The Intelligent Flexible Cloud

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WHITE PAPER

The Intelligent Flexible Cloud

New enabling technology standards. Heterogeneous system on a chip (SoC) architectures. The benefits of distributing intelligence towards the network edge. And other best practices to address next-generation services and the Internet of Things.

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Network applications and services today are nearly unrecognizable from a few decades ago. The diversity, scale, and dynamic evolution of apps, services, data, and devices have led to a corresponding evolution within service provider environments. Public, private, and hybrid clouds are ascendant. Software-based approaches to more agile network management and efficiency are being pioneered by industry standards bodies. As for hardware, a transition where multiple processor architectures, instruction set architectures (ISAs), and boutique implementations popular a decade ago have consolidated to just a few architectures to lower hardware and software costs; predominantly ARM solutions that deliver unique functionality and power optimization, and x86. In the data center, consolidation has accelerated to such an extent towards homogeneous compute clusters where flexibility has been achieved but at the cost of workload efficiency. As a result, cautiously evolving infrastructure networks and distributed new cloud applications cannot depend on standard x86 platforms to scale or support new features, services and business models that operators need.

This white paper is about the emerging capabilities underlying network architectures in operators’ pursuit of what ARM calls “the intelligent, flexible cloud” ― where new features, services, and business models evolve on enhanced equipment at sustainable levels of operational overhead or capital expenditure. The intelligent flexible cloud allows the distribution of applications anywhere in the network based on optimal efficiency, bandwidth availability, location of data, and overhead. This paper begins with an overview of programmable networking technologies, standards, and architectures. It explores the anticipated lower total cost of ownership, faster deployment and troubleshooting, superior performance, and new business opportunities possible by processing network, subscriber intelligence, and Internet of Things application behaviors between the edge and the aggregation layers of the network. The intelligent flexible cloud architecture requires a mix of server-class processing platforms and highly-integrated, system on a chip (SoC) platforms with heterogeneous processing capabilities supported by a common layer of enabling software. As mobile and rich media applications along with the interconnections of the Internet of Things continue to grow and change, this mix of new architecture and technologies provides the cost-efficiencies and flexibility operators need to support the requirements of next-generation services.

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Leaders in the networking industry have talked about intelligent networks for years. Today’s reality has evolved beyond putting intelligence in individual devices to distributing systemic intelligence in network layers and functions, end-to-end topologies, and services. The ability to create simplified management and orchestration layers that abstract underlying hardware and software complexity is here, bringing great excitement to the idea of programmable networks where services, resources, and capacity can be spun up or down on demand. The great leaps forward in the definition of network intelligence are due in large part to three key enabling technology standards or strategies:

 Software defined networking (SDN) is a set of standards developed initially by the Open Networking Foundation that provides an abstraction layer of network functionality by separating the control plane from the data plane. Network management and operations can be centralized instead of dispersed among different network layers. Centralized control via a simplified abstraction software layer has led to benefits such as lower operational expenditures (OpEx) and more automation, control, flexibility, agility, and application innovation.

 Network Function Virtualization (NFV) has extended virtualization to networking functions such as firewalls, virtual switches, customer premise equipment, and wireless access point controllers. With NFV, these and other functions can be moved from proprietary hardware appliances to more standardized servers, switches, and storage. And as software the intent is that they can be easily located on platforms in data centers, network nodes, or customer premises to take advantage of global network efficiencies. Expected NFV benefits therefore include lower capital expenditures (CapEx) and OpEx through less reliance on proprietary and single-purpose hardware. Using NFV can accelerate time-to-market for services due to faster configuration, testing, and integration.

 Distributed intelligence involves bringing together workload optimized hardware and a common software foundation to enable network, storage, and compute functions to be distributed across network nodes. Workload-optimized hardware based on highly integrated SoCs with heterogeneous processing capabilities, makes it possible to add intelligence anywhere in the network – even scaling down to the most power and form factor constrained locations. A common software platform enables developers and IT users to deploy services at cloud scale velocity.

These technology standards and architectures are part of the foundation of the intelligent flexible cloud (Figure 1). It is flexible because it can easily and quickly address diverse network requirements. It is intelligent because it leverages business, customer, and network data to enhance existing services and to use as the basis to create a dynamic environment for the enablement of new services that will be highly innovative and competitive.

