COINRG M. McBride
Internet-Draft Futurewei
Intended status: Standards Track D. Kutscher
Expires: January 14, 2021 Emden University
E. Schooler
Intel
CJ. Bernardos
UC3M
D. Lopez
Telefonica
July 13, 2020
Edge Data Discovery for COIN
draft-mcbride-edge-data-discovery-overview-04
Abstract
This document describes the problem of distributed data discovery in
edge computing, and in particular for computing-in-the-network
(COIN), which may require both the marshalling of data at the outset
of a computation and the persistence of the resultant data after the
computation. Although the data might originate at the network edge,
as more and more distributed data is created, processed, and stored,
it becomes increasingly dispersed throughout the network. There
needs to be a standard way to find it. New and existing protocols
will need to be developed to support distributed data discovery at
the network edge and beyond.
Status of This Memo
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This Internet-Draft will expire on January 14, 2021.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Edge Data . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Background . . . . . . . . . . . . . . . . . . . . . . . 3
1.3. Requirements Language . . . . . . . . . . . . . . . . . . 4
1.4. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4
2. Edge Data Discovery Problem Scope . . . . . . . . . . . . . . 5
2.1. A Cloud-Edge Continuum . . . . . . . . . . . . . . . . . 5
2.2. Types of Edge Data . . . . . . . . . . . . . . . . . . . 6
3. Edge Scenarios Requiring Data Discovery . . . . . . . . . . . 7
4. Edge Data Discovery . . . . . . . . . . . . . . . . . . . . . 7
4.1. Types of Discovery . . . . . . . . . . . . . . . . . . . 7
4.2. Naming the Data . . . . . . . . . . . . . . . . . . . . . 8
5. Use Cases of Edge Data Discovery . . . . . . . . . . . . . . 9
5.1. Autonomous Vehicles . . . . . . . . . . . . . . . . . . . 9
5.2. Video Surveillance . . . . . . . . . . . . . . . . . . . 10
5.3. Elevator Networks . . . . . . . . . . . . . . . . . . . . 10
5.4. Service Function Chaining . . . . . . . . . . . . . . . . 10
5.5. Ubiquitous Witness . . . . . . . . . . . . . . . . . . . 12
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 13
7. Security Considerations . . . . . . . . . . . . . . . . . . . 13
8. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 13
9. Normative References . . . . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
1. Introduction
Edge computing is an architectural shift that migrates Cloud
functionality (compute, storage, networking, control, data
management, etc.) out of the back-end data center to be more
proximate to the IoT data being generated and analyzed at the edges
of the network. Edge computing provides local compute, storage and
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connectivity services, often required for latency- and bandwidth-
sensitive applications. Thus, Edge Computing plays a key role in
verticals such as Energy, Manufacturing, Automotive, Video
Surveillance, Retail, Gaming, Healthcare, Mining, Buildings and Smart
Cities.
1.1. Edge Data
Edge computing is motivated at least in part by the sheer volume of
data that is being created by endpoint devices (sensors, cameras,
lights, vehicles, drones, wearables, etc.) at the very network edge
and that flows upstream, in a direction for which the network was not
originally designed. In fact, in dense IoT deployments (e.g., many
video cameras are streaming high definition video), where multiple
data flows collect or converge at edge nodes, data is likely to need
transformation (to be transcoded, subsampled, compressed, analyzed,
annotated, combined, aggregated, etc.) to fit over the next hop link,
or even to fit in memory or storage. Note also that the act of
performing computation on the data creates yet another new data
stream! Preservation of the original data streams is needed
sometimes but not always.
In addition, data may be cached, copied and/or stored at multiple
locations in the network on route to its final destination. With an
increasing percentage of devices connecting to the Internet being
mobile, support for in-the-network caching and replication is
critical for continuous data availability, not to mention efficient
network and battery usage for endpoint devices.
Additionally, as mobile devices' memory/storage fill up, in an edge
context they may have the ability to offload their data to other
proximate devices or resources, leaving a bread crumb trail of data
in their wakes. Therefore, although data might originate at edge
devices, as more and more data is continuously created, processed and
stored, it becomes increasingly dispersed throughout the physical
world (outside of or scattered across managed local data centers),
increasingly isolated in separate local edge clouds or data silos.
Thus, there needs to be a standard way to find it. New and existing
protocols will need to be identified/developed/enhanced for these
purposes. Being able to discover distributed data at the edge or in
the middle of the network will be an important component of Edge
computing.
