Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Dynamic Virtual Clusters in a Grid Site Manager
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
A Framework for Resource Allocation in Grid Computing
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
Dynamic Provisioning of Multi-tier Internet Applications
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
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
A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Scientific Programming - Scientific Workflows
Feedback-controlled resource sharing for predictable eScience
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters
Proceedings of the 18th ACM international symposium on High performance distributed computing
Automated control in cloud computing: challenges and opportunities
ACDC '09 Proceedings of the 1st workshop on Automated control for datacenters and clouds
Dynamic resource allocation for shared data centers using online measurements
IWQoS'03 Proceedings of the 11th international conference on Quality of service
Elastic Site: Using Clouds to Elastically Extend Site Resources
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Characterizing Cloud Federation for Enhancing Providers' Profit
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Optimal Resource Allocation in Clouds
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Early observations on the performance of Windows Azure
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
See spot run: using spot instances for mapreduce workflows
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Bag-of-Tasks Scheduling under Budget Constraints
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Predictable High-Performance Computing Using Feedback Control and Admission Control
IEEE Transactions on Parallel and Distributed Systems
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)
Infrastructure outsourcing in multi-cloud environment
Proceedings of the 2012 workshop on Cloud services, federation, and the 8th open cirrus summit
Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Budget constrained resource allocation for non-deterministic workflows on an iaas cloud
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Characterizing and profiling scientific workflows
Future Generation Computer Systems
Rebalancing in a multi-cloud environment
Proceedings of the 4th ACM workshop on Scientific cloud computing
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
Intelligent Randomize Round Robin for Cloud Computing
International Journal of Cloud Applications and Computing
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
Cost adaptive workflow scheduling in cloud computing
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
SpeQuloS: a QoS service for hybrid and elastic computing infrastructures
Cluster Computing
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A goal in cloud computing is to allocate (and thus pay for) only those cloud resources that are truly needed. To date, cloud practitioners have pursued schedule-based (e.g., time-of-day) and rule-based mechanisms to attempt to automate this matching between computing requirements and computing resources. However, most of these "auto-scaling" mechanisms only support simple resource utilization indicators and do not specifically consider both user performance requirements and budget concerns. In this paper, we present an approach whereby the basic computing elements are virtual machines (VMs) of various sizes/costs, jobs are specified as workflows, users specify performance requirements by assigning (soft) deadlines to jobs, and the goal is to ensure all jobs are finished within their deadlines at minimum financial cost. We accomplish our goal by dynamically allocating/deallocating VMs and scheduling tasks on the most cost-efficient instances. We evaluate our approach in four representative cloud workload patterns and show cost savings from 9.8% to 40.4% compared to other approaches.