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This e-book constitutes the refereed court cases of the ACM/IFIP/USENIX seventh overseas Middleware convention 2006, held in Melbourne, Australia, in November/December 2006.
The 21 revised complete papers awarded have been conscientiously reviewed and chosen from 122 submissions. The papers are geared up in topical sections on functionality, composition, administration, publish/subscribe expertise, databases, cellular and ubiquitous computing, defense, and information mining techniques.
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Extra resources for Middleware 2006: ACM/IFIP/USENIX 7th International Middleware Conference, Melbourne, Australia, November 27-December 1, 2006. Proceedings
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QoSReqID is -1 if there is no request to send. Used by threaded proxies. void prRemoveRequest Explicitly remove a pending request from Proteus. (QoSReqID, AppReqID) The running time of Proteus is around 150 μs on our machines, a low overhead to incur at every sample interval. The sample interval is 1 s which we found to work well empirically. The overhead of Proteus incurred when registering requests and replies is negligible. 5 Evaluation In this section, we present our prototype implementation Proteus and experimental results showing that it can achieve the goals stated in the introduction.
It has been shown [12] that this STR is stable iff the following is true of the system: (i) the initial delay (d0 ) is known and bounded; (ii) the system is minimum phase1 ; (iii) the signs of the triangular elements of B0 are known; (iv) the upper bound on the order (n) of the system is known; and (v) the measurements are linearly related to the actuators. If true, our control-loop is stable and the performance converges to the performance goals in steady state. Independently of the system we run on, we know that the diagonal of B0 is positive for throughput and negative for latency, and that throughput is linearly related to the share, while latency is nonlinearly related.
The unknown model parameters Ai and Bi are estimated using Recursive LeastSquares (RLS) estimation [12]. This is a standard, computationally fast estimation technique that fits (2) to a number of measurements, so that the sum of squared errors between the measurements and the model is minimized. RLS is able to estimate even the performance correlation between the classes (the anti-diagonals of Ai and Bi ). The control law is a function that, based on the estimated system model (2), decides what the actuator settings should be so that objective function (1) is minimized.