| Internet-Draft | COIN Use Cases | June 2023 |
| Kunze, et al. | Expires 1 January 2024 | [Page] |
Computing in the Network (COIN) comes with the prospect of deploying processing functionality on networking devices, such as switches and network interface cards. While such functionality can be beneficial, it has to be carefully placed into the context of the general Internet communication and it needs to be clearly identified where and how those benefits apply.¶
This document presents some use cases to demonstrate how a number of salient COIN-related applications can benefit from COIN. It further identifies their essential requirements, using normative language to provide a formalized structure for a subsequent analysis. It is a product of the Computing in the Network Research Group (COINRG). It is not an IETF product and it is not a standard.¶
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The Internet was designed as a best-effort packet network, forwarding packets from source to destination with limited guarantees regarding their timely and successful reception. Data manipulation, computation, and more complex protocol functionality is generally provided by the end-hosts while network nodes are kept simple and only offer a "store and forward" packet facility. This simplicity of purpose of the network has shown to be suitable for a wide variety of applications and has facilitated the rapid growth of the Internet.¶
However, with the rise of new services, some of which are described in this document, there are more and more fields that require more than best-effort forwarding including strict performance guarantees or closed-loop integration to manage data flows. In this context, allowing for a tighter integration of computing and networking resources for enabling a more flexible distribution of computation tasks across the network, e.g., beyond 'just' endpoints, may help to achieve the desired guarantees and behaviors as well as increase overall performance.¶
The vision of 'in-network computing' and the provisioning of such capabilities that capitalize on joint computation and communication resource usage throughout the network is part of the move from a telephone network analogy of the Internet into a more distributed computer board architecture. We refer to those capabilities as 'COIN capabilities' in the remainder of the document.¶
We believe that this vision of 'in-network computing' can be best outlined along four dimensions of use cases, namely those that (i) provide new user experiences through the utilization of COIN capabilities (referred to as 'COIN experiences'), (ii) enable new COIN systems, e.g., through new interactions between communication and compute providers, (iii) improve on already existing COIN capabilities, and (iv) enable new COIN capabilities. Sections 3 through 6 capture those categories of use cases and provide the main structure of this document. The goal is to present how computing resources inside the network impact existing services and applications or allow for innovation in emerging fields.¶
By delving into some individual examples within each of the above categories, we outline opportunities and propose possible research questions for consideration by the wider community when pushing forward 'in-network computing' architectures. Furthermore, identifying requirements for an evolving solution space of COIN capabilities is another objective of the use case descriptions. To achieve this, the following taxonomy is proposed to describe each of the use cases:¶
This document discusses these six aspects along a number of individual use cases to demonstrate the diversity of COIN applications. It is intended as a basis for further analyses and discussions within the Computing in the Network Research Group (COINRG) and the wider research community. A companion document [USECASEANALYSIS] is tasked with performing a cross-use case analysis, i.e., summarizing the key research questions and identifying key requirements across all use cases. This document represents the consensus of COINRG.¶
This document uses the terminology outlined in [TERMINOLOGY].¶
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 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.¶
The 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 that also incorporate input data received from sensors in the VR headset.¶
Once this application is partitioned into constituent (COIN) programs and deployed throughout a COIN system, utilizing the COIN execution environment, only the "display" (COIN) program may be left in the headset, while the compute intensive real-time VR content processing (COIN) program can be offloaded to a nearby resource rich home PC or a PND in the operator's access network, for a better execution (faster and possibly higher resolution generation).¶
Partitioning a mobile application into several constituent (COIN) programs allows for denoting the application as a collection of (COIN) functions for a flexible composition and a distributed execution. In our example above, 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 (COIN) programs of a mobile application and expose them to the (COIN) system and its constituent (COIN) execution environments. When the (COIN) program sequence is executed on a single device, the outcome is what you see today as applications running on mobile devices.¶