1.2. Background
Several IETF T2T RG Edge Computing discussions have been held over
the last couple years. A comparative study on the definition of Edge
computing was presented in multiple sessions in T2T RG in 2018 and an
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Edge Computing I-D was submitted early 2019. An IETF BEC (beyond
edge computing) effort has been evaluating potential gaps in existing
edge computing architectures. Edge Data Discovery is one potential
gap that was identified and that needs evaluation and a solution.
The newly proposed COIN RG highlights the need for computations in
the network to be able to marshal potentially distributed input data
and to handle resultant output data, i.e., its placement, storage
and/or possible migration strategy.
1.3. Requirements Language
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].
1.4. Terminology
o Edge: The edge encompasses all entities not in the back-end cloud.
The device edge represents the very leaves of the network and
encompasses the entities found in the last mile network. Sensors,
gateways, compute nodes are included. Because the things that
populate the IoT can be both physical and/or cyber, in some
solutions, particularly in software-defined or digital-twin
contexts, the device edge can include logical (vs physical)
entities. The infrastructure edge includes equipment on the
network operator side of the last mile network including cell
towers, edge data centers, cable headends, POPs, etc. See
Figure 1 for other possible tiers of edge clouds between the
device edge and the back-end cloud data center.
o Edge Computing: Distributed computation that is performed near the
network edge, where nearness is determined by the system
requirements. This includes high performance compute, storage and
network equipment on either the device or infrastructure edge.
o Edge Data Discovery: The process of finding required data from
edge entities, i.e., from databases, file systems, and device
memory that might be physically distributed in the network, and
providing access to it logically as if it were a single unified
source, perhaps through its namespace, that can be evaluated or
searched.
o ICN: Information Centric Networking. An ICN-enabled network
routes data by name (vs address), caches content natively in the
network, and employs data-centric security. Data discovery may
require that data be associated with a name or names, a series of
descriptive attributes, and/or a unique identifier.
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2. Edge Data Discovery Problem Scope
Our focus is on how to define and scope the edge data discovery
problem. This requires some discussion of the evolving definition of
the edge as part of a cloud-to-edge continuum and in turn what is
meant by edge data, as well as the meta-data about the edge data.
2.1. A Cloud-Edge Continuum
Although Edge Computing data typically originates at edge devices,
there is nothing that precludes edge data from being created anywhere
in the cloud-to-edge computing continuum (Figure 1). New edge data
may result as a byproduct of computation being performed on the data
stream anywhere along its path in the network. For example,
infrastructure edges may create new edge data when multiple data
streams converge upon this aggregation point and require
transformation (e.g., to fit within the available resources, to
smooth raw measurements to eliminate high-frequency noise, or to
obfuscate data for privacy).
Initially our focus is on discovery of edge data that resides at the
Device Edge and the Infrastructure Edge.
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+-------------------------------+
| Back-end Cloud Data Center |
+-------------------------------+
*** Cloud
* * Interconnect
***
+-------------------------------+
| Core Data Center |
+-------------------------------+
*** Backbone
* * Network
***
+-------------------------------+
| Regional Data Center |
+-------------------------------+
*** Metropolitan
* * Network
***
+-------------------------------+
| Infrastructure Edge |
+-------------------------------+
*** Access
* * Network
***
+-------------------------------+
| Device Edge |
+-------------------------------+
Figure 1: Cloud-to-edge computing continuum
2.2. Types of Edge Data
Besides classically constrained IoT device sensor and measurement
data accumulating throughout the edge computing infrastructure, edge
data may also take the form of higher frequency and higher volume
streaming data (from a continuous sensor or from a camera), meta data
(about the data), control data (regarding an event that was
triggered), and/or an executable that embodies a function, service,
or any other piece of code or algorithm. Edge data also could be
created after multiple streams converge at an edge node and are
processed, transformed, or aggregated together in some manner.
Regardless of edge data type, a key problem in the Cloud-Edge
continuum is that data is often kept in silos. Meaning, data is
often sequestered within the Edge where it was created. A goal of
this discussion is to consider the prospect that different types of
edge data will be made accessible across disparate edges, for example
to enable richer multi-modal analytics. But this will happen only if
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data can be described, searched and discovered across heterogeneous
edges in a standard way. Having a mechanism to enable granular edge
data discovery is the problem that needs solving either with existing
or new protocols. The mechanisms shouldn't care to which flavor
cloud or edge the request for data discovery is made.
3. Edge Scenarios Requiring Data Discovery
1. A set of data resources appears (e.g., a mobile node hosting data
joins a network) and they want to be discoverable by an existing
but possibly virtualized and/or ephemeral data directory
infrastructure.
2. A device wants to discover data resources available at or near
its current location. As some of these resources may be mobile,
the available set of edge data may vary over time.
