[Docs] [txt|pdf|xml|html] [Tracker] [Email] [Nits]

Versions: 00

COIN                                                               J. He
Internet-Draft                                                   R. Chen
Intended status: Informational                                    Huawei
Expires: April 14, 2019                                M. Montpetit, Ed.
                                                          Triangle Video
                                                        October 11, 2018


                    In-Network Data-Center Computing
                      draft-he-coin-datacenter-00

Abstract

   This draft wants to review the existing research and the open issues
   that relate to the addition of data plane programmability in Data
   Center.  While some of the research hypotheses that are at the center
   of in-network-computing have been investigated since the time of
   active networking, recent developments in software defined
   networking, virtualization programmable switches and new network
   programming languages like P4 have generated a new enthusiasm in the
   research community and a flourish of new projects in systems and
   applications alike.  This is what this draft is addressing.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on April 14, 2019.

Copyright Notice

   Copyright (c) 2018 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of



He, et al.               Expires April 14, 2019                 [Page 1]


Internet-Draft                                              October 2018


   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   3
   2.  In Network Computing and Data Centers . . . . . . . . . . . .   3
   3.  State of the Art in DC Programmability  . . . . . . . . . . .   4
     3.1.  In-Network Computing  . . . . . . . . . . . . . . . . . .   4
     3.2.  In-Network Caching  . . . . . . . . . . . . . . . . . . .   6
     3.3.  In Network Consensus  . . . . . . . . . . . . . . . . . .   7
     3.4.  Research Topics in Next Generation Data Centers . . . . .   8
     3.5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . .  10
   4.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  10
     4.1.  Normative References  . . . . . . . . . . . . . . . . . .  10
     4.2.  Informative References  . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

1.  Introduction

   It is now a given in the computing and networking world that
   traditional approaches to cloud and client-server architectures lead
   to complexity and scalability issues.  New solutions are necessary to
   address the growth of next generation network operation (in data
   centers and edge devices alike) including automation, self-
   management, orchestration across components and federation across
   network nodes to enable emerging services and applications.

   Mobility, social network and big data and AI/ML as well as emerging
   content application in the XR (virtual, augmented and mixed reality)
   require more scalable, available and reliable solution not only in
   real time, anywhere and over a wide variety of end devices.  While
   these solutions involve edge resources for computing, rendering and
   distribution, this paper focuses on the data center what are the
   current research approaches to create more flexible solutions.  We
   must define what we understand by data centers.  In this draft, we
   are not going to limit them to single location cloud resources but
   add multiple locations as well as interwork with edge resources to
   enable the network programmability that is central to next generation
   DCs in term of supported services and dynamic resilience.  This leads
   to innovative research opportunities, including but not limited to:

      - Software defined networking (SDN) in distributed environments.



He, et al.               Expires April 14, 2019                 [Page 2]


Internet-Draft                                              October 2018


      - Security and trust models.

      - Data plane programmability for consensus and key-value
      operations.

      - High Level abstractions as in network computing should focus on
      primitives, which can be widely re-used in a class of applications
      and workloads, and identify those high level abstractions to
      promote deployment.

      - Machine Learning (ML) and Artificial Intelligence (AI) to detect
      faults and failures and allow rapid responses as well as implement
      network control and analytics.

      - New services for mixed reality (XR) deployment with in-network
      optimization and advanced data structures and rendering for
      interactivity, security and resiliency.

1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

2.  In Network Computing and Data Centers

   As DC hardware components becoming interchangeable, the advent of
   software-defined technologies suggests that a change is underway.  In
   the next-generation data center, an increasing percentage of critical
   business and management functions will be activated in the software
   layer rather than the underlying hardware.  This will allow
   organizations to move away from the current manual configurations to
   handle more dynamic, rules-based configurations.  Hence,
   virtualization and cloud computing have redefined the datacenter (DC)
   boundaries beyond the traditional hardware-centric view [SAPIO].
   Servers, storage, monitoring and connectivity are becoming one.  The
   network is more and more the computer.

