Conference Paper

Forwarding Metamorphosis: Fast Programmable Match-Action Processing in Hardware for SDN

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Abstract

In Software Defined Networking (SDN) the control plane is physically separate from the forwarding plane. Control software programs the forwarding plane (e.g., switches and routers) using an open interface, such as OpenFlow. This paper aims to overcomes two limitations in current switching chips and the OpenFlow protocol: i) current hardware switches are quite rigid, allowing ``Match-Action'' processing on only a fixed set of fields, and ii) the OpenFlow specification only defines a limited repertoire of packet processing actions. We propose the RMT (reconfigurable match tables) model, a new RISC-inspired pipelined architecture for switching chips, and we identify the essential minimal set of action primitives to specify how headers are processed in hardware. RMT allows the forwarding plane to be changed in the field without modifying hardware. As in OpenFlow, the programmer can specify multiple match tables of arbitrary width and depth, subject only to an overall resource limit, with each table configurable for matching on arbitrary fields. However, RMT allows the programmer to modify all header fields much more comprehensively than in OpenFlow. Our paper describes the design of a 64 port by 10 Gb/s switch chip implementing the RMT model. Our concrete design demonstrates, contrary to concerns within the community, that flexible OpenFlow hardware switch implementations are feasible at almost no additional cost or power.

