Rajkumar Kettimuthu

The Globus Striped GridFTP Framework and Server

The GridFTP extensions to the File Transfer Protocol define a general-purpose mechanism for secure, reliable, high-performance data movement. We report here on the Globus striped GridFTP framework, a set of client and server libraries designed to support the construction of data-intensive tools and applications. We describe the design of both this framework and a striped GridFTP server constructed within the framework. We show that this server is faster than other FTP servers in both single-process and striped configurations, achieving, for example, speeds of 27.3 Gbit/s memory-to-memory and 17 Gbit/s disk-to-disk over a 60 millisecond round trip time, 30 Gbit/s network. In another experiment, we show that the server can support 1800 concurrent clients without excessive load. We argue that this combination of performance and modular structure make the Globus GridFTP framework both a good foundation on which to build tools and applications, and a unique testbed for the study of innovative data management techniques and network protocols.

Distributed job scheduling on computational Grids using multiple simultaneous requests

Even though middleware support for grid computing has been the subject of extensive research, scheduling policies for the grid context have not been much studied. In addition to processor utilization, it is important to consider the response times of jobs in evaluating the performance of grid scheduling strategies. In this paper we propose distributed scheduling algorithms that use multiple simultaneous requests at different sites. Trace-based simulations show that the use of multiple simultaneous requests provides significant performance benefits. We also show how this scheme can be adapted to provide priority to local jobs, without much loss of performance.

Software as a service for data scientists

Globus Online manages fire-and-forget file transfers for big-data, high-performance scientific collaborations.

Characterization of backfilling strategies for parallel job scheduling

Although there is wide agreement that backfilling produces significant benefits in scheduling of parallel jobs, there is no clear consensus on which backfilling strategy is preferable e.g. should conservative backfilling be used or the more aggressive EASY backfilling scheme; should a first-come first-served (FCFS) queue-priority policy be used, or some other such as shortest job first (SF) or expansion factor (XF); In this paper we use trace-based simulation to address these questions and glean new insights into the characteristics of backfilling strategies for job scheduling. We show that by viewing performance in terms of slowdowns and turnaround times of jobs within various categories based on their width (processor request size), length (job duration) and accuracy of the users estimate of run time, some consistent trends may be observed.

Selective Reservation Strategies for Backfill Job Scheduling

Although there is wide agreement that backfilling produces significant benefits in scheduling of parallel jobs, there is no clear consensus on which backfilling strategy is preferable - should conservative backfilling be used or the more aggressive EASY backfilling scheme. Using trace-based simulation, we show that if performance is viewed within various job categories based on their width (processor request size) and length (job duration), some consistent trends may be observed. Using insights gleaned by the characterization, we develop a selective reservation strategy for backfill scheduling. We demonstrate that the new scheme is better than both conservative and aggressive backfilling.We also consider the issue of fairness in job scheduling and develop a new quantitative approach to its characterization. We show that the newly proposed schemes are also comparable or better than aggressive backfilling with respect to the fairness criterion.

Scheduling of Parallel Jobs in a Heterogeneous Multi-site Environment

Most previous research on job scheduling for heterogeneous systems considers a scenario where each job or task is mapped to a single processor. On the other hand, research on parallel job scheduling has concentrated primarily on the homogeneous context. In this paper, we address the scheduling of parallel jobs in a heterogeneous multi-site environment, where each site has a homogeneous cluster of processors, but processors at different sites have different speeds. Starting with a simple greedy scheduling strategy, we propose and evaluate several enhancements using trace driven simulations. We consider the use of multiple simultaneous reservations at different sites, use of relative job efficacy as a queuing priority, and compare the use of conservative versus aggressive backfilling. Unlike the single-site case, conservative backfilling is found to be consistently superior to aggressive backfilling for the heterogeneous multi-site environment.

Selective buddy allocation for scheduling parallel jobs on clusters

In this paper we evaluate the performance implications of using a buddy scheme for contiguous node allocation, in conjunction with a backfilling job scheduler for clusters. When a contiguous node allocation strategy is used, there is a trade-off between improved run-time of jobs (due to reduced link contention and lower communication overhead) and increased wait-time of jobs (due to external fragmentation of the processor system). Using trace-based simulation, a buddy strategy for contiguous node allocation is shown to be unattractive compared to the standard noncontiguous allocation strategy used in all production job schedulers. A simple but effective scheme for selective buddy allocation is then proposed, that is shown to perform better than non-contiguous allocation.

A data transfer framework for large-scale science experiments

Modern scientific experiments can generate hundreds of gigabytes to terabytes or even petabytes of data that may furthermore be maintained in large numbers of relatively small files. Frequently, this data must be disseminated to remote collaborators or computational centers for data analysis. Moving this data with high performance and strong robustness and providing a simple interface for users are challenging tasks. We present a data transfer framework comprising a high-performance data transfer library based on GridFTP, a data scheduler, and a graphical user interface that allows users to transfer their data easily, reliably, and securely. This system incorporates automatic tuning mechanisms to select at runtime the number of concurrent threads to be used for transfers. Also included are restart mechanisms capable of dealing with client, network, and server failures. Experimental results indicate that our data transfer system can significantly improve data transfer performance and can recover well from failures.

Using overlays for efficient data transfer over shared wide-area networks

Data-intensive applications frequently transfer large amounts of data over wide-area networks. The performance achieved in such settings can often be improved by routing data via intermediate nodes chosen to increase aggregate bandwidth. We explore the benefits of overlay network approaches by designing and implementing a service-oriented architecture that incorporates two key optimizations -- multi-hop path splitting and multi-pathing - within the GridFTP file transfer protocol. We develop a file transfer scheduling algorithm that incorporates the two optimizations in conjunction with the use of available file replicas. The algorithm makes use of information from past GridFTP transfers to estimate network bandwidths and resource availability. The effectiveness of these optimizations is evaluated using several application file transfer patterns: one-to-all broadcast, all-to-one gather, and data redistribution, on a wide-area testbed. The experimental results show that our architecture and algorithm achieve significant performance improvement.

Selective preemption strategies for parallel job scheduling

Although theoretical results have been established regarding the utility of pre-emptive scheduling in reducing average job turn-around time, job suspension/restart is not much used in practice at supercomputer centers for parallel job scheduling. A number of questions remain unanswered regarding the practical utility of pre-emptive scheduling. We explore this issue through a simulation-based study, using job logs from a supercomputer center We develop a tunable selective-suspension strategy, and demonstrate its effectiveness. We also present new insights into the effect of pre-emptive scheduling on different job classes and address the impact of suspensions on worst-case slowdown.