   Hence, there is now a number of distributed networking and computing
   systems which are the basis of big-data and AI-related applications
   in DCs in particular.  They include Distributed file system (e.g. the
   Hadoop Distributed File System or HDFS [HADOOP]), distributed memory
   database (e.g.  MemCached [MEM]), distributed computing system (e.g.
   mapReduce from Hadoop, [HADOOP], Tensorflow [TENSOR], and Spark
   GraphX [SPARK]), as well as distributed trust systems on the
   blockchain, such as hyperledgers and smart contracts.





He, et al.               Expires April 14, 2019                 [Page 3]


Internet-Draft                                              October 2018


   In parallel the emergence of the P4 language [P4] and programmable
   switches facilitates innovation and triggers new research.  For
   example, the latest programmable switches make the concept of the
   totally programmable and dynamically reconfigurable network closer to
   reality.  And, as distributed systems are increasingly based on
   memory instead of hard disks, distributed system-based application
   performance is increasingly constrained by network resources not
   computing.

   However, there are some challenges when introducing in-network
   computing and caching:

      - Limited memory size: tor example, the SRAM size [TOFINO] can be
      as small as tens of MBs.

      - Limited instruction sets: the operations are mainly simple
      arithmetic, data (packet) manipulation and hash operation.  Some
      switches can provide limited floating-point operation.  This
      enables network performance tools like forward error correction
      but limits more advanced applications such as machine learning for
      congestion control for example.

      - Limited speed/CPU processing capabilities: only a few operations
      can be performed on each packet to ensure line speed (tens of
      nano-seconds on fast hardware).  Looping could allow a processed
      packet to re-enter the ingress queue, but with a cost of
      increasing latency and reducing forwarding capabilities.

      - Performance: for devices located on the links of the distributed
      network; it is to be evaluated how on-path processing can reduce
      the FCT (Flow Completion Time) in data center network hence reduce
      the network traffic/congestion and increase the throughput.

   The next sections of this draft review how some of these questions
   are currently being addressed in the research community.

3.  State of the Art in DC Programmability

   Recent research has shown that in-network computing can greatly
   improve the DC network performance of three typical scenarios:
   aggregate on path- computing, key-value (K-V) cache, and strong
   consistency.  Some of these research results are summarized below.

3.1.  In-Network Computing

   The goals on on-path computing in DC is 1. to reduce delay and/or
   increase throughput for improved performance by allowing advanced
   packet processing and 2. to help reduce network traffic and alleviate



He, et al.               Expires April 14, 2019                 [Page 4]


Internet-Draft                                              October 2018


   congestion by implementing better traffic and congestion management
   [REXFORD][SOULE][SAPIO].

   However, in terms of research and implementation, there are still
   open issues that need to be addressed in order to fulfill these
   promises beyond what was mentioned in the previous section.  In
   particular, the end-to-end principle which has driven most of the
   networking paradigms of the last 20 years is challenged when in-
   network computing devices are inserted on the ingress-egress path.
   This is still an open discussion topic.

   The type of computing that can be performed to improve the DC
   performance is another of the open topics.  Computing should improve
   performance but not at the expense of existing application
   degradation Computing should also enable new applications to be
   developed.  At the time of this writing those include data intensive
   applications in workload mode with partition and aggregation
   functionality.

   Data-intensive applications include big data analysis (e.g. data
   reduction, deduplication and machine learning), graph processing, and
   stream processing.  They support scalability by distributing data and
   computing to many worker servers.  Each worker performs computing on
   a part of the data, and there is a communication phase to update the
   shared state or complete the final calculation.  This process can be
   executed iteratively.  It is obvious that communication cost and
   availability of bottleneck resources will be one of the main
   challenges for such applications to perform well as a large amount of
   data need to be transmitted frequently in many-to-many mode.  But
   already, there are several distributed frameworks with user-defined
   aggregation functions, such as mapReduce from Haddop [HADOOP], Pregel
   from Google [PREGEL], and DryadLinq from Microsoft [DRYAD].  These
   functions enable application developers to reduce the network load
   used for messaging by aggregating all single messages together and
   consequently reduce the task execution time.  Currently, these
   aggregation functions are used only at the worker level.  If they are
   used at the network level, a higher traffic reduction ratio can be
   reached.