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Figure 1. The Key Elements of The Intelligent Flexible Cloud

In the intelligent flexible cloud, SDN, NFV, and distributed computing intelligence are combined in a variety of ways. Network nodes can be traditional dedicated network nodes, NFV appliances, or a combination of both. SDN controllers are integrated into each of the different node types as a fixed part of a dedicated hardware node or as a more dynamic software addition to a more software-defined network node. These SDN controllers will reside in a cloud in some optimal location.

All nodes in the intelligent flexible cloud will include compute platforms. Nodes with NFV capabilities will have by definition compute platforms to host software-defined virtual-network functions (VNFs). In the case of more traditional dedicated hardware network functions additional compute platforms will be co-located. The distribution of intelligence is about the ability to host a variety of applications on the compute platforms of the intelligent flexible cloud’s network nodes. These applications can seamlessly extend out from the cloud. In all cases the applications in the cloud, including the SDN controller, will be connected by virtual networks or overlays that tunnel through the packet flows between the network nodes.

The intelligent flexible cloud is inevitable. With relentlessly increasing traffic and diverse applications, existing networking infrastructures are not capable of scaling to handle future requirements. These include the Internet of Things, which stretches network topologies to

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encompass numerous different devices. As the network extends to serve applications in sensors providing telemetry, appliances in home energy monitoring, computers in automobiles providing navigation and component health checks, and many other use cases, complex requirements will have to be satisfied. These include privacy, performance, security, and integration.

The advent of 5G wireless standards bring new challenges as performance metrics are redefined. Networks will be expected to deliver much greater throughput, much lower latency, ultra-high reliability, much higher connectivity density, and a broader mobility range. This enhanced performance is expected along with the capability to control a highly heterogeneous environment and ensure security, trust, identity, and privacy. The 5G architecture is expected to evolve to include modular network functions that could be deployed and scaled on demand to accommodate various use cases in an agile and cost-efficient manner.

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Today operators are moving data between devices, access points, and data centers at the network core or the cloud for activities such as analysis, troubleshooting, and traffic monitoring. This saps network bandwidth, causes congestion, and increases latency. A more efficient alternative is to move compute, storage, and applications closer to where data is being generated, at the network edge. There it can be used to support faster troubleshooting of network issues, near real-time reallocation of resources in response to shifting demand, to meet latency critical demands, and to provide a vertical business enabling layer as the basis of new services.

Figure 2 shows that application data that is easy to manage, with low processing velocity requirements, is ideally processed in the data center. Other application data, with high processing velocity requirements, is best distributed as far out to the edge as possible.

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Figure 2. Moving Intelligence Closer to the Edge of the Intelligent Flexible Cloud

The ease of data manageability is determined by a combination of the volume of data, the structure of the data, plus the degree of data burst and the regularity of the data flow. Difficult to manage data results from a large volume of data, unstructured and of different types, delivered in unpredictable bursts. Easy to manage data results from low volumes of one type of data delivered in a predictable manner.

Processing velocity is based on the combination of latency of delivery, the time required to generate a result from the delivery of the data, and whether or not the data is real-time or non-real-time. High-velocity processing is latency sensitive, requiring the shortest time possible to deliver the results of real-time data analytics. Low-velocity processing means that delivering a result non-real-time is acceptable.

As intelligence gets pushed to the edge it essentially mitigates the challenges of transporting difficult data to the cloud (including network congestion, global power consumption, and high latency). Processing data at the edge reduces the data manageability challenge by turning difficult data into easily manageable data that is better suited for the longer and more costly journey to the data center for processing and storage.

For example, video from multiple surveillance cameras could be processed in real-time close to the source of data at the edge of the intelligent flexible cloud where a surveillance algorithm could determine which video frames of interest should be sent back to the data center. Applications such as real-time measurement or adjustment of Internet of Things infrastructure such as street lights or traffic signals could be controlled at the edge of the network for faster performance and lower latency. To perform radio access optimizations, moving large quantities of radio data to run interference, coordination and cooperation algorithms for LTE and 5G to the

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data center is extremely challenging to backhaul/fronthaul and in many locations not feasible. The volumes of radio data (encapsulated by the CPRI protocol) are enormous and when carried over Ethernet-based infrastructure stress even the newest fiber deployments in terms of meeting latency and jitter targets. The real-time decisions required to manage the radio algorithms are best computed as close to the edge access points as possible. In order to efficiently process this application, the compute nodes need as much flexibility as possible to balance the need for centralization to create uniform visibility verses the need for distribution to get as close to the edge as possible. Furthermore these nodes need to process complex algorithms by fusing a raw high performance heterogeneous computing capability with general purpose compute.

