Others such as Google are less convinced and are focused on building networks and capacity one step back from the edge – to the 'near edge.' Some say that edge datacenters will be relatively niche, citing the main use case as CDNs because of their high data volumes. In most other cases, they say, data volumes and latency needs will not need a 'dedicated' edge datacenter. They say the buildout of new large and hyperscale datacenters, supported by reliable networks, will bring the edge much closer to users and 'things.'
Both arguments will likely bear out over time: the edge will almost certainly require many different datacenter types – and probably many of them. They will range from hyperscale cloud and large colocation facilities that are sited near or near enough to the point of use to support many applications, to new micro-modular datacenters at the edge, to smaller clusters of capacity that are not large or critical enough to even be described as datacenters.
The 451 Take
Where is the edge?
The location of the edge itself is largely defined at the highest level by the application and workload function of the compute. It can include the physical or virtual location of the following:
- Data: Where data is initially generated – including by sensors and other devices (things) – and processed, analyzed, consumed and stored.
- Connections: Carrier-neutral or carrier-specific connectivity services with cross connects, cloud exchanges or direct connects (including dark fiber to other datacenters). The physical point where data is integrated can also be considered the edge.
- Telecom gateways: Entry or access points to local/WANs, including fiber and cellular, as well as to cloud computing and other IT service environments on networks.
Distributed datacenters at the edge
The volume of traffic in some distributed edge datacenters will be very high, and many data sets and applications may be involved. Failures or congestion in networks may cause serious problems in machines, devices and user experience – driving requirements for flexible, agile networking approaches, such as software-defined networking. Two-tier, leaf-and-spine architectures inside the datacenter will be ideal for optimized throughput and redundancy because of the vastly increased traffic between different services.
To understand the different types of datacenters required at the edge, it is useful to differentiate between edge and near edge.
Edge datacenter functions
The true edge is sometimes defined as where near-real-time response and action is needed, measured in sub-2 milliseconds (low or ultra-low latency). Ideally, there would be no – or very few – network paths or 'hops' and very little or no use of shared communications infrastructure between the user/data collection point and the true edge point of processing/aggregation. (When 451 Research has asked enterprise customers what they mean by edge, answers have varied: Some say at the device; others at the first point of aggregation; and others at the point where CIO control, in the form of performance or security, etc., and ownership begins.)
In some cases, telecom gateways such as 4G and 5G base stations and towers will require dedicated datacenter capacity to be collocated very close by, most likely in the form of micro-modular datacenters. These will meet the needs of east-west traffic at the edge where fiber is not an economic or otherwise practical option, such as, for example, between end-user devices and services near each other.
Some edge applications, including process manufacturing and the teleoperation of vehicles, generate data that requires rapid response and action. They need platforms that transform the data streams into formats that can be processed by applications that analyze and act on the data in real time. Wireless networks to support this with management software, protocols and processing/storage are known as ultra-reliable low-latency communications. In these environments, compute and storage capacity is required very close to the point of data generation – these are true edge computing functions.
This true edge processing capability is also referred to as 'fog computing,' which involves performing the required analysis and taking the resultant action as closely as possible to the data – or things of IoT. (In other words, below the level of the centralized cloud).
To address this need, manufacturers such as Intel, HPE, Nokia, Dell EMC, Lenovo and Huawei, among others, sell local gateways that are essentially access routers with special interfaces (MODBUS, CANBUS, etc.) with varying degrees of compute and storage capability and that have the capability for VMs or containers to run applications. While this is a good and possibly the only viable solution for many environments, the scale of some potential IoT deployments will require hundreds or thousands of gateways, representing a considerable capital expense, in addition to the ongoing management and security of the gateways. In these cases, localized datacenter capacity will be needed that is one step back – the near edge.
Near-edge datacenter functions
The near edge can be broadly defined as the 'zone' where sub-millisecond latency, or low-single-digit latency, cannot (usually) be guaranteed across available networks, but performance and security has been architected to securely process, analyze, store and forward larger amounts of data – and possibly to connect to other applications and data sources – without going back to a centralized cloud service. Near-edge datacenters will likely be a mix of microdatacenters and much larger facilities, including enterprise, colocation and cloud datacenters that are sited deliberately or coincidentally near the user of the data.
