RTGWG D.H. Daniel
Internet-Draft ZTE Corporation
Intended status: Standards Track Z.P.D. Zongpeng
Expires: 9 January 2023 China Mobile
C.Z. Chen
Purple Moutain Laborotary
8 July 2022
Hierarchical segment routing solution of CAN
draft-huang-two-segment-routing-solution-of-can-00
Abstract
CAN (Computing Aware Network) is designed to enable the routing
network to be aware of computing status thus deliver the service flow
accordingly. Nevertheless, computing and networking is quite
different in terms of resource granularity as well as its status
stability. It would gain significant benefits to accommodate the
computing status to that of networking by employing a hierarchical
computing routing segment scheme. The network-accommodated computing
status could be maintained at remote CAN nodes while the rest could
reside at local CAN nodes. By enabling the network to schedule and
route computing services in a compatible way with the current IP
routing network, CAN would bring benefits to the industry by both
efficiently pooling the computing resources and rendering services
through perspective of converged networking and computing.
Status of This Memo
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This Internet-Draft will expire on 9 January 2023.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Two-segment CAN routing solution . . . . . . . . . . . . . . 4
3.1. Hierarchical granularity routing scheme . . . . . . . . . 5
3.2. Two-segment routing and forwarding . . . . . . . . . . . 6
3.3. Cross-domain computing routing and forwarding . . . . . . 7
3.4. CSI routing . . . . . . . . . . . . . . . . . . . . . . . 7
3.5. Traffic affinity . . . . . . . . . . . . . . . . . . . . 8
4. Hierarchical CAN computing status update work flow . . . . . 8
4.1. Computing resource and service update work flow . . . . . 8
4.2. Service flow routing and forwarding work flow . . . . . . 9
5. Control plane . . . . . . . . . . . . . . . . . . . . . . . . 9
5.1. Centralized control plane . . . . . . . . . . . . . . . . 9
5.2. Distributed control plane . . . . . . . . . . . . . . . . 9
5.3. Hybrid control plane . . . . . . . . . . . . . . . . . . 10
6. Data plane . . . . . . . . . . . . . . . . . . . . . . . . . 10
6.1. CSI encapsulation . . . . . . . . . . . . . . . . . . . . 10
6.2. CSI for CAN-R, CAN-M and CAN-L . . . . . . . . . . . . . 10
7. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 11
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
10. Security Considerations . . . . . . . . . . . . . . . . . . . 11
11. Informative References . . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
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1. Introduction
Computing-related services have been provided in such a way that
computing resources either are confined within isolated sites (data
centers, MECs etc.) without coordination among multiple sites or they
are coordinated and managed within specific and closed service
systems without fine-grained networking facilitation, while the
industry develops into an era in which the computing resources start
migrating from centralized data centers to distributed edge nodes.
Therefore substantial benefits in light of both cost and efficiency
resulting from scale of economy, would be brought into multiple
industries by intelligently and dynamically connecting the
distributed computing resources and rendering the coordinated
computing resources as a unified and virtual resource pool. On top
of the cost and efficiency gains, applications as well as services
would be served in a more sophisticated way in which computing and
networking resources could be aligned more efficiently and agilely
than conventional way in which the two are delivered in separate
systems.Some impressive drafts such as [I-D.liu-dyncast-ps-usecases]
and [I-D.li-dyncast-architecture] analyze the benefits of routing
related solution, and give the reference architecture and preliminary
test results. End applications could be served not only by fine-
grained computing services but also fine-grained networking services
rather than the best-effort networking services without routing
network involved otherwise. The cost is the burden of maintaining
and sensing computing resource status in the networking layer. The
proposal is designed to be as much smoothly compatible with the
ongoing routing architecture as possible.
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 [RFC2119].
2. Terminology
* CAN Remote Node (CAN-R): routing node maintaining computing
resource as well as service status from remote cloud sites, and
executing the cross-site routing policies in terms of the
aforementioned status as well as the identification of computing
service. CAN-R usually resides at the network edge and works as
ingress of the end to end computing service flow.
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* CAN Local Node (CAN-L): routing node maintaining computing
resource as well as service status from the geographically local
cloud sites and being responsible for the last hop of the service
flow towards the computing service instance in the specific cloud
site. CAN-L usually resides at the network edge and works as
egress of the end to end computing service flow.
* CAN Mid Node (CAN-M): routing node unaware of computing resource
and service status and disregarding encapsulation of the
identification of computing service. CAN-M usually resides
between CAN-R and CAN-L and works as ordinary routing nodes.