However, the execution of a (COIN) program may be moved to other (e.g., more suitable) devices, including PNDs, which have exposed the corresponding (COIN) program as individual (COIN) program instances to the (COIN) system by means of a 'service identifier'. The result is the equivalent to '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) by selecting more suitably placed (COIN) program instances in the overall (COIN) system.¶
Figure 1 shows one realization of the above scenario, where a 'DPR app' is running on a mobile device (containing the partitioned Display(D), Process(P) and Receive(R) COIN programs) over an SDN network. The packaged applications are made available through a localized 'playstore server'. The mobile application installation is realized as a 'service deployment' process, combining the local app installation with a distributed deployment (and orchestration) of one or more (COIN) programs on most suitable end systems or PNDs ('processing server').¶
+----------+ Processing Server
Mobile | +------+ |
+---------+ | | P | |
| App | | +------+ |
| +-----+ | | +------+ |
| |D|P|R| | | | SR | |
| +-----+ | | +------+ | Internet
| +-----+ | +----------+ /
| | SR | | | /
| +-----+ | +----------+ +------+
+---------+ /|SDN Switch|_____|Border|
+-------+ / +----------+ | SR |
| 5GAN |/ | +------+
+-------+ |
+---------+ |
|+-------+| +----------+
||Display|| /|SDN Switch|
|+-------+| +-------+ / +----------+
|+-------+| /|WIFI AP|/
|| D || / +-------+ +--+
|+-------+|/ |SR|
|+-------+| /+--+
|| SR || +---------+
|+-------+| |Playstore|
+---------+ | Server |
TV +---------+
Such localized deployment could, for instance, be provided by a visiting site, such as a hotel or a theme park. Once the 'processing' (COIN) program is terminated on the mobile device, the 'service routing' (SR) elements in the network route (service) requests instead to the (previously deployed) 'processing' (COIN) program running on the processing server over an existing SDN network. Here, capabilities and other constraints for selecting the appropriate (COIN) program, in case of having deployed more than one, may be provided both in the advertisement of the (COIN) program and the service request itself.¶
As an extension to the above scenarios, we can also envision that content from one processing (COIN) program may be distributed to more than one display (COIN) program, e.g., for multi/many-viewing scenarios, thereby realizing a service-level multicast capability towards more than one (COIN) program.¶
The ETSI Mobile Edge Computing (MEC) [ETSI] suite of technologies provides solutions for mobile function offloading by allowing mobile applications to select resources in edge devices to execute functions instead of the mobile device directly. For this, ETSI MEC utilizes a set of interfaces for the selection of suitable edge resources, connecting to so-called MEC application servers, while also allowing for sending data for function execution to the application server.¶
However, the technologies do not utilize micro-services as described in our use case and also do not allow for the dynamic selection and redirection of micro-service calls to varying edge resources rather than a single MEC application server.¶
Also, the selection of the edge resource (the app server) is relatively static, relying on DNS-based endpoint selection, which does not cater to the requirements of the example provided above, where the latency for redirecting to another device lies within few milliseconds for aligning with the framerate of the display micro-service.¶
Lastly, MEC application servers are usually considered resources provided by the network operator through its MEC infrastructure, while our use case here also foresees the placement and execution of micro-services in end user devices.¶
There also exists a plethora of mobile offloading platforms provided through proprietary platforms, all of which follow a similar approach as ETSI MEC in that a selected edge application server is being utilized to send functional descriptions and data for execution.¶
The draft at [APPCENTRES] outlines a number of enabling technologies for the use case, some of which have been realized in an Android-based realization of the micro-services as a single application, which is capable to dynamically redirect traffic to other micro-service instances in the network. This capability, together with the underlying path-based forwarding capability (using SDN) was demonstrated publicly, e.g., at the Mobile World Congress 2018 and 2019.¶
Virtual Reality (VR), Augmented Reality (AR), and immersive media, now globally referred to as Extended Reality (XR) and the bases for the metaverse, are the drivers of a number of advances in interactive technologies. While initially associated with gaming and entertainment, metaverse applications now include remote diagnosis, maintenance, telemedicine, manufacturing and assembly, intelligent agriculture, smart cities, and immersive classrooms. XR is one example of the multisource-multidestination problem that combines video and haptics in interactive multi-party interactions under strict delay requirements.¶