3. A device wants to discover to where best in the edge
infrastructure to opportunistically upload its data, for example
if a mobile device wants to offload its data to the
infrastructure (for greater data availability, battery savings,
etc.).
4. Edge Data Discovery
How can we discover data on the edge and make use of it? There are
proprietary implementations that collect data from various databases
and consolidate it for evaluation. We need a standard protocol set
for doing this data discovery, on the device or infrastructure edge,
in order to meet the requirements of many use cases. We will have
terabytes of data on the edge and need a way to identify its
existence and find the desired data. A user requires the need to
search for specific data in a data set and evaluate it using their
own tools. The tools are outside the scope of this document, but the
discovery of that data is in scope.
4.1. Types of Discovery
There are many aspects of discovery and many different protocols that
address each aspect.
Discovery of new devices added to an environment. Discovery of their
capabilities/services in client/server environments. Discovery of
these new devices automatically. Discovering a device and then
synchronizing the device inventory and configuration for edge
services. There are many existing protocols to help in this
discovery: UPnP, mDNS, DNS-SD, SSDP, NFC, XMPP, W3C network service
discovery, etc.
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Edge devices discover each other in a standard way. We can use DHCP,
SNMP, SMS, COAP, LLDP, and routing protocols such as OSPF for devices
to discover one another.
Discovery of link state and traffic engineering data/services by
external devices. BGP-LS is one such solution.
The question is if one or more of these protocols might be a suitable
contender to extend to support edge data discovery?
4.2. Naming the Data
Information-Centric Networking (ICN) RFC 7927 [RFC7927] is a class of
architectures and protocols that provide "access to named data" as a
first-order network service. Instead of host-to-host communication
as in IP networks, ICNs often use location-independent names to
identify data objects, and the network provides the services of
processing (answering) requests for named data with the objective to
finally deliver the requested data objects to a requesting consumer.
Such an approach has profound effects on various aspects of a
networking system, including security (by enabling object-based
security on a message/packet level), forwarding behavior (name-based
forwarding, caching), but also on more operational aspects such as
bootstrapping, discovery etc.
The CCNx and NDN (https://named-data.net) variants of ICN are based
on a request/response abstraction where consumers (hosts, application
requesting named data) send INTEREST messages into the network that
are forwarded by network elements to a destination that can provide
the requested named data object. Corresponding responses are sent as
so-called DATA messages that follow the reverse INTEREST path.
Each unique data object is named unambiguously in a hierarchical
naming scheme and is authenticated through Public-Key cryptography
(data objects can also optionally be encrypted in different ways).
The naming concept and the object-based security approach lay the
foundation for location-independent operation. The network can
generally operate without any notion of location, and nodes
(consumers, forwarders) can forward requests for named data objects
directly, i.e., without any additional address resolution. Location
independence also enables additional features, for example the
possibility to replicate and cache named data objects. On-path
caching is a standard feature in many ICN systems -- typically for
enhancing reliability and performance.
In CCNx and NDN, forwarders are stateful, i.e., they keep track of
forwarded INTEREST to later match the received DATA messages.
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Stateful forwarding (in conjunction with the general named-based and
location-independent operation) also empowers forwarders to execute
individual forwarding strategies and perform optimizations such as
in-network retransmissions, multicasting requests (in cases there are
several opportunities for accessing a particular named data object)
etc.
Naming data and application-specific naming conventions are naturally
important aspects in ICN. It is common that applications define
their own naming convention (i.e., semantics of elements in the name
hierarchy). Such names can often be directly derived from
application requirements, for example a name like /my-home/living-
room/light/switch/main could be relevant in a smart home setting, and
corresponding devices and applications could use a corresponding
convention to facilitate controllers finding sensors and actors in
such a system with minimal user configuration.
The aforementioned features make ICN amenable to data discovery.
Because there is no name/address chasm as in IP-based systems, data
can be discovered by sending an INTEREST to named data objects
directly (assuming a naming convention as described above).
Moreover, ICN can authenticate received data objects directly, for
example using local trust anchors in the network (for example in a
home network).
Advanced ICN features for data discovery include the concept of
manifests in CCNx, i.e., ICN objects that describe data collections,
and data set synchronization protocols in NDN (https://named-
data.net/publications/li2018sync-intro/) that can inform consumers
about the availability of new data in a tree-based data structure
(with automatic retrieval and authentication). Also, ICN is not
limited to accessing static data. Frameworks such as Named Function
Networking (http://www.named-function.net) and RICE can provide the
general ICN feature for discovery not only for data but also for name
functions (for in-network computing) and for their results.
