Internet DRAFT - draft-liu-can-gap-reqs
draft-liu-can-gap-reqs
rtgwg P. Liu
Internet-Draft T. Jiang
Intended status: Informational China Mobile
Expires: 26 April 2023 P. Eardley
D. Trossen
C. Li
Huawei Technologies
G. Huang
ZTE
23 October 2022
Computing-Aware Networking (CAN) Gap Analysis and Requirements
draft-liu-can-gap-reqs-00
Abstract
This document provides gap analysis and requirements for the problems
and use cases that champion the joint optimization of both network
and computing resources as outlined in[I-D.liu-can-ps-usecases]. It
also identifies the key engineering investigation areas which require
adequate architectures and protocols to achieve balanced computing
and networking resource utilization among facilities providing the
services.
Status of This Memo
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This Internet-Draft will expire on 26 April 2023.
Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definition of Terms . . . . . . . . . . . . . . . . . . . . . 4
3. Gap Analysis of Existing Solutions . . . . . . . . . . . . . 5
3.1. Gap Analysis of DNS and GSLB . . . . . . . . . . . . . . 5
3.2. Gap Analysis of Load Balancer . . . . . . . . . . . . . . 6
3.3. Gap Analysis of ALTO . . . . . . . . . . . . . . . . . . 7
3.4. Gap Analysis of Message Broker . . . . . . . . . . . . . 8
3.5. Gap Analysis of Client Based Solution . . . . . . . . . . 8
3.6. Summary of Gap Analysis . . . . . . . . . . . . . . . . . 9
3.6.1. Dynamicity of Relations . . . . . . . . . . . . . . . 9
3.6.2. Efficiency . . . . . . . . . . . . . . . . . . . . . 10
3.6.3. Complexity and Accuracy . . . . . . . . . . . . . . . 10
3.6.4. Metric Exposure and Use . . . . . . . . . . . . . . . 11
3.6.5. Security . . . . . . . . . . . . . . . . . . . . . . 11
4. Requirements . . . . . . . . . . . . . . . . . . . . . . . . 12
4.1. Support dynamic and effective selection among mutiple
serivce instances . . . . . . . . . . . . . . . . . . . . 12
4.2. Support Agreement on Metric Representation . . . . . . . 13
4.3. Support Moderate Metric Distributing . . . . . . . . . . 13
4.4. Support Flexible Use of Metrics . . . . . . . . . . . . . 14
4.5. Support Session and Service Continuity . . . . . . . . . 14
4.6. Preserve Communication Confidentiality . . . . . . . . . 16
5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 16
6. Security Considerations . . . . . . . . . . . . . . . . . . . 16
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 17
9. Informative References . . . . . . . . . . . . . . . . . . . 17
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 18
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 18
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1. Introduction
Computing service instances deployed at multiple geographically
distributed edge sites are used to better realize an edge computing
service in Computing-Aware Networking(CAN) use cases, as shown in
[I-D.liu-can-ps-usecases]. A fundamental requirement in this type of
deployment is to optimally deliver a service request to the most
appropriate service instance, which would be dynamically selected by
taking into consideration both the available computing resources and
the quality of various network paths. Moreover, the potential
requirement of the service & session continuity for a client
transaction over its lifetime, possibly consisting of multiple
requests, suggests some mechanism(s) be in place to maintain the
service affinity between the client and the dynamically chosen
service instance.
Overall, traditional techniques to manage the load distribution or
balancing of clients requests include either the choose-the-closest
or the round- robin mode. Solutions derived from these techniques
are relatively static, which may lead to an unbalanced distribution
in terms of network utilization and computational load among
available resources. For example, DNS-based load balancing usually
configures a domain in the Domain Name System (DNS) such that client
requests to that domain name will be statically resolved to one of
several pre-provisioned IP addresses, with each IP corresponding to
one node out of a group of servers. Successively, the client loads
are distributed to the selected server, without further considering
the dynamism of the server environment.
