Short-lived trac surges, known as microbursts, can cause periods of unexpectedly high packet delay and loss on a link. Today, preventing microbursts requires deploying switches with larger packet buers (incurring higher cost) or running the network at low utilization (sacricing eciency). Instead, we argue that switches should detect microbursts as they form, and take corrective action before the situation gets worse. This requires an ecient way for switches to identify the particular ows responsible for a microburst, and handle them automatically (e.g., by pacing, marking, or rerouting the packets). However, collecting ne-grained statistics about queue occupancy in real time is challenging, even with emerging programmable data planes. We present Snappy, which identies the ows responsible for a microburst in real time. Snappy maintains multiple snapshots of the occupants of the queue over time, where each snapshot is a compact data structure that makes ecient use of data-plane memory. As each new packet arrives, Snappy updates one snapshot and also estimates the fraction of the queue occupied by the associated ow. Our simulations with data-center packet traces show that Snappy can target the ows responsible for microbursts at the sub-millisecond level.
The development of the Internet of things brings exponential growth of wireless traffic, which puts great pressure on the backhaul link. The proactive caching of some contents in the edge device of mobile network can effectively reduce the repeated transmission of the same contents and relieve the burden of the backhaul link. Moreover, the introduction of recommendation mechanisms can reshape user's request and improve cache hit ratio. However, the optimization of recommendation and caching decisions is highly dependent on the users’ preference information for files. Here, a joint optimization algorithm of recommendation and caching based on users’ preference prediction with multiple base stations cooperative caching is proposed. To improve the caching efficiency, the Deep Crossing model is adopted to predict users’ preferences. Under the constraints of cache capacity, recommendation quantity and bandwidth, an optimization problem to minimize the total transmission delay of the system is formulated. Then, the NP‐hardness of the proposed optimization problem is proved and it is decoupled it into three sub‐problems, namely recommendation, user access and caching optimization sub‐problems. Simulation results show that the proposed algorithm can effectively reduce the total transmission delay of the system.
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