Equinix, for example, positions its colocation and interconnection facilities as offering cost and management benefits over fully distributed edge environments (one or more aggregation points or devices at the interconnect versus hundreds or thousands of smaller devices to manage) and performance benefits over fully centralized cloud models. One of the notable moves by the company in this area is its Data Hub, which combines its interconnection and colocation service with tiered cloud-integrated storage to provide a potential aggregation layer for large amounts of IoT and other edge computing data. The service offers the opportunity for near-local storage, analysis and action of data, including data integration, as well as potential summarization of a large amount of data prior to backhaul to a centralized cloud.
Networking and datacenter topologies at the edge
Generally speaking, datacenter and network topologies to support low and medium latency and high traffic will vary. The deployment of 5G networks (likely to begin in 2019/2020 in pioneering countries such as South Korea and Japan) will reshape connectivity architectures for edge datacenters. In principle, 5G enables very good connectivity coverage from multiple gateways (cell towers) – if one gateway fails, the signal is picked up by another. With its 10Gbps design goal, one potential application of 5G is to replace the need for local high-bandwidth fiber to the edge, especially in some remote locations.
Ideally, 5G networking routes will be optimized, hopping intelligently between different 5G networks and cell towers. To optimize and utilize bandwidth more effectively, network management is increasingly being virtualized and separated from switches and devices. If visualized, these edge network and edge datacenter architectures may look something like the schema below, with true edge microdatacenters, and IoT and telecom gateways (at the very bottom) performing aggregation, control and analytics.
Key data and data needed by other applications and people will in some cases be made available at the near edge – including in large colocation and other metro datacenters. Cloud heavyweights are rapidly building hyperscale facilities with direct fiber links to leased colocation sites. These direct connects reduce latency and increase security and reliability, bringing hyperscale cloud capacity closer to the edge – effectively functioning as near-edge datacenter capacity.
Once consumed or integrated, data will then typically be moved or streamed into large or hyperscale, remote datacenters to be aggregated, analyzed (including through integration with other data and applications) and archived. These large facilities represent the 'core layer.'
The figure below shows a broad schema of different types of datacenters and data paths for IoT and other types of edge computing, spanning the true and near edge and core layers.
- Converged edge infrastructure incorporating IT, communications and facilities
- Prefabrication and micro-modular packaging
- New designs for embedding intelligence in infrastructure
- Software tools specifically designed to analyze, store, integrate and forward data
- Policy-based software designed for initiating automated action at the edge
- Remote and integrated facilities and IT management, including 'as a service'
- Distributed resiliency and availability tools
Suppliers are lining up behind the edge opportunity: there were 44 deals involving edge computing technologies in 2015 and 2016, according to 451 Research's M&A KnowledgeBase.
This report is an extract from our 'Datacenters at the Edge' Technology & Business Insight report.
Rhonda Ascierto is Research Director for the Datacenter Technologies and Eco-Efficient IT Channel at 451 Research. She has spent more than 15 years at the crossroads of IT and business as an analyst, speaker, adviser and editor covering the technology and competitive forces that shape the global IT industry. Rhonda’s focus is on innovation and disruptive technologies in datacenters and critical infrastructure, including those that enable the efficient use of all resources.
Daniel Bizo is a Senior Analyst for Datacenter Technologies Channel at 451 Research. His research focuses on advanced datacenter design, build and operations, such as prefabricated modular datacenters, highly efficient cooling and integrated facilities and IT management to achieve superior economics.Daniel is also a regular contributor to 451 Research's silicon and systems technology research in the Systems and Software Infrastructure Channel.
Keith Dawson is a principal analyst in 451 Research's Customer Experience & Commerce practice, primarily covering marketing technology. Keith has been covering the intersection of communications and enterprise software for 25 years, mainly looking at how to influence and optimize the customer experience.