Computing-Aware Traffic Steering (CATS) Problem Statement, Use Cases and Requirements

3.1. Multi-deployment of Edge Sites and Service

Since edge computing aims at a closer computing service based on the shorter network path, there will be more than one edge sites with the same application in the city/province/state, a number of representative cities have deployed multi-edge sites and the typical applications, and there are more edge sites to be deployed in the future. Before deploying edge sites, there are some factors need to be considered, such as:¶

o The exsiting infrastructure capacities, which could be used to update to edge sites, e.g. operators' machine room.¶

o The amount and frequency of computing resource that is needed.¶

o The network resource status linked to computing resource.¶

When the edge sites are deployed, to improve the effectiveness of service deployment, the problem of how to choose optimal edge node to deploy services needs to be solved. More stable static information should be considered in service deployment, [I-D.contreras-alto-service-edge] introduces the consideration of depoly applications or functions to the edge, such as the type of instance, compute flavor of CPU/GPU, etc, optional storage extension, optional hardware acceleration characteristics. Besides those, more network and service factors may be considered, such as:¶

o Network and computing resource topology: the overall consideration of network access, connectivity, path protection or redundancy. and the location and overall distribution of computing resources in network, and the relative position towards network topology.¶

o Location: the number of users brought, the differentiation of service types and number of connections requested by users, etc. For edge nodes located in popular area, which with large amount of users and service requests, the service duplication can be deployed more than other areas.¶

o Capacity of multiple edge nodes: not only a single node, but also the total number of requests that can be processed by the resource pool composed of multiple nodes¶

o Service category: For example, whether the business is multi-user interaction, such as video conferencing, games, or just resource acquisition, such as short video viewing Alto can help to obtain one or more of the above information, so as to provide suggestions or formulate principles and strategies for service deployment.¶

For the collection of those information, it could periodically collects the total consumption of computing resources, or the total number of sessions accessed, to notify where to deploy more VMS or containers. Unlike the scheduling of request, service deployment should still follow the principle of proximity. The more local access, the more resources should be deployed. If the resources are insufficient, the operator can be informed to increase the hardware resources.¶

3.2. Traffic Steering among Edges Sites and Service Instances

This section shows the necessity of traffic steering among different edges in the real city, considering the mobility of the people in different time slot, events, etc.¶

Traffic needs to be steered to the appropriate edge sites to ensure the application demands. Though the computing resource and network resource are considered when deploy the edge sites and service, but the reference resource information are more static, which can't meet the real-time or near real-time service request. That is, in some cases, the ‘closest’ is not the ‘best’, there will be the variable statues of computing and network could be summarized as:¶

o Closest site may not have enough resource, the load may dynamically change.¶

o Closest site may not have related resource, heterogeneous hardware in different sites.¶

Therefore, more enhancement based on edge computing is need. Because for edge computing, the service request always be steered to the closest edge site.¶

We assume that clients access one or more services with an objective to meet a desired user experience. Each participating service may be realized at one or more places in the network (called, service instances). Such service instances are instantiated and deployed as part of the overall service deployment process, e.g., using existing orchestration frameworks, within so-called edge sites, which in turn are reachable through a network infrastructure via an edge router.¶

When a client issues a service request to a required service, the request is being steered to one of the available service instances. Each service instance may act as a client towards another service, thereby seeing its own outbound traffic steered to a suitable service instance of the request service and so on, achieving service composition and chaining as a result.¶

The aforementioned selection of one of candidate service instances is done using traffic steering methods , where the steering decision may take into account pre-planned policies (assignment of certain clients to certain service instances), realize shortest-path to the 'closest' service instance, or utilize more complex and possibly dynamic metric information, such as load of service instances, latencies experienced or similar, for a more dynamic selection of a suitable service instance.¶

It is important to note that clients may move throughout the execution of a service, which may, as a result, position other service instance 'better' in terms of latency, load, or other metrics. This creates a (physical) dynamicity that will need to be catered for.¶

Figure 1 shows a common way to deploy edge sites in the metro. There is an edge data center for metro area which has high computing resource and provides the service to more UEs at the working time. Because more office buildings are in the Metro area. And there are also some remote edge sites which have limited computing resource and provide the service to the UEs closed to them.¶

The application such as the AR/VR, video recognition could be deployed in both the edge data center in metro area and the remote edge sites. In this case, the service request and the resource are matched well. Some potential traffic steering may needed just for special service request or some small scheduling demand.¶