* Global Computing Resource and Service Status (GCRS): General cloud
site status of the computing resource and service which consists
of overall resource occupation and types of computing service
(algorithms, functions etc.) the specific cloud site provides.
GCRS is maintained at CAN-R and expected to remain relatively
stable and change in slow frequency.
* Local Computing Resource and Service Status (LCRS): fine-grained
cloud site status of the computing resource and service which
consists of status of each active computing service instance as
well as its parameters which impact the way the instance would be
selected and visited by CAN-L. LCRS is maintained at CAN-L and
expected to stay quite active and change in high frequency.
* Computing Service Identification (CSI): a globally unique
identification of a computing service with optional parameters,
and it could be an IPv6-like address or specifically designed
identification structure.
* Instantiated Computing Service (ICS): an active instance of a
computing service identification which resides in a host usually
purporting to a server, container or virtual machine.
3. Two-segment CAN routing solution
Routing network is enabled sensing the computing resource and service
from the cloud sites and routing the service flow according to both
network and computing status as illustrated in figure 1. The
proposed solution is a horizontal convergence of cloud and network,
while the latter maintains the converged resource status and thus is
able to achieve an end to end routing and forwarding policy from a
perspective of cloud and network resource. PE1 maintains GCRS with a
whole picture of the multiple cloud sites, and executes the routing
policy for the network segment between PE1 and PE2 or PE3, namely
between CAN-R and CAN-L, while PE2 maintains LCRS with a focus
picture of the cloud site where S1 resides, and establishes a
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connection towards S1. S1 is an active instance of a specific
computing service type (CSI). On top of the role of CAN-L which
maintains LCRS, PE2 and PE3 also fulfill the role CAN-R which
maintains GCRS from neighboring cloud sites. P provides traditional
routing and forwarding functionality for computing service flow, and
remains unaware of any computing-related status as well as CSI
encapsulations.
+--------+ +--------+
+------>|CAN-R/L |------->| ICS |
| +--------+ +--------+
+--------+ +--------+ | PE2 S1
|CAN-R |--->| CAN-M |--+
+--------+ +--------+ | PE3 S2
PE1 P | +--------+ +--------+
+------>|CAN-R/L |------->| ICS |
+--------+ +--------+
|<------------ Network domain --------------->|<--Computing->|
domain
Figure 1
3.1. Hierarchical granularity routing scheme
Status updates of computing resource and service in the cloud sites
extend in a quite broad range from relatively stable service types
and overall resource occupation to extremely dynamic capacity changes
as well as busy and idle cycle of service instances. It would be a
disaster to build all of the status updates in the network layer
which would bring overburdened and volatile routing tables and ruined
its stability.
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It should be reasonable to divide the wide range of computing
resource and services into different categories with differentiated
characteristics from routing perspective. GCRS and LCRS correspond
to cross-site domain and local site domain respectively, and GCRS
aggregates the computing resource and service status with low update
frequency from multiple cloud sites while LCRS focuses only upon the
status with high frequency in the local sites. Under this two-
granularity scheme, computing-related routing table of GCRS in the
CAN-R remains in a position roughly as stable as the traditional
routing table, and the LCRS in the CAN-L maintains a near
synchronized state table of the highly dynamic updates of computing
service instances in the local cloud site. Nonetheless, LCRS
focusing upon a single and local cloud site is the normal case while
upon multiple sites should be exemption if not impossible.
3.2. Two-segment routing and forwarding
When it comes to end to end service flow routing and forwarding,
there is an status information gap between GCRS and LCRS, therefore a
two-segment mechanism has to be in place in line with the two-
granularity routing scheme demonstrated in 3.1. As is illustrated in
figure 2, R1 as an ingress determines the specific service flow's
egress which turns out to be R2 according to policy calculation from
GCRS. In particular, the CSI from both in-band (user plane) and out-
band (control plane) is the only index for R1 to calculate and
determine the egress, it's highly possible to make this egress
calculation in terms of both networking (bandwidth, latency etc) and
computing Service Agreement Level. Nevertheless, the two SLA routing
optimization could be decoupled to such a degree that the traditional
routing algorithms could remain as they are. The convergence of the
SLA policies as well as the methods to make CAN-R aware of the two
SLA is out of scope of this proposal.