Certainly, there do exist some dynamic solutions to distribute client
requests to servers that best fit somewhat service-specific metrics,
such as the best available resources, the most powerful GPUs, the
minimal platform load, and so on. These solutions usually involve
the Layer 4 - Layer 7 handling of packets, such as through DNS-based
or indirection servers. Unfortunately, this category of approaches
is inefficient for large number of short connections. Another
disadvantage (of the approaches) falls in their lacking of effective
ways to retrieve the desired metrics, such as the runtime status of
network devices, in a real-time way. Therefore, the choice of the
service node is almost entirely determined by the computing status,
rather than the comprehensive considerations of both computing and
network metrics or makes rather long-term decisions due to the (upper
layer) overhead in the decision making itself.
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Distributing service requests to specific services that have multiple
service instances residing at multiple edges, while taking into
account both computing and service-specific metrics in the
distribution decision, is seen as a problem of dynamically
dispatching service requests, without prescribing the use of a
routing solution.
At the same time, with new technologies such as serverless computing
and container based virtual functions, a service node at an edge site
can be easily instantiated and terminated in a sub-second scale,
which in turn dynamically changes the availability of computing
resources for services over time. This is further impacting the
possibly "best" decision on where to send a service request from a
client.
As the use cases in [I-D.liu-can-ps-usecases] , for some applications
and in some use case, considering both the computing resource and
network resource status to make the traffic steering decisions is
necessary to meet the demands of latency. Before that, those status
could be collocted and then be used together. This draft provides
the requirements to realize the potential Computing- Aware Networking
by addressing the challenges as demonstrated by typical use cases in
CAN from the perspective of network that is aware of the computing
resource status.
2. Definition of Terms
Computing-Aware Networking(CAN): Aiming at computing and network
resource optimization by being aware of not only routing metric but
also computing resource metric in deploying computing and network
resource, steering traffic to appropriate computing resources, etc.
CAN Components: The network devices and functions that could realize
CAN's demands & objectives.
Service: A monolithic functionality that is provided by an endpoint
according to the specification for said service. A composite
service can be built by orchestrating monolithic services.
Service instance: Running environment (e.g., a node) that makes the
functionality of a service available. One service can have several
instances running at different network locations.
Service identifier: Used to uniquely identify a service, at the same
time identifying the whole set of service instances that each
represent the same service behaviour, no matter where those service
instances are running.
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Service transaction: Has one or more several service request that
has several flows which require the affinity because of the
transaction related state.
Instance affinity: To maintain the request of several flows belongs
to the same service transaction to the same service instance.
Anycast: An addressing and packet sending methodology that assign an
"anycast" identifier for one or more service instances to which
requests to an "anycast" identifier could be routed, following the
definition in [RFC4786] as anycast being "the practice of making a
particular Service Address available in multiple, discrete,
autonomous locations, such that datagrams sent are routed to one of
several available locations".
3. Gap Analysis of Existing Solutions
There are a number of problems that may occur when realizing the use
cases based on existing solutions. This section analyzes the gap of
DNS, load balancer, etc. and suggests a classification for those
problems to aid the possible identification of solution components
for addressing them.
3.1. Gap Analysis of DNS and GSLB
DNS [RFC1035] uses 'early binding' to explicitly bind from the
service identification to a network address. It uses 'geographical
location' to pick up the closest candidate and applies 'health check'
to preventing the single point failure and also realizing load
balance.
Computing resource information may be collected by DNS servers for
some static use cases, such as computing resource deployment. But it
can not meet the use cases that needs to update or adjust frequently.
For the Early binding, clients resolve IP address first and then
steer traffic accordingly to the selected edge site. Not
surprisingly, most of the time, a cached copy at the client side will
be used. The consequence is that sometimes stale info obtained a
couple of minutes ago could be used, which makes almost impractical
choose the appropriate edge site. Further, it is fairly common that
a resolver and a Load Balancer (or LB) are separate entities. The
incurred signaling flow between them introduces additional overhead
to the decision making procedure that is comprised of sequentially
resolving first and redirecting to LB second. What's more, an IP
resolution is normally at the Layer 7 and being a less-efficient app-
level decision process, e.g., the database lookup that is originally
intended for control but not data plane speed!
