Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this article, we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.

Energy management has become one of the great challenges in portable computing. This is the result of the increasing energy requirements of modern portable devices without a corresponding increase in battery technology. uSleep is an energy reduction technique for handheld devices that is most effective when the handheld's processor is lightly loaded, such as when the user is reading a document or looking at a web page. When possible, rather than using the processor's idle mode, uSleep tries to put the processor in sleep mode for short periods (less than one second) without affecting the user's experience. To enhance the perception that the system is on, an image is maintained on the display and activity is resumed as a result of external events such as touch-screen and button activity. We have implemented, analyzed and evaluated uSleep on a prototype pocket computer, where it has reduced energy consumption by up to 60%.

A battery-powered portable electronic system shuts down once the battery is discharged; therefore, it is important to take the battery behavior into account. A system designer needs an adequate high-level battery model to make battery-aware decisions targeting the maximization of the system's online lifetime. We propose such a model that allows a designer to analytically predict the battery time-to-failure for a given load. Our model also allows for a tradeoff between the accuracy and the amount of computation performed. The quality of the proposed model is evaluated using typical pocket computer applications and a detailed low-level simulation of a lithium-ion electrochemical cell. In addition, we verify the proposed model against actual measurements taken on a real lithium-ion battery.

Predicting the time of full discharge of a finite-capacity energy source, such as a battery, is important for the design of portable electronic systems and applications. In this paper we present a novel analytical model of a battery that not only can be used to predict battery lifetime, but also can serve as a cost function for optimization of the energy usage in battery-powered systems. The model is physically justified, and involves only two parameters, which are easily estimated. The paper includes the results of extensive experimental evaluation of the model with respect to numerical simulations of the electrochemical cell, as well as measurements taken on a real battery. The model was tested using constant, interrupted, periodic and non-periodic discharge profiles, which were derived from standard applications run on a pocket computer.

Application-specific safe message handlers (ASHs) are designed to provide applications with hardware-level network performance. ASHs are user-written code fragments that safely and efficiently execute in the kernel in response to message arrival. ASHs can direct message transfers (thereby eliminating copies) and send messages (thereby reducing send-response latency). In addition, the ASH system provides support for dynamic integrated layer processing (thereby eliminating duplicate message traversals) and dynamic protocol composition (thereby supporting modularity). ASHs offer this high degree of flexibility while still providing network performance as good as, or (if they exploit application-specific knowledge) even better than, hard-wired in-kernel implementations. A combination of user-level microbenchmarks and end-to-end system measurements using TCP demonstrates the benefits of the ASH system.

Application-specific safe message handlers (ASHs) are designed to provide applications with hardware-level network performance. ASHs are user-written code fragments that safely and efficiently execute in the kernel in response to message arrival. ASHs can direct message transfers (thereby eliminating copies) and send messages (thereby reducing send-response latency). In addition, the ASH system provides support for dynamic integrated layer processing (thereby eliminating duplicate message traversals) and dynamic protocol composition (thereby supporting modularity). ASHs provide this high degree of flexibility while still providing network performance as good as, or (if they exploit application-specific knowledge) even better than, hard-wired in-kernel implementations. A combination of user-level microbenchmarks and end-to-end system measurements using TCP demonstrate the benefits of the ASH system.

Low-overhead message passing is critical to the performance of many applications. Active Messages reduce the software overhead for message handling: messages are run as handlers instead of as threads, which avoids the overhead of thread management and the unnecessary data copying of other communication models. Scheduling the execution of Active Messages is typically done by disabling and enabling interrupts, or by polling the network. This primitive scheduling control, combined with the fact that handlers are not schedulable entities, puts severe restrictions on the code that can be run in a message handler. This paper describes a new software mechanism, Optimistic Active Messages (OAM), that eliminates these restrictions; OAMs allow arbitrary user code to execute in handlers, and also allow handlers to block. Despite this gain in expressiveness, OAMs perform as well as Active Messages.

article Free Access Share on CRL: high-performance all-software distributed shared memory Authors: K. L. Johnson MIT Laboratory for Computer Science, Cambridge, MA MIT Laboratory for Computer Science, Cambridge, MAView Profile , M. F. Kaashoek MIT Laboratory for Computer Science, Cambridge, MA MIT Laboratory for Computer Science, Cambridge, MAView Profile , D. A. Wallach MIT Laboratory for Computer Science, Cambridge, MA MIT Laboratory for Computer Science, Cambridge, MAView Profile Authors Info & Claims ACM SIGOPS Operating Systems ReviewVolume 29Issue 5Dec. 3, 1995 pp 213–226https://doi.org/10.1145/224057.224073Published:03 December 1995Publication History 161citation782DownloadsMetricsTotal Citations161Total Downloads782Last 12 Months60Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The MIT J-Machine multicomputer has been constructed to study the role of a set of primitive mechanisms in providing efficient support for parallel computing. Each J-Machine node consists of an integrated multicomputer component, the Message-Driven Processor (MDP), and 1 MByte of DRAM. The MDP provides mechanisms to support efficient communication, synchronization, and naming. A 512 node J-Machine is operational and is due to be expanded to 1024 nodes in March 1993. In this paper we discuss the design of the J-Machine and evaluate the effectiveness of the mechanisms incorporated into the MDP. We measure the performance of the communication and synchronization mechanisms directly and investigate the behavior of four complete applications.

The use of edge detection and localizing filters for the recognition of lines (narrow contrasting strips) in visual scenes is shown to produce systematic errors while being suboptimum with respect to noise. The authors derive optimum filters for the detection and localization of lines based on matched filtering in the line normal direction and a Wiener filter in the direction tangential to the line. The matched filters for the detection and localization of line normals in white noise have the form of the system response and its first derivative, respectively. The orthogonal least-restrictive Wiener filter is also closely approximated by the form of the system response in cased where Gaussian response and white noise dominate. Close approximations to the optimum filters for line recognition under these common conditions are readily realized. Line filters were integrated with the Boie-Cox edge detector. Results for images with mixed lines and edges show that the integrated Boie-Cox system is free of the systematic errors common to other edge recognition systems.>

It is shown that an efficient practical registration algorithm previously described by H.G. Barrow et al. (1977) can provide high-accuracy registration. Experiments with a quantized video image of a solid triangle yielded registration that was accurate to 3% of the interpixel spacing (i.e. accurate to 0.5 mils) in the x and y directions and 0.015 degrees in rotation. It may also be important to predict the accuracy in advance, to see whether specifications can be met and to estimate accuracy during registration, in order to control quality. The authors provide practical formulas for both purposes for two kinds of image point data: edge detection (ED) data and direct measurement (DM) data. In two experiments using ED data, the predicted, estimated, and observed accuracies are all in agreement. The prediction theory developed suggests five precautions to avoid loss of registration accuracy. Perhaps most important is Precaution c, the necessity of the ED case not to have a large fraction of the total segment length of the model aligned with the horizontal or vertical directions of the pixel grid. When the model consists largely of horizontal and vertical segments, a good way to observe this precaution is to tilt the pixel grid a few degrees away from perfect alignment, e.g. by tilting the video camera. A third experiment verifies that violating Precaution c can seriously degrade accuracy.>