Dynamic time delay models for load balancing, Part II: A stochastic analysis of the effect of delay uncertainty

In large-scale distributed computing systems, in which the computational elements are physically or virtually distant from each other, there are communication-related delays that can significantly alter the expected performance of load-balancing policies that do not account for such delays. This is a particularly significant problem in systems for which the individual units are connected by means of a shared broadband communication medium (e.g., the Internet, ATM, wireless LAN or wireless Internet). In such cases, the delays, in addition to being large, fluctuate randomly, making their one-time accurate prediction impossible. In this work, the stochastic dynamics of a load-balancing algorithm in a cluster of computer nodes are modeled and used to predict the effects of the random time delays on the algorithm’s performance. A discrete-time stochastic dynamical-equation model is presented describing the evolution of the random queue size of each node. Monte Carlo simulation is also used to demonstrate the extent of the role played by the magnitude and uncertainty of the various time-delay elements in altering the performance of load balancing. This study reveals that the presence of delay (deterministic or random) can lead to a significant degradation in the performance of a load-balancing policy. One way to remedy such a problem is to weaken the load-balancing mechanism so that the load-transfer between nodes is down-scaled (or discouraged) appropriately

The effect of time delays on the stability of load balancing algorithms for parallel computations

A deterministic dynamic nonlinear time-delay system is developed to model load balancing in a cluster of computer nodes used for parallel computations. The model is shown to be self consistent in that the queue lengths cannot go negative and the total number of tasks in all the queues and the network are conserved (i.e., load balancing can neither create nor lose tasks). Further, it is shown that using the proposed load balancing algorithms, the system is stable in the sense of Lyapunov. Experimental results are presented and compared with the predicted results from the analytical model. In particular, simulations of the models are compared with an experimental implementation of the load balancing algorithm on a distributed computing network

On reliable control system designs.

Thesis. 1978. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.Includes bibliographical references.Ph.D

Anisotropic Diffusion: An Integrated Approach

Abstract. In this paper, a new model is proposed for image segmentation that integrates the curve evolution and anisotropic diffusion methods. The curve evolution method, utilizing both gradient and region information, segments an image into multiple regions. During the evolution of the curve, anisotropic diffusion is adaptively applied to the image to remove noise while preserving boundary information. Coupled partial differential equations (PDE’s) are used to implement the method. Experimental results show that the proposed model is successful for complex images with high noise

hierarchical image segmentation using region competition and the Mumford-Shah functional

Abstract This paper generalizes the methods in a previous pape