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For the Health check, it is designed based on infrequent periodicity
with the checking interval more than 1 second. This for sure will
lead to slow or not-timely switching over upon failure. On the other
aspect, limited computing resources at edges render it definitely
cost-prohibitive to set up any more frequent health check.
Moreover, a Load Balancer at edge usually focuses on server load to
select the 'optimal' server node first (could be virtual), and then
adopts the lowest-latency (or lowest-cost) routing to reach the
selected server (via IP address). Obviously, this type of standalone
sequential steps lacks the organic way to combine and then jointly
consider both compute/server load & routing latency (and/or cost) for
a better E2E guarantee . And the last but not least, how to obtain
necessary metrics from mattered entities for decision is also
critical .
There is also the DNS-SD[RFC6763] and Multi-cast DNS[RFC6762] that
could be used to dicover the service, which might be extended to
collect the computing information. However, in most cases, they are
used in the LAN environment. They need enhanced work and improvement
should we intend to apply them in a wider network. Moreover, the
instance selection will be pushed back to the client but rely on
decision criteria being multicast to all clients , so there is a
scalability limit. The gap of client based solution could be found
at Section 3.4.
Generally speaking, DNS is not designed for the computing information
collection. The potential enhancement could only support the limited
further usage because DNS usually takes several minutes to propagate
an update while clients in our targeted scenarios require frequent
resolution of binding. Unfortunately, updates to the mapping between
a service identifier to a service instance address cannot be pushed
quickly enough into the DNS. If DNS is enforced to meet this level
of dynamism, frequent resolving of the same service name would likely
lead to an overload of the system. These issues are also discussed
in Section 5.4 of[I-D.sarathchandra-coin-appcentres]. Some work like
CDNI[RFC7336] is also based on the DNS/HTTP redirection, which has
the similar problems and may not be suitable for CAN.
3.2. Gap Analysis of Load Balancer
A Load balancer could be seen as the external components of a
network, which is designed for and deployed in a computing domain to
support balanced load distribution. It may also be based on DNS
system and require app level query.
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For the existing load balancer solutions, there are two common ways.
One way is to deploy a single load balancer at a central location for
all service instances across different sites. It is the common way
and is the easiest to implement. However, it bears the risk of the
single point of failure. Plus, the network path from the (centrally-
located) LB to server instances at (remote) sites might not always be
optimal. The second way is to deploy an individual load balancer in
each site, with its scope of application only to service instances in
the site. It is still relatively easy to deploy. But, its main
deficiency lies in no more inter-site load balancing that could
prevent the achievement of better traffic steering across sites.
While most load-balancing solutions revolve around the egress-side
load dispatching, there exist other designs, especially in 5G mobile
networks, that conforms to the ingress-side principle by putting
distributed load balancers closer to UPFs, with either 1:1 or 1:N
mapping. Thru some higher-level coordination with a centralized
load-balance controller residing in the mobile system, the
distributed load balancers could help steer the traffic according to
the running status of UPFs. Of course, further enhancement are
needed to collect network status in order to support the joint
optimization. More details will be explored to realize the solution
and verify the feasibility.
Generally, to achieve the joint optimization of network and computing
resources, a load balancer should also learn the network path status,
which would lead to the problem of how to learn and use them in an
efficient way.
3.3. Gap Analysis of ALTO
ALTO [RFC7285]addresses the problem of selecting the 'optimal'
service instance as an off-path solution, which can be seen as an
alternative way of tackling the problem space of CAN at the
Application Layer. So in that respect, even if both ALTO and CAN
target at the common problem, they have reached different approaches;
further, they impose different needs with different assumptions on
how applications and networks may interact.
