Optimal deployment of geographically distributed workflow engines on the cloud

When orchestrating Web service workflows, the geographical placement of the orchestration engine (s) can greatly affect workflow performance. Data may have to be transferred across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper, we present a framework that, given a DAG-based workflow specification, computes the optimal Amazon EC2 cloud regions to deploy the orchestration engines and execute a workflow. The framework incorporates a constraint model that solves the workflow deployment problem, which is generated using an automated constraint modelling system. The feasibility of the framework is evaluated by executing different sample workflows representative of scientific workloads. The experimental results indicate that the framework reduces the workflow execution time and provides a speed up of 1.3x-2.5x over centralised approaches.

Operator and Workflow Optimization for High-Performance Analytics

We make a case for studying the impact of intra-node parallelism on the performance of data analytics. We identify four performance optimizations that are enabled by an increasing number of processing cores on a chip. We discuss the performance impact of these opimizations on two analytics operators and we identify how these optimizations affect each another.