Patent Publication Number: US-2022231952-A1

Title: OPTIMAL SELECTION OF A CLOUD-BASED DATA MANAGEMENT SERVICE FOR IoT SENSORS

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to computer networks, and, more particularly, to the optimal selection of a cloud-based management service for Internet of Things (IoT) sensors. 
     BACKGROUND 
     The Internet of Things, or “IoT” for short, represents an evolution of computer networks that seeks to connect many everyday objects to the Internet. Notably, there has been a recent proliferation of ‘smart’ devices that are Internet-capable such as thermostats, lighting, televisions, cameras, and the like. In many implementations, these devices may also communicate with one another. For example, an IoT motion sensor may communicate with one or more smart lightbulbs, to actuate the lighting in a room when a person enters the room. Vehicles are another class of ‘things’ that are being connected via the IoT for purposes of sharing sensor data, implementing self-driving capabilities, monitoring, and the like. 
     As the IoT evolves, the variety of IoT devices will continue to grow, as well as the number of applications associated with the IoT devices. For instance, multiple cloud-based, business intelligence (BI) applications may take as input measurements captured by a particular IoT sensor. To this end, data pipelines are often constructed from the edge device(s) of the IoT network to the destination cloud provider. Typically, the nearest point of presence (POP) is selected, without regard to the overall destination of the sensor data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which: 
         FIG. 1  illustrate an example network; 
         FIG. 2  illustrates an example network device/node; 
         FIG. 3  illustrates an example network architecture for edge to multi-cloud processing and governance; 
         FIGS. 4A-4B  illustrate examples of data processing by an edge device in a network; 
         FIGS. 5A-5G  illustrates an example of the selection of a point of presence (POP); and 
         FIG. 6  illustrates an example simplified procedure for selecting an optimal POP. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Overview 
     According to one or more embodiments of the disclosure, a first device coordinates probing of network paths that extend between an edge device and a data collection point via a plurality of points of presence. The first device receives a set of probing results that are indicative of one or more performance metrics associated with the network paths. The first device selects, based on the set of probing results, a particular point of presence from among the plurality of points of presence through which the edge device should send traffic to the data collection point. The first device instructs the edge device to send traffic to the data collection point via the particular point of presence. 
     Description 
     A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC), and others. Other types of networks, such as field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. may also make up the components of any given computer network. 
     In various embodiments, computer networks may include an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” (or “Internet of Everything” or “IoE”) refers to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the IoT involves the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network. 
     Often, IoT networks operate within a shared-media mesh networks, such as wireless or PLC networks, etc., and are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained. That is, LLN devices/routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. IoT networks are comprised of anything from a few dozen to thousands or even millions of devices, and support point-to-point traffic (between devices inside the network), point-to-multipoint traffic (from a central control point such as a root node to a subset of devices inside the network), and multipoint-to-point traffic (from devices inside the network towards a central control point). 
     Edge computing, also sometimes referred to as “fog” computing, is a distributed approach of cloud implementation that acts as an intermediate layer from local networks (e.g., IoT networks) to the cloud (e.g., centralized and/or shared resources, as will be understood by those skilled in the art). That is, generally, edge computing entails using devices at the network edge to provide application services, including computation, networking, and storage, to the local nodes in the network, in contrast to cloud-based approaches that rely on remote data centers/cloud environments for the services. To this end, an edge node is a functional node that is deployed close to IoT endpoints to provide computing, storage, and networking resources and services. Multiple edge nodes organized or configured together form an edge compute system, to implement a particular solution. Edge nodes and edge systems can have the same or complementary capabilities, in various implementations. That is, each individual edge node does not have to implement the entire spectrum of capabilities. Instead, the edge capabilities may be distributed across multiple edge nodes and systems, which may collaborate to help each other to provide the desired services. In other words, an edge system can include any number of virtualized services and/or data stores that are spread across the distributed edge nodes. This may include a master-slave configuration, publish-subscribe configuration, or peer-to-peer configuration. 
     Low power and Lossy Networks (LLNs), e.g., certain sensor networks, may be used in a myriad of applications such as for “Smart Grid” and “Smart Cities.” A number of challenges in LLNs have been presented, such as: 
     1) Links are generally lossy, such that a Packet Delivery Rate/Ratio (PDR) can dramatically vary due to various sources of interferences, e.g., considerably affecting the bit error rate (BER); 
     2) Links are generally low bandwidth, such that control plane traffic must generally be bounded and negligible compared to the low rate data traffic; 
     3) There are a number of use cases that require specifying a set of link and node metrics, some of them being dynamic, thus requiring specific smoothing functions to avoid routing instability, considerably draining bandwidth and energy; 
     4) Constraint-routing may be required by some applications, e.g., to establish routing paths that will avoid non-encrypted links, nodes running low on energy, etc.; 
     5) Scale of the networks may become very large, e.g., on the order of several thousands to millions of nodes; and 
     6) Nodes may be constrained with a low memory, a reduced processing capability, a low power supply (e.g., battery). 
