Patent Description:
Software defined networking (SDN) represents an evolution of computer networks away from a decentralized architecture to one of centralized, software-based control. More specifically, in traditional computer networks, the control plane (e.g., selection of the routing path) and the data plane (e.g., forwarding packets along the selected path) are intertwined, with control plane decisions being made in a decentralized manner via signaling between the networking devices. In contrast, control plane decisions in an SDN-based network architecture are made by a centralized controller and pushed to the networking devices, as needed.

While applicable to any number of different types of network deployments, SDN is particularly of relevance to cloud service provider networks. Indeed, in a traditional client-server architecture, the network need only support traffic between the client and the server. However, with cloud computing, each transaction with a client may result in a large amount of "east-west" traffic between nodes in the cloud (e.g., to perform a query or computation in parallel, etc.), as well as the traditional "north-south" traffic between the cloud and the client. In addition, the very nature of cloud computing environments allows for the rapid scaling of resources with demand, such as by spinning new nodes up or down. In such situations, centralized control over the control plane results in better network performance over that of decentralized control.

With a <NUM> cellular network, the core of the network takes on a sliced, flat structure, and the data centers that serve it must align with this model. A typical SDN implementation allows for dedicating cores to various slices in the servers, but the network is a shared, flat CLOS/Fat-Tree model where incast and drops due to one slice overheating will affect the others. Overlays enable end-to-end communications, and it is possible to build more than one overlay to serve different slices. However, it is not currently possible to associate physical resources to the overlays, since routing is decided in the underlay. In order to really fit the slicing model, each overlay would need its own underlay with its own slice of the physical resources.

<CIT> is directed to a method for operating an industrial automation system communication network comprising a plurality of communication devices, and control unit. In order to operate an industrial automation system communication network comprising a plurality of communication devices, at least one control unit controls functions of a plurality of assigned communication devices and is assigned to at least one partition of the communication network. Partitions each comprise predefinable parts of communication devices assigned to system resources for predefinable resource periods of use. Access periods and repetition cycles for transmit queues are set by the control unit according to the resource periods of use for the partitions in the assigned communication devices. Possible partitions are determined for the path reservation requests on the basis of matching classifications of access periods and repetition cycles. In the case of sufficient system resources, the particular path reservation request is assigned to a determined partition.

According to one or more embodiments of the disclosure, a device configures a plurality of subinterfaces for each of a plurality of physical ports of a software defined network (SDN). The device allocates a fixed amount of bandwidth to each of the subinterfaces. The device forms a plurality of midlays for the SDN by assigning subsets of the plurality of subinterfaces to each of the midlays. The device assigns a network slice to one or more of the midlays, based on a bandwidth requirement of the network slice.

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, with the types 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), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE <NUM>, IEEE P1901. <NUM>, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may further be interconnected by an intermediate network node, such as a router, to extend the effective "size" of each network.

Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or "AMI" applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.

<FIG> is a schematic block diagram of an example computer network <NUM> illustratively comprising nodes/devices, such as a plurality of routers/devices interconnected by links or networks, as shown. For example, customer edge (CE) routers <NUM> may be interconnected with provider edge (PE) routers <NUM> (e.g., PE-<NUM>, PE-<NUM>, and PE-<NUM>) in order to communicate across a core network, such as an illustrative network backbone <NUM>. For example, routers <NUM>, <NUM> may be interconnected by the public Internet, a multiprotocol label switching (MPLS) virtual private network (VPN), or the like. Data packets <NUM> (e.g., traffic/messages) may be exchanged among the nodes/devices of the computer network <NUM> over links using predefined network communication protocols such as the Transmission Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relay protocol, or any other suitable protocol. 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.

In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN, thanks to a carrier network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories:.

