vary widely due to network congestion, link failure, and other problems. In such an unpredictable environment, the traditional iterator-based query execution model performs poorly. We have developed a class of methods, called query scrambling, for dealing explicitly with the problem of unpredictable response times. Query scrambling dynamically modifies query execution plans on-the-fly in reaction to unexpected delays in data access. In this paper we focus on the dynamic scheduling of query operators in the context of query scrambling. We explore various choices for dynamic scheduling and examine, through a detailed simulation, the effects of these choices. Our experimental environment considers pipelined and non-pipelined join processing in a client with multiple remote data sources and delayed or possibly bursty arrivals of data. Our performance results show that scrambling rescheduling is effective in hiding the impact of delays on query response time for a number of different delay scenarios.
61655346
Statistics on Logic Simulation
The high costs associated with logic simulation of large VLSI based systems have led to the need for new computer architectures tailored to the simulation task. Such architectures have the potential for significant speedups over standard software based logic simulators. Several commercial simulation engines have been produced to satisfy needs in this area. To properly explore the space of alternative simulation architectures, data is required on the simulation process itself. This paper presents a framework for such data gathering activity by first examining possible sources of speedup in the logic simulation task, examining the sort of data needed in the design of simulation engines, and then presenting such data. The data contained in the paper includes information on subtask times found in standard discrete event simulation algorithms, event intensities, queue length distributions and simultaneous event distributions.
18137632
Collecting Data About Logic Simulation
Design of high-performance hardware and software-based gate-switch-level logic simulators requires knowledge about the logic simulation process itself. Unfortunately, little data is publicly available concerning key aspects of this process. An example of this is the lack of published empirical measurements relating to the time distribution of events generated by such simulators. This paper presents a gate-switch-level logic simulator lsim which is oriented towards the collection of data about the simulation process. The basic components of lsim are reviewed, and its relevant data gathering facilities are discussed. An example is presented which illustrates the use of lsim in gathering data on event distributions and on communications requirements under alternative logic circuit partitionings.
14976201
A General Matrix Iterative Model for Dynamic Load Balancing
Abstract Many applications require some form of load balancing in order to obtain good performance on parallel computing systems. A number of load balancing algorithms have been proposed, and implemented on real systems. However, application developers are in need of a performance tool that compares and evaluates various balancing algorithms, and helps select the best algorithm for a given application and computing platform. In this paper, we propose a matrix iterative model that can represent a wide range of dynamic load balancing algorithms. The model captures the change in task distribution over the processors due to both the application imbalance and load balancing algorithm. We also define performance measures that capture the costs of executing the parallel application and load balancing algorithm on a given computing platform. The model and the performance measures are used to compare and evaluate various load balancing algorithms. In this paper, the model is parameterized to represent three load balancing algorithms — the random, diffusion and redistribution algorithms. Model predictions and experimental measurements are compared for the parallel N -body simulation application. This application is representative of a wide class of scientific applications where time-varying workload distributions are present. The model predictions are within 20% of measured values. The performance of the three algorithms are compared, and optimal algorithm parameters are derived showing how the model can be used to both select an algorithm and set its parameters.
15258862
Accelerator design for protein sequence HMM search
Profile Hidden Markov models (HMMs) are a powerful approach to describing biologically significant functional units, or motifs, in protein sequences. Entire databases of such models are regularly compared to large collections of proteins to recognize motifs in them. Exponentially increasing rates of genome sequencing have caused both protein and model databases to explode in size, placing an ever-increasing computational burden on users of these systems.Here, we describe an accelerated search system that exploits parallelism in a number of ways. First, the application is functionally decomposed into a pipeline, with distinct compute resources executing each pipeline stage. Second, the first pipeline stage is deployed on a systolic array, which yields significant fine-grained parallelism. Third, for some instantiations of the design, parallel copies of the first pipeline stage are used, further increasing the level of coarse-grained parallelism.A naïve parallelization of the first stage computation has serious repercussions for the sensitivity of the search. We present a pair of remedies to this dilemma and quantify the regions of interest within which each approach is most effective. Analytic performance models are used to assess the overall speedup that can be attained relative to a single-processor software solution. Performance improvements of 1 to 2 orders of magnitude are predicted.
4770738
Parallel Simulated Annealing using Speculative Computation
A parallel simulated annealing algorithm that is problem-independent, maintains the serial decision sequence, and obtains speedup which can exceed log/sub 2/P on P processors is discussed. The algorithm achieves parallelism by using the concurrency technique of speculative computation. Implementation of the parallel algorithm on a hypercube multiprocessor and application to a task assignment problem are described. The simulated annealing solutions are shown to be, on average, 28% better than the solutions produced by a random task assignment algorithm and 2% better than the solutions produced by a heuristic. >