]> Nokia Bell Labs

sabine.randriamasy@nokia-bell-labs.com

Telefonica

luismiguel.contrerasmurillo@telefonica.com

Qualcomm Europe, Inc.

jros@qti.qualcomm.com

next generation unicorn sparkling distributed ledger Service providers are starting to deploy computing capabilities across the network for hosting applications such as AR/VR, vehicle networks, IoT, and AI training, among others. In these distributed computing environments, information about computing and communication resources is necessary to determine both the proper deployment location of each application and the best server location on which to run it. This information is used by numerous different implementations with different interpretations. This document proposes an initial approach towards a common understanding and exposure scheme for metrics reflecting compute capabilities. About This Document The latest revision of this draft can be found at . Status information for this document may be found at . Source for this draft and an issue tracker can be found at .

Introduction Operators are starting to deploy distributed computing environments in different parts of the network with the objective of addressing different service needs including latency, bandwidth, processing capabilities, storage, etc. This translates in the emergence of a number of data centers (both in the cloud and at the edge) of different sizes (e.g., large, medium, small) characterized by distinct dimension of CPUs, memory, and storage capabilities, as well as bandwidth capacity for forwarding the traffic generated in and out of the corresponding data center. The proliferation of the edge computing paradigm further increases the potential footprint and heterogeneity of the environments where a function or application can be deployed, resulting in different unitary cost per CPU, memory, and storage. This increases the complexity of deciding the location where a given function or application should be best deployed or executed. This decision should be jointly influenced on the one hand by the available resources in a given computing environment, and on the other hand by the capabilities of the network path connecting the traffic source with the destination. Network and compute aware function placement and selection has become of utmost importance in the last decade. The availability of such information is taken for granted by the numerous service providers and bodies that are specifying them. However, deployments may reach out to data centers running different implementations with different understandings and representations of compute capabilities and smooth operation is a challenge. While standardization efforts on network capabilities representation and exposure are well-advanced, similar efforts on compute capabilitites are in their infancy. This document proposes an initial approach towards a common understanding and exposure scheme for metrics reflecting compute capabilities. It aims at leveraging on existing work in the IETF on compute metrics definitions to build synergies. It also aims at reaching out to working or research groups in the IETF that would consume such information and have particular requirements.

Conventions and Definitions The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 when, and only when, they appear in all capitals, as shown here.

Problem Space and Needs Visibility and exposure of both (1) network and (2) compute resources to the application is critical to enable the proper functioning of the new class of services arising at the edge (e.g., distributed AI, driverless vehicles, AR/VR, etc.). To understand the problem space and the capabilities that are lacking in today's protocol interfaces needed to enable these new services, we focus on the life cycle of a service. At the edge, compute nodes are deployed near communication nodes (e.g., co-located in a 5G base station) to provide computing services that are close to users with the goal to (1) reduce latency, (2) increase communication bandwidth, (3) enable privacy/personalization (e.g., federated AI learning), and (4) reduce cloud costs and energy. Services are deployed on the communication and compute infrastructure through a two-phase life cycle that involves first a service deployment stage and then a service selection stage ().

Service life cycle. Service +------> Service | | Service | | Deployment | | Selection | | | | | | | +-------------+ +--------------+ +-------------+ ]]>

Service deployment. This phase is carried out by the service provider, and consists in the deployment of a new service (e.g., a distributed AI training/inference, an XR/AR service, etc.) on the communication and compute infrastructure. The service provider needs to properly size the amount of communication and compute resources assigned to this new service to meet the expected user demand. The decision on where the service is deployed and how many resources are requested from the infrastructure depends on the levels of QoE that the provider wants to guarantee to the user base. To make a proper deployment decision, the provider must have visibility on the resources available from the infrastructure, including communication resources (e.g., latency and bandwidth) and compute (e.g., CPU, GPU, memory, storage). For instance, to run a Large Language Model (LLM) with 175 billion parameters, a total aggregated memory of 400GB and 8 GPUs are needed. The service provider needs an interface to query the infrastructure, extract the available compute and communication resources, and decide which subset of resources are needed to run the service. Service selection. This phase is initiated by the user, through a client application that connects to the deployed service. There are two main decisions that must be performed in the service selection stage: compute node selection and path selection. In the compute node selection step, as the service is generally replicated in N locations (e.g., by leveraging a microservices architecture), the application must decide which of the service replicas it connects to. Similar to the service deployment stage, this decision requires knowledge about communication and compute resources available in each replica. On the other hand, in the path selection decision, the application must decide which path it chooses to connect to the service. This decision depends on the communication properties (e.g., bandwidth and latency) of the available paths. Similar to the service deployment case, the service provider needs an interface to query the infrastructure and extract the available compute and communication resources, with the goal to make informed node and path selection decisions. It is also important to note that, ideally, the node and path selection decisions should be jointly optimized, since in general the best end-to-end performance is achieved by jointly taking into account both decisions. In some cases, however, such decisions may be owned by different players. For instance, in some network environments, the path selection may be decided by the network operator, wheres the node selection may be decided by the application. Even in these cases, it is crucial to have a proper interface (for both the network operator and the service provider) to query the available compute and communication resources from the system. summarizes the problem space, the information that needs to be exposed, and the stakeholders that need this information. Problem space, needs, and stakeholders.

Action to take	Information needed	Who needs it
Service placement	Compute and communication	Service provider
Service selection/node selection	Compute	Network/service provider and/or application
Service selection/path selection	Communication	Network/service and/or application

Guiding Principles The driving principles for designing an interface to jointly extract network and compute information are as follows: P1. Leverage metrics across working groups to avoid reinventing the wheel. For instance:

• RFC 9439 leverages IPPM metrics from RFC 7679.
• Section 5.2 of considers delay as a good metric, since it is easy to use in both compute and communication domains. RFC 9439 also defines delay as part of the performance metrics.
• Section 6 of proposes to represent the network structure as graphs, which is similar to the ALTO map services in .