<FIG> illustrates an example of network <NUM> in greater detail, according to various embodiments. As shown, network backbone <NUM> may provide connectivity between devices located in different geographical areas and/or different types of local networks. For example, network <NUM> may comprise local networks <NUM>, <NUM> that include devices/nodes <NUM>-<NUM> and devices/nodes <NUM>-<NUM>, respectively, as well as a data center/cloud environment <NUM> that includes servers <NUM>-<NUM>. Notably, local networks <NUM>-<NUM> and data center/cloud environment <NUM> may be located in different geographic locations.

Servers <NUM>-<NUM> may include, in various embodiments, a network management server (NMS), a dynamic host configuration protocol (DHCP) server, a constrained application protocol (CoAP) server, an outage management system (OMS), an application policy infrastructure controller (APIC), an application server, etc. As would be appreciated, network <NUM> may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc..

The techniques herein may also be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc. Further, in various embodiments, network <NUM> may include one or more mesh networks, such as an Internet of Things network. Loosely, the term "Internet of Things" or "IoT" refers 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, 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.

Notably, shared-media mesh networks, such as wireless networks, etc., 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. In particular, LLN routers typically operate with highly constrained resources, e.g., processing power, memory, and/or energy (battery), and their interconnections are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (e.g., between devices inside the LLN), point-to-multipoint traffic (e.g., from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (e.g., from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network <NUM> may be an LLN in which CE-<NUM> operates as a root node for nodes/devices <NUM>-<NUM> in the local mesh, in some embodiments.

<FIG> is a schematic block diagram of an example device/apparatus <NUM> that may be used with one or more embodiments described herein, e.g., as any of the computing devices shown in <FIG>, particularly the PE routers <NUM>, CE routers <NUM>, nodes/device <NUM>-<NUM>, servers <NUM>-<NUM> (e.g., a network controller located in a data center, etc.), any other computing device that supports the operations of network <NUM> (e.g., switches, etc.), or any of the other devices referenced below. The device/apparatus <NUM> may also be any other suitable type of device depending upon the type of network architecture in place, such as IoT nodes, etc. Device/apparatus <NUM> comprises one or more network interfaces <NUM>, one or more processors <NUM>, and a memory <NUM> interconnected by a system bus <NUM>, and is powered by a power supply <NUM>.

The network interfaces <NUM> include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network <NUM>. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface <NUM> may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art.

The memory <NUM> comprises a plurality of storage locations that are addressable by the processor(s) <NUM> and the network interfaces <NUM> for storing software programs and data structures associated with the embodiments described herein. The processor <NUM> may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures <NUM>. An operating system <NUM> (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc. , another operating system, etc.), portions of which are typically resident in memory <NUM> and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a network slicing process <NUM>.

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 processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.

Software defined networking (SDN) represents an evolution of computer networks that centralizes control plane decisions with a supervisory device. For example, in Application Centric Infrastructure (ACI), an SDN-based architecture from Cisco Systems, Inc. , control plane decisions may be made by a centralized APIC. However, even with centralized control, there still exists the potential for seasonal congestion to occur on certain links in the network fabric.

In general, an SDN-based network fabric may utilize a leaf-spine architecture, such as CLOS and Fat-Tree architectures. This is particularly true in the case of data center and cloud networks that are poised to deliver the majority of computation and storage services in the future. In a Fat-Tree, nodes are organized in a tree structure with branches becoming 'fatter' towards the top of the hierarchy. In the context of computer networks, this increasing 'fatness' typically corresponds to increasing bandwidth towards the top of the hierarchy. CLOS networks typically involve multiple stages (e.g., an ingress stage, a middle stage, and an egress stage), with 'crossbar' switches at different stages that are interwoven such that multiple paths are available for switching, so that one traffic flow does not block another.

As would be appreciated, an SDN fabric that implements a leaf-spine architecture may operate by emulating a very large switch by interleaving many smaller switches, resulting in much lower cost and higher scalability. The benefits of such designs include, but are not limited to, the availability of an equal cost multi-path (ECMP) based switching fabric, a simplified network, and fully utilized link bandwidth on each network node. It also allows the networks to scale and grow incrementally, on demand. Cisco's next generation SDN based data center network fabric architecture, ACI, is also based on CLOS design principles.

