WORLDCOMP'09 Tutorial: Prof. H. J. Siegel

What does “robust” mean? Often people state that their system software component, piece of hardware, application code, or technique is “robust,” but never define what they mean by “robust.” How does one determine if a claim of robustness is true when it is not defined? Furthermore, without a definition, robustness cannot be quantified, so if two people claim to have robust computing systems, for example, how can one decide which is the more robust? These are the types of issues we address in this tutorial. We study robustness in the context of resource allocation in heterogeneous parallel and distributed computing systems, but the robustness concepts presented have broad applicability.

In heterogeneous parallel and distributed computing environments, a collection of different machines is interconnected and provides a variety of computational capabilities. These capabilities can be used to execute a workload composed of different types of applications, each of which may consist of multiple tasks, where the tasks have diverse computational requirements. The execution times of a task may vary from one machine to the next, and just because some machine A is faster than some machine B for task 1 does not mean it will be faster for task 2. Furthermore, there can be inter-task data dependencies. Tasks must share the computing and communication resources of the system. A critical research problem for heterogeneous computing is how to assign tasks to machines and schedule the order of their execution.

The resources in heterogeneous parallel and distributed computing systems should be allocated to the computational tasks in a way that optimizes some given system performance measure. However, allocation decisions and associated performance prediction are often based on estimated values of task and system parameters. The actual values of these parameters are uncertain, and may differ from the estimates. For example, the estimates may represent only average values, the models used to generate the estimates may have limited accuracy, or there may be changes in the environment. Because the actual values of these parameters are uncertain, the actual system performance may differ from the predicted performance. Thus, it is important to develop resource management strategies that strive to meet particular system performance requirements even when such uncertainties are present.

To address this problem, we have designed two models for deriving the degree of robustness of a resource allocation. One model is based on having deterministic estimates of the parameters whose exact values are uncertain. In this case, the degree of robustness of a resource allocation is quantified as the maximum amount of collective difference between actual and estimated values in these system parameters within which a user-specified level of system performance (QoS) can be guaranteed. The second model assumes that stochastic information is available about the values of these parameters whose actual values are uncertain. With this model, the degree of robustness is quantified as the probability that a user-specified level of system performance can be met.

Both robustness models, and the robustness metrics associated with each, will be presented. It will be shown how they can be used to evaluate and compare the robustness of different resource allocations. In addition, it will be demonstrated how these models can be incorporated into resource management heuristics that produce robust allocations to optimize some user-specified performance criterion. Robust resource allocation heuristics for a variety of environments will be discussed and compared. This will be done for both static heuristics, which are executed off-line for production environments, and dynamic heuristics, which are executed on-line for environments where tasks must be assigned resources as they arrive into the system.

The tutorial material is applicable to various types of heterogeneous computing and communication environments, including parallel, distributed, cluster, grid, Internet, cloud, embedded, multicore, content distribution networks, wireless networks, and sensor networks. Furthermore, the robustness models, concepts, and metrics presented are generally applicable to design problems throughout various scientific and engineering fields.

• Understand the problem of robust resource allocation in heterogeneous parallel and distributed computing systems
• Ask the “three robustness questions” that must be answered whenever anyone makes robustness claims
• Apply the appropriate model of robustness depending on the information available about the system uncertainties
• Develop and use robustness metrics to quantify the robustness of a particular resource allocation for a given computational environment
• Design resource allocation heuristics that incorporate robustness, for both static (off-line) and dynamic (on-line) environments
• Use the concepts of robustness in a variety of problem domains in the scientific and engineering fields

This course is intended for faculty, engineers, scientists, and graduate students who want to learn how to define, model, and quantify robustness when designing and using heterogeneous suites of computers (including clusters and clouds) to execute applications in a way that will optimize some performance criterion.

H. J. Siegel is the George T. Abell Endowed Chair Distinguished Professor of Electrical and Computer Engineering at Colorado State University (CSU), where he is also a Professor of Computer Science. He is the Director of the CSU Information Science and Technology Center (ISTeC), a university-wide organization for enhancing CSU’s research, education, and outreach activities pertaining to the design and innovative application of computer, communication, and information systems. From 1976 to 2001, he was a professor in the School of Electrical and Computer Engineering at Purdue University. He received two B.S. degrees from the Massachusetts Institute of Technology (MIT), and the M.A., M.S.E., and Ph.D. degrees from Princeton University. He is a Fellow of the IEEE and a Fellow of the ACM. Prof. Siegel has co-authored over 360 published technical papers in the areas of parallel and distributed computing and communications. He was a Coeditor-in-Chief of the Journal of Parallel and Distributed Computing, and was on the Editorial Boards of the IEEE Transactions on Parallel and Distributed Systems and the IEEE Transactions on Computers. For more information, please see www.engr.colostate.edu/~hj.

Professor H. J. Siegel
Department of Electrical and Computer Engineering and Department of Computer Science
Colorado State University
Fort Collins, CO 80523-1373
Office: (970) 491-7982
Fax: (970) 491-2249
E-mail: hj@colostate.edu
www.engr.colostate.edu/~hj