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Minutes IETF110: coinrg
minutes-110-coinrg-01

Meeting Minutes Computing in the Network Research Group (coinrg) RG
Date and time 2021-03-12 16:00
Title Minutes IETF110: coinrg
State Active
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Last updated 2021-03-29

minutes-110-coinrg-01
# IETF 110 Computing-in-the-Network (COIN) RG
COIN RG: https://datatracker.ietf.org/rg/coinrg/about/
COIN RG documents: https://datatracker.ietf.org/rg/coinrg/documents/
Meeting materials: https://datatracker.ietf.org/rg/coinrg/meetings/
These notes: https://codimd.ietf.org/notes-ietf-110-coinrg

Notetaker: Eve Schooler

Chairs update, reminder of IPR policies, today's agenda is research focused.

# 1. Research Presentations (15 mins each)

## a) FlowLens - Diogo Barradas (U. Lisbon)
Joint work with Nuno Santos, Luís Rodrigues, Salvatore Signorello, Fernando M.
V. Ramos and André Madeira Paper from NDSS'21:
https://www.ndss-symposium.org/ndss-paper/flowlens-enabling-efficient-flow-classification-for-ml-based-network-security-applications/

Research questions:
Can we collect packet distributions within programmable switches? And to do so
eficiently and generically? Given that it does not seem feasible to obtain
packet distributions in programmable switches at scale.

Contributions: FLowLens a flow classification system for generic ML-based
security tasks - Flow markers: compact representation of pkt distr in
programmable switches - Flow marker accumulator: implementation of flow marker
collection in switching hw - automatic profiling: app-tailored config of flow
markers - Evaluation: tested in 3 diff ML-based security tasks: covert channel
detection, website fingerprinting and botnet detection.

What does it take to compress packet distributions efficiently?
Produce flow markers with two operators: quantization and truncation.
Up to 150x size reduction.
How are flow markers collected in the switch?
Feed forward pipeline. Truncation requires only an additional pipeline stage.

Leverage Bayesian optimization to reduce the large configuration space of
quantization x truncation. Saves many hours of testing sub-optimal configs.

FlowLens scales the amount of inspected flows and retains accuracy.
[See slides for full details on detection and accuracy measurements.]

The experimentation artifacts are publicly available.
[See the code in the slides, as well as very interesting Discussion questions]

## b) Forwarding and Routing with Packet Subscriptions - Theo Jepsen (Stanford)
CoNEXT'20 paper: http://www.cs.yale.edu/homes/soule/pubs/conext2020-jepsen.pdf

We now have fast, programmable networks. We can use them for more expressive
routing, by using packet subscriptions. Challenges: How to make practical? How
to eval rules? How to route rules? How to populate tables efficiently? How to
make them compact? Compile subscription rules and convert into a BDD (binary
decision diagram). Each node in the BDD tree is a predicate of the rule.

Key contribution: How to execute the rules on a switch.
The output of the rules is state; state is passed from table to table.
The talk presents 2 schemes for routing these packet subscriptions.
A Traffic reduction scheme and a Memory reduction scheme. See the paper for
another scheme relying on Approximation, and the tradeoffs.

Questions:
- Are packet subscriptions useful to applications (and do they supply the
interfaces they need)? Looked at apps: Nasdaq (market feed filtering), hICN
(video streaming) and in-band network telemetry. - Do these subscriptions
provide performance benefits? in-net filtering reduces tail latency. - Whether
memory is used efficiently (FIBs)? Overall the compiler uses memory
efficiently. No difference between the 2 schemes.

Conclusion: Pkt subscriptions provide the net abstraction used by apps, improve
perf by using net resources efficiently, scales to large net topologies.

Try it out!
[See slides for pointers]

Q: Alexander Clemm. How do you deal with encrypted traffic? Can specify to the
compiler the header format in P4. Some limitations i.e. how deep can look into
header. Subscriptions currently in plain text. Looking at homomorphic
encryption.

Q: Dirk Kutscher. What about congestion control? Would argue it is orthogonal,
but could add CC to packet subscription. Another related issue is reliability.
Difficult to handle in subscription systems, which have 1-N, N-N models. So, CC
should probably be handled at a different layer than packet subscriptions.

Q: Tianji Jiang. For the example used with Nasdaq, see the following paper:
Too Fast or Too Slow? Determining the Optimal Speed of Financial Markets Austin
Gerig US Securities and Exchange Commission Co-author: Daniel Fricke

[From the chat]
Q: David Oran: Apologize since I didn't read the paper - but do you reference
the old DIFANE work that trades off memory for forwarding tables against
stretch? That way you don't need full rule sets in every switch? Theo: 1) I
already addressed this question. 2) For fragmentation, packet subscriptions
supports fragmentation at the application level. This requires the application
placing the header fields in all fragments. Since packet subscriptions does not
track IP frag sequence numbers, it can't reassemble a stream at that level.
David Oran: so the application needs to be changed to use this, which I guess
is fine. This could also help with the crypto question, as you might check the
ICN work on name privacy through obfuscation which can hide the actual matching
values without crypto, and get some privacy (but not defend against
linkability). Theo Jepsen: thank you for bringing up DIFANE. I am aware of that
work. We do something similar with our approximation routing scheme that we
discuss at the end of section 4 in our paper:
http://www.cs.yale.edu/homes/soule/pubs/conext2020-jepsen.pdf