+--------+ +--------+ +--------+ +--------+
| GCRS |--->| |--->| LCRS |--->| ICS |
+--------+ +--------+ +--------+ +--------+
R1 R
|<---------- GCRS segment ---------->|<- LCRS ->|
segment
Figure 2
When the service flow arrives at R2 which terminates the GCRS segment
routing and determines S1 which is the service instance selected
according to LCRS maintained at R2. Again CSI is the only index for
LCRS segment routing process.
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3.3. Cross-domain computing routing and forwarding
Co-ordinated computing resource scheduling among multiple regions
which are usually connected by multiple network domains, as
illustrated in section 1, is an important part of intended scenarios
with regard to why computing-based scheduling and routing is proposed
in the first place. The two-segment routing and forwarding scheme
illustrated in 3.2 is a typical use case of cross-domain computing
routing and forwarding and a good building block for the full-domain
scenario solution. Computing status information is brought into
network domain to enable the latter scheduling routing policies
beyond network. However, a particular scheme has to be put in place
to ensure mild and acceptable impacts upon the ongoing IP routing
scheme. A consistent CSI across terminal, network (multiple domains)
and cloud along with hierarchical CSI-associated computing resource
and service status which corresponds with different network domains,
is the enhanced full-domain routing and forwarding solution. Each
domain maintains a corresponding computing resource and service
status at its edge node and makes the computing-based routing for the
domain-related segment which should be connected by the neighboring
segments.
3.4. CSI routing
CSI encapsulated in the headers and maintained in LCRS and GCRS
indicates an abstract service type rather than a geographically
explicit destination label, thus the routing scheme based upon CSI is
actually a two-part and two-layer process in which CSI only indicates
the routing intention of user's requested computing service type
where routing does not actually materialize in forwarding plane and
the explicit routing destination would be determined by LCRS and
GCRS. Therefore the actual routing falls within the traditional
routing scheme which remains intact.
Apart from the indication of computing service routing intention, CSI
could also indicates a specific network service requirements by
associating the networking service policy indexed by the routing
table of the CAN control plane which would therefore schedule the
network resources such as an SR tunnel, guaranteed bandwidth etc.
Therefore, GCRS and LCRS in control plane along with CSI
encapsulation in user plane enables an logical computing routing sub-
layer which is able to be aware of the computing from cloud sites and
forward the service flow in terms of computing resources as well as
networking resources. Nevertheless, this logical sub-layer remains
only relevant at CAN-R and CAN-L and is simply about computing nodes
selection rather than executing the actual forwarding and routing
actions.
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3.5. Traffic affinity
CSI holds the only semantics of the service type that could be
deployed as multiple instances within specific cloud site or across
multiple cloud sites, CSI is not explicit enough for all of the
service flow packets to be forwarded to a specific destination.
Traffic affinity has to be guaranteed at both CAN-R and CAN-L. Once
the egress is determined at CAN-R, the binding relationship between
the egress and the service flow's unique identification (5-tuple or
other specifically designed labels) is maintained and the subsequent
flow could be forwarded upon this binding table. Likewise CAN-L
maintains the binding relationship between the service flow
identification and the selected service instance.
Traffic affinity could be guaranteed by mechanisms beyond routing
layer, but they will not be in the scope of this proposal.
4. Hierarchical CAN computing status update work flow
4.1. Computing resource and service update work flow
The full range of computing resource and service status from a
specific cloud site is registered at CAN-L which maintains LCRS in
itself and notifies the part of GCRS to remote CAN-R where GCRS would
be thus maintained and updated. As is illustrated in Figure 3, CAN-R
in R1 from site1 and site 2 is updated by R2 and R3, while LCRS of
site 1 in R2 is updated by S1 and LCRS of site 2 in R3 is updated by
S2. GCRS in R2 and R3 is updated by each other. Edge routers
associating with local cloud site establish a mesh fabric to update
the according GCRS among the whole network domain, the computing
resource and services in distributed cloud sites thus are connected
and could be utilized as a single pool for the applications rather
than the isolated islands.
