1、 ETSI GS MEC-IEG 004 V1.1.1 (2015-11) Mobile-Edge Computing (MEC); Service Scenarios Disclaimer This document has been produced and approved by the Mobile-Edge Computing (MEC) ETSI Industry Specification Group (ISG) and represents the views of those members who participated in this ISG. It does not
2、necessarily represent the views of the entire ETSI membership. GROUP SPECIFICATION ETSI ETSI GS MEC-IEG 004 V1.1.1 (2015-11) 2 Reference DGS/MEC-IEG004 Keywords ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N 348 623 562 0001
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9、tered and owned by the GSM Association. ETSI ETSI GS MEC-IEG 004 V1.1.1 (2015-11) 3 Contents Intellectual Property Rights 4g3Foreword . 4g3Modal verbs terminology 4g3Introduction 4g31 Scope 5g32 References 5g32.1 Normative references . 5g32.2 Informative references 5g33 Definitions and abbreviations
10、 . 5g33.1 Definitions 5g33.2 Abbreviations . 6g34 Service scenarios 6g34.1 Intelligent video acceleration service scenario . 6g34.1.1 Description of scenario . 6g34.1.2 Motivation. 6g34.1.3 Problem statement 6g34.1.4 Relation to MEC . 6g34.2 Video stream analysis service scenario 7g34.2.1 Descriptio
11、n of scenario . 7g34.2.2 Motivation. 7g34.2.3 Problem statement 7g34.2.4 Relation to MEC . 7g34.3 Augmented reality service scenario 8g34.3.1 Description of scenario . 8g34.3.2 Motivation. 8g34.3.3 Problem statement 8g34.3.4 Relation to MEC . 8g34.4 Assistance for intensive computation . 9g34.4.1 De
12、scription of scenario . 9g34.4.2 Motivation. 9g34.4.3 Problem statement 9g34.4.4 Relation to MEC . 9g34.5 Enterprise deployment of MEC 10g34.5.1 Description of scenario . 10g34.5.2 Motivation. 10g34.5.3 Problem statement 10g34.5.4 Relation to MEC . 10g34.6 Connected vehicles . 11g34.6.1 Description
13、of scenario . 11g34.6.2 Motivation. 11g34.6.3 Problem statement 11g34.6.4 Relation to MEC . 12g34.7 IoT gateway service scenario . 12g34.7.1 Description of scenario . 12g34.7.2 Motivation. 12g34.7.3 Problem statement 13g34.7.4 Relationship to MEC 13g3Annex A (informative): Authors Essential, or pote
14、ntially Essential, IPRs notified to ETSI in respect of ETSI standards“, which is available from the ETSI Secretariat. Latest updates are available on the ETSI Web server (http:/ipr.etsi.org). Pursuant to the ETSI IPR Policy, no investigation, including IPR searches, has been carried out by ETSI. No
15、guarantee can be given as to the existence of other IPRs not referenced in ETSI SR 000 314 (or the updates on the ETSI Web server) which are, or may be, or may become, essential to the present document. Foreword This Group Specification (GS) has been produced by ETSI Industry Specification Group (IS
16、G) Mobile-Edge Computing (MEC). Modal verbs terminology In the present document “shall“, “shall not“, “should“, “should not“, “may“, “need not“, “will“, “will not“, “can“ and “cannot“ are to be interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of prov
17、isions). “must“ and “must not“ are NOT allowed in ETSI deliverables except when used in direct citation. Introduction Mobile-Edge Computing provides IT service environment and cloud-computing capabilities within the Radio Access Network (RAN) in close proximity to mobile device. MEC aims to reduce l
18、atency, ensure highly efficient network operation, service delivery and ultimate personal experience. The term “Edge“ in this context means the radio base station itself (eNodeB, RNC, etc.), and servers within the radio network (e.g. at “aggregation points“). The presence of MEC server at the edge o
19、f the RAN allows exposure to real-time radio and network information (such as subscriber location, cell load, etc.) that can be leveraged by applications and services to offer context-related services; these services are capable of differentiating the mobile broadband experience. The Mobile-Edge Com
20、puting environment creates a new value chain and an energized ecosystem, which in turn can create new opportunities for mobile operators, application and content providers whilst enabling them to play complementary and profitable roles. Based on innovation and business value, this value chain will a
21、llow all players to benefit from greater cooperation and better monetize the mobile broadband experience. Mobile Operators can open up the radio network edge to third-party partners, allowing them to rapidly deploy innovative applications and services towards mobile subscribers, enterprises and othe
22、r vertical segments. For application developers and content providers, the RAN edge offers a service environment with ultra-low latency and high bandwidth as well as direct access to real-time radio and network information. Mobile-Edge Computing allows content, services and applications to be accele
23、rated, increasing responsiveness from the edge. The customer experience can be proactively maintained through efficient network and service operations, based on insight into the radio and network conditions. Network vendors and technology providers can provide RAN equipment and base stations enhance
24、d with cloud-computing capabilities, by offering more powerful and flexible network elements able to satisfy the increasing needs of the communication world. The goal of the present document is to describe some service scenarios that can be delivered through the use of Mobile-Edge Computing. These s
25、erve as examples of how MEC can stimulate innovative services and applications that would create a better quality of experience for the end user. MEC will enable a large number of new kinds of applications and services for multiple sectors (such as consumer, enterprise, health, etc.). ETSI ETSI GS M
26、EC-IEG 004 V1.1.1 (2015-11) 5 1 Scope The present document introduces a number of service scenarios that would benefit from the introduction of Mobile-Edge Computing (MEC) technology. Mobile-Edge Computing enhances the Mobile Access Network with MEC servers that enable applications to run in the mob
27、ile edge. Due to the proximity of the server into the Access network, there is a latency reduction of delivering such applications or services. Such applications can expose information that can be used to optimize the network and services, reduce latency, and support creating personalized and contex