The critical aspect is the signaling latency and the control plane
load that a service-instance selection process may incur, in both on-
and off-path solutions. This in turn may impact the frequency with
which applications will query ALTO server(s), especially in the
mobile system where UEs may move to different cell sites (gNBs) or
even roam to different mobile networks that would trigger the
switchover to different network paths.
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As a result, off-path systems, e.g., ALTO, which are based on
receiving replies for applications/services before traffic could be
delivered, might not keep optimal or even valid after the handover.
So, ALTO need more improvement, including possible extension to
support multi-domain deployment, quick interaction among all involved
entities (like applications, service instances, etc.), and the
integration of more performance metric information into the system,
etc.
3.4. Gap Analysis of Message Broker
Message brokers (MBs) could be used to dispatch the incoming service
requests from clients to a suitable service instance, where such
dispatching could be controlled by service-specific metrics, such as
computing load. However, MBs will face the following adversities:
May use richer computing metrics (such as load) but may lack the
necessary network metrics.
May lead to 'middleman' adverse effects on efficiency, specifically
when it comes to additional latencies as experienced by clients due
to the extra but necessary communication with the broker. This
introduces the 'path stretch' compared to the possible direct path
between client and service instance.
Preventing the DDoS attack would be entirely limited to the cases of
service instances being hidden by the broker.
3.5. Gap Analysis of Client Based Solution
A solution that leaves the collection of computing and network
resource and further dispatching of service requests entirely to the
client itself may be possible to achieve the needed dynamism.
However, it does bear some drawbacks: e.g., the individual
destination, i.e., the network identifier for a service instance,
must be known to the client a priori for direct service dispatching.
While this may be viable for certain applications, it cannot
generally scale to a large number of clients. Furthermore, there
would exist undesirable reasons for clients to learn the identifiers
of all available service instances in a service domain.
It may be undesirable for clients to learn all available service
instance identifiers for reasons of Service Providers' being
reluctant to expose their 'valuable' information to clients.
It may be undesirable for clients to learn all available network
paths that could be obtained either directly from the operators'
exposure or indirectly by clients' self measurement.
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For scalability concern if the number of service instances and
network paths are very high.
3.6. Summary of Gap Analysis
3.6.1. Dynamicity of Relations
CAN is desired to be aware of multiple edge sites' computing resource
status, to provide the further opportunity of traffic steering based
on the specific routing decision. So the dynamicity of relations
among the multiple edge sites or service instance is the basic
attributes of the potential CAN system/functions. For the different
further using, the degree of the dynamicity may be different.
Especially the traffic steering demands a more frequently information
collection and routing decision.
The mapping from a service identifier to a specific service instance
that may execute the service request for a client usually happens
through resolving the service identification into a specific IP
address at which the service instance is reachable.
Application layer solutions can be foreseen, using an application
server to resolve the binding updates. While the viability of these
solutions will generally subject to the additional latency that is
being introduced by the resolution of the mapping via the said
application server, the potentially higher frequencies of changing
the mapping relation every a few service requests is seen as
difficult to be practical.
Moreover, we can foresee scenarios in which such relationship may
change so frequently that it occurs even at the level of each service
request. One possible factor might be the frequently changing
metrics for a decision making process, e.g., the latency and load
(metrics) as reported from all mattered service instances. Further,
the client mobility creates a natural & physical dynamics with the
consequence that a 'better' service instances may become available,
or, vice versa, the previous assignment of the client to a service
instance may turn less optimal, leading to the reduced performance
that could root in the increased latency.
Existing solutions exhibit limitations in providing the dynamic
'instance affinity'. These limitations are inherently embedded in
the solution design that is used for the mapping between a service
identifier and the address of a candidate service instance. This is
particularly noticeable upon relying on an indirection point in the
form of a resolution or load balancing server. These limitations may
result in the static 'instance stickiness' that would span many
service requests or even last for the lifetime of a client session.