Operators can monetize presence and customer profile data in partnership with owners of venues (e.g., retail malls, sports stadiums, hotels, restaurants, city centers) and advertisers (e.g., retailers, content providers, news organizations, political campaigns) to push discount coupons or special offers to mobile users. Performance and other operational information can be processed faster at the edge to shift resources and bandwidth so that users experience quality services at all times. These and other solutions can generate new revenues for operators while also speeding up network troubleshooting, optimizing resources, lowering operational costs, and reducing customer churn.

According to the International Data Corporation (IDC) December 3, 2014 report IDC FutureScape: Worldwide Internet of Things 2015 Predictions, the intelligence at the edge strategy is well accepted. The report found that by 2018, more than 90% of all Internet of Things data will be hosted on service provider platforms as cloud computing reduces the complexity of supporting the Internet of Things.

The Internet of Things is a diverse number of solutions. It will include many different types of endpoints generating diverse data types and with various requirements for security, latency, session management, and throughput. Nokia has estimated it will have to support 1000 times its 2014 data capacity by 2020. Ericsson currently says that video represents 45% of its mobile traffic and expects it to grow by eight times in volume by 2020. 5G will require less than 1ms of latency and produce up to 100 times higher data rates at the edge than today’s rates. Huge amounts of unstructured data will be generated. The services and business models for the Internet of Things will also be very diverse. Traditionally, networks have been planned based on models for traffic types and projected traffic growth but with very few variables in terms of services evolution, demographic differences, and diverse functionality for varied traffic types. This explosion of variables within the Internet of Things will require a more agile infrastructure that can adapt and evolve through software advances―not only for latency, control, and bandwidth requirements but also for application requirements to enable new services.

For example, vehicle to infrastructure (V2I) applications in an intelligent flexible cloud could include the transfer of road, weather, and vehicle information from these endpoints to the edge infrastructure and then through to the aggregation layer and the cloud. This is highly inefficient. With the bursty, unpredictable, and turbulent nature of Internet of Things applications, this back and forth movement of data puts bandwidth pressure on the entire network. This

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reduces the feasibility of providing any services that are expected to perform effectively in a latency-sensitive environment. But the distributed intelligence of the intelligent flexible cloud will enable data to be filtered and analyzed locally leading to higher decision making performance. Processing data closer to the edge has multiple benefits: only global time-sensitive information needs to be transferred back to the core or the cloud with any priority. The rest can be transferred when the network utilization is low. Data can be given context so it becomes a structured and actionable data set. Critical information with key insights based on network performance and user behavior data can be communicated back to infrastructure controls at the edge (e.g., lights, signs) in real-time without concern for the prioritization of traffic or other factors throughout the aggregation and cloud or core layers of the network. This also has significant impact on global power consumption as it is easier to move a small application to where a large volume of data is than to move a volume of data to an application all the way back in a data center.

Without the agility of an intelligent flexible cloud, the servicing of that edge equipment would be relatively fixed at deployment time. If an automotive manufacturer added intelligent new capabilities to its cars and linked them to a municipality’s, insurance company’s, or public health organization’s data and wanted a new service instead of a software update, it would take a considerable investment to reconfigure the software and most likely require the deployment of new hardware.

According to IDC, by 2018, 50% of networks will go from having the excess capacity available to handle Internet of Things devices to becoming network constrained, with 10% of sites completely overwhelmed with data. To address this, IDC predicts that 40% of Internet of Things data will be stored, processed, analyzed, and applied near or at the network edge by 2018. The benefits of the intelligent flexible cloud extend beyond the Internet of Things and cloud applications. They can also help simplify and optimize network management for LAN and WAN applications in general.