<FIG> illustrates a simplified example of an SDN fabric <NUM> that uses a leaf-spine architecture. As shown, the network switches S1-S4 and L1-L6 may be organized according to CLOS design principles. In particular, switches S1-S4 may form a superspine <NUM>. This layer is also sometimes called the Top of Fabric (ToF) layer, such as in RIFT. At the south of fabric <NUM> is a leaf layer <NUM> that comprises switches L1-L6 and provide connectivity to the various clients of fabric <NUM>, such as endpoints or virtual machines (VMs), and implement Layer <NUM> bridging and Layer <NUM> routing functions. Likewise, S1-S4 in superspine layer <NUM> may be fully meshed with L1-L6 in leaf layer <NUM> via connections <NUM>, which are not actual links, in the physical sense. During operation, S1-S4 may provide redundant paths and connectivity from a previous lower-level stage switch in the network fabric.

<FIG> illustrates another example SDN fabric <NUM> that uses a CLOS-based approach. As shown, at the top of fabric <NUM> are switches S1-S4 that form a superspine layer <NUM> that are connected to a middle layer <NUM> comprising switches M1-M6 which are, in turn, connected to a leaf layer <NUM> comprising switches L1-Lc. The overall function of fabric <NUM> may be similar to that of fabric <NUM> in <FIG>, with the addition of middle layer <NUM> that may perform, for example, aggregation functions. Leaf switches and their corresponding switches in middle layer <NUM> may also form pods, such as pod 318a shown.

Today, a large, virtualized data center fabric might be comprised of approximately <NUM>-<NUM> leaf switches and as many as approximately <NUM>-<NUM> spine switches servicing many of its tenant's virtual networks on the shared, physical network infrastructure. Each leaf switch, in turn, may be connected to between <NUM>-<NUM> physical hypervisor servers, with each server hosting approximately <NUM> virtual servers/endpoints that estimate to between <NUM>-<NUM> endpoints connected per leaf switch. In such a shared network deployment, network access security becomes an important factor for consideration.

More specifically, in virtualized data center deployments, like ACI, the movement of endpoints from one leaf port to another, or from one endpoint group (typically tied to the dot1q VLAN the vSwitch tags to outgoing packets) to another within the same leaf or across leaf switches of the network fabric, is very common. In such loosely-coupled network connectivity models, where the locality of the endpoints is not fixed, the network fabric and the endpoints become vulnerable to attacks by the rogue devices. For example, if the initial network access or the subsequent endpoint moves are allowed without any verification, it might lead to severe security issues. This enforces an important requirement on the underlying first hop switches that are responsible for network connectivity: to grant network access only to authorized endpoints and deny connectivity to unauthorized devices.

To limit the number of ports per leaf switch, leaves are grouped in pods, such as pod 318a. As would be appreciated a pod in an SDN fabric is a cross bar of smaller switches and can be seen as a large, virtual leaf node, characterized by its Radix.

<FIG> illustrates an example pod <NUM> that can be formed by linking switches in middle layer <NUM> with those in leaf layer <NUM>. As shown, pod <NUM> has (K = Radix / <NUM>) ports connecting upward and as many downward, with Kleaf =<NUM> top nodes of Ktop ports down connected to Ktop leaves with Kleaf ports upward. This creates a virtual leaf node of (Kp = Kleaf * Ktop) ports. From there, pods of pods can be constructed recursively within the SDN fabric.

A Fat-Tree has a number of pods interconnected by a superspine. In an ideal fabric, there is at least one port per Top of Pod (ToP) switch on every Top-of-Fabric (ToF) switch in the superspine. This means that every northbound port of a leaf has a path to every spine node. In that case, the superspine is fully meshed with the pod top switches, and the fabric is NOT partitioned. For example, in <FIG>, assume that M1-M2 and M3-M4 in middle layer <NUM> also form pods with pod 318a. In such a case, the network would not be considered partitioned, as each of the pods is fully meshed with superspine layer <NUM>.