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This is normally undesirable from the perspective of a service
provider in terms of achieving the best balanced request handling
across many or all possible service instances.
3.6.2. Efficiency
For different use case of further utilize the collected computing
resource information, there will be different demand to meet the
efficiency issues. If the computing resource information is used for
service deployment or joint resource management, there is no critical
latency demand for receive and refresh the information. If the
computing resource information is used for traffic steering of
service to different edge sites/service instance, it requires the
real-time or near real-time information, and the frequecy of refresh
also needs to be quick and depend on the applications' specific
demand.
The use of external resolvers, such as application layer repositories
in general, also affects the efficiency of the overall service
request. Extra signaling process is required between a client and
the resolver, possibly through application layer solutions that
result in not only more message exchanges but also increased latency
thanks to the involvement of additional resolutions. Further,
accommodating the instance affinities for a large number of short-
live client sessions will exacerbate this additional signaling
process and worsen the latencies, thus impacting the overall
efficiency of the service transactions.
Existing solutions may introduce additional latencies and
inefficiencies in packet transmission due to the need for additional
resolution steps or indirection points, and will lead to the accuracy
problems to select the appropriate edge.
3.6.3. Complexity and Accuracy
As we can see from the efficiency discussion in the previous
subsection, at the moment when external resolvers have succeeded in
collecting the necessary information and processing them to select
the edge node, the network and computing resource status may have
changed already. Accordingly, any additional control decision on
which service instance to choose and for which incoming service
request requires careful planning in order to address the potential
inefficiencies that are caused by extra latencies and path
stretching, at a minimum. Additional control plane elements, such as
brokers, are usually neither well nor optimally placed in relation to
the data path that a service request will ultimately traverse.
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Existing solutions require careful planning for the placement of
necessary control plane functions in relation to the resulting data
plane traffic to improve the accuracy; a problem often intractable in
scenarios of varying service demands.
3.6.4. Metric Exposure and Use
Some systems may use the geographical location, as deduced from an IP
prefix, to pick up the closest edge. The issue here is that
different edge sites may not be far apart in some field deployments,
which renders it hard to deduce the geo-locations from IP addresses.
Furthermore, the geo-location itself may not be the key
distinguishing metric to be considered, particularly if the
geographic co-location does not necessarily mean the congruency of
various network topologies. Also, "geographically closer" cannot
exclude those closer yet more loaded nodes, consequently leading to
possibly worse performance for the end user.
Some solutions may also perform 'health checks' on an infrequent base
(>1s) to reflect the service node status and switch over in service-
degrading or failing situations. Health checks, however,
inadequately reflect the overall computing status of a service
instance. It may therefore not reflect at all the fundamental yet
meaningful basis a suitable service instance will act upon, e.g.,
insufficiently using the number of ongoing sessions as the indicator
of load. Infrequent checks would for sure lead to too coarse
granularity to support high-accurate applications, e.g., applications
requiring mobility-induced dynamics such as the Intelligent
transportation scenario of Section 4.2 in[I-D.liu-can-ps-usecases].
Existing solutions lack the necessary information to make the right
decisions on the selection of the suitable service instance due to
the limited semantic or due to information not being exposed across
boundaries between, e.g., service and network providers.
3.6.5. Security
Resolution systems open up two dimensions of attacks, namely
attacking the mapping system itself, and attacking the service
instance directly after having been resolved. The latter is
particularly critical for a service provider with significantly
deployed service infrastructure. A resolved (global) IP address will
not only enable a (malicious) client to directly attack the
corresponding service instance, but also offer the client the
opportunity to infer (over time) information about available service
instances in the service infrastructure, which might nurture even
wider and coordinated Denial-of-Service (DoS) attacks.
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Existing solutions may expose control as well as data plane to the
possibility of a distributed Denial-of-Service attack on the
resolution system as well as service instance. Localizing the attack
to the data plane ingress point would be desirable from the
perspective of securing service request routing, which is not
achieved by existing solutions.