5. Use Cases of Edge Data Discovery
5.1. Autonomous Vehicles
Autonomous vehicles rely on the processing of huge amounts of complex
data in real-time for fast and accurate decisions. These vehicles
will rely on high performance compute, storage and network resources
to process the volumes of data they produce in a low latency way.
Various systems will need a standard way to discover the pertinent
data for decision making.
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5.2. Video Surveillance
The majority of the video surveillance footage will remain at the
edge infrastructure (not sent to the cloud data center). This
footage is coming from vehicles, factories, hotels, universities,
farms, etc. Much of the video footage will not be interesting to
those evaluating the data. A mechanism, perhaps a set of protocols,
is needed to identify the interesting data at the edge. What
constitutes interesting will be context specific, e.g., a video frame
might be considered interesting if and only if it includes a car, or
person, or bicyclist, or a backyard nocturnal creature, or etc.
Interesting video data may be stored longer in storage systems at the
very edge of the network and/or in networking equipment further away
from the device edge that has access to data in flight as it transits
the network.
5.3. Elevator Networks
Elevators are one of many industrial applications of edge computing.
Edge equipment receives data from hundreds of elevator sensors. The
data coming into the edge equipment is vibration, temperature, speed,
level, video, etc. We need the ability to identify where the data we
need to evalute is located.
5.4. Service Function Chaining
Service function chaining (SFC) allows the instantiation of an
ordered set of service functions (SFs) and the subsequent "steering"
of traffic through them. Service functions are expected to be
deployed at the edge of the network, as a feasible deployment of
"Compute In the Network", with multiple types of potential use cases
(e.g., fog robotics, Industry 4.0 automation, etc). Service
functions provide a specific treatment of received packets, therefore
they need to be discoverable so they can be used in a given service
composition via SFC. In addition, these functions can be producers
and/or consumers of data. So far, how the functions are discovered
and composed has been out of the scope of discussions in the IETF.
While there are some mechanisms that can be used and/or extended to
provide this functionality, more work needs to be done. An example
of this can be found in [I-D.bernardos-sfc-discovery].
In an SFC environment deployed at the edge, the discovery protocol
may also need the following kind of meta-data information per
(service) function:
o Service Function Type: identifying the category of function
provided.
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o SFC-aware: Yes/No. Indicates if the function is SFC-aware.
o Route Distinguisher (RD): IP address indicating the location of
the function.
o Pricing/costs details.
o Migration capabilities of the function: whether a given function
can be moved to another provider (potentially including
information about compatible providers topologically close).
o Mobility of the device hosting the function, with e.g. the
following sub-options:
Level: no, low, high; or a corresponding scale (e.g., 1 to 10).
Current geographical area (e.g., GPS coordinates, post code).
Target moving area (e.g., GPS coordinates, post code).
o Power source of the device hosting the function, with e.g. the
following sub-options:
Battery: Yes/No. If Yes, the following sub-options could be
defined:
Capacity of the battery (e.g., mmWh).
Charge status (e.g., %).
Lifetime (e.g., minutes).
Discovery of resources in an NFV environment: virtualized resources
do not need to be limited to those available in traditional data
centers, where the infrastructure is stable, static, typically
homogeneous and managed by a single admin entity. Computational
capabilities are becoming more and more ubiquitous, with terminal
devices getting extremely powerful, as well as other types of devices
that are close to the end users at the edge (e.g., vehicular onboard
devices for infotainment, micro data centers deployed at the edge,
etc.). It is envisioned that these devices would be able to offer
storage, computing and networking resources to nearby network
infrastructure, devices and things (the fog paradigm). These
resources can be used to host functions, for example to offload/
complement other resources available at traditional data centers, but
also to reduce the end-to-end latency or to provide access to
specialized information (e.g., context available at the edge) or
hardware. Similar to the discovery of functions, while there are
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mechanisms that can be reused/extended, there is no complete solution
yet defined. An example of work in this area is
[I-D.bernardos-intarea-vim-discovery]. The availability of this
meta-data about the capabilities of nearby physical as well as
virtualized resources can be made discoverable through edge data
discovery mechanisms.
5.5. Ubiquitous Witness
Ubiquitous Witness (UW) is the name of a use case that has been
presented in past COINRG and ICNRG meetings at the IETF. It
describes what might occur in dense IoT deployments when an anomaly
occurs. There are many "witnesses" to report on what happened within
a limited region of interest and around an approximate point in time.
The use case highlights the need for upstream data discovery and
management. It is agnostic to where the dense IoT deployment
resides, whether in a factory, home, commercial building, city,
entertainment venue, et cetera. For example, as cameras and other
sensors have become ubiquitous in Smart Cities, it would be helpful
to be able to discover and examine data from all devices and sensors
that witnessed an accident in a city intersection; this could be data
from cameras mounted at the intersection itself, on nearby buildings,
in cars, and cell phones of individuals on location.
