Internet DRAFT - draft-sarathchandra-coin-appcentres

draft-sarathchandra-coin-appcentres



 



COIN                                                    C. Sarathchandra
INTERNET-DRAFT                                         InterDigital Inc.
Intended Status: Informational                                D. Trossen
Expires: August 28, 2020                                          Huawei
                                                             M. Boniface
                                               University of Southampton
                                                       February 28, 2020


          In-Network Computing for App-Centric Micro-Services
                 draft-sarathchandra-coin-appcentres-02


Abstract

   The application-centric deployment of 'Internet' services has
   increased over the past ten years with many million applications
   providing user-centric services, executed on increasingly more
   powerful smartphones that are supported by Internet-based cloud
   services in distributed data centres, the latter mainly provided by
   large scale players such as Google, Amazon and alike. This draft
   outlines a vision of evolving those data centres towards executing
   app-centric micro-services; we dub this evolved data centre as an
   AppCentre. Complemented with the proliferation of such AppCentres at
   the edge of the network, they will allow for such micro-services to
   be distributed across many places of execution, including mobile
   terminals themselves, while specific micro-service chains equal
   today's applications in existing smartphones. We outline the key
   enabling technologies that needs to be provided for such evolution to
   be realized, including references to ongoing IETF work in some
   areas.


Status of this Memo

   This Internet-Draft is submitted to IETF in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF), its areas, and its working groups.  Note that
   other groups may also distribute working documents as
   Internet-Drafts.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

 


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Table of Contents

   1  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . .  4
   2  Terminology . . . . . . . . . . . . . . . . . . . . . . . . . .  5
   3.  Use Cases  . . . . . . . . . . . . . . . . . . . . . . . . . .  5
     3.1 Mobile Application Function Offloading . . . . . . . . . . .  5
     3.2  Collaborative Gaming  . . . . . . . . . . . . . . . . . . .  7
     3.3. Distributed AI  . . . . . . . . . . . . . . . . . . . . . .  7
     3.4. Content Delivery Networks . . . . . . . . . . . . . . . . .  8
     3.5. Compute-Fabric-as-a-Service (CFaaS) . . . . . . . . . . . .  8
     3.6. Requirements Derived from Use Cases . . . . . . . . . . . .  9
   4  Enabling Technologies . . . . . . . . . . . . . . . . . . . . . 10
     4.1  Application Packaging . . . . . . . . . . . . . . . . . . . 10
     4.2  Service Deployment  . . . . . . . . . . . . . . . . . . . . 11
     4.3. Compute Inter-Connection  . . . . . . . . . . . . . . . . . 12
     4.4. Dynamic Contracts . . . . . . . . . . . . . . . . . . . . . 12
     4.5  Service Routing . . . . . . . . . . . . . . . . . . . . . . 12
     4.6  Service Pinning . . . . . . . . . . . . . . . . . . . . . . 13
     4.7. Opportunistic Multicast . . . . . . . . . . . . . . . . . . 13
     4.8  State Synchronization . . . . . . . . . . . . . . . . . . . 13
   5  Security Considerations . . . . . . . . . . . . . . . . . . . . 13
   6  IANA Considerations . . . . . . . . . . . . . . . . . . . . . . 13
   7  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . . . 13
   8  References  . . . . . . . . . . . . . . . . . . . . . . . . . . 14
     8.1  Normative References  . . . . . . . . . . . . . . . . . . . 14
 


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     8.2  Informative References  . . . . . . . . . . . . . . . . . . 14
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 15














































 


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1  Introduction

   With the increasing dominance of smartphones and application markets,
   the end-user experiences today have been increasingly centered around
   the applications and the ecosystems that smartphone platforms create.
   The experience of the 'Internet' has changed from 'accessing a web
   site through a web browser' to 'installing and running an application
   on a smartphone'. This app-centric model has changed the way services
   are being delivered not only for end-users, but also for business-to-
   consumer (B2C) and business-to-business (B2B) relationships.

