Flexible Control of Parallelism in a Multiprocessor PC Router
Proceedings of the General Track: 2002 USENIX Annual Technical Conference
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Load balancing for parallel forwarding
IEEE/ACM Transactions on Networking (TON)
Survey and taxonomy of packet classification techniques
ACM Computing Surveys (CSUR)
Energy-Efficient Scheme for Multiprocessor-Based Router Linecards
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Conserving network processor power consumption by exploiting traffic variability
ACM Transactions on Architecture and Code Optimization (TACO)
Reducing network energy consumption via sleeping and rate-adaptation
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NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Low power architecture for high speed packet classification
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Proceedings of the 5th international conference on Emerging networking experiments and technologies
Reducing dynamic power dissipation in pipelined forwarding engines
ICCD'09 Proceedings of the 2009 IEEE international conference on Computer design
Low-power TCAMs for very large forwarding tables
IEEE/ACM Transactions on Networking (TON)
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IEEE Communications Magazine
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This paper studies the optimization problem of distributing traffic load onto multiple engines in parallel packet forwarding systems. Given that the power dissipation of each engine can be formulated as a function of traffic load going through that engine, we develop a theoretical framework to minimize the overall power consumption while satisfying the throughput demand. We consider two types of power functions, which are different in terms of whether supporting sleep mode or not. Accordingly, the two power functions lead to two different mathematical programming problems: one can be solved via linear programming (LP), the other is modeled as non-linear programming and can be solved via mixed integer programming (MIP). Our simulation using a 18-hour real-life traffic trace shows that our solution can achieve significant power/ energy reduction compared with the traditional parallel forwarding scheme based on load balancing. We also discuss the system design issues and identify the challenges for real implementation. We believe our optimization framework and algorithmic solutions are applicable for load distribution in other parallel systems e.g. data centers and clusters.