Scheduling real-time transactions: a performance evaluation
ACM Transactions on Database Systems (TODS)
Eliminating receive livelock in an interrupt-driven kernel
ACM Transactions on Computer Systems (TOCS)
An admission control scheme for predictable server response time for web accesses
Proceedings of the 10th international conference on World Wide Web
Session-Based Admission Control: A Mechanism for Peak Load Management of Commercial Web Sites
IEEE Transactions on Computers
A QoS-Sensitive Approach for Timeliness and Freshness Guarantees in Real-Time Databases
ECRTS '02 Proceedings of the 14th Euromicro Conference on Real-Time Systems
Qos-aware real-time data management
Qos-aware real-time data management
A method for transparent admission control and request scheduling in e-commerce web sites
Proceedings of the 13th international conference on World Wide Web
Managing Deadline Miss Ratio and Sensor Data Freshness in Real-Time Databases
IEEE Transactions on Knowledge and Data Engineering
Assessing the Robustness of Self-Managing Computer Systems under Highly Variable Workloads
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
How to Determine a Good Multi-Programming Level for External Scheduling
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Autonomic computing through analytic performance models
Autonomic computing through analytic performance models
Web server support for tiered services
IEEE Network: The Magazine of Global Internetworking
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We denote as QoS-max the control of a request processing system to try to maximize QoS qualities and we focus on external, non-intrusive approaches with statistics on readily measurable quantities. In order to do this, the controller characterizes requests in terms of response times (or resource use) and uses that characterization to try to achieve QoS-max. However, measures vary both between different requests and for different runs of the same request. In this paper we show how we incorporated these for robust statistical QoS-max control. We use a simulator and requests with varied arrival and duration distributions to show the effectiveness of the variability handling approach.