4. Requirements
In the following, we outline the requirements for the CAN system to
overcome the observed problems in the realization of the use cases
described in [I-D.liu-can-ps-usecases].
4.1. Support dynamic and effective selection among mutiple serivce
instances
The basic requirement of CAN is to support the dynamic access to
different service instances residing in multiple computing sites and
then being aware of their status , which is also the fundamental
model to enable the traffic steering and to further optimize the
network and computing services. A unique service identifier is used
by all the service instances for a specific service no matter which
edge site an instance may attach to. The mapping of this service
identifier to a network locator makes sure the data packet can
potentially reach any of the service instances deployed in various
edge sites.
Moreover, according to the use case stated in
[I-D.liu-can-ps-usecases], some applications require the E2E low
latency, which warrants a quick mapping of the service identifier to
the network locator. This leads to naturally the in-band methods,
involving the consideration of metrics to make the selection
mechanism either service-specific or category-specific, or both.
Therefore, a desirable system
o MUST provide a discovery and resolving methodology for the mapping
of a service identifier to a specific address.
o MUST provide an mapping methods for further quickly selecting the
service instance.
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4.2. Support Agreement on Metric Representation
Computing metrics can have many different semantics, particularly for
being service- specific. Even the notion of a "computing load"
metric could be represented in many different ways. Such
representation may entail information on the semantics of the metric
or it may be purely one or more semantic- free numerals. Agreement
of the chosen representation among all service and network elements
participating in the service-specific instance selection decision is
important. Therefore, a desirable system
o MUST agree on the service-specific metrics and their representation
among service elements in the participating edges.
o MAY include network metrics
4.3. Support Moderate Metric Distributing
Network path costs in the current routing system usually do not
change very frequently. However, computing load and service-specific
metrics in general can be highly dynamic, e.g., changing rapidly with
the number of sessions, the CPU/GPU utilization and the memory
consumption, etc. It has to be determined at what interval or based
on what events such information needs to be distributed. Overly
frequent distribution with more accurate synchronization may result
in unnecessary overhead in terms of signalling.
Moreover, depending on the service-specific decision logic, one or
more metrics will need to be conveyed in a CAN domain. Problems to
be addressed here may be the loop avoidance of any advertisement of
metrics as well as the frequency of such conveyance, thanks to the
comprehensive load that a signalling process may add to the overall
network traffic. While existing routing protocols may serve as a
baseline for signalling metrics, other means to convey the metrics
can equally be considered and even be realized. Specifically, a
desirable system
o MUST provide mechanisms to distribute the metrics
o MUST realize means for rate control for distributing of metrics
o MUST implement mechanisms for loop avoidance in distributing
metrics, when necessary
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4.4. Support Flexible Use of Metrics
Considering computing resources assigned to a service instance on a
server, which might be related to some critical metrics like the
processing delay, is crucial in addition to the network delay in some
cases, as described in[I-D.liu-can-ps-usecases]. Therefore, the CAN
components might use both the network and computing metrics for
service instance selection. For this, a computing semantic model
should be defined for the mapping selection.
We recognize that different network nodes, e.g., routers, switches,
etc., may have diversified capabilities even in the same routing
domain, let alone in different administrative domains. So, the
service-specific metrics that have been adopted by some nodes may not
be supported by others, either due to technical reasons,
administrative reasons, or something else. There exist scenarios in
which a node supporting service-specific metrics might prefer some
type of metrics to others[TR22.874]. Of course, specific metrics
might not be utilized at all in other scenarios. Hence, there must
exist flexibility in term of metrics definition and utilization for
the selection of service instance. Therefore, a desirable system
o MUST set up metric information that can be understood by CAN
components.
o MUST use network and computing metrics in a flexible way that
includes a default action for the interoperation of network nodes
which may or may not support the specific metrics.