Indeed, XR applications require real-time interactivity for immersive and increasingly mobile immersive applications with tactile and time-sensitive data. Because high bandwidth is needed for high resolution images and local rendering for 3D images and holograms, they are difficult to run over traditional networks which limits some of its potential benefits in the collaborative space. As a consequence, innovation is needed to unlock the full potential of the applications.¶
XR experiences, especially those involving collaboration, are difficult to deliver with a client-server cloud-based solution as they require a combination of: stream synchronization, low delays and delay variations, means to recover from losses and optimized caching and rendering as close as possible to the user at the network edge. Furthermore, when XR deals with personal information and protected content, XR application must also provide a secure environment and ensure user privacy. Additionally, the sheer amount of data needed for and generated by the XR applications, such as video holography, put them squarely in the realm of data-driven applications that can use recent trend analysis and mechanisms, as well as machine learning to find the optimal caching and processing solution and hopefully reduce the size of the data that needs transiting through the network. Other mechanims such as data filtering and reduction, functional distribution and partitioning are also needed to accommodate the low delay needs for the same applications.¶
Those characterisitics of XR makes it ideal to profit from some COIN capabilities to enable XR over networks. COIN can enable the distribution of the service components across different nodes on the path from content source to rendering destination. For example, data filtering, image rendering, and video processing leveraging different HW capabilities with combinations of CPU and GPU at the network edge and in the fog, where the content is consumed, represent possible remedies for the high bandwidth demands of XR. Machine learning across the network nodes can better manage the data flows by distributing them over more adequate paths. In order to provide adequate quality of experience, multi-variate and heterogeneous resource allocation and goal optimization problems need to be solved, likely requiring advanced analysis and articificial intelligence. For the purpose of this document, it is important to note that the use of COIN for XR does not imply a specific protocol but targets an architecture enabling the deployment of the services. In this context, similar considerations as for Section 3.1 apply.¶
XR profits from extensive research in the past years in gaming, machine learning, network telemetry, high resolution imaging, smart cities, and IoT. Information Centric Networking (and related) approaches that combine publish subscribe and distributed storage are also very suited for the multisource-multidestination applications of XR. Hence XR solutions exist and are more and more deployed outside entertainment, and with the focus on the Metaverse the number of publications related to XR has skyrocketed.¶
However, in terms of networking which is the focus of this document current deployments remain mostly one-way: information is sent to the destination, rendered and displayed based on local processing. A lot of the video information goes upstream. There are still very little truly interactive immersive media applications over networks except within a single subnetwork that is characteristic of some smart cities, manufacturing applications or training.¶
While delay is inherently related to information transmission and if we continue the analogy of the computer board to highlight some of the COIN capabilities in terms of computation and storage but also allocation of resources, there are some opportunities that XR could take advantage of:¶
This use case covers live productions of the performing arts where the performers and audience are in different physical locations. The performance is conveyed to the audience through multiple networked streams which may be tailored to the requirements of individual audience members; and the performers receive live feedback from the audience.¶
There are two main aspects: i) to emulate as closely as possible the experience of live performances where the performers and audience are co-located in the same physical space, such as a theater; and ii) to enhance traditional physical performances with features such as personalization of the experience according to the preferences or needs of the audience members.¶
Examples of personalization include:¶
There are several chained functional entities which are candidates for being deployed as (COIN) programs:¶
Personalization functions¶
These are candidates for deployment as (COIN) Programs in PNDs rather than being located in end-systems (at the performers' site, the audience members' premises or in a central cloud location) for several reasons:¶
Note: Existing solutions for some aspects of this use case are covered in the Mobile Application Offloading, Extended Reality, and Content Delivery Networks use cases.¶
