Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Cost-Based Scheduling of Scientific Workflow Application on Utility Grids
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Autonomic virtual resource management for service hosting platforms
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
Auto-scaling to minimize cost and meet application deadlines in cloud workflows
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
A Performance Study of Virtual Machines on Multicore Architectures
PDP '12 Proceedings of the 2012 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing
Cost-Driven Scheduling of Grid Workflows Using Partial Critical Paths
IEEE Transactions on Parallel and Distributed Systems
Enhancing a strategy of virtualized resource assignment in adaptive resource cloud framework
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
IPDPS '13 Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing
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In cloud computing, it remains a challenge to allocate virtualized resource with financial cost minimization and acceptable Quality of Service assurance. In general, the VM instance is allocated to cloud service users based on not actual job processing time but the fixed resource allocation time predetermined by cloud pricing policy in contrast to grid environment. In this case, the unnecessary cost dissipation is occurred by the wasted partial instance hours of allocated resource. To address this problem, we propose the heuristic based workflow scheduling scheme considering cloud-pricing model in this paper. Our scheme is composed of two phases: VM packing and MRSR (Multi Requests to Single Resource) phases. In VM-packing phase, preassigned multi tasks are aggregated into the common VM instance sequentially, and these tasks are merged in parallel by MRSR phase. By using our proposed schemes, we are able to reduce the number of required VM instances and achieve the significant cost saving while we guarantee the user's SLA (Service Level Agreement) in terms of workflow deadline. Our proposed schemes cannot only reduce the cost by 30% compared to traditional workflow scheduling schemes but also assure user's SLA.