Team grand-large

Overall Objectives
Scientific Foundations
Application Domains
New Results
Other Grants and Activities

Section: Software


Participants : Gilles Fedak [ correspondant ] , Paul Malécot.

Since the late 1990's, DG systems, such as SETI@Home, have been the largest and most powerful distributed computing systems in the world, offering an abundance of computing power at a fraction of the cost of dedicated, custom-built supercomputers. Many applications from a wide range of scientific domains –including computational biology, climate prediction, particle physics, and astronomy– have utilized the computing power offered by DG systems. DG systems have allowed these applications to execute at a huge scale, often resulting in major scientific discoveries that would otherwise had not been possible.

The computing resources that power DG systems are shared with the owners of the machines. Because the resources are volunteered, utmost care is taken to ensure that the DG tasks do not obstruct the activities of each machine's owner; a DG task is suspended or terminated whenever the machine is in use by another person. As a result, DG resources are volatile in the sense that any number of factors can cause the task of a DG application to not complete. These factors include mouse or keyboard activity, the execution of other user applications, machine reboots, or hardware failures. Moreover, DG resources are heterogeneous in the sense that they differ in operating systems, CPU speeds, network bandwidth, memory and disk sizes. Consequently, the design of systems and applications that utilize these systems is challenging.

The long-term overall goal of XtremLab is to create a testbed for networking and distributed computing research. This testbed will allow for computing experiments at unprecedented scale (i.e., thousands of nodes or more) and accuracy (i.e., nodes that are at the "ends" of the Internet).

Currently, the short-term goal of XtremLab is to determine a more detailed picture of the Internet computing landscape by measuring the network and CPU availability of many machines. While DG systems consist of volatile and heterogeneous computing resources, it is unknown exactly how volatile and heterogeneous these computing resources are. Previous characterization studies on Internet-wide computing resources have not taken into account causes of volatility such as mouse and keyboard activity, other user applications, and machine reboots. Moreover, these studies often only report coarse aggregate statistics, such as the mean time to failure of resources. Yet, detailed resource characterization is essential for determining the usefulness of DG systems for various types of applications. Also this characterization is a prerequisite for the simulation and modelling of DG systems in a research area where many results are obtained via simulation, which allow for controlled and repeatable experimentation.

For example, one direct application of the measurements is to create a better BOINC CPU scheduler, which is the software component responsible for distributing tasks of the application to BOINC clients. We plan to use our measurements to run trace-driven simulations of the BOINC CPU scheduler in effort to identify ways it can be improved, and for testing new CPU schedulers before they are widely deployed.

We conduct availability measurements by submitting real compute-bound tasks to the BOINC DG system. These tasks are executed only when the host is idle, as determined by the user's preferences and controlled the BOINC client. These tasks continuously perform computation and periodically record their computation rates to file. These files are collected and assembled to create a continuous time series of CPU availability for each participating host. Utmost care will be taken to ensure the privacy of participants. Our simple, active trace method allows us to measure exactly what actual compute power a real, compute-bound application would be able to exploit. Compared to other passive measurement techniques, our method is not as susceptible to OS idiosyncrasies (e.g. with process scheduling) and takes into account keyboard and mouse activity, and host load, all of which directly impact application execution.

The XtremLab project is available at


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