In the case in which each pod is fully connected to superspine layer <NUM>, a spine node has a Radix (number of ports) Rs = Np * Kleaf, where Np is the number of pods. This makes the connectivity from any spine node to any leaf node resilient to Kleaf-<NUM> breakages in between. However, Rs rapidly becomes a gating factor for scalability, limiting the number of pods that can be attached to the superspine, in many implementations.

In large fabric, or fabrics built from switches with a low Radix, the ToF is often partitioned in planes. <FIG> illustrates an example SDN fabric <NUM> in which the fabric is partitioned into two separate planes: Plane <NUM> and Plane <NUM>. As shown, while each ToF switch in superspine layer <NUM> is still connected to each leaf in leaf layer <NUM>, not every ToF switch in superspine layer is connected to every ToP switch in middle layer <NUM>. This means that the redundancy is reduced, in comparison to non-partitioned fabrics.

<FIG> illustrates another example SDN fabric 320a in which the fabric has been partitioned into P+<NUM> number of planes: plane <NUM>, plane <NUM>, and P-number of other planes. As would be appreciated, the decision as to which plane to use in SDN fabric 320a is left to the leaves in leaf layer <NUM>. In addition, a failure of an intermediate link or switch typically affects one or more planes, but not all.

The minimum connectivity for an SDN fabric, such as fabric 320a, is when each leaf in leaf layer <NUM> has a single path to each node in superspine layer <NUM>, which happens when every ToF node connects to only one ToP node in each pod. This means that, at a maximum, there are exactly as many planes as there are northbound ports on a leaf Node (Kleaf = P*R). In that case, the ToF is maximally partitioned.

As noted above, <NUM> networks are increasingly moving towards a model that uses network slicing to effectively create multiple virtual networks on top of a shared, physical network. In general, network slicing leverages SDN and network functions virtualization (NFV) techniques, to form an end-to-end virtual network that encompasses the networking functions, as well as the storage and compute functions of the network.

By way of example, consider a deployment of IoT sensors that each have their own <NUM> transceiver, to report sensor readings to a cloud-based monitoring and analytics service. Today, each transceiver would have its own associated data plan and share the same resources of the network with many other devices outside of the deployment (e.g., people's phones, tablets, etc.). Such an approach does not, however, take into account the specific needs of the different devices. Indeed, mobile phones may be far less tolerant of network latency than that of the IoT sensors. Moreover, the IoT sensors may only report readings very sporadically, whereas the phone may constantly send and receive data throughout the day. Network slicing allows the physical network to be divided into different virtual networks, such as an "IoT sensor" slice and a "mobile phone" slice, effectively creating different "lanes" for the two different types of traffic.

Overlays enable end-to-end communications, and it is possible to build more than one overlay to serve different network slices. However, it is not currently possible to associate physical resources to the overlays, since routing is decided in the underlay (e.g., the physical network). In order to really fit the slicing model, each overlay would need its own underlay with its own slice of the physical resources.

The techniques herein leverage the concept of a midlay between the underlay and overlay of a network to implement network slicing. In some aspects, subinterfaces can be configured for the physical ports in the underlay and used to form the midlays, effectively forming virtual underlays on which the overlays can be built. This enables real slices to be implemented and, potentially, a Fat Tree-as-a-service model.

Specifically, according to one or more embodiments of the disclosure as described in detail below, a device configures a plurality of subinterfaces for each of a plurality of physical ports of a software defined network (SDN). The device allocates a fixed amount of bandwidth to each of the subinterfaces. The device forms a plurality of midlays for the SDN by assigning subsets of the plurality of subinterfaces to each of the midlays. The device assigns a network slice to one or more of the midlays, based on a bandwidth requirement of the network slice.

Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the network slicing process <NUM>, which may include computer executable instructions executed by the processor <NUM> (or independent processor of interfaces <NUM>) to perform functions relating to the techniques described herein.