4.5. Support Session and Service Continuity
In the CAN system, a service may be provided by one or more service
instances that would be deployed at different locations in the
network. Each instance provides equivalent service functionality to
their respective clients. The decision logic of the instance
selection are subject to the normal packet level communication and
packets are forwarded based on the operating status of both network
and computing resources. This resource status will likely change
over time, leading to individual packets potentially being sent to
different network locations, possibly segmenting individual service
transactions and breaking service-level semantics. Moreover, when a
client moves, the access point might change and successively lead to
the same result of the change of service instance. If execution
changes from one (e.g., virtualized) service instance to another,
state/context needs transfer to another. Such required transfer of
state/context makes it desirable to have session persistence (or
instance affinity) as the default, removing the need for explicit
context transfer, while also supporting an explicit state/context
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transfer (e.g., when metrics change significantly). So session as
well as service continuity must be maintained in those situations.
The nature of this continuity is highly dependent on the nature of
the specific service, which could be seen as a 'instance affinity' to
represent the relationship. The minimal affinity of a single request
represents a stateless service, where each service request may be
responded to without any state being held at the service instance for
fulfilling the request.
Providing any necessary information/state in-band as part of the
service request, e.g., in the form of a multi-form body in an HTTP
request or through the URL provided as part of the request, is one
way to achieve such stateless nature.
Alternatively, the affinity to a particular service instance may span
more than one request, as in the AR/VR example in
[I-D.liu-can-ps-usecases], where previous client input is needed to
render subsequent frames.
However, a client, e.g., a mobile UE, may have many applications
running. If all, or majority, of the applications request the CAN-
based services, then the runtime states that need to be created and
accordingly maintained would require high granularity. In the
extreme scenario, this granular requirement could reach the level of
per-UE per-APP per-(sub)flow with regard to a service instance.
Evidently, these fine-granular runtime states can potentially place a
heavy burden on network devices if they have to dynamically create
and maintain them. On the other hand, it is not appropriate either
to place the state-keeping task on clients themselves.
Besides, there might be the case that UE moves to a new (access)
network or the service instance is migrated to another cloud, which
cause the unreachable or inconvenient of the original service
instance. So the UE and service instance mobility also need to be
considered.
Therefore, a desirable system
o MUST maintain "instance affinity" which MAY span one or more
service requests, i.e., all the packets from the same application-
level flow MUST go to the same service instance unless the original
service instance is unreachable
o MUST avoid keeping fine runtime-state granularity in network nodes
for providing session and service continuity.
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o MUST provide mechanisms to minimize client side states in order to
achieve the instance affinity.
o Should support the UE and service instance mobility.
4.6. Preserve Communication Confidentiality
Exposing the information of computing resources to the network may
lead to the leakage of computing domain and application privacy. In
order to prevent it, it need to consider the methods to process the
sensitive information related to computing domain. For instance,
using general anonymous methods, including hiding the key information
representing the identification of devices, or using an index to
represent the service level of computing resources, or using
customized information exposure strategies according to specific
application requirements or network scheduling requirements. At the
same time, when anonymity is achieved, it is also necessary to
consider whether the computing information exposed in the network can
help make full use of traffic steering. Therefore, a CAN system
o MUST preserve the confidentiality of the communication relation
between user and service provider by minimizing the exposure of user-
relevant information according to user needs.
5. Conclusion
As a consequence, the problem of satisfying service-specific metrics
is challenging to allow for selecting the most suitable service
instance among a pool of instances that are available to the service
throughout the network. There are quite a number of observed
problems in existing solutions. The use cases
[I-D.liu-can-ps-usecases] as well as the categorization of the
observed problems may start the process of determining how they are
best explored within the IETF protocol suite or through suitable
extensions to that protocol suite.
This document analyzes the gap of existing solutions and presents
high-level requirements for CAN, where the architecture should
address how to model, represent, distribute and use the resource
information. How to realize appropriate instance selection and
routing actions and how to assure service continuity in a dynamic
environment, based on the holistic consideration of network and
computing metrics, are discussed.
