Artificial Neural Networks (NNs) play an increasingly important role in many services and applications, contributing significantly to compute infrastructures' workloads. When used in latency sensitive services, NNs are usually processed by CPUs since using an external dedicated hardware accelerator would be inefficient. However, with growing workloads size and complexity, CPUs are hitting their computation limits, requiring the introduction of new specialized hardware accelerators tailored to the task. In this paper we analyze the option to use programmable network devices, such as Network Cards and Switches, as NN accelerators in place of purpose built dedicated hardware. To this end, in this preliminary work we analyze in depth the properties of NN processing on CPUs, derive options to efficiently split such processing, and show that programmable network devices may be a suitable engine for implementing a CPU's NN co-processor.
CCS CONCEPTS• Networks → Programmable networks; In-network processing; • Computing methodologies → Machine learning; • Computer systems organization → Neural networks;
When dealing with node or link failures in software-defined networking (SDN), the network capability to establish an alternative path depends on controller reachability and on the round-trip times between controller and involved switches. Moreover, current SDN data plane abstractions for failure detection, such as OpenFlow "Fast-failover," do not allow programmers to tweak switches' detection mechanism, thus leaving SDN operators relying on proprietary management interfaces (when available) to achieve guaranteed detection and recovery delays. We propose SPI-DER, an OpenFlow-like pipeline design that provides (i) a detection mechanism based on switches' periodic link probing and (ii) fast reroute of traffic flows even in the case of distant failures, regardless of controller availability. SPIDER is based on stateful data plane abstractions such as OpenState or P4, and it offers guaranteed short (few milliseconds or less) failure detection and recovery delays, with a configurable trade-off between overhead and failover responsiveness. We present here the SPIDER pipeline design, behavioral model, and analysis on flow tables' memory impact. We also implemented and experimentally validated SPIDER using Open-State (an OpenFlow 1.3 extension for stateful packet processing) and P4, showing numerical results on its performance in terms of recovery latency and packet loss.
When dealing with node or link failures in Software Defined Networking (SDN), the network capability to establish an alternative path depends on controller reachability and on the round trip times (RTTs) between controller and involved switches. Moreover, current SDN data plane abstractions for failure detection (e.g. OpenFlow "Fast-failover") do not allow programmers to tweak switches' detection mechanism, thus leaving SDN operators still relying on proprietary management interfaces (when available) to achieve guaranteed detection and recovery delays. We propose SPIDER, an OpenFlow-like pipeline design that provides i) a detection mechanism based on switches' periodic link probing and ii) fast reroute of traffic flows even in case of distant failures, regardless of controller availability. SPIDER can be implemented using stateful data plane abstractions such as OpenState or Open vSwitch, and it offers guaranteed short (i.e. ms) failure detection and recovery delays, with a configurable trade off between overhead and failover responsiveness. We present here the SPIDER pipeline design, behavioral model, and analysis on flow tables' memory impact. We also implemented and experimentally validated SPIDER using OpenState (an OpenFlow 1.3 extension for stateful packet processing), showing numerical results on its performance in terms of recovery latency and packet losses.
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