Pushpinder Kaur Chouhan

Automatic Middleware Deployment Planning On Clusters

The use of remotely distributed computing resources as a single system offers great potential for compute-intensive applications. Increasingly, users have access to hundreds or thousands of machines at once and wish to utilize those resources concurrently. To provide a reasonable user experience, such systems must provide an effective, scalable scheduling system. Unfortunately, the great majority of job schedulers are centralized and many do not scale well to thousands or even hundreds of nodes. In this paper we study how distributed scheduling systems can be designed most effectively; we focus on the problem of selecting an optimal arrangement of schedulers, or a deployment, for hierarchically organized systems. We show that the optimal deployment is a complete spanning d-ary tree; this result conforms with results from the scheduling literature. More importantly, we present an approach for determining the optimal degree d for the tree. To test our approach, we use DIET, a middleware system that uses hierarchical schedulers. We develop detailed performance models for DIET and validate these models in a real-world environment. Finally, we demonstrate that our approach selects deployments that are near-optimal in practice.

Automatic deployment for hierarchical network enabled servers

Summary form only given. This article focuses on the deployment of grid infrastructures, more specifically problem solving environments (PSE) for numerical applications on the grid. Although the deployment of such an architecture may be constrained e.g., firewall, right access or security, its efficiency heavily depends on the quality of the mapping between its different components and the grid resources. This article proposes a new model based on linear programming to estimate the performance of a deployment of a hierarchical PSE. The advantages of our modeling approach are: evaluate a virtual deployment before a real deployment, provide a decision builder tool (i.e., designed to compare different architectures or add new resources) and take into account the platform scalability. Using our model, it is possible to determine the bottleneck of the platform and thus to know whether a given deployment can be improved or not. We illustrate the model by applying the results to improve performance of an existing hierarchical PSE called DIET.

How should you structure your hierarchical scheduler

In this paper we study how distributed scheduling systems can be designed most effectively; we focus on the problem of selecting an optimal arrangement of schedulers, or a deployment, for hierarchically organized systems. We show that the optimal deployment is a complete spanning d-ary tree; this result conforms with results from the scheduling literature. More importantly, we present an approach for determining the optimal degree d for the tree. We test our approach using DIET, a network-enabled server system that uses hierarchical schedulers. Finally, we demonstrate that our approach selects deployments that are near-optimal in practice