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SDN, NFV, and distributed intelligence are all individual approaches that may be used separately or in different combinations as requirements dictate in specific situations. Building the intelligent flexible cloud does not need to be solely dependent on any one of them. For example, if NFV cannot provide added efficiency and enhance performance then SDN and distributed intelligence overlays can be used to deliver elastic control and extended cloud architectures. If a centralized SDN-like system isn’t chosen, overlays can still be used to quickly create, deploy, and manage services that take advantage of multiple types of intelligence closer to the network edge. If distributed intelligence is not fully accessible, cloud infrastructure, fiber deployments or smaller-scale and localized data centers may be able to locate partitioned applications and process intelligence closer to the edge or in a more distributed fashion.

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SDN distributes control and forwarding actions, effectively factoring IP networking nodes and thereby gaining greater flexibility to change networking behavior. It is mostly used with OpenFlow, one of the leading technologies used to communicate between the control plane and the data plane. OpenFlow separates a switching appliance into data, control, and management planes. The management plane provides one or more centralized views for configuring and monitoring the network dynamically. The data plane forwards packets from input to output ports according to a set of preprogrammed rules as quickly as possible. The control plane is logically centralized and usually physically separated from the multiple data planes that it controls by feeding the rules needed to service every packet that each data plane receives.

One control point with visibility into multiple data planes can correlate behaviors across switches. This can be very useful for traffic engineering to optimize utilization and resource allocation, especially to support virtualization extending from the endpoints all the way through the network. In principle centralization can make it easier to scale networks because the fewer places rules are written or where exceptions are handled, the simpler it is to control many switches. And in the modern virtualized data center and intelligent flexible cloud there can be orders of magnitude more switches due to the advent of overlay networking and virtual switches. In this context centralization can also slow forwarding of new flow types if the system doesn’t want to make a decision about a packet or flow until the controller has seen some of the traffic and sent new policies back to the control plane. Moreover, if the channels between the controller and the data planes are disrupted, the forwarding tables can become inconsistent and network behavior can be corrupted. This can take time to settle to a consistent state in a similar way to routing table propagation challenges today.

The advantage of combining forwarding and control in one physical platform is the ability to function relatively autonomously within a connected sub-network when cut off from the larger network. These autonomous systems continue to function with potentially lower latency than legacy architectures. If the rules used in the forwarding table of the data plane are incomplete or insufficient for new packet types, then the packets are sent back up to the controller for further instructions including whether to drop, alarm, or even create a new rule and reprocess the packet. These delays can cause problems, especially in latency-sensitive applications. The rules that go into the forwarding tables can become quite numerous and therefore difficult to debug when problems occur. Decades of experience in rule-based expert systems have established these challenges and yet determined that the benefits of simple and small rule sets still outweigh reasoning on complex forwarding policies.

SDN should be used in intelligent flexible clouds where it produces clear benefits. So in intelligent flexible cloud architecture, centralization of control should be dynamically adjustable. Controllers should be enabled at appropriate places across a number of data planes as needed and should be dynamically tunable as traffic patterns evolve over time.

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The use of NFV is driven by a real need to reduce CapEx and OpEx in operator networks and to accelerate service deployment and upgrades. NFV allows operators to rapidly deploy services and upgrades without disrupting their network operations by relying on more standard and yet flexible platforms that lower their CapEx and reduce the threat of equipment obsolescence. A flatter IP infrastructure allows NFV-based platforms to expand functionality to easily and dynamically create chained services and simplify scaling using cloud-based technologies. The European Telecommunications Standards Institute (ETSI) NFV architecture created a loose definition of the required underlying hardware and compute platforms. The specifications simply imply the use of data center hardware and associated virtualization technologies. Within this virtualized environment, VNFs are instantiated and perform their primary networking and communications role. Surrounding the platform and the VNF operating environment is an orchestration function that manages the integration and deployment of VNFs creating end user services as chains of VNFs.

The original NFV white paper talked about everything running on standard high volume (x86) servers. Since then, developers have recognized that standard server hardware alone cannot meet their needs and have acknowledged the need for hardware acceleration. Now the hardware environment for NFV has broadened to include highly integrated heterogeneous SoCs combined with a common software abstraction layer. The live operating metrics of power, form-factor, cost, efficiency, and performance cannot be ignored if NFV is to thrive and deliver on its promised benefits. NFV needs to accommodate a spectrum of optimized hardware solutions from scalable servers to fully hardware-assisted network functions.

