This paper examines the costs and potential benefits of long-term prefetching for content distribution. In contrast with traditional short-term prefetching, in which caches use recent access history to predict and prefetch objects likely to be referenced in the near future, long-term prefetching uses long-term steadystate object access rates and update frequencies to identify objects to replicate to content distribution locations. Compared to demand caching, long-term prefetching increases network bandwidth and disk space costs but may benefit a system by improving hit rates. Using analytic models and trace-based simulations, we examine several algorithms for selecting objects for long-term prefetching. We find that although the web's Zipf-like object popularities makes it challenging to prefetch enough objects to significantly improve hit rates, systems can achieve significant benefits at modest costs by focusing their attention on long-lived objects.
OpenFlow is a great concept, but its original design imposes excessive overheads. It can simplify network and traffic management in enterprise and data center environments, because it enables flow-level control over Ethernet switching and provides global visibility of the flows in the network. However, such fine-grained control and visibility comes with costs: the switch-implementation costs of involving the switch's control-plane too often and the distributed-system costs of involving the OpenFlow controller too frequently, both on flow setups and especially for statistics-gathering.In this paper, we analyze these overheads, and show that OpenFlow's current design cannot meet the needs of highperformance networks. We design and evaluate DevoFlow, a modification of the OpenFlow model which gently breaks the coupling between control and global visibility, in a way that maintains a useful amount of visibility without imposing unnecessary costs. We evaluate DevoFlow through simulations, and find that it can load-balance data center traffic as well as fine-grained solutions, without as much overhead: DevoFlow uses 10-53 times fewer flow table entries at an average switch, and uses 10-42 times fewer control messages.
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