     +----------------+    +---+                  +------------+
   +----------------+ |- - |UE1|                +------------+ |
   | +-----------+  | |    +---+             +--|    Edge    | |
   | |Edge server|  | |    +---+       +- - -|PE|            | |
   | +-----------+  | |- - |UE2|       |     +--|   Site 1   |-+
   | +-----------+  | |    +---+                +------------+
   | |Edge server|  | |     ...        |            |
   | +-----------+  | +--+         Potencial      +---+ +---+
   | +-----------+  | |PE|- - - - - - -+          |UEa| |UEb|
   | |Edge server|  | +--+         Steering       +---+ +---+
   | +-----------+  | |    +---+       |                  |
   | +-----------+  | |- - |UE3|                  +------------+
   | |  ... ...  |  | |    +---+       |        +------------+ |
   | +-----------+  | |     ...              +--|    Edge    | |
   |                | |    +---+       +- - -|PE|            | |
   |Edge data center|-+- - |UEn|             +--|   Site 2   |-+
   +----------------+      +---+                +------------+
   High computing resource              Limited computing resource
   and more UE at Metro area            and less UE at Remote area

Figure 2 shows that when it goes to non working time, for example at weekend or daily night, more UEs move to the remote area that are close to their house or for some weekend events. So there will be more service request at remote but with limited computing resource, while the rich computing resource might not be used with less UE in the Metro Area. It is possible for so many people request the AR/VR service at remote are but with the limited computing resource, moreover, as the people move from the metro area to the remote are, the edge sites served the common service such as intelligent transportation will also change, so it need to steer some traffic back to Metro center.¶

     +----------------+                           +------------+
   +----------------+ |                         +------------+ |
   | +-----------+  | |  Steering traffic    +--|    Edge    | |
   | |Edge server|  | |          +-----------|PE|            | |
   | +-----------+  | |    +---+ |           +--|   Site 1   |-+
   | +-----------+  | |- - |UEa| |    +----+----+-+----------+
   | |Edge server|  | |    +---+ |    |           |           |
   | +-----------+  | +--+       |  +---+ +---+ +---+ +---+ +---+
   | +-----------+  | |PE|-------+  |UE1| |UE2| |UE3| |...| |UEn|
   | |Edge server|  | +--+       |  +---+ +---+ +---+ +---+ +---+
   | +-----------+  | |    +---+ |          |           |
   | +-----------+  | |- - |UEb| |          +-----+-----+------+
   | |  ... ...  |  | |    +---+ |              +------------+ |
   | +-----------+  | |          |           +--|    Edge    | |
   |                | |          +-----------|PE|            | |
   |Edge data center|-+  Steering traffic    +--|   Site 2   |-+
   +----------------+                           +------------+
   High computing resource              Limited computing resource
   and less UE at Metro area            and more UE at Remote area

There will also be the common variable of network and computing resources, for someone who is not moving but get a poor latency sometime. Because of other UEs’ moving, a large number of request for temporary events such as vocal concert, shopping festival and so on, and there will also be the normal change of the network and computing resource status. So for some fixed UEs, it is also expected to steer the traffic to appropriate sites dynamiclly.¶

Those problems indicate that traffic needs to be steered among different edge sites, because of the mobility of the UE and the common variable of network and computing resources. Moreover, some apps in the following Section require both low latency and high computing resource usage or specific computing HW capabilities (such as local GPU); hence joint optimization of network and computing resource is needed to guarantee the QoE.¶

4.1. Computing-Aware AR or VR

Cloud VR/AR services are used in some exhibitions, scenic spots, and celebration ceremonies. In the future, they might be used in more applications, such as industrial internet, medical industry, and meta verse.¶

Cloud VR/AR introduces the concept of cloud computing to the rendering of audiovisual assets in such applications. Here, the edge cloud helps encode/decode and render content. The end device usually only uploads posture or control information to the edge and then VR/AR contents are rendered in the edge cloud. The video and audio outputs generated from the edge cloud are encoded, compressed, and transmitted back to the end device or further transmitted to central data center via high bandwidth networks.¶

Edge sites may use CPU or GPU for encode/decode. GPU usually has better performance but CPU is simpler and more straightforward to use as well as possibly more widespread in deployment. Available remaining resources determines if a service instance can be started. The instance's CPU, GPU and memory utilization has a high impact on the processing delay on encoding, decoding and rendering. At the same time, the network path quality to the edge site is a key for user experience of quality of audio/ video and input command response times.¶