     In other words, LLNs are a class of network in which both the routers and their interconnect are constrained: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen and up to thousands or even millions of LLN routers, and support point-to-point traffic (between devices inside the LLN), point-to-multipoint traffic (from a central control point to a subset of devices inside the LLN) and multipoint-to-point traffic (from devices inside the LLN towards a central control point). 
     An example implementation of LLNs is an “Internet of Things” network. Loosely, the term “Internet of Things” or “IoT” may be used by those in the art to refer to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, HVAC (heating, ventilating, and air-conditioning), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., IP), which may be the Public Internet or a private network. Such devices have been used in the industry for decades, usually in the form of non-IP or proprietary protocols that are connected to IP networks by way of protocol translation gateways. With the emergence of a myriad of applications, such as the smart grid advanced metering infrastructure (AMI), smart cities, and building and industrial automation, and cars (e.g., that can interconnect millions of objects for sensing things like power quality, tire pressure, and temperature and that can actuate engines and lights), it has been of the utmost importance to extend the IP protocol suite for these networks. 
       FIG. 1  is a schematic block diagram of an example simplified computer network  100  illustratively comprising nodes/devices at various levels of the network, interconnected by various methods of communication. For instance, the links may be wired links or shared media (e.g., wireless links, PLC links, etc.) where certain nodes, such as, e.g., routers, sensors, computers, etc., may be in communication with other devices, e.g., based on connectivity, distance, signal strength, current operational status, location, etc. 
     Specifically, as shown in the example IoT network  100 , three illustrative layers are shown, namely cloud layer  110 , edge layer  120 , and IoT device layer  130 . Illustratively, the cloud layer  110  may comprise general connectivity via the Internet  112 , and may contain one or more datacenters  114  with one or more centralized servers  116  or other devices, as will be appreciated by those skilled in the art. Within the edge layer  120 , various edge devices  122  may perform various data processing functions locally, as opposed to datacenter/cloud-based servers or on the endpoint IoT nodes  132  themselves of IoT device layer  130 . For example, edge devices  122  may include edge routers and/or other networking devices that provide connectivity between cloud layer  110  and IoT device layer  130 . Data packets (e.g., traffic and/or messages sent between the devices/nodes) may be exchanged among the nodes/devices of the computer network  100  using predefined network communication protocols such as certain known wired protocols, wireless protocols, PLC protocols, or other shared-media protocols where appropriate. In this context, a protocol consists of a set of rules defining how the nodes interact with each other. 
     Those skilled in the art will understand that any number of nodes, devices, links, etc. may be used in the computer network, and that the view shown herein is for simplicity. Also, those skilled in the art will further understand that while the network is shown in a certain orientation, the network  100  is merely an example illustration that is not meant to limit the disclosure. 
     Data packets (e.g., traffic and/or messages) may be exchanged among the nodes/devices of the computer network  100  using predefined network communication protocols such as certain known wired protocols, wireless protocols (e.g., IEEE Std. 802.15.4, Wi-Fi, Bluetooth®, DECT-Ultra Low Energy, LoRa, etc.), PLC protocols, or other shared-media protocols where appropriate. In this context, a protocol consists of a set of rules defining how the nodes interact with each other. 
       FIG. 2  is a schematic block diagram of an example node/device  200  that may be used with one or more embodiments described herein, e.g., as any of the nodes or devices shown in  FIG. 1  above or described in further detail below. The device  200  may comprise one or more network interfaces  210  (e.g., wired, wireless, PLC, etc.), at least one processor  220 , and a memory  240  interconnected by a system bus  250 , as well as a power supply  260  (e.g., battery, plug-in, etc.). 
     Network interface(s)  210  include the mechanical, electrical, and signaling circuitry for communicating data over links coupled to the network. The network interfaces  210  may be configured to transmit and/or receive data using a variety of different communication protocols, such as TCP/IP, UDP, etc. Note that the device  200  may have multiple different types of network connections, e.g., wireless and wired/physical connections, and that the view herein is merely for illustration. Also, while the network interface  210  is shown separately from power supply  260 , for PLC the network interface  210  may communicate through the power supply  260 , or may be an integral component of the power supply. In some specific configurations the PLC signal may be coupled to the power line feeding into the power supply. 
