A multi-level K-cut graph partitioning algorithm is proposed to minimize data transfer across cloud datacenters while satisfying constraints for load balancing and fixed data locations. The algorithm contracts fixed input datasets and tasks within each datacenter, coarsens the graph level-by-level, performs K-cut partitioning to minimize cut size, and projects the partitioned graph back to the original workflow while maintaining load balancing. Evaluation on three real-world workflows shows the algorithm outperforms other state-of-the-art methods.

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A Data and Task Co-scheduling Algorithm for Scientific Cloud Workflows
Abstract
Cloud computing has emerged as a promising computational infrastructure for cost-efficient
workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While
scientific workflows require accessing community-wide resources,they usually need to be
performed in collaborative cloud environments composed of multiple datacenters. Although such
environments facilitate scientific collaboration, the movements of input and intermediate datasets
across geographically distributed datacenters may cause intolerable latency that would hinder
efficient execution of large-scale data-intensive scientific workflows. To address the problem, in
this article we propose a novel multi-level K-cut graph partitioning algorithm to minimize the
volume of data transfer across datacenters while satisfying load balancing and fixed data
constraints. The algorithm first contracts the fixed input datasets in the same datacenter and their
consuming tasks, and coarsens the contracted graph to a predefined scale in a level-by-level
manner. Then, a K-cut algorithm is used to partition the resulted graph into K parts such that the
cut size is minimized. After that, the partitioned graph is projected back to the original workflow
graph, during which the load balancing constraint is maintained. We evaluate our algorithm
using three real-world workflow applications and the results demonstrate that the proposed
algorithm outperforms other state-of-the-art algorithms.
System Specification
System Requirements:
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.

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• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
Software Requirements:
• Operating system : - Windows 7. 32 bit
• Coding Language : C#.net 4.0
• Data Base : SQL Server 2008