A Cloud VR service, such as a mobile gaming service, brings challenging requirements to both network and computing so that the edge node to serve a service request has to be carefully selected to make sure it has sufficient computing resource and good network path. For example, for an entry-level Cloud VR (panoramic 8K 2D video) with 110-degree Field of View (FOV) transmission, the typical network requirements are bandwidth 40Mbps, 20ms for motion-to-photon latency, packet loss rate is 2.4E-5; the typical computing requirements are 8K H.265 real-time decoding, 2K H.264 real-time encoding. We can further divide the 20ms latency budget into:¶

(i) sensor sampling delay(client), which is considered imperceptible by users is less than 1.5ms including an extra 0.5ms for digitalization and end device processing.¶

(ii) display refresh delay(client), which take 7.9ms based on the 144Hz display refreshing rate and 1ms extra delay to light up.¶

(iii) image/frame rendering delay(server), which could be reduced to 5.5ms.¶

(iv) network delay(network), which should be bounded to 20-1.5-5.5-7.9 = 5.1ms.¶

So the the budgets for server(computing) delay and network delay are almost equivalent, which make sense to consider both of the delay for computing and network. And it can’t meet the total delay requirements or find the best choice by either optimize the network or computing resource.¶

Based on the analysis, here are some further assumption as figure 3 shows, the client could request any service instance among 3 edge sites. The delay of client could be same, and the differences of differente edge sites and corresponding network path has different delays:¶

o Edge site 1: The computing delay=4ms based on a light load, and the corresponding network delay=9ms based on a heavy traffic.¶

o Edge site 2: The computing delay=10ms based on a heavy load, and the corresponding network delay=4ms based on a light traffic.¶

o Edge site 3: The edge site 3's computing delay=5ms based on a normal load, and the corresponding network delay=5ms based on a normal traffic.¶

In this case, we can't get a optimal network and computing total delay if choose the resource only based on either of computing or network status:¶

o If choosing the edge site based on the best computing delay it will be the edge site 1, the E2E delay=22.4ms.¶

o If choosing the edge site based on the best network delay it will be the edge site 2, the E2E delay=23.4ms.¶

o If choosing the edge site based on both of the status it will be the edge site 3, the E2E delay=19.4ms.¶

So, the best choice to ensure the E2E delay is edge site 3, which is 19.4ms and is less than 20ms. The differences of the E2E delay is only 3~4ms among the three, but some of them will meet the application demand while some doesn't.¶

The conclusion is that it requires to dynamically steer traffic to the appropriate edge to meet the E2E delay requirements considering both network and computing resource status. Moreover, the computing resources have a big difference in different edges, and the ‘closest site’ may be good for latency but lacks GPU support and should therefore not be chosen.¶

     Light Load          Heavy Load           Normal load
   +------------+      +------------+       +------------+
   |    Edge    |      |    Edge    |       |    Edge    |
   |   Site 1   |      |   Site 2   |       |   Site 3   |
   +-----+------+      +------+-----+       +------+-----+
computing|delay(4ms)          |           computing|delay(5ms)
         |           computing|delay(10ms)         |
    +----+-----+        +-----+----+         +-----+----+
    |  Egress  |        |  Egress  |         |  Egress  |
    | Router 1 |        | Router 2 |         | Router 3 |
    +----+-----+        +-----+----+         +-----+----+
  newtork|delay(9ms)   newtork|delay(4ms)   newtork|delay(5ms)
         |                    |                    |
         |           +--------+--------+           |
         +-----------|  Infrastructure |-----------+
                     +--------+--------+
                              |
                         +----+----+
                         | Ingress |
         +---------------|  Router |--------------+
         |               +----+----+              |
         |                    |                   |
      +--+--+              +--+---+           +---+--+
    +------+|            +------+ |         +------+ |
    |Client|+            |Client|-+         |Client|-+
    +------+             +------+           +------+
                   clien delay=1.5+7.9=9.4ms

Furthermore, specific techniques may be employed to divide the overall rendering into base assets that are common across a number of clients participating in the service, while the client-specific input data is being utilized to render additional assets. When being delivered to the client, those two assets are being combined into the overall content being consumed by the client. The requirements for sending the client input data as well as the requests for the base assets may be different in terms of which service instances may serve the request, where base assets may be served from any nearby service instance (since those base assets may be served without requiring cross-request state being maintained), while the client-specific input data is being processed by a stateful service instance that changes, if at all, only slowly over time due to the stickiness of the service that is being created by the client-specific data. Other splits of rendering and input tasks can be found in[TR22.874] for further reading.¶