     The memory  240  comprises a plurality of storage locations that are addressable by the processor  220  and the network interfaces  210  for storing software programs and data structures associated with the embodiments described herein. The processor  220  may comprise hardware elements or hardware logic adapted to execute the software programs and manipulate the data structures  245 . An operating system  242 , portions of which are typically resident in memory  240  and executed by the processor, functionally organizes the device by, among other things, invoking operations in support of software processes and/or services executing on the device. These software processes/services may comprise an illustrative data management process  248  and/or a data pipeline configuration process  249 , as described herein. 
     It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while the processes have been shown separately, those skilled in the art will appreciate that processes may be routines or modules within other processes. 
       FIG. 3  illustrates an example network architecture  300  for edge to multi-cloud processing and governance, according to various embodiments. As shown, consider the case of an IoT network at IoT layer  130  that comprises a plurality of nodes  132 , such as node  132   a  (e.g., a boiler), node  132   b  (e.g., a metal machine), and node  132   c  (e.g., a pump). Notably, the IoT network at IoT layer  130  may comprise any numbers of sensors and/or actuators. For instance, the network may be located in an industrial setting, such as a factory, port, substation, or the like, a smart city, a stadium, a conference or office building, or any other location in which IoT devices may be deployed. 
     As noted above, as the IoT evolves, the variety of IoT devices will continue to grow, as well as the number of applications associated with the IoT devices. As a result, multiple cloud-based applications may take as input measurements or other data generated by a particular IoT device/node. For instance, as shown, assume that IoT nodes  132   a - 132   c  generate data  302   a - 302   c,  respectively, for consumption by any number of applications  308  hosted by different cloud providers  306 , such as Microsoft Azure, Software AG, Quantela, MQTT/DC, or the like. 
     To complicate the collection and distribution of data  302   a - 302   c,  the different applications  308  may also require different sets of data  304   a - 304   c  from data  302   a - 302   c.  For instance, assume that cloud provider  306   a  hosts application  308   a,  which is a monitoring application used by the operator of the IoT network. In addition, cloud provider  306   a  may also host application  308   b,  which is a developer application that allows the operator of the IoT network to develop and deploy utilities and configurations for the IoT network. Another application, application  308   c,  may be hosted by an entirely different cloud provider  306   b  and be used by the vendor or manufacturer of a particular IoT node  132  for purposes. Finally, a further application, application  308   d,  may be hosted by a third cloud provider  306   c,  which is used by technicians for purposes of diagnostics and the like. 
     From the standpoint of the edge device  122 , such as a router or gateway at the edge of the IoT network, the lack of harmonization between data consumers can lead to overly complicated data access policies, virtual models of IoT nodes  132  (e.g., ‘device twins’ or ‘device shadows’) that are often not portable across cloud providers  306 , and increased resource consumption. In addition, different IoT nodes may communicate using different protocols within the IoT network. For instance, IoT nodes  132   a - 132   c  may communicate using MQTT, Modbus, OPC Unified Architecture (OPC UA), combinations thereof, or other existing communication protocols that are typically used in IoT networks. As a result, the various data pipelines must be configured on an individual basis at edge device  122  and for each of the different combinations of protocols and destination cloud providers  306 . 
       FIG. 4A  illustrates an example architecture  400  for data management process  248 , according to various embodiments. As shown, data management process  248  may comprise any or all of the following components: a plurality of protocol connectors  402 , data mappers  404 , a data transformer  406 , and/or a governance engine  408 . Typically, these components are executed on a single device located at the edge of the IoT network. However, further embodiments provide for these components to be executed in a distributed manner across multiple devices, in which case the combination of devices can be viewed as a singular device for purposes of the teachings herein. Further, functionalities of the components of architecture  400  may also be combined, omitted, or implemented as part of other processes, as desired. 
     During execution, protocol connectors  402  may comprise a plurality of southbound connectors that are able to extract data  302  from traffic in the IoT network sent via any number of different protocols. For instance, protocol connectors  402  may include connectors for OPC UA, Modbus, Ethernet/IP, MQTT, and the like. Accordingly, when the device executing data management process  248  (e.g., device  200 ) receives a message from the IoT network, such as a packet, frame, collection thereof, or the like, protocol connectors  402  may process the message using its corresponding connector to extract the corresponding data  302  from the message. 
     Once data management process  248  has extracted data  302  from a given message using the appropriate connector in protocol connectors  402 , data mappers  404  may process the extracted data  302 . More specifically, in various embodiments, data mappers  404  may normalize the extracted data  302 . Typically, this may entail identifying the data extracted from the traffic in the network as being of a particular data type and grouping the data extracted from the traffic in the network with other data of the particular data type. In some instances, this may also entail associating a unit of measure with the extracted data  302  and/or converting a data value in one unit of measure to that of another. 