   Designing and engineering applications is largely done statically at
   design time, such that achieving significant performance improvements
   thereafter has become a challenge (especially, at runtime in response
   to changing demands and resources). Applications today come
   prepackaged putting them at disadvantage for improving efficiency due
   to the monolithic nature of the application packaging. Decomposing
   application functions into micro-services [MSERVICE1] [MSERVICE2]
   allows applications to be packaged dynamically at run-time taking
   varying application requirements and constraints into consideration.
   Interpreting an application as a chain of micro-services, allows the
   application structure, functionality, and performance to be adapted
   dynamically at runtime in consideration of tradeoffs between quality
   of experience, quality of service and cost.

   Interpreting any resource rich networked computing (and storage)
   capability not just as a pico or micro-data centre, but as an
   application-centric execution data centre (AppCentre), allows
   distributed execution of micro-services where the notion of an
   application constitutes a set of objectives being realized in a
   combined packaging of micro-services under the governance of the
   'application provider'. These micro-services may then be deployed on
   the most appropriate AppCentre (edge/fog/cloud resources) to satisfy
   requirements under varying constraints. In addition, the high degree
   of distribution of application and data partitions, and compute
   resources offered by the execution environment decentralizes control
   between multiple cooperating parties (multi-technology, multi-domain,
   multi-ownership environments). Furthermore, compute resource
   availability may be volatile, particularly when moving along the
   spectrum from well-connected cloud resources over edge data centres
   to user-provided compute resources, such as (mobile) terminals or
   home-based resources such as NAS and IoT devices. 

   We believe that the emergence of AppCentreS will democratize
   infrastructure and service provision to anyone with compute resources
   with the notion of applications providing an element of governing the
   execution of micro-services. This increased distribution will lead to
   new forms of application interactions and user experiences based on
 


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   cooperative AppCentreS (pico-micro and large cloud data centres), in
   which applications are being designed, dynamically composed and
   executed.

2  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

3.  Use Cases

   Although our motivation for the 'AppCentre' term stems from the
   (mobile) application ecosystem, the use cases in this section are not
   limited to mobile applications only. Instead, we interpret
   'applications' as a governing concept of executing a set of micro-
   services where the 'application provider' can reach from those
   realizing mobile applications over novel network applications to
   emerging infrastructure offerings serving a wide range of
   applications in a purpose- (and therefore application-)agnostic
   manner. The following use cases provide examples for said spectrum of
   applications.

3.1 Mobile Application Function Offloading

   Partitioning an application into micro-services allows for denoting
   the application as a collection of functions for a flexible
   composition and a distributed execution, e.g., most functions of a
   mobile application can be categorized into any of three, "receiving",
   "processing" and "displaying" function groups.

   Any device may realize one or more of the micro-services of an
   application and expose them to the execution environment. When the
   micro-service sequence is executed on a single device, the outcome is
   what you see today as applications running on mobile devices.
   However, if any of the three functions are terminated on the device,
   the execution of the rest of the functions may be moved to other
   (e.g., more suitable) devices which have exposed the corresponding
   micro-services to the environment. The result of the latter is
   flexible mobile function offloading, for possible reduction of power
   consumption (e.g., offloading CPU intensive process functions to a
   remote server) or for improved end user experience (e.g., moving
   display functions to a nearby smart TV). 

   The above scenario can be exemplified in an immersive gaming
   application, where a single user plays a game using a VR headset. The
   headset hosts functions that "display" frames to the user, as well as
   the functions for VR content processing and frame rendering combining
 


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   with input data received from sensors in the VR headset. Once this
   application is partitioned into micro-services and deployed in an
   app-centric execution environment, only the "display" micro-service
   is left in the headset, while the compute intensive real-time VR
   content processing micro-services can be offloaded to a nearby
   resource rich home PC, for a better execution (faster and possibly
   higher resolution generation).

   Figure 1 shows one realization of the above scenario, where a 'DPR
   app' running on a mobile device (containing the partitioned
   Display(D), Process(P) and Receive(R) micro services) over an SDN
   network. The packaged applications are made available through a
   localized 'playstore server'. The application installation is
   realized as a 'service deployment' process (Section 4.2.), combining
   the local app installation with a distributed micro-service
   deployment (and orchestration) on most suitable AppCentreS
   ('processing server').