When it comes to the service instances themselves, those may be instantiated on-demand, e.g., driven by network or client demand metrics, while resources may also be released, e.g., after an idle timeout, to free up resources for other services. Depending on the utilized node technologies, the lifetime of such "function as a service" may range from many minutes down to millisecond scale. Therefore computing resources across participating edges exhibit a distributed (in terms of locations) as well as dynamic (in terms of resource availability) nature. In order to achieve a satisfying service quality to end users, a service request will need to be sent to and served by an edge with sufficient computing resource and a good network path.¶

4.2. Computing-Aware Intelligent Transportation

For the convenience of transportation, more video capture devices are required to be deployed as urban infrastructure, and the better video quality is also required to facilitate the content analysis. Therefore, the transmission capacity of the network will need to be further increased, and the collected video data need to be further processed, such as for pedestrian face recognition, vehicle moving track recognition, and prediction. This, in turn, also impacts the requirements for the video processing capacity of computing nodes.¶

In auxiliary driving scenarios, to help overcome the non-line-of- sight problem due to blind spot or obstacles, the edge node can collect comprehensive road and traffic information around the vehicle location and perform data processing, and then vehicles with high security risk can be warned accordingly, improving driving safety in complicated road conditions, like at intersections. This scenario is also called "Electronic Horizon", as explained in[HORITA]. For instance, video image information captured by, e.g., an in-car, camera is transmitted to the nearest edge node for processing. The notion of sending the request to the "nearest" edge node is important for being able to collate the video information of "nearby" cars, using, for instance, relative location information. Furthermore, data privacy may lead to the requirement to process the data as close to the source as possible to limit data spread across too many network components in the network.¶

Nevertheless, load at specific "closest" nodes may greatly vary, leading to the possibility for the closest edge node becoming overloaded, leading to a higher response time and therefore a delay in responding to the auxiliary driving request with the possibility of traffic delays or even traffic accidents occurring as a result. Hence, in such cases, delay-insensitive services such as in-vehicle entertainment should be dispatched to other light loaded nodes instead of local edge nodes, so that the delay-sensitive service is preferentially processed locally to ensure the service availability and user experience.¶

In video recognition scenarios, when the number of waiting people and vehicles increases, more computing resources are needed to process the video content. For rush hour traffic congestion and weekend personnel flow from the edge of a city to the city center, efficient network and computing capacity scheduling is also required. Those would cause the overload of the nearest edge sites if there is no extra method used, and some of the service request flow might be steered to others edge site except the nearest one.¶

4.3. Computing-Aware Digital Twin

A number of industry associations, such as the Industrial Digital Twin Association or the Digital Twin Consortium (https://www.digitaltwinconsortium.org/), have been founded to promote the concept of the Digital Twin (DT) for a number of use case areas, such as smart cities, transportation, industrial control, among others. The core concept of the DT is the "administrative shell" [Industry4.0], which serves as a digital representation of the information and technical functionality pertaining to the "assets" (such as an industrial machinery, a transportation vehicle, an object in a smart city or others) that is intended to be managed, controlled, and actuated.¶

As an example for industrial control, the programmable logic controller (PLC) may be virtualized and the functionality aggregated across a number of physical assets into a single administrative shell for the purpose of managing those assets. PLCs may be virtualized in order to move the PLC capabilities from the physical assets to the edge cloud. Several PLC instances may exist to enable load balancing and fail-over capabilities, while also enabling physical mobility of the asset and the connection to a suitable "nearby" PLC instance. With this, traffic dynamicity may be similar to that observed in the connected car scenario in the previous sub-section. Crucial here is high availability and bounded latency since a failure of the (overall) PLC functionality may lead to a production line stop, while boundary violations of the latency may lead to loosing synchronization with other processes and, ultimately, to production faults, tool failures or similar.¶

Particular attention in Digital Twin scenarios is given to the problem of data storage. Here, decentralization, not only driven by the scenario (such as outlined in the connected car scenario for cases of localized reasoning over data originating from driving vehicles) but also through proposed platform solutions, such as those in [GAIA-X], plays an important role. With decentralization, endpoint relations between client and (storage) service instances may frequently change as a result.¶