   The aggregation functions needed by the data intensive applications,
   have some features that make it suitable to be at least executed in a
   programmable.  They usually reduce the total amount of data by
   arithmetic (add) or logical function (minima/maxima detection) that
   can be parallelized.  Performing these functions in the DC at the
   ingress of the network can be beneficial to reduce the total network
   traffic and lead to reduced congestion.  The challenge is of course
   not to lose important data in the process especially when applied to




He, et al.               Expires April 14, 2019                 [Page 5]


Internet-Draft                                              October 2018


   different parts of the input data without considering the order and
   affect the accuracy of the final result.

   In-network computing can also improve the performance of multipath
   routing by aggregating path capacity to individual flows and
   providing dynamic path selection, improving scalability and
   multitenancy.

   Other data intensive applications that can be improved in terms of
   network load by in-network computing include: machine learning, graph
   analysis, data analytics and map reduce.  For all of those,
   aggregation functions in the computing hardware provides a reduction
   of potential network congestion; in addition, because of the reduced
   load, the overall application performance is improved.  The traffic
   reduction was shown to range from 48% up to 93% [SAPIO].

      Machine learning is a very active research area for in-network
      computing because of the large datasets it both requires and
      generates.  For example, in TensorFlow [TENSOR], parameters
      updates are small deltas that only change a subset of the overall
      tensor and can be aggregated by a vector addition operation.  The
      overlap of the tensor updates, i.e. the portion of tensor elements
      that are updated by multi workers at the same time, is
      representative of the possible data reduction achievable when the
      updates are aggregated inside the network.

      In graph analysis, three algorithms with various characteristics
      have been considered in [SAPIO]: PageRank, Single Source Shortest
      Path (SSSP) and Weakly Connected Components (WCC) with a
      commutative and associative aggregation function.  Experiment
      shows that the potential traffic reduction ratio in the three
      applications is signification.

      Finally, in map-reduce, experiments in the same paper show that
      after aggregation computing, the number of packets received by the
      reducer decreases by 88%~90% compared UDP, by 40% compared to
      using TCP.  There is thus great promise for mapReduce-like to take
      advantage of computing and storage optimization.

3.2.  In-Network Caching

   Key-value stores are ubiquitous and one of their major challenges are
   to process their associated data-skewed workload in a dynamic
   fashion.  As in any caches, popular items receive more queries, and
   the set of popular items can change rapidly, with the occurrence of
   well-liked posts, limited-time offers, and trending events.  The skew
   generated by the dynamic nature of the K-V can lead to severe load
   imbalance and significant performance deterioration.  The server is



He, et al.               Expires April 14, 2019                 [Page 6]


Internet-Draft                                              October 2018


   either overused in an area or underused in another, the throughput
   can decrease rapidly, and the response time latency degrades
   significantly.  When the storage server uses per core sharding/
   partitioning to process high concurrency, this degradation will be
   further amplified.  The problem of unbalanced load is especially
   acute for high performance in memory K-V store.

   The selective replication copying of popular items is often used to
   keep performance high.  However, in addition to more hardware
   resource consumption, selective replication requires a complex
   mechanism to implement data mobility, data consistency and query
   routing.  As a result, system design becomes complex and overhead is
   increased.

   This is where in-network caching can help.  Recent research
   experiments show that K-V cache throughput can be improved by 3~10
   times by introducing in net cache.  Analytical results in [FAN] show
   that a small frontend cache can provide load balancing for N back-end
   nodes by caching only O(N logN) entries, even under worst-case
   request patterns.  Hence, caching O(NlogN) items is sufficient to
   balance the load for N storage servers (or CPU cores).

   In the NetCache system [JIN], a new rack-scale key-value store design
   guarantees billions of queries per second (QPS) with bounded
   latencies even under highly-skewed and rapidly-changing workloads.  A
   programmable switch is used to detect, sort, cache, and obtain a
   hotspot K-V pair to process load balancing between the switch storage
   nodes.

3.3.  In Network Consensus

   Strong consistency and consensus in distributed networks are
   important.  Significant efforts in the in-network computing community
   have been directed towards it.  Coordination is needed to maintain
   system consistency and it requires a large amount of communication
   between network nodes and instances, taking away processing
   capabilities from other more essential tasks.  Performance overhead
   and extra resources often result in a decrease in consistency.  And
   as a result, potential inconsistencies need to be addressed.

