Warren Smith

A Resource Management Architecture for Metacomputing Systems

Metacomputing systems are intended to support remote and/or concurrent use of geographically distributed computational resources. Resource management in such systems is complicated by five concerns that do not typically arise in other situations: site autonomy and heterogeneous substrates at the resources, and application requirements for policy extensibility, co-allocation, and online control. We describe a resource management architecture that addresses these concerns. This architecture distributes the resource management problem among distinct local manager, resource broker, and resource co-allocator components and defines an extensible resource specification language to exchange information about requirements. We describe how these techniques have been implemented in the context of the Globus metacomputing toolkit and used to implement a variety of different resource management strategies. We report on our experiences applying our techniques in a large testbed, GUSTO, incorporating 15 sites, 330 computers, and 3600 processors.

Predicting Application Run Times Using Historical Information

We present a technique for deriving predictions for the run times of parallel applications from the run times of “similar” applications that have executed in the past. The novel aspect of our work is the use of search techniques to determine those application characteristics that yield the best definition of similarity for the purpose of making predictions. We use four workloads recorded from parallel computers at Argonne National Laboratory, the Cornell Theory Center, and the San Diego Supercomputer Center to evaluate the effectiveness of our approach. We show that on these workloads our techniques achieve predictions that are between 14 and 60 percent better than those achieved by other researchers; our approach achieves mean prediction errors that are between 40 and 59 percent of mean application run times.

Scheduling with advanced reservations

Some computational grid applications have very large resource requirements and need simultaneous access to resources from more than one parallel computer. Current scheduling systems do not provide mechanisms to gain such simultaneous access without the help of human administrators of the computer systems. In this work, we propose and evaluate several algorithms for supporting advanced reservation of resources in supercomputing scheduling systems. These advanced reservations allow users to request resources from scheduling systems at specific times. We find that the wait times of applications submitted to the queue increases when reservations are supported and the increase depends on how reservations are supported. Further, we find that the best performance is achieved when we assume that applications can be terminated and restarted, backfilling is performed, and relatively accurate run-time predictions are used.

Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance

On many computers, a request to run a job is not serviced immediately but instead is placed in a queue and serviced only when resources are released by preceding jobs. In this paper, we build on run-time prediction techniques that we developed in previous research to explore two problems. The first problem is to predict how long applications will wait in a queue until they receive resources. We develop run-time estimates that result in more accurate wait-time predictions than other run-time prediction techniques. The second problem we investigate is improving scheduling performance. We use run-time predictions to improve the performance of the least-work-first and backfill scheduling algorithms. We find that using our run-time predictor results in lower mean wait times for the workloads with higher offered loads and for the backfill scheduling algorithm.

Benchmarks and Standards for the Evaluation of Parallel Job Schedulers

The evaluation of parallel job schedulers hinges on the workloads used. It is suggested that this be standardized, in terms of both format and content, so as to ease the evaluation and comparison of different systems. The question remains whether this can encompass both traditional parallel systems and metacomputing systems. This paper is based on a panel on this subject that was held at the workshop, and the ensuing discussion; its authors are both the panel members and participants from the audience. Naturally, not all of us agree with all the opinions expressed here...

Software infrastructure for the I-WAY high-performance distributed computing experiment

High speed wide area networks are expected to enable innovative applications that integrate geographically distributed, high performance computing, database, graphics, and networking resources. However, there is as yet little understanding of the higher level services required to support these applications, or of the techniques required to implement these services in a scalable, secure manner. We report on a large scale prototyping effort that has yielded some insights into these issues. Building on the hardware base provided by the I-WAY, a national scale asynchronous transfer mode (ATM) network, we developed an integrated management and application programming system, called I-Soft. This system was deployed at most of the 17 I-WAY sites and used by many of the 60 applications demonstrated on the I-WAY network. We describe the I-Soft design and report on lessons learned from application experiments.

Prediction Services for Distributed Computing

Users of distributed systems such as the TeraGrid and Open Science Grid can execute their applications on many different systems. We wish to help such users, or the grid schedulers they use, select where to run applications by providing predictions of when tasks will complete if sent to different systems. We make predictions of file transfer times, batch scheduler queue wait times, and application execution times using historical information and instance-based learning techniques. Our prediction errors for data from the TACC lonestar system are 37 percent of mean file transfer time, 115 percent for mean queue wait time, and 72 percent of mean execution time. Our approach achieves significantly lower prediction error on other workloads. We have wrapped these prediction techniques with Web services, making predictions available to users of distributed systems as well as tools such as resource brokers and metaschedulers.

TeraGrid's integrated information service

The NSF TeraGrid project has designed and constructed a federated integrated information service (IIS) to serve its capability publishing and discovery needs. This service has also proven helpful in automating TeraGrids operational activities. We describe the requirements that motivated this work; IISs system architecture, information architecture, and information content; processes that IIS currently supports; and how various layers of the system architecture are being used. We also review motivating use cases that have not yet been satisfied by IIS and outline approaches for future work.