                                  +----------+
                Mobile            |Processing|
             +---------+          | Server   |
             |   App   |          +----------+   
             | +-----+ |              |
             | |D|P|R| |             +--+
             | +-----+ |             |SR|                 Internet
             | +-----+ |             +--+                 / 
             | |  SR | |              |                  /
             | +-----+ |           +----------+     +------+
             +---------+          /|SDN Switch|_____|Border|
                     \ +-------+ / +----------+     |  SR  |
                      \| 5GAN  |/          |        +------+
                       +-------+           |
                                           |	  		   
                                       +----------+			
             +---------+              /|SDN Switch| 
             | +-----+ |   +-------+ / +----------+
             | | SR  | |  /|WIFI AP|/     \     	     
             | +-----+ | / +-------+     +--+     	
             |+-------+|/                |SR|
             ||Display||                /+--+
             ||       ||            +---------+
             |+-------+|            |Playstore|
             +---------+            | Server  |
                  TV                +---------+

          Figure 1: Application Function Offloading Example

   Such localized deployment could, for instance, be provided by a
 


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   visiting site, such as a hotel or a theme park. Once the 'processing'
   micro-service is terminated on the mobile device, the 'service
   routing' (SR) elements in the network (Section 4.3.) route requests
   to the previously deployed 'processing' micro-service running on the
   'processing server' AppCentre over an existing SDN network.

3.2  Collaborative Gaming

   There has been a recent shift from applications that provide single-
   user experiences, such as the ones described in the previous section
   to collaborative/cooperative experiences such as multi-user gaming
   and mixed/virtual reality. The latter leads to increasing amounts of
   interaction where input (e.g., gesture, gaze, touch, movement) and
   output (e.g., visual display, sound, and actuation) needs to be
   processed within strict timing constraints and synchronized to ensure
   temporal and spatial consistency with local and distant users. App-
   centric design allows functions with high data and process coupling
   to be modularized, deployed and executed, such that the subset of
   micro-services is cooperatively executed towards optimizing multi-
   user experiences.

   The same example in previous section can be envisaged from a multi-
   player gaming scenario. Here the micro-services that need to be
   executed cooperatively are executed in a localized and synchronized
   manner for player coordination and synchronizing interaction and
   state between collaborating players.

3.3. Distributed AI

   There is a growing range of use cases demanding for the realization
   of AI capabilities among distributed endpoints. Such demand may be
   driven by the need to increase overall computational power for large-
   scale problems. Other solutions may desire the localization of
   reasoning logic, e.g., for deriving attributes that better preserve
   privacy of the utilized raw input data. Examples for large-scale AI
   problems include biotechnology and astronomy related reasoning over
   massive amounts of observational input data. Examples for localizing
   input data for privacy reasons include radar-like application for the
   development of topological mapping data based on (distributed) radio
   measurements at base stations (and possibly end devices), while the
   processing within radio access networks (RAN) already constitute a
   distributed AI problem to a certain extent albeit with little
   flexibility in distributing the execution of the AI logic.

   Reasoning frameworks, such as TensorFlow, may be utilized for the
   realization of the (distributed) AI logic, building on remote service
   invocation through protocols such as gRPC [GRPC] or MPI [MPI] with
   the intention of providing an on-chip NPU (neural processor unit)
 


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   like abstraction to the AI framework.

3.4. Content Delivery Networks

   Delivery of content to end user often relies on Content Delivery
   Networks (CDNs) storing said content closer to end users for latency
   reduced delivery with DNS-based indirection being utilized to serve
   the request on behalf of the origin server. From the perspective of
   this draft, a CDN can be interpreted as a (network service level)
   application with distributed logic for distributing content from
   origin server to CDN ingress and further to the CDN replication
   points which ultimately serve the user-facing content requests.
   Studies such as those in [FCDN] have shown that content distribution
   at the level of named content, utilizing efficiency Layer 2 multicast
   for replication towards edge CDN nodes, can significantly increase
   the overall network and server efficiency, while reducing indirection
   latency for content retrieval but also reducing required edge storage
   capacity by benefiting from the increased network efficiency to renew
   edge content more quickly against changing demand. 