There are many benefits to having multiple processor designs with a common software framework available in the supply chain. These include price competition, innovation, and greater choices for operators as they build network solutions for next-generation applications. Distributed Intelligence Today

Distributed intelligence involves creating a pool of accessible general purpose compute within the network infrastructure to allow applications to run remotely in the network or to act as part of larger distributed applications that could stretch from the cloud to the network edge. Assuming that every intelligent flexible cloud node has a general purpose compute resource available, distributed intelligence is enabled by providing computing and networking overlays. Software frameworks (e.g., the open source OpenStack) and virtualization tools that can quickly spin up virtual machines or containerized applications are used to create the overlays. Processing, storage, and network applications can then be run in the overlays all the way out to access points and other intelligent flexible cloud nodes at the network edge. These overlays allow operators to provide better quality of service by positioning valuable compute resources closer to the consumer and devices in the Internet of Things. This allows for better gathering of big data for analytics and, in the mobile context, enhanced radio access operational efficiencies

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through linking real-time network performance data to operational algorithms. The overlays also allow operators to place content closer to users for major performance and cost benefits.

The ability to create overlaid virtual compute and network domains has been until now mostly limited to the domain of data center computing. Close to a decade of experience has created technologies and best practices on homogeneous platforms with a limited set ratio of compute, I/O and storage resources.

Unlike a traditional data center, the distributed network infrastructure is a more challenging and diverse environment. In the data center, high performing and uniformly distributed compute platforms enable large collaborative efforts like OpenStack to be used to produce software stacks and frameworks that set the standard for the simple creation and management of virtual compute and networking domains. This behavior doesn’t necessarily translate well to network deployments and creates a number of challenges.

One challenge is achieving the optimal compute density to perform meaningful processing in the network – especially at the power and form factor constrained edge. Distribution of data center platforms through the network isn’t realistic. These platforms model the classic 1U 2P server configurations that have been historically designed to cope with large monolithic x86 processors. Multiple ruggedized operator hardware platforms exist today from OEMs and standards like Advanced Telecommunications Computing Architecture (ATCA) that are designed to support heterogeneous SoC solutions. No change or redesign of hardware platforms is required if the SoCs strive to maintain absolute power and compute efficiency. To achieve this, the SoCs deployed will have to have optimal design points for smallest size, lowest power, and total efficiency. Heterogeneous and multicore compute brings many benefits to in-network application processing when considering the real need to blend highly specific processing for network functions and general purpose compute that has to run a broad range of applications.

Once usable physical compute platforms are distributed then an elastic computing environment needs to be created to host applications and be managed as part of an extended cloud computing environment. This is the domain of compute virtualization. Unlike the confines of a homogeneous data center environment, distributed intelligence allows a variety of tasks and workloads to scale efficiently and likely reduce the overhead of virtualization. It then becomes important to distinguish between the different goals of virtualization (for example, the need to virtualize for efficient use of the compute platform’s processors versus the need to virtualize for simplified hosting, management, and orchestration of applications).

In a typical cloud environment there are many high single-threaded performance processors used as compute platforms. Over time, the market has evolved and these resources are typically virtualized to effectively create more usable or smaller processors in an attempt to increase the platform’s efficiency. However this has generated only minor gains in efficiency with reports of total efficiency not exceeding 40% and even being reported as low as 15%.

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There are multiple factors impacting these underwhelming numbers, usually based on handling diverse types of workloads. This level of virtualization overhead can detract from the efficiency needed in distributed intelligence deployments

In the network, merchant SoCs and processors have been historically designed to be highly efficient and to maximally utilize the benefits of multicore. As a starting point, intelligent flexible cloud compute nodes are more likely to be built from arrays of smaller processors, often with valuable acceleration resources. When using efficient multicore SoCs, the need for virtualization to improve silicon utilization diminishes. This creates the opportunity for new application execution schemes such as containers or other lightweight direct application-hosting hypervisors to be developed and deployed.

On top of the layer of processor virtualization there is a second layer of platform virtualization, similar in concept to an operating system that can download and run many diverse applications, often concurrently. Virtualization for management is newer than processor virtualization and is a rapidly evolving area of technology development. As a higher level of abstraction in the software stack, this level of virtualization will be responsible for isolation and security at scale. On top of the emerging opportunity to greatly improve processor-level virtualization there is a new potential to extend and enhance the best practices from cloud application management and deployment to system orchestration to suit the in-network critical needs of the intelligent flexible cloud.

