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
ConPaaS: an integrated runtime environment for elastic cloud applications
Proceedings of the Workshop on Posters and Demos Track
SpeQuloS: a QoS service for BoT applications using best effort distributed computing infrastructures
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
Time and Cost Sensitive Data-Intensive Computing on Hybrid Clouds
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Online optimization of busy time on parallel machines
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
On modelling and prediction of total CPU usage for applications in mapreduce environments
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Stochastic Tail-Phase Optimization for Bag-of-Tasks Execution in Clouds
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling
Future Generation Computer Systems
Scalable virtual machine deployment using VM image caches
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Scheduling jobs in the cloud using on-demand and reserved instances
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
Fair scheduling of bag-of-tasks applications using distributed Lagrangian optimization
Journal of Parallel and Distributed Computing
SpeQuloS: a QoS service for hybrid and elastic computing infrastructures
Cluster Computing
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Commercial cloud offerings, such as Amazon’s EC2, let users allocate compute resources on demand, charging based on reserved time intervals. While this gives greatflexibility to elastic applications, users lack guidance for choosing between multiple offerings, in order to complete their computations within given budget constraints. In this work, we present BaTS, our budget-constrained scheduler. BaTS can schedule large bags of tasks onto multiple clouds with different CPU performance and cost, minimizing completion time while respecting an upper bound for the budget to be spent. BaTS requires no a-priori information about task completion times, and learns to estimate them at runtime. We evaluate BaTS by emulating different cloud environments on the DAS-3 multi-cluster system. Our results show that BaTS is able to schedule within a user-definedbudget (if such a schedule is possible at all.) At the expense of extra compute time, significant cost savings can be achieved when comparing to a cost-oblivious round-robin scheduler.