The control of physical processes and components of industrial production lines is essential for the growing automation of production and ideally allows for a consistent quality level. Traditionally, the control has been exercised by control software running on programmable logic controllers (PLCs) located directly next to the controlled process or component. This approach is best-suited for settings with a simple model that is focused on a single or few controlled components.¶
Modern production lines and shop floors are characterized by an increasing number of involved devices and sensors, a growing level of dependency between the different components, and more complex control models. A centralized control is desirable to manage the large amount of available information which often has to be pre-processed or aggregated with other information before it can be used. PLCs are not designed for this array of tasks and computations could theoretically be moved to more powerful devices. These devices are no longer close to the controlled objects and induce additional latency. Moving compute functionality onto COIN execution environments inside the network offers a new solution space to these challenges, providing new compute locations with much smaller latencies.¶
A control process consists of two main components as illustrated in Figure 2: a system under control and a controller. In feedback control, the current state of the system is monitored, e.g., using sensors, and the controller influences the system based on the difference between the current and the reference state to keep it close to this reference state.¶
reference
state ------------ -------- Output
----------> | Controller | ---> | System | ---------->
^ ------------ -------- |
| |
| observed state |
| --------- |
-------------------| Sensors | <-----
---------
Apart from the control model, the quality of the control primarily depends on the timely reception of the sensor feedback which can be subject to tight latency constraints, often in the single-digit millisecond range. While low latencies are essential, there is an even greater need for stable and deterministic levels of latency, because controllers can generally cope with different levels of latency, if they are designed for them, but they are significantly challenged by dynamically changing or unstable latencies. The unpredictable latency of the Internet exemplifies this problem if, e.g., off-premise cloud platforms are included.¶
Control functionality is traditionally executed on PLCs close to the machinery. These PLCs typically require vendor-specific implementations and are often hard to upgrade and update which makes such control processes inflexible and difficult to manage. Moving computations to more freely programmable devices thus has the potential of significantly improving the flexibility. In this context, directly moving control functionality to (central) cloud environments is generally possible, yet only feasible if latency constraints are lenient.¶
Early approaches such as [RUETH] and [VESTIN] have already shown the general applicability of leveraging COIN for in-network control.¶
RQ 4.1.1: How to derive simplified versions of the global (control) function?¶
RQ 4.1.2: How to distribute the simplified versions in the network?¶
In modern industrial networks, processes and machines are extensively monitored by distributed sensors with a large spectrum of capabilities, ranging from simple binary (e.g., light barriers) to sophisticated sensors with varying degrees of resolution. Sensors further serve different purposes, as some are used for time-critical process control while others represent redundant fallback platforms. Overall, there is a high level of heterogeneity which makes managing the sensor output a challenging task.¶
Depending on the deployed sensors and the complexity of the observed system, the resulting overall data volume can easily be in the range of several Gbit/s [GLEBKE]. These volumes are often already difficult to handle in local environments and it becomes even more challenging when off-premise clouds are used for managing the data. While large networking companies can simply upgrade their infrastructure to accommodate the accruing data volumes, most industrial companies operate on tight infrastructure budgets and upgrading is hence not always feasible or possible. A major challenge is thus to devise a methodology that is able to handle such amounts of data over limited access links.¶
Data filtering and pre-processing, similar to the considerations in Section 3.2, can be building blocks for new solutions in this space. Such solutions, however, might also have to address the added challenge of business data leaving the premises and control of the company. As this data could include sensitive information or valuable business secrets, additional security measures have to be taken. Yet, typical security measures such as encrypting the data make filtering or pre-processing approaches hardly applicable as they typically work on unencrypted data. Consequently, incorporating security into these approaches, either by adding functionality for handling encrypted data or devising general security measures, is thus an additional auspicious field for research.¶