     In various embodiments, once data  302  has been extracted and normalized, data transformer  406  may apply any number of data transformation to the data. In some embodiments, data transformer  406  may transform data  302  by applying any number of mathematical and/or symbolic operations to it. For instance, data transformer  406  may apply a data compression or data reduction to the extracted and normalized data  302 , so as to summarize or reduce the volume of data transmitted to the cloud. To do so, data transformer  406  may sample data  302  over time, compute statistics regarding data  302  (e.g., its mean, median, moving average, etc.), apply a compression algorithm to data  302 , combinations thereof, or the like. 
     In further embodiments, data transformer  406  may apply analytics to the extracted and normalized data  302 , so as to transform the data into a different representation, such as an alert or other indication. For instance, data transformer  406  may apply simple heuristics and/or thresholds to data  302 , to transform data  302  into an alert. In another embodiment, data transformer  406  may apply machine learning to data  302 , to transform the data. 
     In general, machine learning is concerned with the design and the development of techniques that take as input empirical data (such as network statistics and performance indicators), and recognize complex patterns in these data. One very common pattern among machine learning techniques is the use of an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function would be the number of misclassified points. The learning process then operates by adjusting the parameters a,b,c such that the number of misclassified points is minimal. After this optimization phase (or learning phase), the model M can be used very easily to classify new data points. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data. 
     Data transformer  406  may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data that is used to train the model to apply labels to the input data. For example, the training data may include samples of ‘good’ readings or operations and ‘bad’ readings or operations that are labeled as such. On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes in the behavior. For instance, an unsupervised model may Semi-supervised learning models take a middle ground approach that uses a greatly reduced set of labeled training data. 
     Example machine learning techniques that data transformer  406  can employ may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), singular value decomposition (SVD), multi-layer perceptron (MLP) ANNs (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, deep learning models, or the like. 
     In further embodiments, data transformer  406  may comprise a scripting engine that allows developers to deploy any number of scripts to be applied to data  302  for purposes of the functionalities described above. For instance, an application developer may interface with application  308   b  shown previously in  FIG. 3 , to develop and push various scripts for execution by data transformer  406 , if allowed to do so by policy. In other cases, previously developed scripts may also be pre-loaded into data transformer  406  and/or made available by the vendor or manufacturer of the device executing data management process  248  for deployment to data transformer  406 . 
     According to various embodiments, another potential component of data management process  248  is governance engine  408  that is responsible for sending the data  302  transformed by data transformer  406  to any number of cloud providers as data  304 . In general, governance engine  408  may control the sending of data  304  according to a policy. For instance, governance engine  408  may apply a policy that specifies that data  304  may be sent to a particular cloud provider and/or cloud-based application, but should not be sent to others. In some embodiments, the policy enforced by governance engine  408  may control the sending of data  304  on a per-value or per-data type basis. For instance, consider the case of an IoT node reporting a temperature reading and pressure reading. In such a case, governance engine  408  may send the temperature reading to a particular cloud provider as data  304  while restricting the sending of the pressure reading, according to policy. 
     As would be appreciated, by unifying the policy enforcement via governance engine  408 , the various stakeholders in the data pipelines are able to participate in the creation and maintenance of the enforced policies. Today, the various data pipelines built to support the different network protocols and cloud vendors results in a disparate patchwork of policies that require a level of expertise that not every participant may possess. In contrast, by unifying the policy enforcement via governance engine  408 , personnel such as security experts, data compliance representatives, technicians, developers, and the like can participate in the administration of the policies enforced by governance engine  408 . 
       FIG. 4B  illustrates an example  410  of the operation of data management process  248  during execution, according to various embodiments. As shown, assume that edge device  122  described previously (e.g., a device  200 ) executes data management process  248  at the edge of an IoT network that comprises IoT nodes  132 . During operation, edge device  122  may communicate with IoT nodes  132  in the network that comprise devices from n-number of different vendors. 
     Each set of vendor devices in IoT nodes  132  may generate different sets of data, such as sensor readings, computations, or the like. For instance, the devices from a first machine vendor may generate data such as a proprietary data value, a temperature reading, and a vibration reading. Similarly, the devices from another machine vendor may generate data such as a temperature reading, a vibration reading, and another data value that is proprietary to that vendor. 
     As would be appreciated, the data  302  generated from each group of IoT nodes  132  may use different formats that are set by the device vendors or manufacturers. For instance, two machines from different vendors may both report temperature readings, but using different data attribute labels (e.g., “temp=,” “temperature=,” “##1,” “*_a,” etc.). In addition, the actual data values may differ by vendor, as well. For instance, the different temperature readings may report different levels of precision/number of decimals, use different units of measure (e.g., Celsius, Fahrenheit, Kelvin, etc.), etc. 