4.4. Computing-Aware SD-WAN

SD-WAN provides organizations or enterprises with centralized control over multiple sites which are network endpoints including branch offices, headquarters, data centers, clouds, and more. A enterprise may deploy their services and applications in different locations to achieve optimal performance. The traffic sent by a host will take the shortest WAN path to the closest server. However, the closet server may not be the best choice with lowest cost of network and computing resources for the host. If the path computation element can consider the computing dimension information in path computation, the best path with lowest cost can be provided.¶

The computing related information can be the number of vCPUs of the VM running the application/services, CPU utilization rate, usage of memory, etc.¶

The SD-WAN can be aware of the computing resource of applications deployed in the multiple sites and can perform the routing policy according to the information is defined as the computing-aware SD-WAN.¶

Many enterprises are performing the cloud migration to migrate the applications from data centers to the clouds, including public, private, and hybrid clouds. The clouds resources can be from the same provider or multiple cloud providers which have some benefits including disaster recovery, load balancing, avoiding vendor lock-in.¶

In such cloudification deployments SD-WAN provides enterprises with centralized control over Customer-Premises Equipments(CPEs) in branch offices and the cloudified CPEs(vCPEs) in the clouds.The CPEs connect the clients in branch offices and the application servers in clouds. The same application server in different clouds is called an application instance. Different application instances have different computing resource.¶

SD-WAN is aware of the computing resource of applications deployed in the clouds by vCPEs, and selects the application instance for the client to visit according to computing power and the network state of WAN.¶

Figure 1 below illustrates Computing-aware SD-WAN for Enterprise Cloudification.¶

                                                    +---------------+
   +-------+                      +----------+      |    Cloud1     |
   |Client1|            /---------|   WAN1   |------|  vCPE1  APP1  |
   +-------+           /          +----------+      +---------------+
     +-------+        +-------+
     |Client2| ------ |  CPE  |
     +-------+        +-------+                     +---------------+
   +-------+           \          +----------+      |    Cloud2     |
   |Client3|            \---------|   WAN2   |------|  vCPE2  APP1  |
   +-------+                      +----------+      +---------------+

    Figure 1: Illustration of Computing-aware SD-WAN for Enterprise
                         Cloudification

The current computing load status of the application APP1 in cloud1 and cloud2 is as follows: each application uses 6 vCPUs. The load of application in cloud1 is 50%. The load of application in cloud2 is 20%. The computing resource of APP1 are collected by vCPE1 and vCPE2 respectively. Client1 and Client2 are visiting APP1 in cloud1. WAN1 and WAN2 have the same network states. Considering lightly loaded application SD-WAN selects APP1 in cloud2 for the client3 in branch office. The traffic of client3 follows the path: Client3 -> CPE -> WAN1 -> Cloud2 vCPE1 -> Cloud2 APP1¶

5.2. 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 instance selection decision is important. Therefore, a desirable system¶

o SHOULD agree on using metrics that are oriented towards compute capabilities and resources and their representation among service elements in the participating edges.¶

o MAY include network metrics.¶

5.3. Support Moderate Metric Distributing

Network path costs in the current routing system usually do not change very frequently. However, metrics that are oriented towards compute capabilities and resources 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 related decision logic, one or more metrics need to be conveyed in a CATS 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¶

5.4. 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. Therefore, the CATS 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, metrics that are oriented towards compute capabilities and resources 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 CATS 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.¶

5.6. 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 CATS 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.¶

10.1. 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>.

[RFC7665]

Halpern, J., Ed. and C. Pignataro, Ed., "Service Function Chaining (SFC) Architecture", RFC 7665, DOI 10.17487/RFC7665, October 2015, <https://www.rfc-editor.org/info/rfc7665>.

10.2. Informative References

[I-D.contreras-alto-service-edge]

Contreras, L. M., Randriamasy, S., Ros-Giralt, J., Perez, D. A. L., and C. E. Rothenberg, "Use of ALTO for Determining Service Edge", Work in Progress, Internet-Draft, draft-contreras-alto-service-edge-07, 13 March 2023, <https://datatracker.ietf.org/doc/html/draft-contreras-alto-service-edge-07>.

[TR22.874]

3GPP, "Study on traffic characteristics and performance requirements for AI/ML model transfer in 5GS (Release 18)", 2021.

[HORITA]

Horita, Y., "Extended electronic horizon for automated driving", Proceedings of 14th International Conference on ITS Telecommunications (ITST)", 2015.

[Industry4.0]

Industry4.0, "Details of the Asset Administration Shell, Part 1 & Part 2", 2020.

[GAIA-X]

Gaia-X, ""GAIA-X: A Federated Data Infrastructure for Europe"", 2021.