3.5. Compute-Fabric-as-a-Service (CFaaS)

   App-centric execution environments, consisting of Layer 2 connected
   appcentres in the sense outlined in this draft, provide the
   opportunity for infrastructure providers to offer CFaaS type of
   offerings to application providers for them to utilize the compute
   fabric exposed by this CFaaS offering for the purposes defined
   through their applications. In other words, the compute resources can
   be utilized to execute the desired micro-services of which the
   application is composed, while utilizing the inter-connection between
   those compute resources to do so in a distributed manner. We foresee
   those CFaaS offerings to be tenant-specific, a tenant here defined as
   the provider of at least one application. For this, we foresee an
   interaction between CFaaS provider and tenant to dynamically select
   the appropriate resources to define the demand side of the fabric.
   Conversely, we also foresee the supply side of the fabric to be
   highly dynamic with resources being offered to the fabric through,
   e.g., user-provided resources (whose supply might depend on highly
   context-specific supply policies) or infrastructure resources of
   intermittent availability such as those provided through road-side
   infrastructure in vehicular scenarios. The resulting dynamic demand-
   supply matching establishes a dynamic nature of the compute fabric
   that in turn requires trust relationships to be built dynamically
   between the resource provider(s) and the CFaaS provider. This also
   requires the communication resources to be dynamically adjusted to
   interconnect all resources suitably into the (tenant-specific) fabric
   exposed as CFaaS.

 


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3.6. Requirements Derived from Use Cases

   The following requirements are derived from the presented use cases
   in Section 3.1. to 3.5., numbered according to the section numbers
   although those requirements apply in some cases across more than one
   of the presented use cases. 

   Req 1.1: Any app-centric execution environment MUST provide means for
   routing of service requests between resources in the distributed
   environment.

   Req 1.2: Any app-centric execution environment MUST provide means for
   dynamically choosing the best possible micro-service sequence (i.e.,
   chaining of micro-services) for a given application experience. 

   Req 1.3: Any app-centric execution environment MUST provide means for
   pinning the execution of a specific micro-service to a specific
   resource instance in the distributed environment. 

   Req 1.4: Any app-centric execution environment SHOULD provide means
   for packaging micro-services for deployments in distributed networked
   computing environments, including any constraints regarding the
   deployment of service instances in specific network locations or
   compute resources. Such packaging SHOULD conform to existing
   application deployment models, such as mobile application packaging,
   TOSCA orchestration templates or tar balls or combinations thereof. 

   Req 2.1: Any app-centric execution environment MUST provide means for
   real-time synchronization and consistency of distributed application
   states.

   Req 3.1: Any app-centric execution environment MUST provide means to
   specify the constraints for placing (AI) execution logic in certain
   logical execution points (and their associated physical locations).

   Req 3.2: Any app-centric execution environment MUST provide support
   for app/micro-service specific invocation protocols.

   Req 4.1: Any app-centric execution environment SHOULD utilize Layer 2
   multicast transmission capabilities for responses to concurrent
   service requests. 

   Req 5.1: Any app-specific execution environment SHOULD expose means
   to specify the requirements for the tenant-specific compute fabric
   being utilized for the app execution.

   Req 5.2: Any app-specific execution environment SHOULD allow for
   dynamic integration of compute resources into the compute fabric
 


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   being utilized for the app execution; those resources include, but
   are not limited to, end user provided resources.

   Req 5.3: Any app-specific execution environment MUST provide means to
   optimize the inter-connection of compute resources, including those
   dynamically added and removed during the provisioning of the tenant-
   specific compute fabric. 

   Req 5.4: Any app-specific execution environment MUST provide means
   for ensuring availability and usage of resources is accounted for.


4  Enabling Technologies

   EDITOR NOTE: Section 4 will be updated and extended in the next
   version of the draft, including the addressing of specific
   requirements listed in Section 3.6. 

4.1  Application Packaging

   Applications often consist of one or more sub-elements (e.g., audio,
   visual, hepatic elements) which are 'packaged' together, resulting in
   the final installable software artifact. Conventionally, application
   developers perform the packaging process at design time, by packaging
   a set of software components as a (often single) monolithic software
   package, for satisfying a set of predefined application
   requirements.