If an anomaly were to automatically trigger independent upstream
flows of video data from all of the witnesses (within a proximal
vicinity and time window), the data flows would naturally converge at
shared collection or aggregation points in the network. These edge
nodes might opt to vault any data deemed part of a safety-related
anomaly, which would enable interested parties (the car owner, the
car manufacturer, an insurance company, a city traffic planner) to
investigate the root cause of the anomaly after the fact. The
implication however is that enough meta data has been generated
alongside the data itself (e.g., a data name, an identifier, or a geo
location and timestamp), to allow the retrieval of this distributed
data, provided those asking have proper authorization to access it.
The UW streams are contextually-related and as such it can be
advantageous also to be able to process them simultaneously, at the
time they are first generated. For example if collection nodes could
derive that groups of data streams are contextually-related, they
could stitch streams together to create a 360-degree view of the
anomalous event (e.g., to walk around in the data), or to winnow the
set of vaulted data to only the "best" video (e.g., highest
resolution, unoccluded views) or to perform compute-in-the-network to
enable them to fit within the available resources (e.g., at the
receiving node due to the convergence or implosion of upstream data,
or over the next hoplink). Ubiquitous Witness data doesn't have to
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be video data, but video illustrates why one might want to jointly
process upstream flows in real-time.
6. IANA Considerations
N/A
7. Security Considerations
Security considerations will be a critical component of edge data
discovery particularly as intelligence is moved to the extreme edge
where data is to be extracted.
An assumption is that all data will have associated policies
(default, inherited or configured) that describe access control
permissions. Consequently, the discoverability of data will be a
function of who or what has requested access. In other words, the
discoverable view into the available data will be limited to those
who are authorized. Discovering edge data that is exclusively
private is out of scope of this document, the assumption being that
there will be some edge clouds that do not expose or publish the
availability of their data. Although edge data may be sent to the
back-end cloud as needed, there is nothing that precludes it from
being discoverable if the cloud offers it as public.
8. Acknowledgement
The authors thank Dave Oran for his detailed feedback on an early
version of this draft, as well as inputs from Greg Skinner and Lixia
Zhang.
9. Normative References
[I-D.bernardos-intarea-vim-discovery]
Bernardos, C. and A. Mourad, "IPv6-based discovery and
association of Virtualization Infrastructure Manager (VIM)
and Network Function Virtualization Orchestrator (NFVO)",
draft-bernardos-intarea-vim-discovery-03 (work in
progress), February 2020.
[I-D.bernardos-sfc-discovery]
Bernardos, C. and A. Mourad, "Service Function discovery
in fog environments", draft-bernardos-sfc-discovery-04
(work in progress), March 2020.
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[I-D.irtf-icnrg-ccnxmessages]
Mosko, M., Solis, I., and C. Wood, "CCNx Messages in TLV
Format", draft-irtf-icnrg-ccnxmessages-09 (work in
progress), January 2019.
[I-D.irtf-icnrg-ccnxsemantics]
Mosko, M., Solis, I., and C. Wood, "CCNx Semantics",
draft-irtf-icnrg-ccnxsemantics-10 (work in progress),
January 2019.
[I-D.kutscher-icnrg-rice]
Krol, M., Habak, K., Oran, D., Kutscher, D., and I.
Psaras, "Remote Method Invocation in ICN", draft-kutscher-
icnrg-rice-00 (work in progress), October 2018.
[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>.
[RFC7927] Kutscher, D., Ed., Eum, S., Pentikousis, K., Psaras, I.,
Corujo, D., Saucez, D., Schmidt, T., and M. Waehlisch,
"Information-Centric Networking (ICN) Research
Challenges", RFC 7927, DOI 10.17487/RFC7927, July 2016,
<https://www.rfc-editor.org/info/rfc7927>.
Authors' Addresses
Mike McBride
Futurewei
Email: michael.mcbride@futurewei.com
Dirk Kutscher
Emden University
Email: ietf@dkutscher.net
Eve Schooler
Intel
Email: eve.m.schooler@intel.com
URI: http://www.eveschooler.com
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Carlos J. Bernardos
Universidad Carlos III de Madrid
Av. Universidad, 30
Leganes, Madrid 28911
Spain
Phone: +34 91624 6236
Email: cjbc@it.uc3m.es
URI: http://www.it.uc3m.es/cjbc/
Diego R. Lopez
Telefonica
Email: diego.r.lopez@telefonica.com
URI: https://www.linkedin.com/in/dr2lopez/
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