28、tualized services. Moreover, new and innovative analytics services enable the operator to monitor usage and service quality. Internet of Things (IOT) applications or enterprise communications can benefit greatly from MEC, as it allows service delivery in close proximity to the actual terminal device
29、s. The focus of the present document is to introduce or provide a non-exhaustive set of service scenarios. It is not the intent nor does the present document provide any requirements. 2 References 2.1 Normative references References are either specific (identified by date of publication and/or editi
30、on number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. Referenced documents which are not found to be publicly available in the expected loca
31、tion might be found at http:/docbox.etsi.org/Reference. NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee their long term validity. The following referenced documents are necessary for the application of the present document. Not applicab
32、le. 2.2 Informative references References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including an
33、y amendments) applies. NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee their long term validity. The following referenced documents are not necessary for the application of the present document but they assist the user with regard to a
34、particular subject area. Not applicable. 3 Definitions and abbreviations 3.1 Definitions For the purposes of the present document, the following terms and definitions apply: analytics: processing which makes use of data to provide actions, insights and/or inference augmented reality: live direct or
35、indirect view of a physical, real-world environment whose elements are enhanced, or supplemented, by computer-generated sensory Internet of Things (IoT): device, machine or entity connected to any other device, machine or entity through a network ETSI ETSI GS MEC-IEG 004 V1.1.1 (2015-11) 6 vehicle:
36、human transport including automobile, car, truck, plane, train, motorcycle 3.2 Abbreviations For the purposes of the present document, the following abbreviations apply: DSRC Dedicated Short Range Communications IoT Internet of Things IP-PBX Internet Protocol Private Branch Exchange LAN Local Area N
37、etwork LTE Long Term Evolution MEC Mobile-Edge Computing PBX Private Branch Exchange QoE Quality of Experience RAN Radio Access Network RNC Radio Network Controller TCP Transmission Control Protocol WLAN Wireless Local Area Network 4 Service scenarios 4.1 Intelligent video acceleration service scena
38、rio 4.1.1 Description of scenario Eliminating mobile content delivery inefficiencies by guiding sources as to real-time network capacity. A Radio Analytics application located at the RAN provides the video server with an indication on the throughput estimated to be available at the radio downlink in
39、terface. The information can be used to assist TCP congestion control decisions and also to ensure that the application-level coding matches the estimated capacity at the radio downlink. 4.1.2 Motivation Improve the end users Quality of Experience (QoE) by reducing the contents time-to-start as well
40、 as video-stall occurrences, and guarantee maximum utilization of the radio networks resources. 4.1.3 Problem statement The behaviour of the Transmission Control Protocol (TCP), which assumes that network congestion is the primary cause for packet loss and high delay, can lead to the inefficient use
41、 of a cellular networks resources and degrade application performance and user experience. The root cause for this inefficiency lies in the fact that TCP has difficulty adapting to rapidly varying network conditions. In cellular networks, the bandwidth available for end devices can vary by an order
42、of magnitude within a few seconds due to changes in the underlying radio channel conditions, caused by the movement of devices, as well as changes in system load when other devices enter and leave the network. 4.1.4 Relation to MEC Figure 1 shows an example of the intelligent video acceleration serv
43、ice scenario. In this scenario, a Radio Analytics application, which resides in a MEC server, provides the video server with a near real-time indication on the throughput estimated to be available at the radio downlink interface. ETSI ETSI GS MEC-IEG 004 V1.1.1 (2015-11) 7 Figure 1: Intelligent Vide
44、o Acceleration The video server may use this information to assist TCP congestion control decisions, for example in selecting the initial window size, setting the value of the congestion window during the congestion avoidance phase, and adjusting the size of the congestion window when the conditions
45、 on the “radio link“ deteriorate. In other words, with this additional information, TCP does not need to overload the network when probing for available resources, nor does it need to rely on heuristics to reduce its sending rate after a congestion episode. This information may also be used to ensur
46、e that the application level coding matches the estimated capacity at the radio downlink. The aim of all of these improvements is to enhance the end users quality of experience by reducing the contents time-to-start as well as video-stall occurrences, and to guarantee maximum utilization of the radi
47、o networks resources. Mobile-edge Computing allows the Radio Analytics application to be deployed on top of platforms implemented by different vendors and across multi-operator networks. This ensures efficient utilization of the network resources in addition to enhanced quality of experience for the
48、 vast majority of end users. 4.2 Video stream analysis service scenario 4.2.1 Description of scenario Consider a video based monitoring system such as vehicle license plate recognition for example to monitor vehicles entering and exiting an area of the city, car parks, for security purposes, etc. Li
49、cense plate information can be captured and sent to a cloud based monitoring service. It may be useful to extract other information from such video streams. 4.2.2 Motivation Moving the video analysis away from the video camera reduces the cost of such cameras, especially when numerous cameras need to be deployed. Furthermore, performing the analysis locally, i.e. close to the mobile base station, mitigates the need to transmit high data video streams when only small pieces of information are required to be extracted from these video streams. 4.2.3 Problem statement Cur