     Another way in which data  302  generated by IoT nodes  132  may differ is the network protocol used to convey data  302  in the network. For instance, the devices from one machine vendor may communicate using the OPC UA protocol, while the devices from another machine vendor may communicate using the Modbus protocol. 
     In response to receiving data  302  from IoT nodes  132 , data management process  248  of edge device  122  may process data  302  in three stages: a data ingestion phase  412 , a data transformation phase  414 , and a data governance phase  416 . These three processing phases operate in conjunction with one another to allow edge device  122  to provide data  304  to the various cloud providers  306  for consumption by their respective cloud-hosted applications. 
     During the data ingestion phase  412 , protocol connectors  402  may receive messages sent by IoT nodes  132  in their respective protocols, parse the messages, and extract the relevant data  302  from the messages. For instance, one protocol connector may process OPC UA messages sent by one set of IoT nodes  132 , while another protocol connector may process Modbus messages sent by another set of IoT nodes  132 . Once protocol connectors  402  have extracted the relevant data  302  from the messages, data management process  248  may apply a data mapping  418  to the extracted data, to normalize the data  302 . For instance, data management process  248  may identify the various types of reported data  302  and group them by type, such as temperature measurements, vibration measurements, and vendor proprietary data. In addition, the data mapping  418  may also entail standardizing the data on a particular format (e.g., a particular number of digits, unit of measure, etc.). The data mapping  418  may also entail associating metadata with the extracted data  302 , such as the source device type, its vendor, etc. 
     During its data transformation phase  414 , data management process  248  may apply various transformations to the results of the data ingestion phase  412 . For instance, assume that one IoT node  132  reports its temperature reading every 10 milliseconds (ms). While this may be acceptable in the IoT network, and even required in some cases, reporting the temperature readings at this frequency to the cloud-providers may represent an unnecessary load on the WAN connection between edge device  122  and the cloud provider(s)  306  to which the measurements are to be reported. Indeed, a monitoring application in the cloud may only need the temperature readings at a frequency of once every second, meaning that the traffic overhead to the cloud provider(s)  306  can be reduced by a factor of one hundred by simply reporting the measurements at one second intervals. Accordingly, data transformation phase  414  may reduce the volume of data  304  sent to cloud provider(s)  306  by sending only a sampling of the temperature readings (e.g., every hundred), an average or other statistic(s) of the temperature readings in a given time frame, or the like. 
     During its data governance phase  416 , data management process  248  may apply any number of different policies to the transformed data, to control how the resulting data  304  is sent to cloud provider(s)  306 . For instance, one policy enforced during data governance phase  416  may specify that if the data type=‘Temp’ or ‘Vibration,’ then that data is permitted to be sent to destination=‘Azure,’ for consumption by a BI application hosted by Microsoft Azure. Similarly, another policy may specify that if the machine type=‘Vendor 1’ and the data type=‘proprietary,’ then the corresponding data can be sent to a cloud provider associated with the vendor. 
     In some embodiments, the policy enforced during data governance phase  416  may further specify how data  304  is sent to cloud providers  306 . For instance, the policy may specify that edge device  122  should send data  304  to a particular cloud provider  306  via an encrypted tunnel, using a particular set of one or more protocols (e.g., MQTT), how the connection should be monitored and reported, combinations thereof, and the like. 
     As noted above, IoT data is increasingly managed by a cloud-based system (e.g., Amazon Web Services IoT Core, Azure IoT Hub, Asset Vision and Edge Intelligence by Cisco Systems, Inc., etc.). In this model, the local IoT network sends data to a cloud data aggregation and brokering service, such as an MQTT broker, a LoRaWAN Network Server, or the like. In turn, that data is then passed back to the desired data repository of the owner of the IoT network, such as a data warehouse or big data system, where the data can be analyzed. Typically, the location of the data consumption service is different than the data warehouse, to which the data is ultimately sent. For example, a network operator may want their data passed to an on-site Hadoop, NoSQL, or Spark system in their data center, to a Google machine learning service, or some other place. 
     Optimal Selection of a Cloud-Based Data Management Service for IoT Sensors 
     The techniques introduced herein allow for the optimal selection of a cloud-based point of presence (POP) through which data may be sent from an IoT network to a data collection point. In some aspects, the techniques herein allow for the probing of the various path segments between the IoT network and the data collection point, in a coordinated manner. Based on the probing results, a device can select the optimal POP through which the data from the IoT network should be sent. 
     Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with data management process  248  and/or data pipeline configuration process  249 , which may include computer executable instructions executed by the processor  220  (or independent processor of interfaces  210 ) to perform functions relating to the techniques described herein. 