6. Security Considerations
Section 4.6 discusses some security considerations.
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7. IANA Considerations
No IANA action is required so far.
8. Contributors
The following people have substantially contributed to this document:
Peter Willis
BT
Markus Amend
Deutsche Telekom
Markus.Amend@telekom.de
9. Informative References
[RFC4786] Abley, J. and K. Lindqvist, "Operation of Anycast
Services", BCP 126, RFC 4786, DOI 10.17487/RFC4786,
December 2006, <https://www.rfc-editor.org/info/rfc4786>.
[RFC1035] Mockapetris, P., "Domain names - implementation and
specification", STD 13, RFC 1035, DOI 10.17487/RFC1035,
November 1987, <https://www.rfc-editor.org/info/rfc1035>.
[RFC6762] Cheshire, S. and M. Krochmal, "Multicast DNS", RFC 6762,
DOI 10.17487/RFC6762, February 2013,
<https://www.rfc-editor.org/info/rfc6762>.
[RFC6763] Cheshire, S. and M. Krochmal, "DNS-Based Service
Discovery", RFC 6763, DOI 10.17487/RFC6763, February 2013,
<https://www.rfc-editor.org/info/rfc6763>.
[RFC7285] Alimi, R., Ed., Penno, R., Ed., Yang, Y., Ed., Kiesel, S.,
Previdi, S., Roome, W., Shalunov, S., and R. Woundy,
"Application-Layer Traffic Optimization (ALTO) Protocol",
RFC 7285, DOI 10.17487/RFC7285, September 2014,
<https://www.rfc-editor.org/info/rfc7285>.
[RFC7336] Peterson, L., Davie, B., and R. van Brandenburg, Ed.,
"Framework for Content Distribution Network
Interconnection (CDNI)", RFC 7336, DOI 10.17487/RFC7336,
August 2014, <https://www.rfc-editor.org/info/rfc7336>.
[I-D.liu-can-ps-usecases]
Liu, P., Eardley, P., Trossen, D., Boucadair, M.,
Contreras, L. M., Li, C., and Y. Li, "Computing-Aware
Networking (CAN) Problem Statement and Use Cases", Work in
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Progress, Internet-Draft, draft-liu-can-ps-usecases-00, 23
October 2022,
<https://datatracker.ietf.org/api/v1/doc/document/draft-
liu-can-ps-usecases/>.
[I-D.sarathchandra-coin-appcentres]
Trossen, D., Sarathchandra, C., and M. Boniface, "In-
Network Computing for App-Centric Micro-Services", Work in
Progress, Internet-Draft, draft-sarathchandra-coin-
appcentres-04, 26 January 2021,
<https://www.ietf.org/archive/id/draft-sarathchandra-coin-
appcentres-04.txt>.
[I-D.contreras-alto-service-edge]
Luis Contreras, M., Lachos, D. A., Rothenberg, C. E., and
S. Randriamasy, "Use of ALTO for Determining Service
Edge", Work in Progress, Internet-Draft, draft-contreras-
alto-service-edge-05, 11 July 2022,
<https://www.ietf.org/archive/id/draft-contreras-alto-
service-edge-05.txt>.
[TR22.874] 3GPP, "Study on traffic characteristics and performance
requirements for AI/ML model transfer in 5GS (Release
18)", 2020.
Acknowledgements
The author would like to thank Yizhou Li, Luigi IANNONE, Kaibin Zhang
and Geng Liang for their valuable suggestions to this document.
Authors' Addresses
Peng Liu
China Mobile
Email: liupengyjy@chinamobile.com
Tianji Jiang
China Mobile
Email: jiangtianji@chinamobile.com
Philip Eardley
Email: ietf.philip.eardley@gmail.com
Dirk Trossen
Huawei Technologies
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Email: dirk.trossen@huawei.com
Cheng Li
Huawei Technologies
Email: c.l@huawei.com
Guangping Huang
ZTE
Email: huang.guangping@zte.com.cn
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