A study of deadline scheduling for client-server systems on the Computational Grid

The Computational Grid is a promising platform for the deployment of various high-performance computing applications. A number of projects have addressed the idea of software as a service on the network. These systems usually implement client-server architectures with many servers running on distributed Grid resources and have commonly been referred to as network-enabled servers (NES). An important question is that of scheduling in this multi-client multi-server scenario. Note that in this context most requests are computationally intensive as they are generated by high-performance computing applications. The Bricks simulation framework has been developed and extensively used to evaluate scheduling strategies for NES systems. The authors first present recent developments and extensions to the Bricks simulation models. They discuss a deadline scheduling strategy that is appropriate for the multi-client multi-server case, and augment it with "Load Correction" and "Fallback" mechanisms which could improve the performance of the algorithm. We then give Bricks simulation results. The results show that future NES systems should use deadline scheduling with multiple fallbacks and it is possible to allow users to make a trade-off between failure-rate and cost by adjusting the level of conservatism of deadline scheduling algorithms.

[1]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..

[2]  Henri Casanova,et al.  NetSovle: A Network Server for Solving Computational Science Problems , 1996, Proceedings of the 1996 ACM/IEEE Conference on Supercomputing.

[3]  Vern Paxson,et al.  Fast, approximate synthesis of fractional Gaussian noise for generating self-similar network traffic , 1997, CCRV.

[4]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[5]  Joel R. Stiles,et al.  Monte Carlo simulation of neuro-transmitter release using MCell, a general simulator of cellular physiological processes , 1998 .

[6]  Kenneth L. Calvert,et al.  Modeling Internet topology , 1997, IEEE Commun. Mag..

[7]  Carla E. Brodley,et al.  Predictive application-performance modeling in a computational grid environment , 1999, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469).

[8]  Satoshi Matsuoka,et al.  Overview of a performance evaluation system for global computing scheduling algorithms , 1999, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469).

[9]  D. Rogers,et al.  EGS4 code system , 1985 .

[10]  J.M. Schopf,et al.  Stochastic Scheduling , 1999, ACM/IEEE SC 1999 Conference (SC'99).

[11]  R. F. Freund,et al.  Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems , 1999, J. Parallel Distributed Comput..

[12]  Quinn Snell,et al.  Application placement using performance surfaces , 2000, Proceedings the Ninth International Symposium on High-Performance Distributed Computing.

[13]  Ibrahim Matta,et al.  On the origin of power laws in Internet topologies , 2000, CCRV.

[14]  Jan M. Podivinsky,et al.  SNP: a program for non-parametric time series analysis , 1996 .

[15]  David Abramson,et al.  High performance parametric modeling with Nimrod/G: killer application for the global grid? , 2000, Proceedings 14th International Parallel and Distributed Processing Symposium. IPDPS 2000.

[16]  S Rogers,et al.  A comparison of implicit schemes for the incompressible Navier-Stokes equations with artificial compressibility , 1995 .

[17]  David Abramson,et al.  Modelling Photochemical Pollution using Parallel and Distributed Computing Platforms , 1994, PARLE.

[18]  Francine Berman,et al.  The AppLeS Parameter Sweep Template: User-Level Middleware for the Grid , 2000, ACM/IEEE SC 2000 Conference (SC'00).

[19]  Jeff T. Linderoth,et al.  An enabling framework for master-worker applications on the Computational Grid , 2000, Proceedings the Ninth International Symposium on High-Performance Distributed Computing.

[20]  Richard Wolski,et al.  Implementing a Performance Forecasting System for Metacomputing The Network Weather Service , 1997, ACM/IEEE SC 1997 Conference (SC'97).

[21]  Amitava Majumdar Parallel performance study of Monte Carlo photon transport code on shared-, distributed-, and distributed-shared-memory architectures , 2000, Proceedings 14th International Parallel and Distributed Processing Symposium. IPDPS 2000.

[22]  David Abramson,et al.  An Economy Driven Resource Management Architecture for Global Computational Power Grids , 2000, PDPTA.

[23]  Stuart E. Rogers,et al.  Comparison of Implicit Schemes for the Incompressible Navier-Stokes Equations , 1995 .

[24]  Andrew A. Chien,et al.  The MicroGrid: a Scientific Tool for Modeling Computational Grids , 2000, ACM/IEEE SC 2000 Conference (SC'00).

[25]  Vijay Karamcheti,et al.  Expressing and Enforcing Distributed Resource Sharing Agreements , 2000, ACM/IEEE SC 2000 Conference (SC'00).

[26]  James C. French,et al.  Legion: The Next Logical Step Toward a Nationwide Virtual Computer , 1994 .

[27]  Satoshi Matsuoka,et al.  Performance Evaluation Model for Scheduling in Global Computing Systems , 2000, Int. J. High Perform. Comput. Appl..

[28]  Mitsuhisa Sato,et al.  Ninf: A Network Based Information Library for Global World-Wide Computing Infrastructure , 1997, HPCN Europe.

[29]  Miron Livny,et al.  Harnessing the Capacity of Computational Grids for High Energy Physics , 2000 .

[30]  Henri Casanova,et al.  Simgrid: a toolkit for the simulation of application scheduling , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[31]  Francine Berman,et al.  Application-Level Scheduling on Distributed Heterogeneous Networks , 1996, Proceedings of the 1996 ACM/IEEE Conference on Supercomputing.

[32]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[33]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[34]  Francine Berman,et al.  Using Simulation to Evaluate Scheduling Heuristics for a Class of Applications in Grid Environments , 1999 .

[35]  George Tauchen,et al.  SNP: A Program for Nonparametric Time Series Analysis. Version 8.4. User's Guide , 1995 .

[36]  PaxsonVern Fast, approximate synthesis of fractional Gaussian noise for generating self-similar network traffic , 1997 .