CATS Metrics Definition

Introduction Service providers are deploying computing capabilities across the network for hosting applications such as distributed AI workloads, AR/VR and driverless vehicles, among others. In these deployments, multiple service instances are replicated across various sites to ensure sufficient capacity for maintaining the required Quality of Experience (QoE) expected by the application. To support the selection of these instances, a framework called Computing-Aware Traffic Steering (CATS) is introduced in . CATS is a traffic engineering approach that optimizes the steering of traffic to a given service instance by considering the dynamic nature of computing and network resources. To achieve this, CATS components (C-PS, C-Forwarders, etc.) require performance metrics for both communication and compute resources. Since these resources are deployed by multiple providers, standardized metrics are essential to ensure interoperability and enable precise traffic steering decisions, thereby optimizing resource utilization and enhancing overall system performance. Various considerations for metric definition are proposed in , which are useful for defining computing metrics. This document categorizes the relevant compute and network metrics for CATS into three levels based on their complexity and granularity, following the considerations outlined in .

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. This document uses the following terms defined in :

Computing-Aware Traffic Steering (CATS)
Service
Service contact instance

Definition of Metrics Introducing a definition of metrics requires balancing the following trade-off: if the metrics are too fine-grained, they become unscalable due to the excessive number of metrics that must be communicated through the metrics distribution protocol. (See for a discussion of metrics distribution protocols.) Conversely, if the metrics are too coarse-grained, they may lack the necessary information to make informed decisions. To ensure scalability while providing sufficient detail for effective decision-making, we propose a definition of metrics that incorporates three levels of abstraction:

Level 0 (L0): Raw metrics. These metrics are presented without abstraction, with each metric using its own unit and format as defined by the underlying resource.
Level 1 (L1): Normalized metrics in categories. These metrics are derived by aggregating L0 metrics into multiple categories, such as network, computing, and storage. Each category is summarized with a single L1 metric by normalizing it into a value within a defined range of scores.
Level 2 (L2): Fully normalized metric. These metrics are derived by aggregating lower level metrics (L0 or L1) into a single L2 metric, which is then normalized into a value within a defined range of scores.

Level 0: Raw Metrics Level 0 metrics encompass detailed, raw metrics, including but not limit to:

CPU: Base Frequency, boosted frequency, number of cores, core utilization, memory bandwidth, memory size, memory utilization, power consumption.
GPU: Frequency, number of render units, memory bandwidth, memory size, memory utilization, core utilization, power consumption.
NPU: Computing power, utilization, power consumption.
Network: Bandwidth, capacity, throughput, transmit bytes, receive bytes, host bus utilization.
Storage: Available space, read speed, write speed.
Delay: Time taken to process a request.

L0 metrics can be encoded into an Application Programming Interface (API), such as a RESTful API, and can be solution-specific. Different resources can have their own metrics, each conveying unique information about their status. These metrics can generally have units, such as bits per second (bps) or floating point instructions per second (flops). Regarding network-related information, the IPPM WG has defined various types of metrics in . Additionally, in , the ALTO WG has introduced an extended set of metrics related to packet performance and throughput/bandwidth. For compute metrics, lists a set of cloud resource metrics.

Level 1: Normalized Metrics in Categories L1 metrics are organized into distinct categories, such as computing, networking, storage, and delay. Each L0 metric is classified into one of these categories. Within each category, a single L1 metric is computed using an aggregation function and normalized to a unitless score that represents the performance of the underlying resources according to that category. Potential categories include:

Computing: A normalized value derived from computing-related L0 metrics, such as CPU, GPU, and NPU metrics.
Networking: A normalized value derived from network-related L0 metrics.
Storage: A normalized value derived from storage-related L0 metrics.
Delay: A normalized value derived from computing, networking, and storage metrics, reflecting the end-to-end processing delay of a request.

Editor note: detailed categories can be updated according to the CATS WG discussion. The L0 metrics, such as those defined in , , and , can be categorized into the aforementioned categories. Each category will employ its own aggregation function (e.g., weighted summary) to generate the normalized value. This approach allows the protocol to focus solely on the metric categories and their normalized values, thereby avoiding the need to process solution-specific detailed metrics.