In many cases the applications running in this overlaid and virtualized compute environment will be running their own networking functions that communicate with other parts of a bigger distributed cloud application. Like the networking functions running in the physical network infrastructure, perhaps as NFV applications, they can also benefit from being accelerated, especially for functions like queuing and encryption and decryption, in order to reduce the compute load and increase overall efficiency. By exposing deeply embedded links that reach down into the SoC, any existing network accelerators that are present can be leveraged to improve the overlays performance.

The distribution of intelligence is opening up the possibility to move away from the inefficiencies of brute force cloud-based virtualization and to create a better technology model that leverages the cloud’s best practices taking advantage of the inherent benefits of multi-core and heterogeneous compute platforms. In the ideal condition, the distribution of intelligence will be as abstract as simply selecting the in-network locations for a distributed application based on accessible power, performance, capability, and latency metrics. And as more OpenSource initiatives and software businesses take root (e.g., OpenFlow and VNF software vendors), the functional specifications of an intelligent flexible cloud will be defined by the supporting software combined with the compute metrics of the underlying SoCs. The industry and supporting ecosystem is beginning to collectively rally to put the intelligent flexible cloud on a trajectory of maximum effectiveness.

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The combination of SDN, NFV and distributed intelligence creates a fundamental platform for innovation. Historically, network equipment has been marketed and positioned around the attributes of its primary network function. This will remain but due to the continued deeper levels of functional integration at the SoC level, network level operational performance will be best represented by the SoCs themselves. Increasingly though, the compute efficiency and extensibility of the underlying platforms will be equally critical as intelligent flexible cloud nodes service a diverse range of applications from dedicated network functions to abstract applications like big data and Internet of Things analytics.

SDN can scale across a few large data centers and is controlled by expert users. If a more distributed approach is required, control must migrate back outwards into the network. ARM-based solutions enable software and hardware designs that provide dynamically variable services distribution for the intelligent flexible cloud. This provides great flexibility: the ability to centralize control and visibility to enhance scale and correlation functions for various kinds of monitoring, for example, and the ability to distribute control all the way out to more autonomous systems. By using heterogeneous SoCs that can run an optimized balance of specific networking functions with tightly coupled general purpose compute, administrators have the ability to deploy centralized or decentralized control as optimal application and network requirements dictate ― a more flexible approach to SDN.

The Internet of Things requires scale in terms of endpoints, users, and accessible intelligent flexible cloud nodes. Nodes can have multiple complex roles such as providing access, backhaul, aggregation routing, and carrying traffic across geographies. As intelligence is moved to the network edge in the intelligent flexible cloud, nodes can also take on server-class processing capabilities to collect and process data for analytics related to performance, traffic conditions, subscriber behavior, presence, and many other things. These capabilities must be deployed in nodes that reside in parts of the network that may use lower power edge devices or within difficult-to-manage thermal environments like blades and racks. SoCs with heterogeneous compute, optimized to provide these capabilities for use in intelligent flexible cloud networks, is the answer.

NFV must be able to harness acceleration capabilities in silicon in order to achieve low latency operation within power budgets as part of the intelligent flexible cloud model. This can be achieved in harmony with ETSI NFV standards. Hardware platforms with specialized SoCs that deliver a wide range of optimized and accelerated solutions can be deployed below the VNFs and alongside virtualization layers.

A key tenet of the intelligent flexible cloud architecture is the ability to deploy data and service intelligence anywhere in the network to meet demanding next-generation mobile and Internet of Things requirements (e.g., configurability, bandwidth, low latency, monetization of data). This architecture requires scalable compute platforms based on highly-integrated, heterogeneous and multicore SoC platforms built on a heritage of power optimization and efficiency. At the highest level, the intelligent flexible cloud technology model can be considered a strategy to

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densely pack the largest amount of compute into the network without adversely impacting economic, reliability, and efficiency constraints.

ARM has partnered closely with major OEMs and its silicon partners (fabless semiconductor companies) to create these SoC solutions for specific use cases (Figure 3).