   Maintaining consistency requires multiple communications rounds in
   order to reach agreement, hence the danger of creating messaging
   bottlenecks in large systems.  Even without congestion, failure or
   lost messages, a decision can only be reached as fast as the network
   round trip time (RTT) permits.  Thus, it is essential to find
   efficient mechanisms for the agreement protocols.  One idea is to use
   the network devices themselves.




He, et al.               Expires April 14, 2019                 [Page 7]


Internet-Draft                                              October 2018


   Hence, consensus mechanisms for ensuring consistency are some of the
   most expensive operations in managing large amounts of data [ZSOLT].
   Often, there is a tradeoff that involves reducing the coordination
   overhead at the price of accepting possible data loss or
   inconsistencies.  As the demand for more efficient data centers
   increases, it is important to provide better ways of ensuring
   consistency without affecting performance.  In [ZSOLT] consensus
   (atomic broadcast) is removed from the critical path by moving it to
   hardware.  The Zookeeper atomic broadcast (also in Hadoop) proof of
   concept is implemented at the network level on an FPGA, using both
   TCP and an application specific network protocol.  This design can be
   used to push more value into the network, e.g., by extending the
   functionality of middle boxes or adding inexpensive consensus to in-
   network processing nodes.

   A widely used protocol for consensus is Paxos.  Paxos is a
   fundamental protocol used by fault-tolerant systems, and is widely
   used by data center applications.  In summary, Paxos serializes
   transaction requests from different clients in the leader, ensuring
   that each learner (message replicator) in the distributed system is
   implemented in the same order.  Each proposal can be an atomic
   operation (an inseparable operation set).  Paxos does not care about
   specific content of the proposal.  Recently, some research evaluation
   suggests that moving Paxos logic into the network would yield
   significant performance benefits for distributed applications
   [DANG15].  In this scheme network switches can play the role of
   coordinators (request managers) and acceptors (managed storage
   nodes).  Messages travel fewer hops in the network, therefore
   reducing the latency for the replicated system to reach consensus
   since coordinators and acceptors typically act as bottlenecks in
   Paxos implementations, because they must aggregate or multiplex
   multiple messages.  Experiments suggest that moving consensus logic
   into network devices could dramatically improve the performance of
   replicated systems.  In [DANG15], NetPaxos achieves a maximum
   throughput of 57,457 messages/s, while basic Paxos the coordinator
   being CPU bound, is only able to send 6,369 messages/s.  In [DANG16],
   a P4 implementation of Paxos is presented as a result of Paxos
   implementation with programmable data planes.

   Other papers, have shown the use of in-network processing and SDN for
   Paxos performance improvements using multi-ordered multicast and
   multi-sequencing [LIJ] [PORTS].

3.4.  Research Topics in Next Generation Data Centers

   While the previous section introduced the state of the art in data
   center in-network computing, there are still some open issues that
   need to be addressed.  In this section, some of these questions are



He, et al.               Expires April 14, 2019                 [Page 8]


Internet-Draft                                              October 2018


   listed as well as the impacts that adding in-network computing will
   have on existing systems.

   Adding computing and caching to the network violates the End-to-End
   principle central to the Internet.  And the interaction with
   encrypted systems can limit the scope of what in-network can do to
   individual packet.  In addition, even when programmable, every switch
   is still designed for (line speed) forwarding with the resulting
   limitations, such as lack of floating-point support for advanced
   algorithms and buffer size limitation.  Especially in the high-
   performance datacenters for in-network computing to be successful, a
   balance between functionality, performance and cost must be found.

   Hence the research areas include but are not limited to:

      Protocol design and network architecture: a lot of the current in-
      network work has targeted network layer optimization; however,
      transport protocols will influence the performance of any in-
      network solution.  How can in-network optimization interact (or
      not) with transport layer optimizations?

      Can the end-to-end assumptions of existing transport like TCP
      still be applicable in the in-network compute era?  There is
      heritage in middlebox interactions with existing flows.

      Fixed-point calculation for current application vs. of floating-
      point calculation for more complex operations and services:
      network switches typically do not support floating-point
      calculation.  Is it necessary to introduce this capability and
      scale to the demand?  For example, AI and ML algorithms currently
      mainly use floating-point calculation.  If the AI algorithm is
      changed to fixed-point calculation, will the training stop in
      advance and the training result deteriorate (this needs to be done
      as joint work with the AI community)?