## c) FogStore: Toward a Distributed Data Store for Fog Computing - Harshit
Gupta (Georgia Tech) https://arxiv.org/pdf/1709.07558.pdf

Data management over geo-distributed edge computing infrastructure.
Vision: a continuum of resources, could be owned by multiple providers, dense
geo-distribution, limited capacity at each site. Disruptive to data management
because: situation-awareness apps gen large amnts of data, data exchange for
coordination between connected entities presents challenges. Challenges of edge
computing infrastructures: Highly heterogeneous, dense geo-distribution (as
compared with DCs, where granularity at the level of racks is sufficient), no
support to monitor Client mobility (workload dynamism).

This talk focuses on 2 sytems, two classes of data management.

1) DataFog: geo-distributed spatio-temporal K-V store. Targets
situation-awareness apps, requiring low-latency access to data. Extended Apache
Cassandra and ran emulations for performance evaluation w/ and w/out
extensions. Synchronous replication for consistency. Many latency and fault
tolerance tradeoffs. E.g., because of the tradeoff between latency and fault
tolerance, developed two types of replicas, those in-vicinty and strongly
consistent, and those that are remote and eventually consistent.

2) ePulsar: Topic-based pub-sub system. Geo-distr pub-sub system, similar
interface as Kafka and Pulsar. The source of latency in pub-sub systems is
often a result of being topology unaware. Two main contributions: incorporated
network coordinates protocol to estimate client-broker latencies, and
incorporated the coordinates into the broker selection and adapts to client
mobility.

[See references in the slides.]

Q: Jeffrey He: Which is the major latency? Sychronization or replication? It is
tied to the selection of the replicas and where they are located. So it is
actually a combination of both.

Q: Dirk Kutscher: in general, on a good track and good potential re how the
relevant app-layer system can be better supported by the network. In your
system, we are often interested from the performance of the  network topology
and not so much the geo-location or geographic coordinates. What is your view?
One reason why we moved toward a more net coordinate approach, agnostic of
location, proximity in terms of RTT. As edge provider, wouldn't know about the
innards of the network, e.g., a micro-edge deployed on peering sites.

[From the chat]
Q: Stuart Card: How about latitude, longitude and altitude?
David Oran: Let's replace hop counts with cartesian distance :-)and stop loops
by never getting closer to the source (c.f.
https://en.wikipedia.org/wiki/Geographic_routing). Q: Jianfei(Jeffrey) HE:
Geographical routing? Stuart Card: There is a spotty history of attempts at
"geocast" Jianfei(Jeffrey) HE: routing by computing without tables David Oran:
Geo is likely useful for filtering, but not for routing Dirk Kutscher: yes
Stuart Card: Also routing if it is a homogeneous wireless mesh, otherwise not
so much.

Harshit Gupta (the presenter): We envision that for a heterogeneous edge,
location is not the best method of selecting replicas. A more topology-agnostic
approach like Network Coordinates can help. Network Coords would already take
into account the routing between nodes. Do you consider edge-to-edge
communication without going to the core? Jianfei(Jeffrey) HE: @Harshit, in
Mobile Edge Computing (MEC) context, you can assume edge can communicate with
edges without going up to the core. Philip Eardley: Interesting, thanks! the
part with finding the marbles up the tree reminded me of some old manet routing
work (mobile adhoc networks) and creating Directed acycle graph Harshit Gupta:
@Jeffrey, is it possible to point me to some document that goes into little
more detail about inter-edge communitcation? I have been having a hard time to
get some meaningful network characteristics (topology, RTT) between edge sites.
Jianfei(Jeffrey) HE: @Harshit, that is formally in the 3GPP documents. Tianji
Jiang: @Harshit @ Jeffrey: it is 3gpp TR. But, the version in 3gpp right now is
about to be updated in 1 week
(https://www.3gpp.org/ftp/Specs/archive/23_series/23.748/)

## d) Hierarchical Data Storage and Processing on the Edge of the Network -
Seyed Hossein Mortazavi (U. Toronto) with Eyal de Lara, University of Toronto
https://usenix.org/system/files/conference/hotedge18/hotedge18-papers-mortazavi.pdf
https://cse.buffalo.edu/faculty/tkosar/cse710_spring19/mortavazi-sec17.pdf

Next gen apps req lower latencies, better response time, high bw, as well as
privacy, exact loc detection and scalability. Has led to Edge computing, and
the notion of a hierarchical DC infrastructure. To emulate success of web
(CRUD) apps on the cloud, where apps are partitioned into independent stateless
handlers; this model was enabled by a shared storage layer.

