Almost ten years after the introduction of cloud computing, the cloud is the mainstream computing paradigm. The majority of modern enterprises store data and deploy applications in the cloud in order to benefit from its capacity, scalability, elasticity and pay-as-you-go features. Moreover, emerging applications are, in most cases, cloud-enabled. Despite the huge success of the cloud, there is also a rise of new systems and applications, which drive the conventional cloud paradigm to its limits. Typical examples of such applications are those involving distributed networked devices and data streams at a large scale. When handling distributed data streams and related networked applications (such as Internet-of-Things (IoT) and Big Data applications, centralized cloud paradigms suffer from a significant waste of network and storage resources, as large amounts of data, of low business value, is integrated in the cloud. For example, in cloud-based distributed sensing applications most sensors are producing large volumes of useless data, which unnecessarily consumes cloud storage and network bandwidth. This is typically the case when sensor data, that does not frequently change (e.g., temperature information), is streamed into the cloud.
In order to cope with these distinct requirements of IoT/Big Data applications the cloud industry has recently introduced edge computing paradigm (also called “fog computing”), which extends conventional centralized cloud infrastructures with an additional storage and processing layer that enables execution of application logic close to end-users and/or devices. Edge computing foresees the deployment of edge (or fog) nodes, between the cloud and the devices. Different types of edge nodes are envisaged in the edge computing paradigm, ranging from embedded devices with limited storage, memory and processing capacity, to conventional computers and computing clusters. According to the OpenFog consortium, edge/fog computing is aimed at supporting multiple industry verticals and applications, while enabling services and applications to be distributed anywhere between the cloud and devices, including deployments close to the devices layer.
For specific classes of IoT and Big Data applications, edge computing provides distinct benefits over conventional cloud architectures, including:
The edge computing paradigm is targeting specific classes of applications with the following characteristics:
Hence, while there are still a large number of applications that are adequately supported by cloud computing infrastructures and services (e.g., transactional enterprise applications), there are also a considerable number of emerging data intensive applications (e.g., smart cities, smart manufacturing, and connected car) that need to leverage the benefits of edge deployments.
The momentum of edge computing applications is clearly reflected on the proliferating industry initiatives about edge computing, including standards, products and services. Prominent examples include:
In a world of billions of connected devices and high-volume streaming datasets, deploying cloud in not enough. Enterprises planning to engage or already dealing with large scale IoT/Big Data applications should also get ready for the edge. Fortunately, industry experts are already supporting this new endeavor and can help you make the most of it.
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