      What are the gains brought by aggregation in distributed and
      decentralized networks?  The built-in buffer of the network device
      is often limited, and, for example the AI and XR application
      parameters and caches can reach hundreds of megabytes.  There is a
      trade-off between aggregation of the data on a single network
      device and its distribution across multiple nodes in terms of
      performance.

      What is the relationship between the depth of packet inspection
      and not only performance but security and privacy?  There is a
      need to find what application layer cryptography is ready to
      expose to other layers and even collaborating nodes; this is also
      related to trust in distributed networks.



He, et al.               Expires April 14, 2019                 [Page 9]


Internet-Draft                                              October 2018


      Relationship between the speed of creating tables on the data
      plane and the performance.

3.5.  Conclusion

   In-network computing as it applies to data centers is a very current
   and promising research area.  Thus, the proposed Research Group
   creates an opportunity to bring together the community in
   establishing common goals, identify hurdles and difficulties, provide
   paths to new research especially in applications and linkage to other
   new networking research areas at the edge.  More information is
   available in [COIN].

4.  References

4.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

4.2.  Informative References

   [CHAN]     Chan et al., M., "Network Support for DNN Training", 2018.

   [COIN]     He, J., Chen, R., and M. Montpetit, "Computing in the
              Network, COIN, proposed IRTF group", 2018.

   [DANG15]   Dang et al., T., "NetPaxos: Consensus at Network Speed",
              2015.

   [DANG16]   Dang et al., T., "Paxos Made Switch-y", 2016.

   [DRYAD]    Microsoft, "DryadLinq", 2018.

   [FAN]      Fan et al., B., "Small Cache, Big Effect: Provable Load
              Balancing for Randomly Partitioned Cluster Services",
              2011.

   [FORSTER]  Forster, N., "To be included", 2018.

   [GRAHAM]   Graham, R., "Scalable Hierarchical Aggregation Protocol
              (SHArP): A Hardware Architecture for Efficient Data
              Reduction.", 2016.

   [HADOOP]   Hadoop, "Hadoop Distributed Filesystem", 2016.




He, et al.               Expires April 14, 2019                [Page 10]


Internet-Draft                                              October 2018


   [JIN]      Jin et al., X., "NetCache: Balancing Key-Value Stores with
              Fast In-Network Caching", 2017.

   [LIJ]      Li et al., J., "NetCache: Balancing Key-Value Stores with
              Fast In-Network Caching", 2017.

   [LIX]      Li et al., X., "Be fast, cheap and in control with
              SwitchKV", 2016.

   [MEM]      memcached.org, "Memcached", 2018.

   [P4]       p4.org, "P4 Language", 2018.

   [PORTS]    Ports, D., "Designing Distributed Systems Using
              Approximate Synchrony in Data Center Networks", 2015.

   [PREGEL]   github.com/igrigorik/pregel, "Pregel", 2018.

   [REXFORD]  Rexford, J., "Sigcomm 2018 Keynote Address", 2018.

   [SAPIO]    Sapio et al., A., "In net computing is a dumb idea whose
              time has come", 2017.

   [SOULE]    Soule, R., "Sigcomm 2018 Netcompute Workshop Keynote
              Address", 2018.

   [SPARK]    Apache, "Spark Graph X", 2018.

   [SUBEDI]   Subedi et al., T., "OpenFlow-based in-network Layer-2
              adaptive multipath aggregation in data centers", 2015.

   [TENSOR]   tensorflow.org, "Tensorflow", 2018.

   [TOFINO]   BarefootNetworks, "Tofino", 2018.

   [ZSOLT]    Zsolt et al., I., "Consensus in a Box", 2018.

Authors' Addresses

   Jeffrey He
   Huawei

   Email: jeffrey.he@huawei.com








He, et al.               Expires April 14, 2019                [Page 11]


Internet-Draft                                              October 2018


   Rachel Chen
   Huawei

   Email: chenlijuan5@huawei.com


   Marie-Jose Montpetit (editor)
   Triangle Video
   Boston, MA
   US

   Email: marie@mjmontpetit.com







































He, et al.               Expires April 14, 2019                [Page 12]


Html markup produced by rfcmarkup 1.128, available from https://tools.ietf.org/tools/rfcmarkup/