Internet DRAFT - draft-wang-cats-green-challenges
draft-wang-cats-green-challenges
cats J. Wang
Internet-Draft Y. Fu
Intended status: Informational China Mobile
Expires: 5 September 2024 C. Li
Huawei Technologies
4 March 2024
Green Challenges in Computing-Aware Traffic Steering (CATS)
draft-wang-cats-green-challenges-03
Abstract
As mobile edge computing networks sink computing tasks from cloud
data centers to the edge of the network, tasks need to be processed
by computing resources close to the user side. Therefore, CATS was
raised. Reducing carbon footprint is a major challenge of our time.
Networks are the main enablers of carbon reductions. The
introduction of computing dimension in CATS makes it insufficient to
consider the energy saving of network dimension in the past, so the
green for CATS based on network and computing combination is worth
exploring. This document outlines a series of challenges and
associated research to explore ways to reduce carbon footprint and
reduce network energy based on CATS.
Status of This Memo
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document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Definition of Terms . . . . . . . . . . . . . . . . . . . . . 3
3. Challenges . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. Computing Resource Energy Consumption Modeling . . . . . 3
3.2. Joint Optimization of Computing and Network . . . . . . . 4
3.3. Service Experience Guarantee . . . . . . . . . . . . . . 4
3.4. Energy Consumption of Other Equipment . . . . . . . . . . 4
3.5. Evaluation of Computing Equipment Energy Consumption
Performance . . . . . . . . . . . . . . . . . . . . . . . 4
4. Observation . . . . . . . . . . . . . . . . . . . . . . . . . 5
5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 5
6. Security Considerations . . . . . . . . . . . . . . . . . . . 6
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 6
9. Informative References . . . . . . . . . . . . . . . . . . . 6
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction
With the continuous development and progress of the Internet, a large
amount of computing resources is required to complete data
processing. In order to disperse the pressure of cloud data centers,
computing power gradually moves from the center to the edge, forming
scattered computing resources in mobile networks. In order to make
full use of scattered computing resources and provide better
services, Computing-Aware Traffic Steering (CATS) is proposed to
support steering the traffic among different edge sites according to
both the real-time network and computing resource status as mentioned
in [I-D.yao-cats-ps-usecases] and [I-D.yao-cats-gap-reqs]. It
requires the network to be aware of computing resource information
and select a service instance based on the joint metric of computing
and networking.
Green has become a global topic. The United Nations and the vast
majority of governments agree that climate change and the need to
curb greenhouse gas emissions are the major challenges of our time.
Therefore, improving energy efficiency and reducing electricity
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consumption are becoming increasingly important for society and many
industries. The networking industry is no exception. The IETF
conducted a study on the energy costs of the IETF meeting three times
a year. The results showed that it was found that 99% of energy
consumption came from air travel.
In addition, there are several papers that discuss green networks,
and some work [I-D.cx-green-ps] summarizes the energy-saving
possibilities that exist in the network. However, there is no
discussion of joint optimization of green and energy savings with
computing and networking. Therefore, this document outlines a series
of challenges and related research to explore ways to reduce carbon
emissions and reduce network energy based on CATS.
2. Definition of Terms
Computing-Aware Traffic Steering (CATS): Aiming at computing and
network resource optimization by steering traffic to appropriate
computing resources considering not only routing metric but also
computing resource metric.
Service: A monolithic functionality that is provided by an endpoint
according to the specification for said service. A composite
service can be built by orchestrating monolithic services.
Service instance: Running environment (e.g., a node) that makes the
functionality of a service available. One service can have several
instances running at different network locations.
3. Challenges
Considering energy savings in CATS creates challenges in the
following aspects
3.1. Computing Resource Energy Consumption Modeling
Computing resource status is considered in Cats, so it is necessary
to research the modeling of computing resource energy consumption in
order to save energy. The energy consumption of the equipment is
different when the load is different. For example, the energy
efficiency of equipment is different when it is not loaded or at full
load. Therefore, it is also a challenge to consider which factors to
consider when modeling the energy consumption of computing resources.
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3.2. Joint Optimization of Computing and Network
The magnitude of computing energy consumption may differ from the
magnitude of network energy consumption. Therefore, when computing
and network are jointly optimized, how to weigh the two in joint
optimization becomes a challenge. When the computing energy
consumption is large enough, the impact of network energy consumption
on the joint optimization results is negligible.
3.3. Service Experience Guarantee
The service experience that takes energy saving factors into account
in CATS is distinct from the service experience that does not
consider energy saving factors. The implementation of energy
conservation may come with sacrifices in user service experience.
Users have limitations on factors such as latency when making
requests. Therefore, when conducting joint optimization, how to
guarantee user service experience while conserving energy is also a
challenge.
