Loading...
Please wait, while we are loading the content...
An end-to-end probabilistic network calculus with moment generating functions
| Content Provider | CiteSeerX |
|---|---|
| Author | Fidler, Markus |
| Description | Network calculus is a min-plus system theory for performance evaluation of queuing networks. Its elegance stems from intuitive convolution formulas for concatenation of deterministic servers. Recent research dispenses with the worstcase assumptions of network calculus to develop a probabilistic equivalent that benefits from statistical multiplexing. Significant achievements have been made, owing for example to the theory of effective bandwidths, however, the outstanding scalability set up by concatenation of deterministic servers has not been shown. This paper establishes a concise, probabilistic network calculus with moment generating functions. The presented work features closed-form, end-to-end, probabilistic performance bounds that achieve the objective of scaling linearly in the number of servers in series. The consistent application of moment generating functions put forth in this paper utilizes independence beyond the scope of current statistical multiplexing of flows. A relevant additional gain is demonstrated for tandem servers with independent cross-traffic. |
| File Format | |
| Language | English |
| Publisher Institution | IN PROC. IEEE 14TH INTERNATIONAL WORKSHOP ON QUALITY OF SERVIC (IWQOS |
| Access Restriction | Open |
| Subject Keyword | Statistical Multiplexing Consistent Application Probabilistic Network Calculus Probabilistic Performance Bound Intuitive Convolution Formula Effective Bandwidth Tandem Server Min-plus System Theory Current Statistical Multiplexing Relevant Additional Gain Outstanding Scalability End-to-end Probabilistic Network Calculus Worstcase Assumption Probabilistic Equivalent Performance Evaluation Significant Achievement Recent Research Network Calculus Abstract Network Calculus Presented Work Feature Deterministic Server |
| Content Type | Text |
| Resource Type | Article |