Operationally, the techniques herein introduce the concept of network midlays constructed from subinterfaces having fixed allocated bandwidths, to implement network slicing in an adjustable manner.

<FIG> illustrates an example network <NUM> having a physical underlay <NUM> comprising a leaf layer <NUM>, middle layer <NUM>, and a (super)spine layer <NUM>, as described previously. A key aspect of the techniques herein is the concept of a "midlay" that acts as an intermediate layer between the fabric underlay and overlay(s). For example, as shown, a midlay <NUM> may be formed on top of underlay <NUM> of network <NUM> that essentially divides network <NUM> into subsets/segments. In various embodiments, midlay <NUM> may take the form of a collection of Fat Trees implemented over circuits between leaf layer <NUM> (e.g., leaf layer <NUM> in underlay <NUM>) and spine layer <NUM> (e.g., spine layer <NUM> in underlay <NUM>).

The midlay circuits of midlay <NUM> may be bidirectional and installed by a central controller (e.g., device/apparatus <NUM>) over the physical fabric/underlay <NUM>. In various embodiments, midlay <NUM> may be implemented over underlay <NUM> using a multi-topology routing technique, using virtual routing and forwarding (VRF) or similar for the routing and MPLS or virtual LAN (VLAN) tagging to indicate midlay <NUM> in the forwarding plane. In another embodiment, midlay <NUM> can also be formed using a less elastic approach, such as using Flexible Ethernet (FlexE).

As detailed below, the concept of a midlay can be extended to form Fat Tree slices and enable a Fat Tree-as-a-service model whereby a cloud provider not only hosts VMs, but also hosts full dedicated fabrics with the same ease of use to add and modify fabrics as in the overlay. The gist of the techniques herein is that subinterfaces on the physical ports in the underlay can be used to create midlays for each network slice. This is in contrast, for example, to simply implementing the midlay using tunnels. While the 'diameters' of tunnels can be increased to afford more bandwidth, which is adequate for statistical QoS processing, doing so may not be adequate to allocate more bandwidth to network slices.

Using the techniques herein, a midlay may comprise a collection of subinterfaces of a larger underlay, whereby each subinterface has a fixed bandwidth. This can be enforced, for instance, using FlexE or Time Sensitive Networking (TSN)/<NUM>. <FIG> illustrates an example of a physical port <NUM> for which an SDN controller or other supervisory device/apparatus (e.g., device/apparatus <NUM>) may configure any number of bandwidth-controlled subinterfaces <NUM>. To form a midlay, the controller may first dynamically add ingress subinterfaces to leaves of the network, as well as adding subinterfaces all the way to the spine, and allocate a set amount of bandwidth to each interface. To the overlay above, the miday will look like a fabric that is a subset of the underlay fabric. In other words, the teachings herein propose using a model in which a virtual CLOS is associated to each network slice, which can be adapted dynamically in capacity according to the bandwidth requirements of the slice.

To further highlight the teachings herein, consider the case shown in <FIG>. As shown, assume that a cloud provider associates VMs with three different sets of endpoints (e.g., three different customer networks). Packets <NUM> for these sets of endpoints are represented in <FIG> as solid, dashed, and dotted arrow, accordingly. Under a traditional approach, packets <NUM> will share the communal network resources, such as physical port <NUM>. During operation, packets <NUM> received on physical port <NUM> may be statistically multiplexed. However, this means that a given flow could still suffer from incast in the network due to the activity of other slices.

To avoid one slice consuming the bandwidth needed by another slice, <FIG> illustrates a potential fabric configuration <NUM> in which the slices are assigned to dedicated pods. For example, one slice may be assigned to pod 318a, another to pod 318b, and a third to pod 318c. Doing so makes it such that in-slice traffic does not leak into superspine layer <NUM> and underutilizes the superspine. While the arrangement shown in <FIG> is functional, better arrangements are also possible, as a failure in one pod <NUM> means that the entire slice will go down.