In essence, the described monitoring systems consist of sensors that produce large volumes of monitoring data. This data is then transmitted to additional components that provide data processing and analysis capabilities or simply store the data in large data silos.¶
As sensors are often set up redundantly, part of the collected data might also be redundant. Moreover, sensors are often hard to configure or not configurable at all which is why their resolution or sampling frequency is often larger than required. Consequently, it is likely that more data is transmitted than is needed or desired, prompting the deployment of filtering techniques. For example, COIN programs deployed in the on-premise network could filter out redundant or undesired data before it leaves the premise using simple traffic filters, thus reducing the required (upload) bandwidths. The available sensor data could be scaled down using packet-based sub-sampling or using filtering as long as the sensor value is in an uninteresting range while forwarding with a higher resolution once the sensor value range becomes interesting (cf. [KUNZE-SIGNAL]). While the former variant is oblivious to the semantics of the sensor data, the latter variant requires an understanding of the current sensor levels. In any case, it is important that end-hosts are informed about the filtering so that they can distinguish between data loss and data filtered out on purpose.¶
In practice, the collected data is further processed using manifold computations. Some of them are very complex or need the complete sensor data during the computation, but there are also simpler operations which can already be done on subsets of the overall dataset or earlier on the communication path as soon as all data is available. One example is finding the maximum of all sensor values which can either be done iteratively at each intermediate hop or at the first hop, where all data is available. Using expert knowledge about the exact computation steps and the concrete transmission path of the sensor data, simple computation steps can thus be deployed in the on-premise network, again reducing the overall data volume.¶
Current approaches for handling such large amounts of information typically build upon stream processing frameworks such as Apache Flink. While they allow for handling large volume applications, they are tied to performant server machines and upscaling the information density also requires a corresponding upscaling of the compute infrastructure.¶
RQ 4.2.2: How to design COIN programs for (semantic) packet filtering?¶
RQ 4.2.3: Which kinds of COIN programs can be leveraged for (pre-)processing steps?¶
RQ 4.2.6: How to incorporate the (pre-)processing and filtering steps into the overall system?¶
Despite an increasing automation in production processes, human workers are still often necessary. Consequently, safety measures have a high priority to ensure that no human life is endangered. In traditional factories, the regions of contact between humans and machines are well-defined and interactions are simple. Simple safety measures like emergency switches at the working positions are enough to provide a good level of safety.¶
Modern factories are characterized by increasingly dynamic and complex environments with new interaction scenarios between humans and robots. Robots can either directly assist humans or perform tasks autonomously. The intersect between the human working area and the robots grows and it is harder for human workers to fully observe the complete environment. Additional safety measures are essential to prevent accidents and support humans in observing the environment.¶
Industrial safety measures are typically hardware solutions because they have to pass rigorous testing before they are certified and deployment-ready. Standard measures include safety switches and light barriers. Additionally, the working area can be explicitly divided into 'contact' and 'safe' areas, indicating when workers have to watch out for interactions with machinery.¶
These measures are static solutions, potentially relying on specialized hardware, and are challenged by the increased dynamics of modern factories where the factory configuration can be changed on demand. Software solutions offer higher flexibility as they can dynamically respect new information gathered by the sensor systems, but in most cases they cannot give guaranteed safety. COIN systems could leverage the increased availability of sensor data and the detailed monitoring of the factories to enable additional safety measures with shorter response times and higher guarantees. Different safety indicators within the production hall could be combined within the network so that PNDs can give early responses if a potential safety breach is detected. For example, the positions of human workers and robots could be tracked and robots could be stopped when they get too close to a human in a non-working area or if a human enters a defined safety zone. More advanced concepts could also include image data or combine arbitrary sensor data.¶
Due to the importance of safety, there is a wide range of software-based approaches aiming at enhancing security. One example are tag-based systems, e.g., using RFID, where drivers of forklifts can be warned if pedestrian workers carrying tags are nearby. Such solutions, however, require setting up an additional system and do not leverage existing sensor data.¶