     Specifically, in various embodiments, a first device coordinates probing of network paths that extend between an edge device and a data collection point via a plurality of points of presence. The first device receives a set of probing results that are indicative of one or more performance metrics associated with the network paths. The first device selects, based on the set of probing results, a particular point of presence from among the plurality of points of presence through which the edge device should send traffic to the data collection point. The first device instructs the edge device to send traffic to the data collection point via the particular point of presence. 
     Operationally,  FIGS. 5A-5G  illustrates an example of the selection of a point of presence (POP), according to various embodiments. Continuing the previous examples, assume that there is an edge device  122  located at the edge of a network comprising any number of IoT nodes  132 , such as IoT node  132   d  shown. Further, assume that there are a plurality of cloud-hosted POPs that aggregate data from the IoT network (e.g., sensor data from IoT node  132   d,  etc.). For instance, as shown, there may be a first cloud aggregation POP  308   e  hosted by a first cloud provider  306   e,  a second cloud aggregation POP  308   f  hosted by a first cloud provider  306   f,  and a third cloud aggregation POP  308   g  hosted by a third cloud provider  306   g.  In some instances, cloud providers  306   e - 306   g  may be separate cloud providers. However, in further cases, cloud providers  306   e - 306   g  may be associated with the same cloud provider (e.g., Amazon, Microsoft, etc.). Regardless, assume for purposes of illustration that each of POPs  308   e - 308   g  are located in different geographic locations (e.g., the U.S., Europe, Asia, etc.). 
     In various embodiments, POPs  308   e - 308   g  may comprise a data aggregation service and/or data brokering service, such as an MQTT broker, LoRaWAN network server, or the like. Thus, during operation, edge device  122  may publish data to a particular one of POPs  308   e - 308   g  sourced from an IoT node in the local IoT network, such as IoT node  132   d  (e.g., sensor data, operational data, etc.). In addition, edge device  122  may perform the various data ingestion, transformation, and/or governance functions described previously with respect to  FIGS. 4A-4B . 
     As shown, there may also be a data collection point  502  to which the data from edge device  122  will ultimately be sent by the receiving POP. For instance, data collection point  502  may take the form of a data warehouse system and/or data analytics system that stores and/or analyzes the data sent by edge device  122 . Examples of data collection point  502  may include, but are not limited to, a Hadoop, NoSQL, or Spark system in a data center, a Google machine learning service, or the like. In some instances, data collection point  502  may be a data center associated with the operator of the operator of the IoT network. In other cases, data collection point  502  may be another cloud service hosted somewhere on the Internet. 
     Today, POP selection by an IoT edge device is typically based on the distance between the edge device and the POP. In other words, the closest POP is typically the one to which an IoT edge device sends its traffic. For instance, assume that POP  308   e  is located in the U.S., POP  308   f  is located in Europe, and POP  308   g  is located in Asia. If edge device  122  is also located in the U.S., it may simply select POP  308   e  as its data aggregator. However, assume now that data collection point  502  is located in Japan. While POP  308   e  may be the closest POP to edge device  122  and, consequently, the path between them offers the shortest round-trip time (RTT), the overall RTT of the path extending from edge device  122  to POP  308   e  to data collection point  502  may be sub-optimal. Indeed, sending the IoT data to POP  308   g  may actually result in a shorter overall RTT to data collection point  502 . Thus, an opportunity exists to optimize the POP selection process by taking into account the full end-to-end path metrics between the edge device and the data collection point. 
     According to various embodiments, the selection of the optimal POP for use by an IoT edge device may proceed as follows. First, the operator of the local IoT network may sign up for a data aggregation service, such as Cisco Asset Vision (based on LoRaWAN), Cisco Edge Intelligence, Amazon Web Services (AWS) IoT Core, Microsoft Azure IoT Hub, or the like. In general, these cloud services collect data, process it, and broker it to its final destination(s), where the operator of the IoT network may store, analyze, and visualize their data. In further embodiments, the operator of the IoT network may also specify POP preferences that can be used during the POP selection process. For instance, the operator may also specify a list of POPs that can be used, a list of one or more POPs to exclude, a preferred POP, or the like. 
     As part of the onboarding process with the data aggregation service, the operator may specify the final destination(s) of the data. In turn, when the operator on-boards an edge device to the data aggregation service, the edge device may first register with the closest POP of the aggregation service. For instance, assume that the operator of the IoT network in which IoT node  132   d  is located onboards edge device  122  to a data aggregation service associated with POPs  308   e - 308   g.  In such a case, edge device  122  may send a registration  504  to POP  308   e,  as it is the closest POP to edge device  122 . 