Level 2: Fully Normalized Metric. The L2 metric is a single score value derived from the lower level metrics (L0 or L1) using an aggregation function. Different implementations may employ different aggregation functions to characterize the overall performance of the underlying compute and communication resources. The definition of the L2 metric simplifies the complexity of collecting and distributing numerous lower-level metrics by consolidating them into a single, unified score. TODO: Some implementations may support configuration of Ingress CATS-Forwarders with the metric normalizing method so that it can decode the affection from the L1 or L0 metrics. Figure 1 shows the logic of metrics in Level 0, Level 1, and Level 2.

Logic of CATS Metrics in levels

Representation of Metrics The representation of metrics is a key component of the CATS architecture. It defines how metrics are encoded and transmitted over the network. The representation should be flexible enough to accommodate different types of metrics and their associated units and precisions. This section includes the detailed representation of the CATS metrics. The design follows similar principles used in other similar IETF specifications, such as the network performance metrics defined in .

Definition of CATS Metric A CATS metric is represented using a set of fields, each describing a property of the metric. The following fields are introduced:

CATS Metric Definition Next, we describe each field in more detail:

Type (type): This field specifies the category or kind of CATS metric being measured, such as computational resources, storage capacity, or network bandwidth. It serves as a label for network devices to recognize what the metric is.
Format standard (format_std, optional): This optional field indicates the standard used to encode and decode the value field according to the format field. It is only required if the value field is encoded using a specific standard, and knowing this standard is necessary to decode the value field. Examples of format standards include ieee_754 and ascii. This field ensures that the value can be accurately interpreted by specifying the encoding method used.
Format (format): This field indicates the data encoding format of the metric, such as whether the value is represented as an integer, a floating-point number, or has no specific format.
Length (length): This field indicates the size of the value field measured in octets (bytes). It specifies how many bytes are used to store the value of the metric. Examples include 4, 8, 16, 32, and 64. The length field is important for memory allocation and data handling, ensuring that the value is stored and retrieved correctly.
Units (units): This field defines the measurement units for the metric, such as frequency, data size, or data transfer rate. It is usually associated with the metric to provide context for the value.
Source (source, optional): This field describes the origin of the information used to obtain the metric:
- 'nominal'. Similarly to , "a 'nominal' metric indicates that the metric value is statically configured by the underlying devices. Not all metrics have reasonable "nominal" values. For example, throughput can have a nominal value, which indicates the configured transmission rate of the involved devices; latency typically does not have a nominal value."
- 'estimation'. The 'estimation' source indicates that the metric value is computed through an estimation process.
- 'directly measured'. This source indicates that the metric can be obtained directly from the underlying device. The value can be dynamic and it does not need to be estimated.
- 'normalization'. The 'normalization' source indicates that the metric value was normalized. For instance, a metric could be normalized to take a value from 0 to 1, from 0 to 10, or to take a percentage value. This type of metrics do not have units.
- 'aggregation'. This source indicates that the metric value was obtained by using an aggregation function.
Nominal metrics have inherent physical meanings and specific units without any additional processing. Aggregated metrics may or may not have physical meanings, but they retain their significance relative to the directly measured metrics. Normalized metrics, on the other hand, might have physical meanings but lack units; they are simply numerical values used for making node and path selection decisions.
Value (value): This field represents the actual numerical value of the metric being measured. It provides the specific data point for the metric in question.

Level 0 Metric Representation Several definitions have been developed within the compute and communication industry and through various standardizations, such as those by the , that could be used as L0 metrics. In this section, we provide examples.

Compute Raw Metrics The metric type of compute resources are named as “compute_type: CPU” or “compute_type: GPU”. Their frequency unit is GHZ, the compute capabilities unit is FLOPS. Format should support integer and FP8. It will occupy 4 octets. Example:

An Example for Compute Raw Metrics

Storage Raw Metrics The metric type of storage resources like SSD are named as “storage_type: SSD”. The storage space unit is megaBytes(MBs). Format is integer. It will occupy 2 octets. The unit of read or write speed is denoted as MB per second. Example:

An Example for Storage Raw Metrics

Network Raw Metrics The metric type of network resources like bandwidth are named as "network_type: Bandwidth”. The unit is gigabits per second(Gb/s). Format is integer. It will occupy 2 octets. The unit of TXBytes and RXBytes is denoted as MB per second. Example:

An Example for Network Raw Metrics

Delay Raw Metrics Delay is a kind of synthesized metric which is influenced by computing, storage access, and network transmission. It is named as “delay_raw”. Format should support integer and FP8. Its unit is microsecond. It will occupy 4 octets. Example:

An Example for Delay Raw Metrics

Considerations on the Sources of Metrics and the Statistics The sources of L0 metrics can be nominal, directly measured, or aggregated. Nominal L0 metrics are provided initially by resource providers. Dynamic L0 metrics are measured and updated during service stage. L0 metrics also support aggregation, in case that there are multiple service instances. The statistics of L0 metrics will follow the definition of Section 3.2 of .

