A Microeconomic Approach to Optimal Resource Allocation in Distributed Computer Systems
IEEE Transactions on Computers
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
User-Centric Performance Analysis of Market-Based Cluster Batch Schedulers
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Market-based Proportional Resource Sharing for Clusters
Market-based Proportional Resource Sharing for Clusters
The application of microeconomics to the design of resource allocation and control algorithms
The application of microeconomics to the design of resource allocation and control algorithms
Libra: a computational economy-based job scheduling system for clusters
Software—Practice & Experience
Balancing Risk and Reward in a Market-Based Task Service
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Resource overbooking and application profiling in shared hosting platforms
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Profitable services in an uncertain world
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Tycoon: An implementation of a distributed, market-based resource allocation system
Multiagent and Grid Systems
A break in the clouds: towards a cloud definition
ACM SIGCOMM Computer Communication Review
A QoS and profit aware cloud confederation model for IaaS service providers
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Services in cloud computing systems are typically categorized into three types—software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS) These services can be prepared in the form of virtual machine (VM) images; and they can be deployed and run dynamically as clients request Since the cloud service provider has to deal with a diverse set of clients, including both regular and new/one-off clients, and their requests most likely differ from one another, the judicious scheduling of these requests plays a key role in the efficient use of resources for the provider to maximize its profit In this paper, we address the problem of scheduling arbitrary service requests of those three different types—taking into account the maximization of profit—in cloud environments, and present the client satisfaction oriented scheduling (CSoS) algorithm Our algorithm effectively exploits different characteristics of those three service types and the availability of third-party cloud service providers who have (or are capable of having) identical service offerings (using virtual machine images) Our main contribution is the incorporation of client satisfaction into our request scheduling; this incorporation enables to increase profit by avoiding the discontinuation of service requests from those unsatisfied clients due to the poor quality of service.