     As shown in  FIG. 5B , after receiving registration  504  from edge device  122 , POP  308   e  may coordinate proving of the various paths available between edge device  122  and data collection point  502 , according to various embodiments. To do so, POP  308   e  may instruct edge device  122  to send a registration request to all of the other POPs (e.g., POP  308   f  and POP  308   g ), or those POPs that were specified by preference by the operator of the IoT network, and to timestamp those packets. By timestamping the packets of the registration requests, the receiving POP can calculate metrics such as the round-trip time (RTT) between that POP and edge device  122 . In other words, the initial POP  308   e  may send an instruction  506   a  to edge device  122  to probe the various path segments between itself and the POPs that are available to it. 
     In addition to instructing edge device  122  to probe its paths to POPs  308   f - 308   g,  POP  308   e  may also send an instruction  506   b  to data collection point  502  to probe its paths with POPs  306   e - 306   g,  likewise. Such a synthetic probe may also be timestamped by data collection point  502 , to allow each receiving POP to compute the RTT and/or other path metrics for these path segments. 
     In various embodiments, POP  308   e  may also send instructions  508   a - 508   b  to POPs  306   f - 306   g,  respectively, that instructs these POPs to receive the incoming probes from edge device  122  and data collection point  502  and to return the results to POP  308   e,  when completed. 
     Thus, as shown in  FIG. 5C , edge device  122  may perform probing  510   a - 510   c  of the paths between itself and POPs  308   e - 308   g,  or a subset thereof. Preferably, the probing of the other POPs  308   f - 308   g  may comprise registration requests that include the information needed to onboard edge device  122  to either of them. However, this is not a strict requirement and other forms of path probing may be used, in the alternate. 
     Similarly, as shown in  FIG. 5D , data collection point  502  may also initiate its own probing  510   d - 510   f  of its respective paths to POPs  308   e - 308   g.  For instance, data collection point  502  may send timestamped packets to each of POPs  308   e - 308   g.    
     As shown in  FIG. 5E , in various embodiments, POPs  308   f - 308   g  may send the results  512   a - 512   b  of the probing to POP  308   e.  For instance, POP  308   f  may compute the RTT between itself and edge device  122 , as well as between itself and data collection point  502 , and include these metrics in results  512   a.  Similarly, POP  308   g  may compute the RTT between itself and edge device  122 , as well as between itself and data collection point  502 , and include these metrics in results  512 . POP  308   e  may perform similar computations with respect to itself, edge device  122 , and data collection point  502 . In further embodiments, results  512   a - 512   b  may simply include the raw measurement data and POP  308   e  may perform the RTT computations, itself. 
     In further embodiments, results  512   a - 512   b  may also include any other path metrics that may be obtained from the probing performed in  FIGS. 5C-5D  and used for the POP selection. For instance, results  512   a - 512   b  may also include metrics such as latency, jitter, packet loss, minimum bandwidth or other bandwidth measurements, reliability metrics, or the like. 
     Computation of the end-to-end metrics for the paths between edge device  122  and data collection point  502  may simply entail combining the metrics for their constituent path metrics. For instance, the RTT between edge device  122  and data collection point  502  via POP  308   e  can be computed by summing the RTT of the path segment between POP  308   e  and edge device  122  with the path segment between POP  308   e  and data collection point  502 . Similar computations may also be made with respect to the end-to-end paths that extend from edge device  122  to data collection point  502  via POPS  308   f - 308   g.  Other path metrics can be combined to form end-to-end path metrics, such as by taking an average of the metrics for the path segments, summing the metrics for the path segments, computing other aggregated statistics, or the like. 
     In various embodiments, POP  308   e  may select the optimal POP for edge device  122  to use, based on the results of the path probing. In one embodiment, POP  308   e  may simply provide the path metrics to a user interface for review by the operator of the IoT network and, in turn, receive a selection of the optimal POP or end-to-end path between edge device  122  and data collection point  502 . In further embodiments, POP  308   e  may use the probing results as input to an objective function configured to select the preferred POP and end-to-end path. For instance, such an objective function may take into account the end-to-end RTT, latency, jitter, bandwidth, etc. Thus, POP  308   e  may generate a ranking of the top n-number of POPs for edge device  122  based on their associated end-to-end path metrics. In turn, POP  308   e  may simply select the top POP from the list or seek confirmation of the selection via the user interface, first. 
     Once the best POP  308   e  has identified the optimal POP for use by edge device  122 , it may send a redirect instruction  514  to edge device  122 , as shown in  FIG. 5F . For instance, assume that POP  308   g  is selected as the optimal POP for edge device  122 . In such a case, redirect instruction  514  may instruct edge device  122  to send its traffic data to POP  308   g.  In response, as shown in  FIG. 5G , edge device  122  may move its tunnel termination to POP  308   g,  thereby switching its end-to-end path with data collection point  502  to the path that extends via POP  308   g.    
