Packet classification on general purpose CPUs remains expensive regardless of advances in classification algorithms. Unless the packet forwarding pipeline is both simple and static in function, finetuning the system for optimal forwarding is a time-consuming and brittle process. Network virtualization and network function virtualization value general purpose CPUs exactly for their flexibility: in such systems, a single x86 forwarding element does not implement a single, static classification step but a sequence of dynamically reconfigurable and potentially complex forwarding operations. This leaves a software developer looking for maximal packet forwarding throughput with few options besides flow caching. In this paper, we consider the problem of flow caching and more specifically, how to cache forwarding decisions that depend on packet fields with high entropy (and therefore, change often); to this end, we arrive at algorithms that allow us to efficiently compute near optimal flow cache entries spanning several transport connections, even if forwarding decisions depend on transport protocol headers.
Network failures are inevitable. Interfaces go down, devices crash and resources become exhausted. It is the responsibility of the control software to provide reliable services on top of unreliable components and throughout unpredictable events. Guaranteeing the correctness of the controller under all types of failures is therefore essential for network operations. Yet, this is also an almost impossible task due to the complexity of the control software, the underlying network, and the lack of precision in simulation tools.Instead, we argue that testing network control software should follow in the footsteps of large scale distributed systems, such as those of Netflix or Google, which deliberately induce live failures in their production environments during working hours, and analyze how their control software reacts.In this paper, we describe Armageddon, a framework for introducing sustainable and systematic chaos in networks. When we cause failures, we do so without violating some operator-specified network invariants (e.g., end-to-end connectivity). The injected failures also guarantee some notion of coverage. If the controller can sustain all of the failures, then it can be considered resilient with a high degree of confidence. We describe efficient algorithms to compute failure scenarios and implemented them in a prototype. Applied to real-world networks, our algorithms a coverage of 80% of the links within only three iterations of failures.
Packet classification on general purpose CPUs remains expensive regardless of advances in classification algorithms. Unless the packet forwarding pipeline is both simple and static in function, fine-tuning the system for optimal forwarding is a time-consuming and brittle process. Network virtualization and network function virtualization value general purpose CPUs exactly for their flexibility: in such systems, a single x86 forwarding element does not implement a single, static classification step but a sequence of dynamically reconfigurable and potentially complex forwarding operations. This leaves a software developer looking for maximal packet forwarding throughput with few options besides flow caching. In this paper, we consider the problem of flow caching and more specifically, how to cache forwarding decisions that depend on packet fields with high entropy (and therefore, change often); to this end, we arrive at algorithms that allow us to efficiently compute near optimal flow cache entries spanning several transport connections, even if forwarding decisions depend on transport protocol headers.
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