A PTAS for the multiple knapsack problem
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Tight approximation algorithms for maximum general assignment problems
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
An economic-based resource management framework in the grid context
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
Mirage: a microeconomic resource allocation system for sensornet testbeds
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
Reducing Costs of Spot Instances via Checkpointing in the Amazon Elastic Compute Cloud
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Resource Allocation with Supply Adjustment in Distributed Computing Systems
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
See spot run: using spot instances for mapreduce workflows
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
SpotMPI: a framework for auction-based HPC computing using amazon spot instances
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
ACM SRC poster: SpotMPI: auction-based high performance cloud computing
Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis Companion
Pricing and Resource Allocation in a Cloud Computing Market
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
pCloud: an adaptive i/o resource allocation algorithm with revenue consideration over public clouds
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
Characterizing spot price dynamics in public cloud environments
Future Generation Computer Systems
Deconstructing Amazon EC2 Spot Instance Pricing
ACM Transactions on Economics and Computation
A QoS and profit aware cloud confederation model for IaaS service providers
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Self-Adaptive Resource Allocation in Cloud Applications
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Cloud computing promises on-demand provisioning of resource to applications and services. To deal with dynamically fluctuating resource demands, market-driven resource allocation has been proposed and recently implemented by commercial cloud providers like Amazon EC2. In this environment, cloud resources are offered in distinct types of virtual machines (VMs) and the cloud provider runs a continuous market-driven mechanism for each VM type with the goal of achieving maximum revenue over time. However, as demand of each VM type can fluctuate independently at run time, it becomes a challenging problem to dynamically allocate data center resources to each spot market to maximize cloud provider's total revenue. In this paper, we present a solution to this problem that consists of 2 parts: (1) market analysis for forecasting the demand for each spot market, and (2) a dynamic scheduling and consolidation mechanism that allocate resource to each spot market to maximize total revenue. As optimally allocating resources for revenue maximization is a NP-hard problem, we show our algorithms can approximate the optimal solutions to this problem under both fixed and variable pricing schemes. Simulation studies confirm the effectiveness of our approach.