     Note that, in some cases, the initial POP (e.g., POP  308   e ) may be the optimal POP for edge device  122 . When this occurs, POP  308   e  may send a confirmation to edge device  122  that it should continue to use POP  308   e.  Alternatively, POP  308   e  may simply omit sending such a notification or a redirect instruction  514  to edge device  122 , allowing edge device  122  to continue using POP  308   e.    
     When a different POP is selected for use by edge device  122 , the initial POP may also replicate its probe database with the results of the path probing to the selected POP. This allows the selected POP to continue to monitor the end-to-end path metrics of the current path between edge device  122  and data collection point  502  and, if necessary, select an alternate path. For instance, if the performance of the end-to-end path between edge device  122  and data collection point  502  via POP  308   g  degrades, POP  308   g  may send a redirect instruction to edge device  122  for the next best POP, according to the probing results. In further embodiments, POP  308   g  may also initiate new probing of each end-to-end path, such as when the existing probing results reach a threshold age and are considered stale. 
     Note that while the probing coordination and POP selection are shown in  FIGS. 5A-5G  as being performed by POP  308   e,  any device associated with POP  308   e  may perform these functions, either directly as a part of POP  308   e  or in communication therewith. 
       FIG. 6  illustrates an example simplified procedure for selecting an optimal POP, in accordance with one or more embodiments described herein. The procedure  600  may start at step  605 , and continues to step  610 , where, as described in greater detail above, a specifically-configured, first device (e.g., device  200 ) may coordinate probing of network paths that extend between an edge device and a data collection point via a plurality of points of presence (POPs). For instance, in some embodiments, the first device may is itself be associated with one of the POPs. In such cases, the first device may coordinate the probing, after receiving a registration request from the edge device. Typically, the edge device is a router or other networking device at the edge of an IoT network. In various embodiments, the first device may coordinate the probing by any or all of the following: instructing the edge device to send probes to the plurality of POPs, instructing the data collection points to send probes to the plurality of POPs, and/or notifying the POPs to expect probes. 
     At step  615 , as detailed above, the first device may receive a set of probing results that are indicative of one or more performance metrics associated with the network paths. For instance, the probing results may be indicative of the round-trip times (RTTs), latency, jitter, bandwidth, other measures of reliability, combinations thereof, or the like. In some embodiments, the first device may receive at least a portion of the set of probing results from one or more of the plurality of POPs. In further embodiments, the set of probing results comprises a first subset of probing results associated with path segments between the edge device and the plurality of points of presence and a second subset of probing results associated with path segments between the data collection point and the plurality of points of presence. 
     At step  620 , the first device may select, based on the set of probing results, a particular POP from among the plurality of POPs through which the edge device should send traffic to the data collection point, as described in greater detail above. For instance, the first device may select a particular cloud-based data aggregation service or cloud-based brokering service (e.g., an MQTT broker, a LoRaWAN network server, etc.) to which the edge device should send its data, based on the performance associated with the path extending from the edge device to the data collection point via that POP. In a simple case, the first device may simply select the POP based on the RTT associated with its corresponding path. However, in more complex implementations, the optimization may entail optimizing an objective function that takes into account a plurality of path metrics (e.g., latency, jitter, etc.). In further cases, the first device may also filter out any POPs from consideration whose paths exhibit one or more characteristics that exceed a predefined threshold (e.g., number of packet drops, etc.). 
     At step  625 , as detailed above, the first device may instruct the edge device to send traffic to the data collection point via the particular POP. In turn, the edge device may send its traffic, such as data from any number of IoT devices in the IoT network, to the data collection point via the optimal POP. Procedure  600  then ends at step  630 . 
     It should be noted that while certain steps within procedure  600  may be optional as described above, the steps shown in  FIG. 6  are merely examples for illustration, and certain other steps may be included or excluded as desired. Further, while a particular order of the steps is shown, this ordering is merely illustrative, and any suitable arrangement of the steps may be utilized without departing from the scope of the embodiments herein. 
     The techniques described herein, therefore, provide for the optimal selection of a cloud-based POP to which an edge device should send traffic, by also taking into account the data collection point that serves as the final destination of the data in the traffic. 
     While there have been shown and described illustrative embodiments for the optimal selection of a cloud-based data management service for IoT sensors, it is to be understood that various other adaptations and modifications may be made within the intent and scope of the embodiments herein. For example, while specific protocols are used herein for illustrative purposes, other protocols and protocol connectors could be used with the techniques herein, as desired. Further, while the techniques herein are described as being performed by certain locations within a network, the techniques herein could also be performed at other locations, such as at one or more locations fully within the local network (e.g., by the edge device), etc. 
     The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable is medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true intent and scope of the embodiments herein.