CloudPath - a platform that enables the exec of 3rd party apps, advocating a
separation between app code and data. Developers: organize apps as a collection
of stateless fns. CloudPath: on-demand replication of code and data. Provides a
common run time on all flavors of cloud nodes. Ran a face detection application
in their experiments. Although tremendous gains, there are however issues with
routing overhead due to its implimentation over HTTP.

Discussion - Network requirements
- Routing reqs for apps based on fns
- Synchronizing clocks between DCs
- Providing locking service for data consistency
    - better guarantees for data can be provided (if there is a service inside
    the network itself)

[From chat]
Jianfei(Jeffrey) HE: @Seyed, when you say for networks to provide "locker"
service, can be a global sequencer insider the network equivelent to that
locker manager? Global sequencer: receive all requests and assign a global
unique sequence to each. Seyed Mortazavi: @jianfei yes, I think a lock manager
can be appropriate, maybe different levels of lock managers to keep locality.I
have to think about the global sequencer, but a lock manager I think is more
appropriate compared to a full blown paxos solution for the edge.The key here
is the local reads/write. If we don't have local read/writes then the edge
becomes less useful. One may think why use the edge and have all the
complexities when one can simply send to the cloud.

## e) The connected intelligent machines’ technology journey  - Edgar Ramos
(Ericsson)
https://www.ericsson.com/4af428/assets/local/reports-papers/consumerlab/reports/2020/ericsson-10-hct-report-connected-intelligent-machines.pdf

This presentation is more about research questions for the future, comparing
what the network is today vs what it should look like in the future.
Crowdsourced a set of questions to researchers at Ericsson and this
presentation is the result.

10 exponential technological forces: Ubiquitous connectivity, IoT, AI, Cloud &
edge, trustworthiness, robotics, new ways of interaction (think goggles), new
computing paradigms, alt energy tech, smart materials Combinatorial effects of
their interaction and intersection

Re COIN:
The challenge: will the future network match machine evolution?
Integration of more autonomous, more generic devices. Yet ack the challenge to
facilitate customization whilst maintaining interoperability. The network
services needed: discovery, exposure, trust and self-organization. When
everything is intelligent, there will be a need for mediation and arbitration.
Question if the network will be relevant in this vision of the future? In a
highly intelligent world, the net relevance will depend on the incentives
provided, to avoid being by-passed.

The discussion today: Connected intelligent machines (en route to 6G).

What do we mean by intelligent machines? Encompasses multi-sensing deeply
specialized machines, machines learning from other machines, regenerative
self-managed machines, environment- & intent-driven machines, decentralized
intelligence & collectively reasoning machines, machines moving twds general
intelligence.

What are the ranges of enhanced net platform features and services needed in
this intelligent machine vision?

If everything is connected, there are additional dimensions of the problem
space to consider, such as: interoperability, human-machine co-existence,
trustworthiness (where trustworthy means responsible, resilient, secure, safe,
explainable, unbiased, fair and ethical).

[See the slides for a detailed and very interesting visualization of the
Connected intelligent machine relationships matrix.]

A high-level AI-2-AI network stack is presented.

[From the chat]
Stuart Card: This is a remarkably broad vision, including what appears to be
acceptance of "runaway AI". I agree, so am focused on "friendly AI" and
trustworthiness. Edgar Ramos: @Stuart..and well AI is also biased as humans are
in many aspects, and we can expect that there will be conflicts on how agents
will act depending on who produce them even if not ill-intended (from for
example cultural crash perspective).

# 2. Drafts Updates

## a) draft-sarathchandra-coin-appcentres-04 - Dirk Trossen - 10 minutes
“In-Network Computing for App-Centric Micro-Services”
https://datatracker.ietf.org/doc/draft-sarathchandra-coin-appcentres/

Moved the use cases into use case draft. Just a stub reference now. The
requirements have been linked more clearly to Section 5, which now has a
mixture of refs to research and standards. Section 6 is entirely new.

Requirements were put into technology areas (vs use cases previously). Also
discussing how to get those requirements into the use case discussions. A
mapping of requirements to standardization efforts, although extensive, it is
not exhaustive of course.

Future: Link to other drafts, updating additional related research, even more
SDO efforts, esp identify what standards have NOT done so far.

## b) draft-hsingh-coinrg-reqs-p4comp-03 - ran out of time
Hemant Singh - 10 minutes
Requirements for P4 Program Splitting for Heterogeneous Network Node”
https://datatracker.ietf.org/doc/draft-hsingh-coinrg-reqs-p4comp-03

## c) draft-hsingh-coinrg-p4use-00 - ran out of time
Hemant Singh - 5 minutes
“Use of P4 Programs in IETF Specifications”
https://datatracker.ietf.org/doc/draft-hsingh-coinrg-p4use-00

# 3. Discussion - 10 minutes

Recurring feedback: Need to leave more time for discussions!!!

# 4. Conclusions and Future Plans
- Interim and IETF 111 - targeted for May.
- Action item: I-D authors of expired drafts, please let co-chairs know whether
you intend to progress the drafts - or not. - Goal for the next Interim: Update
Milestones.