Figure 3. ARM’s Heterogeneous SoC Architecture Framework

Today, more than 100 different SoCs are available. They include highly optimized wireless radio head and base station devices through a wide range of multicore and accelerated gateway, switching and routing devices to server class processors for control processing and high-performance high core count data center usage. Each of these devices includes two major benefits of the ARM business model:

 The common ARM ISA, providing the first level of commonality required to minimize software development efforts for intelligent flexible cloud application developers.

 Specific competitive differentiation to leverage ARM’s low power heritage combined with unique network and workload specific acceleration from the deep and carrier-fielded expertise of ARM’s silicon partners.

ARM’s technology portfolio provides a framework to build heterogeneous SoCs. It includes:  A scalable number of cores and types: Cortex-A7, -A53, -A57, -A72 to create platforms

that scale from highly efficient and cost optimized edge applications, through balanced midrange performance to high performance network and server applications.

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 System hooks to accept the addition of ARM’s silicon partner’s optimized and differentiated accelerators (e.g., traffic management, deep packet inspection, packet classification and cryptography).

ARM Partner SoCs (Figure 4) represent a wide range of specialization points to meet real-world needs while providing a common ARM-based processing platform to host the diverse needs of intelligent flexible cloud applications. These include differing application requirements for compute, storage, and acceleration where needed.

Figure 4. Heterogeneous SoCs in the Intelligent Flexible Cloud

To further extend ARM’s open SoC design to intelligent flexible cloud application developers, ARM and its ecosystem partners (including Broadcom, Cisco, Linaro, ENEA, ZTE, Montavista, Applied Micro, HiSilicon, AMD, Freescale, Cavium, and Nokia) are driving development and broad adoption of the Open Data Plane (ODP) open source software standard, a cross-architecture, cross-platform set of APIs that provide a common layer of enabling software. Just as Open Graphics Library (OpenGL) became a broadly-used API for the graphics industry, ODP makes it possible for OEMs to build highly optimized solutions as part of service offerings that incorporate VNFs and create service chains where the supporting underlying hardware can transparently support hardware and software acceleration.

This combination of inherent SoC level efficiency and open enabling layer software initiatives supports the maximum compute density possible in the intelligent flexible cloud.

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With global IP traffic projected to grow by a factor of three by 2018 (reaching 1.6 zettabytes per year according to the Cisco Visual Networking Index: Forecast and Methodology, 2013–2018) combined with the Internet of Things and the coming of 5G, innovation is needed to transform networks to meet next-generation requirements. SDN, NFV, and distributed intelligence are necessary, creative advances. To meet the demanding new use cases, there is also an important role for heterogeneous SoCs which can be distributed throughout network layers to provide an array of features and functions that can optimize performance and network resources (Figure 5).

Figure 5. An Optimized Architecture for the Intelligent Flexible Cloud

The intelligent flexible cloud concept is a pragmatic response, not only recognized by ARM – but it’s ecosystem of partners, to the growing requirements, traffic volumes, complexity, and costs of network services. Its list of potential benefits are imperative: lower CapEx and OpEx, greater reliability and better performance, faster infrastructure troubleshooting and responiveness for reliability, lower power requirements, reduction in global network toggle rates and east/west traffic, and through the creation of easily accessible compute overlays the ability to monetize data in new ways.

Moving data from the edge all the way to the core, where the compute capability is in place today, adds unnecessary latency and cost. Application and service innovation and efficiencies are happening at the network edge. Virtualization, distributed intelligence, and optimized hardware platforms can together help meet the bandwidth, latency, power, and configurability requirements of the intelligent flexible cloud. High throughput heterogeneous SoCs and scalable multicore processors for application compute and data throughput provide critical enabling silicon components.

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The intelligent flexible cloud will be realized by different operators in different ways based on their subscriber bases, offerings, geography, and other factors. Aside from enabling technologies and innovative architectures, settling on server-class commodity platforms exclusively won’t work. The best hardware strategy is a mix of power efficient general purpose processors and specialized processors targeting critical network applications. Heterogeneous-based SoC platforms provide many benefits and support freedom of choice and competition in the supply chain. ARM technology utilizing the ODP API is a powerful technology toolkit needed to craft and support the SoCs that will drive the principles of intelligent flexible cloud to maximum effectiveness.

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For more information on the intelligent flexible cloud and ARM processor design, visit