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... Programmable switches, which allow the data plane behavior to be reconfigured, provide the necessary flexibility. The RMT-based Protocol-Independent Switch Architecture (PISA) [10] has emerged as the de facto standard for programmable switch architecture. ...
... Banzai provides implementations only for the functional units, not for the entire switch chip, so we are unable to directly evaluate the impact of our modifications on the full chip design. However, prior work suggests that ALUs take up only a small portion (i.e., ∼ 10%) of the power/area budget for the entire chip [10]; from this we infer that our modifications would have negligible impact. In other words, this hardware enhancement is feasible today, and is unlikely to become a bottleneck in future hardware generations. ...
... Extending switches' processing capability. Proposed enhancements to the RMT architecture [10] include transactions [105], disaggregated memory [15], and better stateful data plane support [29]. While many focus on improving stateful computations, none address floating point operations. ...
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... In this paper, we propose a data-plane algorithm that produces a provably unbiased delay distribution, specifically designed for programmable switches using the Protocol Independent Switch Architecture (PISA) [4]. Our algorithms tackle the bias by keeping track of the probability of getting each sample, and applying a correction factor inversely proportional to this probability when computing the distribution. ...
... 4.1 Using many pipeline stages per fridge. On a PISA programmable switch [4], we are limited to accessing only one index per register array when processing a packet. Algorithms often span multiple pipeline stages and allocate multiple register arrays to improve performance. ...
... Reconfigurable Match Tables (RMTs): The RMT pipelined architecture proposed by Forwarding Metamorphosis [4] consists of: a parser that produces a packet header vector, a series of logical stages that perform Match-Action operations, a recombination block to reattach headers to packets, and configurable output queues. Logical stages are mapped to one, multiple, and/or fractions of physical stages. ...
... This was chosen using lean levels; the details are omitted for space reasons.4 Previous optimal hybrid trees were used; the conversion factor for hybridization was 3 and 8 for IPv4 and IPv6 respectively. ...
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... The advent of fully programmable switches [20,21] and high-level programming languages [22] has solved the problem of poor scalability of traditional switches. Nick McKeown proposed programming protocol-independent packet processors (P4) [23] and the corresponding forwarding model [24,25] that allows administrators to customize the packet-forwarding behavior of switches and improve the programmability of data plane and the flexibility of packet processing. Signorello proposed NDN.P4 [26], which first implemented the native NDN packet parsing and forwarding function in the software switch [27] by using P4_14 [28]. ...
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... This model is motivated by specialized TCAM hardware such as [15], and has been studied intensively [9], [10], [11], [14]. As detailed in [16], common programmable switch architectures such as the RMT and Intel's FlexPipe have tables of different types and in particular tables dedicated to longest prefix matching [17], [18]. In this setting a pattern is in fact a prefix of bits that matches all addresses that start with this prefix. ...
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We revisit the problem of scaling software routers, motivated by recent advances in server technology that enable high-speed parallel processing---a feature router workloads appear ideally suited to exploit. We propose a software router architecture that parallelizes router functionality both across multiple servers and across multiple cores within a single server. By carefully exploiting parallelism at every opportunity, we demonstrate a 35Gbps parallel router prototype; this router capacity can be linearly scaled through the use of additional servers. Our prototype router is fully programmable using the familiar Click/Linux environment and is built entirely from off-the-shelf, general-purpose server hardware.
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We present PacketShader, a high-performance software router framework for general packet processing with Graphics Processing Unit (GPU) acceleration. PacketShader exploits the massively-parallel processing power of GPU to address the CPU bottleneck in current software routers. Combined with our high-performance packet I/O engine, PacketShader outperforms existing software routers by more than a factor of four, forwarding 64B IPv4 packets at 39 Gbps on a single commodity PC. We have implemented IPv4 and IPv6 forwarding, OpenFlow switching, and IPsec tunneling to demonstrate the flexibility and performance advantage of PacketShader. The evaluation results show that GPU brings significantly higher throughput over the CPU-only implementation, confirming the effectiveness of GPU for computation and memory-intensive operations in packet processing.
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A crucial problem that needs to be solved is the allocation of memory to processors in a pipeline. Ideally, the processor memories should be totally separate (i.e., one-port memories) in order to minimize contention; however, this minimizes memory sharing. Idealized sharing occurs by using a single shared memory for all processors but this maximizes contention. Instead, in this paper we show that perfect memory sharing of shared memory can be achieved with a collection of two-port memories, as long as the number of processors is less than the number of memories. We show that the problem of allocation is NP-complete in general, but has a fast approximation algorithm that comes within a factor of \frac 32\frac 32 asymptotically. The proof utilizes a new bin packing model, which is interesting in its own right. Further, for important special cases that arise in practice a more sophisticated modification of this approximation algorithm is in fact optimal. We also discuss the online memory allocation problem and present fast online algorithms that provide good memory utilization while allowing fast updates.
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We present a simple dictionary with worst case constant lookup time, equaling the theoretical performance of the classic dynamic perfect hashing scheme of Dietzfelbinger et al. [SIAM J. Comput. 23 (4) (1994) 738–761]. The space usage is similar to that of binary search trees. Besides being conceptually much simpler than previous dynamic dictionaries with worst case constant lookup time, our data structure is interesting in that it does not use perfect hashing, but rather a variant of open addressing where keys can be moved back in their probe sequences. An implementation inspired by our algorithm, but using weaker hash functions, is found to be quite practical. It is competitive with the best known dictionaries having an average case (but no nontrivial worst case) guarantee on lookup time.
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We present PacketShader, a high-performance software router framework for general packet processing with Graphics Processing Unit (GPU) acceleration. PacketShader exploits the massively-parallel processing power of GPU to address the CPU bottleneck in current software routers. Combined with our high-performance packet I/O engine, PacketShader outperforms existing software routers by more than a factor of four, forwarding 64B IPv4 packets at 39 Gbps on a single commodity PC. We have implemented IPv4 and IPv6 forwarding, OpenFlow switching, and IPsec tunneling to demonstrate the flexibility and performance advantage of PacketShader. The evaluation results show that GPU brings significantly higher throughput over the CPU-only implementation, confirming the effectiveness of GPU for computation and memory-intensive operations in packet processing.
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Internet routers and switches need to maintain millions of (e.g., per prefix) counters at up to OC-768 speeds that are essential for traffic engineering. Unfortunately, the speed requirements require the use of large amounts of expensive SRAM memory. Shah et al [1] introduced a cheaper statistics counter architecture that uses a much smaller amount of SRAM by using the SRAM as a cache together with a (cheap) backing DRAM that stores the complete counters. Counters in SRAM are periodically updated to the DRAM before they overflow under the control of a counter management algorithm. Shah et al [1] also devised a counter management algorithm called LCF that they prove uses an optimal amount of SRAM. Unfortunately, it is difficult to implement LCF at high speeds because it requires sorting to evict the largest counter in the SRAM. This paper removes this bottleneck in [1] by proposing a counter management algorithm called LR(T) (Largest Recent with threshold T) that avoids sorting by only keeping a bitmap that tracks counters that are larger than threshold T. This allows LR(T) to be practically realizable using only at most 2 bits extra per counter and a simple pipelined data structure. Despite this, we show through a formal analysis, that for a particular value of the threshold T, the LR(T) requires an optimal amount of SRAM, matching LCF. Further, we also describe an implementation, based on a novel data structure called aggregated bitmap, that allows the LR(T) algorithm to be realized at line rates.
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N. Dukkipati. Rate Control Protocol (RCP). PhD thesis, Stanford University, 2008.
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P. Bosshart. Low power TCAM. US Patent 8,125,810, Feb. 2012.
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IETF. NVGRE: Network Virtualization using Generic Routing Encapsulation, Feb. 2013. https://tools.ietf. org/html/draft-sridharan-virtualization-nvgre-02.
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Netronome. NFP-6xxx Flow Processor. http://www.netronome.com/pages/flow-processors/.
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