Delivery of content to end users often relies on Content Delivery Networks (CDNs). CDNs store said content closer to end users for latency-reduced delivery and they often utilize DNS-based indirection 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) set of (COIN) programs. These programs implement a distributed logic for first distributing content from the origin server to the CDN ingress and then further to the CDN replication points which ultimately serve the user-facing content requests.¶
CDN technologies have been well described and deployed in the existing Internet. Core technologies like Global Server Load Balancing (GSLB) [GSLB] and Anycast server solutions are used to deal with the required indirection of a content request (usually in the form of an HTTP request) to the most suitable local CDN server. Content is replicated from seeding servers, which serve as injection points for content from content owners/producers, to the actual CDN servers, who will eventually serve the user's request. The replication architecture and mechanisms itself differs from one (CDN) provider to another, and often utilizes private peering or network arrangements in order to distribute the content internationally and regionally.¶
Studies such as those in [FCDN] have shown that content distribution at the level of named content, utilizing efficient (e.g., Layer 2) multicast for replication towards edge CDN nodes, can significantly increase the overall network and server efficiency. It also reduces indirection latency for content retrieval as well as required edge storage capacity by benefiting from the increased network efficiency to renew edge content more quickly against changing demand.¶
In addition to the research questions in Section 3.1.5:¶
Requirements 3.1.1 through 3.1.6 also apply for CDN service access. In addition:¶
Layer 2 connected compute resources, e.g., in regional or edge data centers, base stations, and even end user devices, provide the opportunity for infrastructure providers to offer CFaaS-like offerings to application providers. App and service providers may utilize the compute fabric exposed by this CFaaS offering for the purposes defined through their applications and services. In other words, the compute resources can be utilized to execute the desired (COIN) programs of which the application is composed, while utilizing the interconnection 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 suitably interconnect all resources into the (tenant-specific) fabric exposed as CFaaS.¶
There exist a number of technologies to build non-local (wide area) Layer 2 networks, which in turn allows for connecting compute resources for a distributed computational task. 5G-LAN [SA2-5GLAN] specifies a cellular L2 bearer for interconnecting L2 resources within a single cellular operator. The work in [ICN5GLAN] outlines using a path-based forwarding solution over 5G-LAN as well as SDN-based LAN connectivity together with an ICN-based naming of IP and HTTP-level resources to achieve computational interconnections, including scenarios such as those outlined in Section 3.1. L2 network virtualization (see, e.g., [L2Virt]) is one of the methods used for realizing so-called 'cloud-native' applications for applications developed with 'physical' networks in mind, thus forming an interconnected compute and storage fabric.¶
In addition to the research questions in Section 3.1.5:¶
Requirements 3.1.1 through 3.1.6 also apply for the provisioning of services atop the CFaaS. In addition:¶
The term "virtual network programming" is proposed to describe mechanisms by which tenants deploy and operate COIN programs in their virtual network. Such COIN programs can, e.g., be P4 programs, OpenFlow rules, or higher layer programs. This feature can enable other use cases described in this draft to be deployed using virtual networks services, over underlying networks such as datacenters, mobile networks, or other fixed or wireless networks.¶
For example, COIN programs could perform the following on a tenant's virtual network:¶
To provide a concrete example of virtual COIN programming, we consider a use case using a 5G underlying network, the 5GLAN virtualization technology, and the P4 programming language and environment. Section 5.1 of [I-D.ravi-icnrg-5gc-icn] provides a description of the 5G network functions and interfaces relevant to 5GLAN, which are otherwise specified in [TS23.501] and [TS23.502]. From the 5GLAN service customer/tenant standpoint, the 5G network operates as a switch.¶
In the use case depicted in Figure 3, the tenant operates a network including a 5GLAN network segment (seen as a single logical switch), as well as fixed segments. The mobile devices (or User Equipment nodes) UE1, UE2, UE3 and UE4 are in the same 5GLAN, as well as Device1 and Device2 (through UE4). This scenario can take place in a plant or enterprise network, using, e.g., a 5G Non-Public Network. The tenant uses P4 programs to determine the operation of both the fixed and 5GLAN switches. The tenant provisions a 5GLAN P4 program into the mobile network, and can also operate a controller.¶
..... Tenant ........
