Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Introduction to Algorithms
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
Service-Oriented System Engineering: A New Paradigm
SOSE '05 Proceedings of the IEEE International Workshop
Dynamic placement for clustered web applications
Proceedings of the 15th international conference on World Wide Web
Decentralized Workflow Execution for Virtual Enterprises in Grid Environment
GCCW '06 Proceedings of the Fifth International Conference on Grid and Cooperative Computing Workshops
Dynamic estimation of CPU demand of web traffic
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
A scalable application placement controller for enterprise data centers
Proceedings of the 16th international conference on World Wide Web
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Effective load balancing for cluster-based servers employing job preemption
Performance Evaluation
Design patterns for decentralised coordination in self-organising emergent systems
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Service Middleware for Self-Managing Large-Scale Systems
IEEE Transactions on Network and Service Management
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In a shared cluster, each application runs on a subset of nodes and these subsets can overlap with one another. Resource management in such a cluster should adaptively change the application placement and workload assignment to satisfy the dynamic applications workloads and optimize the resource usage. This becomes a challenging problem with the cluster scale and application amount growing large. This paper proposes a novel self-adaptive resource management approach which is inspired from human market: the nodes trade their shares of applications' requests with others via auction and bidding to decide its own resource allocation and a global high-quality resource allocation is achieved as an emergent collective behavior of the market. Experimental results show that the proposed approach can ensure quick responsiveness, high scalability, and application prioritization in addition to managing the resources effectively.