P2. Aim for simplicity, while ensuring the combined efforts don’t leave technical gaps in supporting the full life cycle of service deployment and selection. For instance, the CATS working group is covering path selection from a network standpoint, while ALTO (e.g., ) covers exposing of network information to the service provider and the client application. However, there is currently no effort being pursued to expose compute information to the service provider and the client application for service placement or selection.

Related Work Some existing work has explored compute-related metrics. They can be categorized as follows:

• References providing raw compute infrastructure metrics: includes references to cloud management solutions (i.e., OpenStack, Kubernetes, etc) which administer the virtualization infrastructure, providing information about raw compute infrastructure metrics. Furthermore, describes processor, memory and network interface usage metrics.
• References providing compute virtualization metrics: provides several metrics as part of the Management Information Base (MIB) definition for managing virtual machines controlled by a hypervisor. The objects there defined make reference to the resources consumed by a particluar virtual machine serving as host for services or applications. Moreover, provides metrics associated to virtualized network functions.
• References providing service metrics including compute-related information: proposes metrics associated to services running in compute infrastructures. Some of these metrics do not depend on the infrastructure behavior itself but from where such compute infrastructure is topologically located.

GAP Analysis From this related work it is evident that compute-related metrics can serve several purposes, ranging from service instance instantiation to service instance behavior, and then to service instance selection. Some of the metrics could refer to the same object (e.g., CPU) but with a particular usage and scope. In contrast, the network metrics are more uniform and straightforward. It is then necessary to consistently define a set of metrics that could assist to the operation in the different concerns identified so far, so that networks and systems could have a common understanding of the perceived compute performance. When combined with network metrics, the combined network plus compute performance behavior will assist informed decisions particular to each of the operational concerns related to the different parts of a service life cycle.

Security Considerations TODO Security

IANA Considerations This document has no IANA actions.

References Normative References Applications using the Internet already have access to some topology information of Internet Service Provider (ISP) networks. For example, views to Internet routing tables at Looking Glass servers are available and can be practically downloaded to many network application clients. What is missing is knowledge of the underlying network topologies from the point of view of ISPs. In other words, what an ISP prefers in terms of traffic optimization -- and a way to distribute it. The Application-Layer Traffic Optimization (ALTO) services defined in this document provide network information (e.g., basic network location structure and preferences of network paths) with the goal of modifying network resource consumption patterns while maintaining or improving application performance. The basic information of ALTO is based on abstract maps of a network. These maps provide a simplified view, yet enough information about a network for applications to effectively utilize them. Additional services are built on top of the maps. This document describes a protocol implementing the ALTO services. Although the ALTO services would primarily be provided by ISPs, other entities, such as content service providers, could also provide ALTO services. Applications that could use the ALTO services are those that have a choice to which end points to connect. Examples of such applications are peer-to-peer (P2P) and content delivery networks. Huawei Yale University Samsung Huawei Nokia Bell Labs Telefonica The cost metric is a basic concept in Application-Layer Traffic Optimization (ALTO), and different applications may use different types of cost metrics. Since the ALTO base protocol (RFC 7285) defines only a single cost metric (namely, the generic "routingcost" metric), if an application wants to issue a cost map or an endpoint cost request in order to identify a resource provider that offers better performance metrics (e.g., lower delay or loss rate), the base protocol does not define the cost metric to be used. This document addresses this issue by extending the specification to provide a variety of network performance metrics, including network delay, delay variation (a.k.a. jitter), packet loss rate, hop count, and bandwidth. There are multiple sources (e.g., estimations based on measurements or a Service Level Agreement) available for deriving a performance metric. This document introduces an additional "cost-context" field to the ALTO "cost-type" field to convey the source of a performance metric. China Mobile China Mobile Huawei Technologies ZTE New H3C Technologies This document describes the considerations and the potential architecture of the computing information that needs to be notified into the network in Computing-Aware Traffic Steering (CATS). In many standards track documents several words are used to signify the requirements in the specification. These words are often capitalized. This document defines these words as they should be interpreted in IETF documents. This document specifies an Internet Best Current Practices for the Internet Community, and requests discussion and suggestions for improvements. RFC 2119 specifies common key words that may be used in protocol specifications. This document aims to reduce the ambiguity by clarifying that only UPPERCASE usage of the key words have the defined special meanings. Informative References Telefonica Nokia Bell Labs Qualcomm Europe Benocs Unicamp Service providers are starting to deploy computing capabilities across the network for hosting applications such as AR/VR, vehicle networks, IoT, and AI training, among others. In these distributed computing environments, knowledge about computing and communication resources is necessary to determine the proper deployment location of each application. This document proposes an initial approach towards the use of ALTO to expose such information to the applications and assist the selection of their deployment locations. This document defines a portion of the Management Information Base (MIB) for use with network management protocols in the Internet community. In particular, this specifies objects for managing virtual machines controlled by a hypervisor (a.k.a. virtual machine monitor). Futurewei Microsoft Huawei Huawei Futurewei This draft describes the 5G Edge computing use cases for CATS and how BGP can be used to propagate additional IP layer detectable information about the 5G edge data centers so that the ingress routers in the 5G Local Data Network can make path selections based on not only the routing distance but also the IP Layer relevant metrics of the destinations. The goal is to improve latency and performance for 5G Edge Computing (EC) services even when the detailed servers running status are unavailable.

Acknowledgments TODO acknowledge.