A network calculus with effective bandwidth
IEEE/ACM Transactions on Networking (TON)
Towards an FBM model based network calculus framework with service differentiation
Mobile Networks and Applications
New perspectives on network calculus
ACM SIGMETRICS Performance Evaluation Review
FBM model based network-wide performance analysis with service differentiation
The Fourth International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness & Workshops
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Network calculus and queueing theory: two sides of one coin: invited paper
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Strategies for adaptive smoothing and rebuffering via dynamic network calculus
Computer Networks: The International Journal of Computer and Telecommunications Networking
Dynamic demultiplexing in network calculus-Theory and application
Performance Evaluation
On superlinear scaling of network delays
IEEE/ACM Transactions on Networking (TON)
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cross-layer analysis of the end-to-end delay distribution in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Perspectives on network calculus: no free lunch, but still good value
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
Perspectives on network calculus: no free lunch, but still good value
ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12
On applying stochastic network calculus
Frontiers of Computer Science: Selected Publications from Chinese Universities
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The network calculus offers an elegant framework for determining worst-case bounds on delay and backlog in a network. This paper extends the network calculus to a probabilistic framework with statistical service guarantees. The notion of a statistical service curve is presented as a probabilistic bound on the service received by an individual flow or an aggregate of flows. The problem of concatenating per-node statistical service curves to form an end-to-end (network) statistical service curve is explored. Two solution approaches are presented that can each yield statistical network service curves. The first approach requires the availability of time scale bounds at which arrivals and departures at each node are correlated. The second approach considers a service curve that describes service over time intervals. Although the latter description of service is less general, it is argued that many practically relevant service curves may be compliant to this description