   Decomposing micro-services of an application, and then executing them
   on peer execution points in AppCentreS (e.g., on an app-centric
   serverless runtime [SRVLESS]) can be done with design-time planning.
   Micro-service decomposition process involves, defining clear
   boundaries of the micro-service (e.g., using wrapper classes for
   handling input/output requests), which could be done by the
   application developer at design-time (e.g., through Android app
   packaging by including, as part of the asset directory, a service
   orchestration template [TOSCA] that describes the decomposed micro-
   services). Likewise, the peer execution points could be 'known' to
   the application (e.g., using well-known and fixed peer execution
   points on AppCentreS) and incorporated with the micro-services by the
   developer at design-time. 

   Existing programming frameworks address decomposition and execution
   of applications centering around other aspects such as concurrency
   [ERLANG]. For decomposing at runtime, application elements can be
   profiled using various techniques such as dynamic program analysis or
   dwarf application benchmarks. The local profiler information can be
   combined with the profiler information of other devices in the
 


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   network for improved accuracy. The output of such a profiler process
   can then be used to identify smaller constituting sub-components of
   the application in forms of pico-services, their interdependencies
   and data flow (e.g., using caller/callee information, instruction
   usage). Due to the complex nature of resulting application structure
   and therefore its increased overhead, in most cases, it may not be
   optimal to decompose applications at the pico level. Therefore, one
   may cluster pico-services into micro-services with common
   characteristics, enabling a meaningful (e.g., clustering pico-
   services with same resource dependency) and a performant
   decomposition of applications. Characteristics of micro-services can
   be defined as a set of concepts using an ontology language, which can
   then be used for clustering similar pico-services into micro-
   services. Micro-services may then be partitioned along their
   identified borders. Moreover, mechanisms for governance, discovery
   and offloading can be employed for 'unknown' peer execution points on
   AppCentreS with distributed loci of control.

   Therefore, with this app-centric model, application packaging can be
   done at runtime by constructing micro-service chains for satisfying
   requirements of experiences (e.g., interaction requirements), under
   varying constraints (e.g., temporal consistency between multiple
   players within a shared AR/VR world)[SCOMPOSE]. Such packaging
   includes mechanisms for selecting the best possible micro-services
   for a given experience at runtime in the multi-X environment. These
   run-time packaging operations may continuously discover the 'unknown'
   and adapt towards an optimal experience. Such decision mechanisms
   handle the variability, volatility and scarcity within this multi-X
   framework.

4.2  Service Deployment

   The service function chains, constituting each individual
   application, will need deployment mechanisms in a true multi-X
   (multi-user, multi-infrastructure, multi-domain) environment
   [SDEPLOY1][SDEPLOY2]. Most importantly, application installation and
   orchestration processes are married into one, as a set of procedures
   governed by device owners directly or with delegated authority.
   However, apart from extending towards multi-X environments, the
   process also needs to cater for changes in the environment, caused,
   e.g., by movement of users, new pervasive sensors/actuators, and
   changes to available infrastructure resources. Methods to deploy
   service functions as executable code into chosen service execution
   points, supporting the various endpoint realizations (e.g., device
   stacks, COTS stacks, etc.), and service function endpoint realization
   through utilizing existing and emerging virtualization techniques.

   A combination of application installation procedure and orchestrated
 


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   service deployment can be achieved by utilizing the application
   packaging with integrated service deployment templates described in
   Section 4.1 such that the application installation procedure on the
   installing device is being extended to not only install the local
   application package but also extract the service deployment template
   for orchestrating with the localized infrastructure, using, for
   instance, REST APIs for submitting the template to the orchestrator. 

4.3. Compute Inter-Connection  

   NOTE: left for future revision

4.4. Dynamic Contracts

   NOTE: left for future revision

4.5  Service Routing

   Service routing within a combined compute and network infrastructure
   that will enable true end-to-end experiences across distributed
   application execution points provisioned on distant cloud, edge and
   device-centric resources (e.g., using ICN/name-based routing
   methods), is a key aspect of app-centric micro-service execution.
   Once the micro-services are packaged and deployed in such highly
   distributed micro-data centres, the routing mechanisms will ensure
   efficient information exchange (e.g., for satisfying application
   requirements) between corresponding micro-services within the multi-X
   execution environment.