<FIG> illustrates another example <NUM> of using midlays to allocate bandwidth in an SDN to network slices, according to various embodiments. Similar to the example of <FIG>, midlays may be built and assigned to each network slice, allocating bandwidth on each hop for each slice. Routing can also be communalized in the underlay or done per slice in each midlay. Further, a fixed amount of bandwidth can be allocated in each ingress and each infrastructure link can be associated to a slice. Such a configuration can be enforced, for example, using TSN shapers or FlexE mechanisms.

In <FIG>, assume again that there are three different slices, represented as solid, dashed, and dotted lines, accordingly. In some embodiments, rather than assign each slice to its own pod as in <FIG>, in the arrangement shown in <FIG>, the controller may form midlays for the slices that divide the risk across the network. For simplicity, assume that the controller configures subinterfaces for each physical port such that the overall bandwidth is divided evenly and forms midlays for each of the slices, accordingly.

As a result, each slice is still allocated the same overall bandwidth as in <FIG>, but the slices can now utilize their own share of all resources of the fabric, including super spine layer <NUM>. Thus, only a third of the traffic for any given slice now flows through any particular device in the network, greatly reducing the damage if an equipment failure occurs.

In other words, the arrangement shown has the added benefit that the consequences of a failure of a physical device (e.g., loss of a percentage of ECMP bandwidth, etc.) is now divided across the network. The risk sharing approach shown also isolates the risks of incast due to a particular slice exceeding its allocated resources, since the subinterfaces associated with each slice have fixed bandwidth allocations, already.

<FIG> illustrate further examples of dynamically allocating bandwidth for a network slice, in accordance with the teachings herein. Currently, it is relatively easy to add VMs to any server connected to the underlay, but cloud switching is perceived as a non blocking infinite resource, which is never actually the case. In further embodiments, the techniques herein can be used to enable a slice-as-a-service model, to paint per-slice midlay fabrics over the underlay, as needed.

As shown in example <NUM> in <FIG>, in further embodiments, the controller may form a midlay for a network slice by allocating subinterfaces on a minimal amount of links while respecting any or all of the following Fat Tree rules:.

Consider, for example, the network slice represented by dotted lines in <FIG>. For illustrative purposes, assume that the controller configures ten subinterfaces per port, each with <NUM>% of the total available bandwidth and that the slice only requires the minimal possible bandwidth in a single pod 318b. In such a case, the controller may form a midlay for the slice that includes only one subinterface per physical port.

Assume now that the bandwidth requirements of the network slice increase over time. In such a case, the controller may take one of two different approaches, to allocate more bandwidth to the slice.

One possible way to dynamically adjust the bandwidth of a network slice is shown in <FIG>. In some embodiments, when the network slice requires more bandwidth than is currently allocated to it, the controller may add more subinterfaces to the midlay associated with the network slice. For example, as shown, the controller may double the number of subinterfaces for the midlay of the network slice, to effectively double the amount of bandwidth for the slice. However, as mentioned previously, diversifying the use of the physical network by the slice also helps to reduce the impact of a device failure on the slice.

Another possible way to dynamically adjust the bandwidth of a network slice is shown in <FIG>. In further embodiments, rather than simply increasing the number of subinterfaces of the current midlay associated with the network slice, the controller may instead adjust the midlay to extend the midlay and number of subinterfaces to other pods. For example, rather than simply double the number of interfaces of the midlay for the network slice in pod 318b, the controller may instead assign additional subinterfaces from pod 318c to the midlay. This arrangement has the added benefit of reducing the impact on the network slice, should one of the physical devices of the underlay (e.g., leaf L1) fails.

Of course, the controller may also take a holistic approach to dynamically allocating bandwidth for network slices, so as to optimize the dispositions of their midlays and serve their various needs. In other words, the controller may take into account any or all of the network slices, when dynamically allocating bandwidth to a particular slice, so as to meet the requirements of all of the slices.