3.4. Energy Consumption of Other Equipment
The computing resources may be in the data center, edge computing
nodes or others. In order to ensure the normal operation of network
and computing equipment, the source of energy consumption is not only
the equipment itself, but also some other equipment, such as :
Cool equipment : computing resources will emit heat into the air
during operation. When the temperature is too high, the operation of
the equipment will be affected. So refrigeration is required to
reduce the temperature of the equipment to ensure that the equipment
operates at a higher performance.
The normal running of computing resources are inseparable from the
support of refrigeration equipment and other equipment. Therefore,
when performing joint optimization of network and computing, the
energy consumption generated by equipment other than network
equipment and computing equipment should also be considered.
3.5. Evaluation of Computing Equipment Energy Consumption Performance
The energy efficiency level requirements of computing equipments can
also be considered when performing traffic steering. Since there is
no standardized definition of computing energy efficiency for
different computing equipments. Therefore, it is difficult to
consider the computing energy efficiency level of computing
equipments when traffic steering.
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4. Observation
Recently, the document [I-D.cx-opsawg-green-metrics] gives some green
networking metrics for network instrumentation to optimize the energy
efficiency of the network. It divides the green metrics into four
categories according to the subject of the metrics, as follows:
At the device/equipment level: The author considers three factors.
The first are energy consumption metrics. Some of these metrics
could be provided by the data sheet that comes with the device or
could be measured simply in a lab, such as power consumption when
idle, power consumption when fully loaded, power consumption at
various loads and so on. The others are not fixed and need to be
accounted according to the actual operation of the network equipment,
such as current power consumption/kB (or gB), current power
consumption/packet, power drawn since system started for the past
minute and so on. The second is green metrics beyond energy
consumption, Wich is related to the power source of the device and
the environment in which the device is located. The third is related
to network instrumentation virtualization. Nowadays, network
instrumentation could be virtualized and hosted (for example) in data
centers.
At the flow level: These metrics are related to flows, such as
amortized energy consumed over the duration of the flow and
Incremental energy consumed over the duration of the flow.
At the path level: These metrics can evaluate the energy consumption
of paths and optimize these paths so that the overall footprint is
minimized. The author gives some candidate metrics, such as energy
rating of a path, current power consumption across a path and
incremental power for a packet over a path.
At the network level: These metrics can reflect the energy usage of
the entire network.
5. Conclusion
This document highlights the green challenges in Cats and summarizes
the latest IETF work which is associated with green networking. As
is well known, Cats not only considers network resource status, but
also computing resource status. Therefore, energy consumption
research of Cats can also consider both network and computing energy
consumption from the device/equipment, path and network level.
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6. Security Considerations
TBD.
7. IANA Considerations
TBD.
8. Acknowledgements
The authors would like to thank thank Adrian Farrel and Peng Liu for
their valuable suggestions to this document. Additionally, the
authors would also like to thank Alexander Clemm and Lijun Dong for
their related work.
9. Informative References
[I-D.yao-cats-ps-usecases]
Yao, K., Trossen, D., Boucadair, M., Contreras, L. M.,
Shi, H., Li, Y., and S. Zhang, "Computing-Aware Traffic
Steering (CATS) Problem Statement, Use Cases, and
Requirements", Work in Progress, Internet-Draft, draft-
yao-cats-ps-usecases-03, 30 June 2023,
<https://datatracker.ietf.org/doc/html/draft-yao-cats-ps-
usecases-03>.
[I-D.yao-cats-gap-reqs]
Yao, K., Jiang, T., Eardley, P., Trossen, D., Li, C., and
D. Huang, "Computing-Aware Traffic Steering (CATS) Gap
Analysis and Requirements", Work in Progress, Internet-
Draft, draft-yao-cats-gap-reqs-00, 3 March 2023,
<https://datatracker.ietf.org/doc/html/draft-yao-cats-gap-
reqs-00>.
[I-D.cx-green-ps]
Clemm, A., Westphal, C., Tantsura, J., Ciavaglia, L., and
M. Odini, "Challenges and Opportunities in Management for
Green Networking", Work in Progress, Internet-Draft,
draft-cx-green-ps-02, 13 March 2023,
<https://datatracker.ietf.org/doc/html/draft-cx-green-ps-
02>.
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[I-D.cx-opsawg-green-metrics]
Clemm, A., Dong, L., Mirsky, G., Ciavaglia, L., Tantsura,
J., Odini, M., Schooler, E., Rezaki, A., and C. Pignataro,
"Green Networking Metrics", Work in Progress, Internet-
Draft, draft-cx-opsawg-green-metrics-01, 15 December 2023,
<https://datatracker.ietf.org/doc/html/draft-cx-opsawg-
green-metrics-01>.
Authors' Addresses
Jing Wang
China Mobile
No.32 XuanWuMen West Street
Beijing
100053
China
Email: wangjingjc@chinamobile.com
Yuexia Fu
China Mobile
No.32 XuanWuMen West Street
Beijing
100053
China
Email: fuyuexia@chinamobile.com
Cheng Li
Huawei Technologies
Huawei Campus, No. 156 Beiqing Rd.
Beijing
100095
China
Email: c.l@huawei.com
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