   Routing becomes a problem of routing the micro-service requests, not
   just packets, as done through IP. Traditionally, the combination of
   the Domain Naming Service (DNS) and IP routing has been used for this
   purpose. However, the advent of virtualization with use cases such as
   those outlined above have made it challenging to further rely on the
   DNS. This is mainly down to the long delay in updating DNS entries to
   'point' to the right micro-service instances. If one was to use the
   DNS, one would be updating the DNS entries at a high rate, caused by
   the diversity of trigger, e.g., through movement. DNS has not been
   designed for such frequent update, rendering it useless for such
   highly dynamic applications. With many edge scenarios in the VR/AR
   space demanding interactivity and being latency-sensitive, efficient
   routing will be key to any solution. 

   Various ongoing work on service request forwarding [nSFF] with the
   service function chaining [RFC7665] framework as well as name-based
   routing [ICN5G][ICN4G] addressing some aspects described above albeit
   with a focus on HTTP as the main invocation protocol. Extensions will
   be required to support other invocation protocols, such as GRPC or
 


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   MPI (for distributed AI use cases, as outlined in Section 3.3.).  

4.6  Service Pinning

   Allocating the right resources to the right micro-services is a
   fundamental task when executing micro-services on such highly
   distributed app-centric micro-data centres (e.g., resource management
   in cloud [CLOUDFED]), particularly in the light of volatile resource
   availability as well as concurrent and highly dynamic resource
   access. Once the specific set of micro-services of an application has
   been identified, during the lifetime of the application, requirements
   (e.g., QoS) must be ensured by the execution environment. Therefore,
   all micro-data centres and the execution environment will realize
   mechanisms for ensuring the utilization of specific resources within
   a pool of resources (i.e., resources in all app-centric micro-data
   centres), for a specific set of micro-services belonging to one
   application, while also ensuring integrity in the wider system.

4.7. Opportunistic Multicast 

   NOTE: left for future revision

4.8  State Synchronization 

   Given the highly distributed nature of app-centric micro-services,
   their state exchange and synchronization is a very crucial aspect for
   ensuring in-application and system wide consistency. Mechanisms that
   ensure consistency will ensure that data is synchronized with
   different spatial, temporal and relational data within a given time
   period.

5  Security Considerations

   N/A

6  IANA Considerations

   N/A

7  Conclusion

   This draft positions the evolution of data centres as one of becoming
   execution centres for the app-centric experiences provided today
   mainly by smart phones directly. With the proliferation of data
   centres closer to the end user in the form of edge-based micro data
   centres, we believe that app-centric experiences will ultimately be
   executed across those many, highly distributed execution points that
   this increasingly rich edge environment will provide, such as smart
 


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   glasses and IoT devices. Although a number of activities are
   currently underway to address some of the challenges for realizing
   such AppCentre evolution, we believe that the proposed COIN research
   group will provide a suitable forum to drive forward the remaining
   research and its dissemination into working systems and the necessary
   standardization of key aspects and protocols.

8  References

8.1  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, DOI
              10.17487/RFC2119, March 1997, <https://www.rfc-
              editor.org/info/rfc2119>.

   [RFC7665]  Halpern, J., Ed., and C. Pignataro, Ed., "Service Function
              Chaining (SFC) Architecture", RFC 7665, DOI
              10.17487/RFC7665, October 2015, <https://www.rfc-
              editor.org/info/rfc7665>.

8.2  Informative References

   [MSERVICE1] Dragoni, N., Giallorenzo, S., Lafuente, A. L., Mazzara,
              M., Montesi, F., Mustafin, R., & Safina, L. (2017).
              Microservices: yesterday,today, and tomorrow. In Present
              and Ulterior Software Engineering (pp. 195-216). Springer,
              Cham.

   [MSERVICE2] Balalaie, A., Heydarnoori, A., & Jamshidi, P. (2016).
              Microservices architecture enables devops: Migration to a
              cloud-native architecture. IEEE Software, 33(3), 42-52.

