Spawn: A Distributed Computational Economy
IEEE Transactions on Software Engineering
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Lottery and stride scheduling: flexible proportional-share resource management
Lottery and stride scheduling: flexible proportional-share resource management
Dynamic tracking of page miss ratio curve for memory management
ASPLOS XI Proceedings of the 11th international conference on Architectural support for programming languages and operating systems
Dynamic selection of application-specific garbage collectors
Proceedings of the 4th international symposium on Memory management
Memory resource management in VMware ESX server
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
Geiger: monitoring the buffer cache in a virtual machine environment
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Greedy bidding strategies for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
Isla Vista Heap Sizing: Using Feedback to Avoid Paging
Proceedings of the International Symposium on Code Generation and Optimization
CRAMM: virtual memory support for garbage-collected applications
OSDI '06 Proceedings of the 7th symposium on Operating systems design and implementation
Resource overbooking and application profiling in a shared Internet hosting platform
ACM Transactions on Internet Technology (TOIT)
Dynamic memory balancing for virtual machines
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Memory overbooking and dynamic control of Xen virtual machines in consolidated environments
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Communications of the ACM
Q-clouds: managing performance interference effects for QoS-aware clouds
Proceedings of the 5th European conference on Computer systems
Mechanisms for multi-unit auctions
Journal of Artificial Intelligence Research
Waste not, want not: resource-based garbage collection in a shared environment
Proceedings of the international symposium on Memory management
Low cost working set size tracking
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
CloudScale: elastic resource scaling for multi-tenant cloud systems
Proceedings of the 2nd ACM Symposium on Cloud Computing
Applications Know Best: Performance-Driven Memory Overcommit with Ginkgo
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Deconstructing Amazon EC2 Spot Instance Pricing
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
On revenue in the generalized second price auction
Proceedings of the 21st international conference on World Wide Web
Exploiting hardware heterogeneity within the same instance type of Amazon EC2
HotCloud'12 Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing
The resource-as-a-service (RaaS) cloud
HotCloud'12 Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing
Application level ballooning for efficient server consolidation
Proceedings of the 8th ACM European Conference on Computer Systems
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Physical memory is the scarcest resource in today's cloud computing platforms. Cloud providers would like to maximize their clients' satisfaction by renting precious physical memory to those clients who value it the most. But real-world cloud clients are selfish: they will only tell their providers the truth about how much they value memory when it is in their own best interest to do so. How can real-world cloud providers allocate memory efficiently to those (selfish) clients who value it the most? We present Ginseng, the first market-driven cloud system that allocates memory efficiently to selfish cloud clients. Ginseng incentivizes selfish clients to bid their true value for the memory they need when they need it. Ginseng continuously collects client bids, finds an efficient memory allocation, and re-allocates physical memory to the clients that value it the most. Ginseng achieves a 6.2×--15.8x improvement (83%--100% of the optimum) in aggregate client satisfaction when compared with state-of-the-art approaches for cloud memory allocation.