In some embodiments, the controller may also dynamically adjust slices without an explicit request to do so, thereby offering a service-as-you-go model. By monitoring the traffic usage per-slide, the controller may dynamically adjust the midlays for the slices and their associated bandwidths, based on their traffic. For example, the controller may allocate K * average-traffic-usage over past T minutes, with K varying from <NUM> to K_Max (K > <NUM> meaning over provisioning). In a further embodiment, the controller could forecast the traffic demand and pre-allocate bandwidth for a slice before ever seeing the traffic of the slice on the fabric.

<FIG> illustrates an example simplified procedure for using a midlay for adjustable segmentation and slicing in a network in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device <NUM>) may perform procedure <NUM> by executing stored instructions (e.g., process <NUM>), such as a controller for an SDN fabric. The procedure <NUM> may start at step <NUM>, and continues to step <NUM>, where, as described in greater detail above, the device may configure a plurality of subinterfaces for each of a plurality of physical ports of the SDN fabric.

At step <NUM>, as detailed above, the device may allocate a fixed amount of bandwidth to each of the subinterfaces. For example, the device may use FlexE or TSN-based commands, to allocate and enforce a specific amount of bandwidth per subinterface. In some cases, the device may allocate proportionate amounts of bandwidth to the subinterfaces. For example, if a given port is divided into five subinterfaces, each subinterface may be allocated <NUM>% of the total bandwidth. However, other embodiments also provide for disproportionate amounts of bandwidth.

At step <NUM>, the device may form a plurality of midlays for the SDN by assigning subsets of the plurality of subinterfaces to each of the midlays, as described in greater detail above. In some embodiments, a midlay may comprise one or more Fat Trees between one or more leaves of the SDN and a spine of the SDN. In addition, a midlay may operate as an intermediate layer between a physical/underlay layer of the SDN and an overlay layer of the SDN. In some embodiments, routes in the SDN can be established for the midlays using VRF and the midlays can be indicated in the forwarding plane of the SDN using MPLS or VLAN tagging.

At step <NUM>, as detailed above, the device may assign a network slice to one or more of the midlays, based on a bandwidth requirement of the network slice. As would be appreciated, by using subinterfaces and midlays, this allows the controller to dynamically adjust the amount of bandwidth for the slice, such as by adding subinterfaces to the midlay(s) of the slice. In some cases, the one or more of midlays to which the network slice is assigned span multiple physical pods of the SDN, also offering additional protection against device failures and other issues. Procedure <NUM> then ends at step <NUM>.

It should be noted that while certain steps within procedure <NUM> may be optional as described above, the steps shown in <FIG> 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, address the shortcomings of existing network slicing approaches by allowing physical resources to be associated with the overlays through the use of 'midlays' comprising sets of subinterfaces with fixed amounts of bandwidth. In some aspects, the techniques herein also allow for bandwidth to be dynamically allocated to a given network slice, letting the bandwidth of the service grow or shrink as needed, by associating more or fewer subinterfaces to the midlay of the slice.

While there have been shown and described illustrative embodiments that provide for using a midlay in an SDN fabric, it is to be understood that various other adaptations and modifications may be made within the scope of the embodiments herein. For example, while certain protocols are shown, such as FlexE, other suitable protocols may be used, accordingly.

Claim 1:
A method comprising:
configuring (<NUM>), by a device, a plurality of subinterfaces for each of a plurality of physical ports of a software defined network, SDN;
allocating (<NUM>), by the device, a fixed amount of bandwidth to each of the subinterfaces;
forming (<NUM>), by the device, a plurality of midlays for the SDN by assigning subsets of the plurality of subinterfaces to each of the midlays, wherein each of the plurality of midlays is arranged to operate as an intermediate layer between a physical layer of the SDN and an overlay layer of the SDN; and
assigning (<NUM>), by the device, a network slice to one or more of the midlays, based on a bandwidth requirement of the network slice.