Internet DRAFT - draft-peng-cats-car-architecture
draft-peng-cats-car-architecture
cats T. Peng
Internet-Draft Y. Ma
Intended status: Informational Beijing Jiaotong University
Expires: 20 August 2024 17 February 2024
The Computing-Aware Routing Architecture in Computing-Aware Traffic
Steering
draft-peng-cats-car-architecture-00
Abstract
This document proposes a compute-aware routing (CAR) architecture for
Computing-Aware Traffic Steering (CATS), designed to support routing
systems with compute resource awareness and provide a standardized
approach for network devices and services.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. The Computing-Aware Routing Headers (CARH) . . . . . . . . . 2
3. Computing-Aware Info (CAI) . . . . . . . . . . . . . . . . . 3
4. Advanced Info . . . . . . . . . . . . . . . . . . . . . . . . 5
4.1. 0xF0 - Composite Performance Score (CPS) . . . . . . . . 5
4.2. 0xF1 - Network Quality Index (NQI) . . . . . . . . . . . 5
5. Security Considerations . . . . . . . . . . . . . . . . . . . 6
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 6
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 6
1. Introduction
With the continuous increase of computing resources and the
popularity of distributed computing, how to fully utilize these
dispersed computing resources to provide better services has become
an important challenge. To address this challenge, Computing-Aware
Traffic Steering (CATS) has been proposed.
CATS requires the network to be able to perceive information about
computing resources and select appropriate service instances based on
the joint indicators of computing and the network, thereby achieving
dynamic control of network traffic. In the implementation of CATS, a
key task is to design a message format that can transmit computing
resource information.
Therefore, the Computing-Aware Routing (CAR) message format is
proposed.
The CAR is a flexible and powerful message transmission method
designed to support computing resource-aware routing systems.
2. The Computing-Aware Routing Headers (CARH)
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0 1 2 3
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
|Version| Time to Live | Length |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| |
| Timestamp (64 bits) |
| |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| CAI Type[0] | CAI Value[0] |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| ... |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| CAI Type[n] | CAI Value[n] |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
The CARH contains the following fields:
* Version Number (4-bit): The version number indicates the CARH
format version.
* Time to Live (8-bit): Time to Live indicates the effective time of
the resource information represented by CARH.
* Length (16-bit): Length of the CARH.
* Timestamp (64-bit): Timestamp for perceiving resource information.
* CAI Type (8-bit): Computing-Aware Info Type (CAI Type) represents
a type of computing resource and the order of magnitude
corresponding to the specific value of that type of resource.
This section corresponds one-to-one with the CAI Value.
* CAI Value (24-bit): Computing-Aware Info Value (CAI Value)
represents the specific value of the resource identified by the
CAI Type.
3. Computing-Aware Info (CAI)
In order to comprehensively evaluate the computing power in the
network, CARH needs to introduce a series of dynamic performance
indicators to capture real-time changes in computing resources.
These indicators need to cover key performance parameters of network
devices during operation, such as CPU Utilization, CPU Frequency, CPU
Cores, Memory Utilization, Memory Frequency, Storage Utilization, GPU
Memory, GPU Utilization, etc.
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In CARH, use Computing-Aware Info (CAI) to carry these key
performance parameters.
Computing-Aware Info (CAI) consists of two parts: Computing-Aware
Info Type (CAI Type) and Computing-Aware Info Value (CAI Value).
Use the CAI Type field to represent the type of resource being
carried, and use the CAI Value field to represent the numerical value
and order of magnitude corresponding to the resource being carried.
Here are some definitions of the CAI Type field.
* 0x01 - CPU Utilization: Indicates the percentage of time the CPU
processor is executing tasks. The calculation formula is the used
CPU time divided by the total CPU time.
* 0x02 - CPU Frequency: Refers to the number of instructions
executed by the CPU per second, usually in hertz (Hz).
* 0x03 - CPU Cores: Represents the number of physical cores in the
CPU processor. Multi-core processors can execute multiple tasks
simultaneously.
* 0x04 - Memory Utilization: Indicates the percentage of system
memory in use. The calculation formula uses memory divided by
total memory.
* 0x05 - Memory Frequency: Refers to the clock speed of RAM,
indicating the amount of data that the memory module can transfer
per second.
* 0x06 - Storage Utilization: Indicates the percentage of storage
capacity being used in the storage system. The calculation
formula uses storage divided by total storage capacity.
* 0x07 - GPU Memory: Refers to the memory on the graphics processing
unit (GPU) used to store data required for graphics and computing
tasks.
* 0x08 - GPU Utilization: Indicates the percentage of time the GPU
processor is performing tasks. The calculation formula is the
used GPU time divided by the total GPU time.
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4. Advanced Info
Dynamic performance indicators can only serve as basic information
transmitted in CARH. In fact, based on performance indicator
information, some advanced performance indicators can be established
according to specific network scenarios. Advanced info is also a
supplementary definition of CAI Type.
4.1. 0xF0 - Composite Performance Score (CPS)
From the perspective of computing power, a computing power weight
allocation mechanism can be introduced to assign appropriate weights
to different computing power factors.
This mechanism gives appropriate weights to different computing power
factors to more accurately reflect their relative importance when
calculating comprehensive performance indicators.
For example, in some scenarios, CPU utilization may be more critical,
while memory usage may be more important in other scenarios. This
can be customized by network administrators according to specific
needs, or adjusted through CATS intelligent algorithms for Self-
Adaptation.
Therefore, a comprehensive scoring model can be proposed to take into
account various computing power factors and generate a comprehensive
score.
This score can be a standardized value that reflects the overall
computing power status of the system. The formulation of a
comprehensive scoring model can combine Data Analysis and network
topology features.
4.2. 0xF1 - Network Quality Index (NQI)
From the perspective of network status, a network quality measurement
mechanism can be introduced to evaluate network quality.
Network Quality Index (NQI) takes into account several important
performance indicators in the network, including latency, packet loss
rate, bandwidth utilization, etc.
The goal of NQI is to provide a single metric to measure the overall
quality of the network, so that network administrators and system
operators can more easily understand the performance of the network
and take corresponding measures to optimize the network.
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The definition of NQI is based on the weighted average of a series of
network performance indicators. Each performance indicator is
assigned a weight, which reflects the relative importance of each
indicator to network quality. Then, by multiplying the values of
each indicator with its corresponding weight and adding all weighted
values, a comprehensive score, namely NQI, is obtained.
5. Security Considerations
TBD.
6. IANA Considerations
This document has no IANA actions.
Acknowledgments
TODO acknowledge.
Authors' Addresses
Tianhao Peng
Beijing Jiaotong University
Email: th.peng@bjtu.edu.cn
Yuyin Ma
Beijing Jiaotong University
Email: mayuyin@bjtu.edu.cn
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