P4 program : :
deployment : Operation :
V :
+-----+ air interface +----------------+ :
| UE1 +----------------+ | :
+-----+ | | :
| | :
+-----+ | | V
| UE2 +----------------+ 5GLAN | +------------+
+-----+ | Logical +------+ Controller |
| Switch | P4 +-------+----+
+-----+ | | runtime |
| UE3 +----------------+ | API |
+-----+ | | |
| | |
+-----+ | | |
+-+ UE4 +----------------+ | |
| +-----+ +----------------+ |
| |
| Fixed or wireless connection |
| P4 runtime API |
| +---------+ +-------------------------------+
+--+ Device1 | |
| +---------+ |
| |
| +---------+ +------+-----+
`--+ Device2 +----+ P4 Switch +--->(fixed network)
+---------+ +------------+
Research has been conducted, for example by [Stoyanov], to enable P4 network programming of individual virtual switches. To our knowledge, no complete solution has been developed for deploying virtual COIN programs over mobile or datacenter networks.¶
Virtual network programming by tenants could bring benefits such as:¶
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. From a COIN perspective, those capabilities may be realized as (COIN) programs and executed throughout the COIN system, including in PNDs.¶
Some solutions may desire the localization of reasoning logic, e.g., for deriving attributes that better preserve privacy of the utilized raw input data. Quickly establishing (COIN) program instances in nearby compute resources, including PNDs, may even satisfy such localization demands on-the-fly (e.g., when a particular use is being realized, then terminated after a given time).¶
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 constitutes 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) like abstraction to the AI framework.¶
A number of activities on distributed AI exist in the area of developing the 5th and 6th generation mobile network with various activities in the 3GPP SDO as well as use cases developed for the ETSI MEC initiative mentioned in previous use cases.¶
In addition to the research questions in Section 3.1.5:¶
Requirements 3.1.1 through 3.1.6 also apply for general distributed AI capabilities. In addition:¶
Deploying COIN solutions to the use cases described in this document may pose a risk for security breaches if the solutions are not deployed with security and authentication mechanisms in place. In particular, many early PND-based approaches work on unencrypted plain text data and often customize packet payload to account for missing capabilities of early-generation PNDs. Such need for operating on unencrypted data either limits the applicability of COIN solutions to those parts of the packet transfer that happens to be unencrypted or poses a strong requirement to user data to not being encrypted for the sake of utilizing COIN capabilities; a requirement hardly aligned with the strong trend to encrypting all user-related data in traversing packets. Thus, even without having analyzed the use cases in more detail in this document, designing meaningful solutions for providing authentication as well as incorporating the rightfully ongoing trend to more and more end-to-end encrypted traffic into COIN will be key.¶
This document presented use cases gathered from several fields that can and could profit from capabilities that are provided by in-network and, more generally, distributed compute capabilities. We distinguished between use cases in which COIN may enable new experiences (Section 3), expose new features (Section 6), or improve on existing system capabilities (Section 5), and other use cases where COIN capabilities enable totally new applications, for example, in industrial networking (Section 4).¶
Beyond the mere description and characterization of those use cases, we identified opportunities arising from utilizing COIN capabilities as well as research questions that may need to be addressed before being able to reap those opportunities. We also outlined possible requirements for building a COIN system that may realize these use cases.¶
We acknowledge that this work offers no comprehensive overview of possible use cases and is thus only a snapshot of what may be possible if COIN capabilities existed.
In fact, the decomposition of many current client-server applications into node by node transit could identify other opportunities for adding computing to forwarding notably in supply-chain, health care, intelligent cities and transportation and even financial services (among others).¶
With this in mind, updates to this document might become necessary or desirable in the future to capture this extended view on what may be possible. We are, however, confident that the current selection of use cases, each describing the dimensions of opportunities, research questions, and requirements, already represents a useful set of scenarios that yield themselves for a subsequent analysis that is currently intended to be performed in [USECASEANALYSIS]. Through this, the use cases presented here together with the intended analysis provide direct input into the milestones of COINRG in terms of required functionalities.¶
The authors would like to thank Chathura Sarathchandra for reviewing the document and David Oran, Phil Eardley, Stuart Card, Jeffrey He, Toerless Eckert, and Jon Crowcroft for providing comments on earlier versions of the document.¶