Level 1 Metric Representation Normalized metrics in categories have physical meanings but they do not have unit. They are numbers after some ways of abstraction, but they can represent their type, in case that in some use cases, some specific types of metrics require more attention.

Normalized Compute Metrics The metric type of normalized compute metrics is “compute_norm”, and its format is integer. It has no unit. It will occupy an octet. Example:

An Example for Normalized Compute Metrics

Normalized Storage Metrics The metric type of normalized compute metrics is “storage_norm”, and its format is integer. It has no unit. It will occupy a octet. Example:

An Example for Normalized Storage Metrics

Normalized Network Metrics The metric type of normalized compute metrics is “network_norm”, and its format is integer. It has no unit. It will occupy a octet. Example:

An Example for Normalized Network Metrics

Normalized Delay The metric type of normalized compute metrics is “delay_norm”, and its format is integer. It has no unit. It will occupy a octet. Example:

An Example for Normalized Delay Metrics

Considerations on the Sources of Metrics and the Statistics The sources of L1 metrics is normalized. Based on L0 metrics, service providers design their own algorithms to normalize metrics. For example, assigning different cost values to each raw metric and do summation. L1 metric do not need further statistical values.

Level 2 Metric Representation A fully normalized metric is a single value which does not have any physical meaning or unit. Each provider may have its own methods to derive the value, but all providers must follow the definition in this section to represent the fully normalized value. Metric type is “norm_fi”. The format of the value is non-negative integer. It has no unit. It will occupy a octet. Example:

An Example for Fully Normalized Metric The fully normalized value also supports aggregation when there are multiple service instances providing these fully normalized values. When providing fully normalized values, service instances do not need to do further statistics.

Comparison of three layers of metric From L0 to L1 to L2, the computing metric is consolidated. Different level of abstraction can meet the requirements from different services. Table 1 shows the comparison among metric levels. Comparison among Metrics Levels

Level	Encoding Complexity	Extensibility	Stability	Accuracy
Level 0	Complicated	Bad	Bad	Good
Level 1	Medium	Medium	Medium	Medium
Level 2	Simple	Good	Good	Medium

Since Level 0 metrics are raw metrics, therefore, different services may have their own metrics, resulting in hundreds or thousands of metrics in total, this brings huge complexity in protocol encoding and standardization. Therefore, this kind of metrics are always used in customized IT systems case by case. In Level 1 metrics, metrics are categorized into several categories and each category is normalized into a value, therefore they can be encoded into the protocol and standardized. Regarding the Level 2 metrics, all the metrics are normalized into one single metric, it is easier to be encoded in protocol and standardized. Therefore, from the encoding complexity aspect, Level 2 and Level 1 metrics are suggested. Similarly, when considering extensibility, new services can define their own new L0 metrics, which requires protocol to be extended as needed. Too many metrics type can create a lot of overhead to the protocol resulting in a bad extensibility of the protocol. Level 1 introduce only several metrics categories, which is acceptable for protocol extension. Level 2 metric only need one single metric, so it brings least burden to the protocol. Therefore, from the extensibility aspect, Level 2 and Level 1 metrics are suggested. Regarding Stability, new Level 0 raw metrics may require new extension in protocol, which brings unstable format for protocol, therefore, this document does not recommend to standardize Level 0 metrics in protocol. Level 1 metrics request only few categories, and Level 2 Metric only introduce one metric to the protocol, so they are preferred from the stability aspect. In conclusion, for computing-aware traffic steering, it is recommended to use the L2 metric due to its simplicity. If advanced scheduling is needed, L1 metric can be used. L2 metrics are the most comprehensive and dynamic, therefore transferring them to network devices is discouraged due to their high overhead. Editor notes: this draft can be updated according to the discussion of metric definition in CATS WG.

Security Considerations TBD

IANA Considerations TBD