IEEE Transactions on Computers
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Runtime Prediction Based Grid Scheduling of Parameter Sweep Jobs
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
A Realistic Integrated Model of Parallel System Workloads
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
International Journal of High Performance Computing Applications
Performing Large Science Experiments on Azure: Pitfalls and Solutions
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Bag-of-Tasks Scheduling under Budget Constraints
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
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
ExPERT: Pareto-Efficient Task Replication on Grids and a Cloud
IPDPS '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium
Fast (re-)configuration of mixed on-demand and spot instance pools for high-throughput computing
Proceedings of the first ACM workshop on Optimization techniques for resources management in clouds
Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Elastic applications like bags of tasks benefit greatly from Infrastructure as a Service (IaaS) clouds that let users allocate compute resources on demand, charging based on reserved time intervals. Users, however, still need guidance for mapping their applications onto multiple IaaS offerings, both minimizing execution time and respecting budget limitations. For budget-controlled execution of bags of tasks, we built Bats, a scheduler that estimates possible budget and make spancombinations using a tiny task sample, and then executes a bag within the user's budget constraints. Previous work has shown the efficacy of this approach. There remains, however, the risk of outlier tasks causing the execution to exceed the predicted make span. In this work, we present a stochastic optimization of the tail phase for Bats' execution. The main idea is to use the otherwise idling machines up until the end of their (already paid-for) allocation time. Using the task completion time information acquired during the execution, BaTS decides which tasks to replicate onto idle machines in the tail phase, reducing the make span and improving the tolerance to outlier tasks. Our evaluation results show that this effect is robust w.r.t. the quality of runtime predictions and is the strongest with more expensive schedules in which many fast machines are available.