Defining the Edge

How might we differentiate Edge computing from Cloud computing?

This is an edited excerpt from my forthcoming book on Trust in Computing and the Cloud for Wiley.

There’s been a lot of talk about the Edge, and almost as many definitions as there are articles out there. As usual on this blog, my main interest is around trust and security, so this brief look at the Edge concentrates on those aspects, and particularly on how we might differentiate Edge computing from Cloud computing.

The first difference we might identify is that Edge computing addresses use cases where consolidating compute resource in a centralised location (the typical Cloud computing case) is not necessarily appropriate, and pushes some or all of the computing power out to the edges of the network, where it can computing resources can process data which is generated at the fringes, rather than having to transfer all the data over what may be low-bandwidth networks for processing. There is no generally accepted single industry definition of Edge computing, but examples might include:

  • placing video processing systems in or near a sports stadium for pre-processing to reduce the amount of raw footage that needs to be transmitted to a centralised data centre or studio
  • providing analysis and safety control systems on an ocean-based oil rig to reduce reliance and contention on an unreliable and potentially low-bandwidth network connection
  • creating an Internet of Things (IoT) gateway to process and analyse data environmental sensor units (IoT devices)
  • mobile edge computing, or multi-access edge computing (both abbreviated to MEC), where telecommunications services such as location and augmented reality (AR) applications are run on cellular base stations, rather than in the telecommunication provider’s centralised network location.

Unlike Cloud computing, where the hosting model is generally that computing resources are consumed by tenants – customers of a public cloud, for instance- in the Edge case, the consumer of computing resources is often the owner of the systems providing them (though this is not always the case). Another difference is the size of the host providing the computing resources, which may range from very large to very small (in the case of an IoT gateway, for example). One important factor about most modern Edge computing environments is that they employ the same virtualisation and orchestration techniques as the cloud, allowing more flexibility in deployment and lifecycle management over bare-metal deployments.

A table comparing the various properties typically associated with Cloud and Edge computing shows us a number of differences.


Public cloud computingPrivate cloud computingEdge computing
LocationCentralisedCentralisedDistributed
Hosting modelTenantsOwnerOwner or tenant(s)
Application typeGeneralisedGeneralisedMay be specialised
Host system sizeLargeLargeLarge to very small
Network bandwidthHighHighMedium to low
Network availabilityHighHighHigh to low
Host physical securityHighHighLow
Differences between Edge computing and public/private cloud computing

In the table, I’ve described two different types of cloud computing: public and private. The latter is sometimes characterised as on premises or on-prem computing, but the point here is that rather than deploying applications to dedicated hosts, workloads are deployed using the same virtualisation and orchestration techniques employed in the public cloud, the key difference being that the hosts and software are owned and managed by the owner of the applications. Sometimes these services are actually managed by an external party, but in this case there is a close commercial (and concomitant trust) relationship to this managed services provider, and, equally important, single tenancy is assured (assuming that security is maintained), as only applications from the owner of the service are hosted[1]. Many organisations will mix and match workloads to different cloud deployments, employing both public and private clouds (a deployment model known as hybrid cloud) and/or different public clouds (a deployment model known as multi-cloud). All these models – public computing, private computing and Edge computing – share an approach in common: in most cases, workloads are not deployed to bare-metal servers, but to virtualisation platforms.

Deployment model differences

What is special about each of the models and their offerings if we are looking at trust and security?

One characteristic that the three approaches share is scale: they all assume that hosts will have with multiple workloads per host – though the number of hosts and the actual size of the host systems is likely to be highest in the public cloud case, and lowest in the Edge case. It is this high workload density that makes public cloud computing in particular economically viable, and one of the reasons that it makes sense for organisations to deploy at least some of their workloads to public clouds, as Cloud Service Providers can employ economies of scale which allow them to schedule workloads onto their servers from multiple tenants, balancing load and bringing sets of servers in and out of commission (a computation- and time-costly exercise) infrequently. Owners and operators of private clouds, in contrast, need to ensure that they have sufficient resources available for possible maximum load at all times, and do not have the opportunities to balance loads from other tenants unless they open up their on premises deployment to other organisations, transforming themselves into Cloud Service Providers and putting them into direct competition with existing CSPs.

It is this push for high workload density which is one of the reasons for the need for strong workload-from-workload (type 1) isolation, as in order to be able to maintain high density, cloud owners need to be able to mix workloads from multiple tenants on the same host. Tenants are mutual untrusting; they are in fact likely to be completely unaware of each other, and, if the host is doing its job well, unaware of the presence of other workloads on the same host as them. More important than this property, however, is a strong assurance that their workloads will not be negatively impacted by workloads from other tenants. Although negative impact can occur in other contexts to computation – such as storage contention or network congestion – the focus is mainly on the isolation that hosts can provide.

The likelihood of malicious workloads increases with the number of tenants, but reduces significantly when the tenant is the same as the host owner – the case for private cloud deployments and some Edge deployments. Thus, the need for host-from-workload (type 2) isolation is higher for the public cloud – though the possibility of poorly written or compromised workloads means that it should not be neglected for the other types of deployment.

One final difference between the models is that for both public and private cloud deployments the physical vulnerability of hosts is generally considered to be low[2], whereas the opportunities for unauthorised physical access to Edge computing hosts are considered to be much higher. You can read a little more about the importance of hardware as part of the Trusted Compute Base in my article Turtles – and chains of trust, and it is a fundamental principle of computer security that if an attacker has physical access to a system, then the system must be considered compromised, as it is, in almost all cases, possible to compromise the confidentiality, integrity and availability of workloads executing on it.

All of the above are good reasons to apply Confidential Computing techniques not only to cloud computing, but to Edge computing as well: that’s a topic for another article.


1 – this is something of a simplification, but is a useful generalisation.

2 – Though this assumes that people with authorised access to physical machines are not malicious, a proposition which cannot be guaranteed, but for which monitoring can at least be put in place.

Author: Mike Bursell

Long-time Open Source and Linux bod, distributed systems security, etc.. Now employed by Red Hat. マイク・バーゼル: オープンソースとLinuxに長く従事。他にも分散セキュリティシステムなども手がける。現在Red Hatのチーフセキュリティアーキテクト

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