Scheduling in Data Intensive and Network Aware (DIANA) Grid Environments

TL;DR

This paper introduces DIANA, a grid scheduling approach that considers data, processing power, and network characteristics to optimize job placement, significantly reducing queue and execution times for data-intensive tasks.

Contribution

The paper presents a novel meta-scheduling method that incorporates network awareness into grid scheduling, improving performance for data-intensive jobs.

Findings

Significant reduction in queue times for data-intensive jobs

Improved execution times through network-aware scheduling

Effective implementation on a Grid testbed

Abstract

In Grids scheduling decisions are often made on the basis of jobs being either data or computation intensive: in data intensive situations jobs may be pushed to the data and in computation intensive situations data may be pulled to the jobs. This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a Grid environment and may result in large processing queues and job execution delays due to site overloads. In this paper we describe a Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites. Through a practical implementation on a Grid testbed, we demonstrate that queue and execution times of data-intensive jobs can be significantly improved when we introduce our proposed DIANA scheduler. The basic scheduling decisions are dictated by a weighting factor for each potential target location which is a calculated function of network characteristics, processing cycles and data location and size. The job scheduler provides a global ranking of the computing resources and then selects an optimal one on the basis of this overall access and execution cost. The DIANA approach considers the Grid as a combination of active network elements and takes network characteristics as a first class criterion in the scheduling decision matrix along with computation and data. The scheduler can then make informed decisions by taking into account the changing state of the network, locality and size of the data and the pool of available processing cycles.