+--------+ +--------+
+---------------------------|CAN-R/L |<-------| ICS |
| +--------+ +--------+
+-----V--+ +--------+ A R2 | S1
|CAN-R | | CAN-M | | |
+-----A--+ +--------+ | R3 V S2
R1 | R +--------+ +--------+
+---------------------------|CAN-R/L |<-------| ICS |
+--------+ +--------+
|<--------- GCRS update domain ----------->|<-----LCRS------>|
domain
Figure 3
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4.2. Service flow routing and forwarding work flow
From perspective of the service work flow, more details have actually
been demonstrated in 3.2 and 3.3. Rather than the traditional
destination-oriented routing mechanism and the segment routing in
which the ingress router is explicitly aware of a specific
destination, CSI as an abstract label without semantics of physical
address works as the required destination from viewpoint of the user
in terms of the intended computing service. Therefore the service
flow has to be routed and forwarded segment by segment in which the
two segment destinations are determined by GCRS and LCRS
respectively.
5. Control plane
5.1. Centralized control plane
LCRS's volatility makes it infeasible to be maintained and controlled
in a centralized entity, GCRS is the chief computing resource and
service status information to be collected and managed in the
controller when it comes to centralized control plane. Routing and
forwarding policies from GCRS calculated in the centralized
controller, as is demonstrated in 3.2, apply only to the segment from
CAN-R to CAN-L, while the second segment routing policy from CAN-L to
the selected service instance in the cloud site is determined by LCRS
at egress.
Hierarchically centralized control plane architecture would be
strongly recommended under the circumstances of nationwide network
and cloud management.
5.2. Distributed control plane
GCRS is updated among the edge routers which have been connected in a
mesh way that each pair of edge routers could exchange GCRS to each
other, while LCRS will be unidirectionally updated from cloud site to
the associated CAN-L in which LCRS is maintained and its update
process is terminated.
Protocol consideration upon which GCRS and LCRS is updated is out of
the scope of this proposal and will be illustrated in future drafts.
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5.3. Hybrid control plane
It should be more efficient to update the GCRS by a distributed way
than a centralized way in terms of routing request and response in a
limited network and cloud domain, but be the opposite case in a
nationwide circumstance. This is how hybrid control plane could be
deployed in such a scheme that overall optimization is achieved.
6. Data plane
6.1. CSI encapsulation
Computing service identification is the predominant index across the
entire computing delivery in routing network architecture under which
a new virtual routing sub-layer is employed with CSI working as the
virtual destination. Data plane indicates the routing and forwarding
orientation with CSI by inquiring GCRS and LCRS at CAN-R and CAN-L
respectively. CSI encapsulation could be achieved by extending the
existing packet header and also achieved by designing a dedicated
shim layer, which along with the specific structure of CSI are out of
the scope of this proposal and will be illustrated in future draft.
6.2. CSI for CAN-R, CAN-M and CAN-L
CAN-R encapsulates CSI in a designated header format as a proxy by
translating the user-originated CSI format, and makes the first
segment routing policy and starts routing and forwarding the service
traffic. CAN-M ignores CSI and simply forwards the traffic as usual.
CAN-L decapsulates CSI and makes the second segment routing policy
and completes the last hop routing and forwarding.
7. Summary
It would significantly benefit the industry by connecting and
coordinating the distributed computing resources and services and
more so by further converging networking and computing resource.
Uncertainty and the potential impacts over the ongoing network
architecture is the main reason for the community to think twice. By
classifying the end to end routing and forwarding path into two
segments, the impacts from computing status are to be reduced to a
degree they would be as acceptable and comfortable enough as they are
as networking status. In particular, employment of CSI enables a new
service routing solution perfectly compatible with the ongoing
routing architecture.
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8. Acknowledgements
To be added upon contributions, comments and suggestions.
9. IANA Considerations
This memo includes no request to IANA.
10. Security Considerations
As information originated from the third party (cloud sites), both
GCRS and LCRS would be frequently updated in the network domain, both
security threats against the routing mechanisms and credibility and
security issues of the computing services should be taken into
account by architecture designing. The detailed analysis as well as
solution consideration will be proposed in the updated version of the
draft.
11. Informative References
[I-D.li-dyncast-architecture]
Li, Y., "Dynamic-Anycast Architecture", February 2021,
.
[I-D.liu-dyncast-ps-usecases]
Liu, Peng., "Dynamic-Anycast (Dyncast) Use Cases and
Problem Statement", February 2021,
.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
.
Authors' Addresses
Daniel Huang
ZTE Corporation
Nanjing
Phone: +86 13770311052
Email: huang.guangping@zte.com.cn
Zongpeng Du
China Mobile
Beijing
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Phone: +86 13811071289
Email: duzongpeng@chinamobile.com
Chen Zhang
Purple Moutain Laborotary
Nanjing
Phone: +86 15300249211
Email: zhangchen@pmlabs.com.cn
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