Grid applications that use a considerable number of processors
for their computations need effective predictions of
the expected computation times on the different nodes. Currently,
there are no effective prediction methods available
that satisfactorily cope with those ever-changing dynamics
of computation times in a grid environment. Motivated
by this, in this paper we develop the Dynamic Exponential
Smoothing (DES) method to predict job processing times in
a grid environment. To compare predictions of DES to those
of the existing prediction methods, we have performed extensive
experiments in a real large-scale grid environment.
The results illustrate a strong and consistent improvement
of DES in comparison with the existing prediction methods.

Grid computing technology connects globally distributed processors to develop
an immense source of computing power, which enables us to run
applications in parallel that would take orders of magnitude more time on a single processor.
Key characteristics of a global-scale grid are the strong burstiness in the amount of load on the resources and on the network capacities, and
the fact that processors may be appended to or removed from the grid at any time.
To cope with these characteristics, it is essential to develop techniques that make applications robust against the dynamics of
the grid environment. For these techniques to be effective, it is important to have an understanding of the statistical properties of the
dynamics of a grid environment. Today, however, the statistical properties of the dynamic behavior of real global-scale grid environments are not well understood.
Our main focus is on highly CPU-intensive grid applications that require huge amounts of processor power for running tasks.
Motivated by this, we have performed extensive measurements in a real, global-scale grid environment to
study the statistical properties of the running times of tasks on processors.
We observe (1) a strong burstiness of the running times over different time scales, (2) a strong heterogeneity of the running-time
characteristics among the different hosts, (3) a strong heterogeneity of the running-time characteristics for the same host
over different time intervals, and (4) the occurrence of sudden level-switches in the running times, amongst others.
These observations are used to develop effective
techniques for the prediction of running times.
They can be used to develop effective control schemes for robust grid
applications.

Connected world-widely distributed computers and data systems establish a global source of processing power and data, called a grid. Key properties of a grid are the fact that computers providing processing power may connect and disconnect at any time, and that demands for processing power may highly
fluctuate over time. This has raised the need for the development of applications that are robust against changing circumstances. In [4] fluctuations in processing speeds on running times has been investigated, and it was found that dynamic load balancing methods provide a promising means to deal with the ever-changing environment in the grid. In this paper we demonstrate with extensive experiments in a real grid environment, Planetlab, that dynamic load balancing based on predictions via Exponential Smoothing indeed lead to significant reductions in running times of parallel applications in a randomly changing grid environment.

Grids functionally combine globally distributed computers and information systems for creating a universal source of computing power and information. A key characteristic of grids is that resources (e.g., CPU cycles and network capacities) are shared among numerous applications, and therefore, the amount of resources available to any given application highly fluctuates over time. In this paper we analyze the impact of the fluctuations in the processing speed on the performance of grid applications. Extensive lab experiments show that the burstiness in processing speeds has a dramatic impact on the running times, which heightens the need for dynamic load balancing schemes to realize good performance. Our results demonstrate that a simple dynamic load balancing scheme based on forecasts via exponential smoothing is highly effective in reacting to the burstiness in processing speeds.