   [SRVLESS]  C. Cicconetti, M. Conti and A. Passarella, "An
              Architectural Framework for Serverless Edge Computing:
              Design and Emulation Tools," 2018 IEEE International
              Conference on Cloud Computing Technology and Science
              (CloudCom), Nicosia, 2018, pp. 48-55. doi:
              10.1109/CloudCom2018.2018.00024

   [TOSCA]    Topology and Orchestration Specification for Cloud
              Applications Version 1.0. 25 November 2013. OASIS
              Standard. <http://docs.oasis-
              open.org/tosca/TOSCA/v1.0/os/TOSCA-v1.0-os.html>.

   [ERLANG]   Armstrong, Joe, et al. "Concurrent programming in ERLANG."
              (1993).

 


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   [SCOMPOSE] M. Hirzel, R. Soule, S. Schneider, B. Gedik, and R. Grimm,
              "A Catalog of Stream Processing Optimizations", ACM 
              Computing Surveys,46(4):1-34, Mar. 2014

   [SDEPLOY1] Lu, H., Shtern, M., Simmons, B., Smit, M., & Litoiu, M.
              (2013, June). Pattern-based deployment service for next
              generation clouds. In 2013 IEEE Ninth World Congress on
              Services (pp. 464-471). IEEE. 

   [SDEPLOY2] Eilam, T., Elder, M., Konstantinou, A. V., & Snible, E.
              (2011, May). Pattern-based composite application
              deployment. In 12th IFIP/IEEE International Symposium on
              Integrated Network Management (IM 2011) and Workshops (pp.
              217-224). IEEE.

   [nSFF]     Trossen, D., Purkayastha, D., Rahman, A., "Name-Based
              Service Function Forwarder (nSFF) component within SFC
              framework", <https://datatracker.ietf.org/doc/draft-
              trossen-sfc-name-based-sff> (work in progress), April
              2019.

   [ICN5G]    Ravindran, R., Suthar, P., Trossen, D., Wang, C., White,
              G., "Enabling ICN in 3GPP's 5G NextGen Core Architecture",
              <https://tools.ietf.org/html/draft-ravi-icnrg-5gc-icn-03>
              (work in progress), March 2019.

   [ICN4G]    Suthar, P., Jangam, Ed., Trossen, D., Ravindran, R.,
              "Native Deployment of ICN in LTE, 4G Mobile Networks",
              <https://tools.ietf.org/html/draft-irtf-icnrg-icn-lte-4g-
              03> (work in progress), March 2019.

   [CLOUDFED] M. Liaqat, V. Chang, A. Gani, S. Hafizah Ab Hamid, M.
              Toseef, U. Shoaib, R. Liaqat Ali, "Federated cloud
              resource management: Review and discussion", Elsevier
              Journal of Network and Computer Applications, 2017.

   [GRPC] High performance open source universal RPC framework,
              https://grpc.io/

   [MPI] A. Vishnu, C. Siegel, J. Daily, "Distributed TensorFlow with
              MPI", https://arxiv.org/pdf/1603.02339.pdf  

   [FCDN] M. Al-Naday, M. J. Reed, J. Riihijarvi, D. Trossen, N. Thomos,
              M. Al-Khalidi, "fCDN: A Flexible and Efficient CDN
              Infrastructure without DNS Redirection of Content
              Reflection", https://arxiv.org/pdf/1803.00876.pdf  

Authors' Addresses
 


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   Chathura Sarathchandra
   InterDigital Europe, Ltd.
   64 Great Eastern Street, 1st Floor
   London EC2A 3QR
   United Kingdom	

   Email: Chathura.Sarathchandra@InterDigital.com



   Dirk Trossen
   Huawei Technologies Duesseldorf GmbH
   Riesstr. 25C
   80992 Munich
   Germany		

   Email: Dirk.Trossen@Huawei.com



   Michael Boniface
   University of Southampton
   University Road
   Southampton SO17 1BJ
   United Kingdom	

   Email: mjb@it-innovation.soton.ac.uk 
























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