Joint Exposure of Network and Compute Information for Infrastructure-Aware Service Deployment

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Table of Contents

1. 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.¶

2. 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 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.¶

3. 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 (Figure 1).¶

 +-------------+      +--------------+      +-------------+
 |             |      |              |      |             |
 |  New        +------>  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.¶

Table 1 summarizes the problem space, the information that needs to be exposed, and the stakeholders that need this information.¶

4. 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 [I-D.ietf-alto-performance-metrics] leverages IPPM metrics from RFC 7679.¶

• Section 5.2 of [I-D.du-cats-computing-modeling-description] 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 [I-D.du-cats-computing-modeling-description] proposes to represent the network structure as graphs, which is similar to the ALTO map services in [RFC7285].¶

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., [RFC7285]) 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.¶

5. Related Work

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

• References providing raw compute infrastructure metrics: [I-D.contreras-alto-service-edge] includes references to cloud management solutions (i.e., OpenStack, Kubernetes, etc) which administer the virtualization infrastructure, providing information about raw compute infrastructure metrics. Furthermore, [NFV-TST] describes processor, memory and network interface usage metrics.¶

• References providing compute virtualization metrics: [RFC7666] 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, [NFV-INF] provides metrics associated to virtualized network functions.¶

• References providing service metrics including compute-related information: [I-D.dunbar-cats-edge-service-metrics] 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.¶

6. 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.¶

7. Security Considerations

TODO Security¶

8. IANA Considerations

This document has no IANA actions.¶

Acknowledgments

TODO acknowledge.¶

Authors' Addresses