In search of scalability, flexibility and security, organizations are increasingly moving their workloads to the cloud. However, data centers are required to modernize and expand their networks for more flexible resource delivery. Edge computing helps these networks enhance their performance – including flexible resource delivery – and ultimately bring cloud technologies closer to the end-user.
Furthermore, technologies such as AI and ML and the Internet of Things accelerate the growth of cloud and edge technologies, since smart devices are ubiquitous today. For example, IoT-created data intensifies the need to move data closer to the edge. It also and highlights the need for cloud to offer solutions to storage, security, collaboration and scalability challenges faced by IoT and edge computing.
Even though the above factors were already influencing the growth of edge and cloud computing, their demand was further accelerated in no small part by the global pandemic and the explosion of digital transformation activities it yielded across the board.
Let’s look at the relationship between these two highly influential technologies, edge and cloud.
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What is Cloud Computing?
Cloud computing refers to executing workloads within a remote infrastructure. This is a type of computing where elastic and scalable IT-enabled capabilities are offered as a service through Internet networks. Today, cloud computing impacts the overall strategy and growth of organizations.
Aside from being faster to set up and having lower upfront costs, cloud services are cheaper to run through the flexible pricing models they offer. The cloud also offers limitless compute on demand and eases IT management.
What is Edge Computing?
Edge computing refers to the practice of running workloads on distributed devices, often on a large network. This computing happens near or at the physical location of either the data source or the user. The edge is a physical computing location at the network edge, supported by hardware and software.
Edge computing brings compute and storage closer to where it is needed. It yields greater bandwidth, faster data processing, lower response times, reduced cost, and assured data sovereignty.
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Cloud vs. Edge
Similarities among cloud and edge computing
Automation represents the future of operations, and both the cloud and edge are enablers of automation. Telecommunications companies are rolling out network automation at the edge, enabled by software-defined networking and 5G. Enterprise edge automation is enabled by AI and ML solutions, an upsurge in the number of smart devices, and the automatic deployment of applications.
Enterprise tools and processes are also automated by cloud platforms to lower or eliminate dependency on manual efforts in enterprise operations. Cloud automation also helps enterprises optimize the security and efficiency of their systems.
Since both edge and cloud computing utilize many advanced analytics methodologies on massive unstructured, semi-structured and structured data sets, they are built for big data analytics. Large-scale edge and cloud infrastructures provide the processing power to easily generate insights from data sets.
Flexible pricing models
Cloud and edge computing vendors offer pricing models that eliminate capital expenditures related to the purchase of hardware and software, setting up these purchases in on-premise data centers and the costs of carrying out infrastructure maintenance. The pricing models offered by some of the top providers depend on the usage and configuration of the solutions.
Straightforward regulatory compliance
Edge and cloud computing platforms have data in motion and data at rest encrypted and processed within the approved jurisdiction. Vendors adhering to a shared responsibility model can use edge and cloud computing requirements to ease compliance with global and local regulations.
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Differences between cloud and edge computing
Compared to cloud networks, edge networks operate in far more dynamic conditions. As a result, for scaling resources, edge networks need robust infrastructure for timely scaling. Additionally, scaling must take into account the heterogeneity of devices in an edge computing ecosystem since devices offer varying energy considerations and levels of performance.
On the other hand, cloud computing allows enterprises to scale networking, processing and storage capabilities quickly and easily without disruption or downtime.
Speed and agility
As edge solutions move their computational and analytical capabilities as close to the data source as possible, edge applications are highly responsive. Their throughput is also boosted and so for certain use cases, cloud-based systems can be outdone by well-conceived edge platforms.
The cloud, however, offers enterprise services that are accessible through self-service, far more than edge. These services are on-demand and deployable in a handful of minutes. The range of services offered by the cloud enable agile innovation and rapid development of new applications.
At the edge, data can be transmitted directly between nodes without any communication with the cloud. To secure edge devices, cloud-independent encryption mechanisms capable of operating on the most resource-constrained devices are required. However, limited interaction with the cloud ensures that there is less likelihood of sensitive data being intercepted in transit.
With cloud computing, providers enhance their cybersecurity posture through the implementation of advanced technologies, frameworks, controls and policies. Data protection is also eased by the relative ease of implementing end-to-end encryption protocols, by comparison to edge computing.
In well-configured edge networks, the loss of a small number of nodes does not limit the efficiency of service. Redundant infrastructure ensures business continuity is maintained. Additionally, edge computing can operate without Internet access.
Cloud computing often offers greater reliability than edge computing. Its centralized architecture makes disaster recovery, data backup and business continuity cheaper than edge computing. However, to operate reliably, cloud computing requires a strong server-side and client-side Internet connection.
Cloud Computing Use Cases
Backup and disaster recovery
Backup as a service (BaaS) and disaster recovery as a service (DRaaS) are some of the core cloud computing use cases. One of the most straightforward methods for organizations to begin their shift to the cloud is investing in a backup solution. Restoring cloud backups is quick and prevents enterprises from disastrous losses.
Disaster recovery makes sure businesses recover from downtime in the event of a disaster. Using the cloud for disaster recovery purposes ensures quick failover without the responsibility of building and maintaining infrastructure.
Software as a service (SaaS)
SaaS technology enables organizations to store, organize and maintain data. Also known as on-demand software, SaaS tools and solutions are hosted in the cloud and can be accessed at any time from anywhere.
Infrastructure as a service (IaaS)
The cloud enables organizations to forego Capex in favor of operational expenditures by hosting data in data centers that are run by service providers. This helps organizations avoid expensive infrastructure investments and simply access data via the cloud.
Edge Computing Use Cases
As vehicles attain higher levels of autonomy, they require greater capabilities for decision-making as they have to make more decisions. Autonomous vehicles are consistently sending, receiving, creating and aggregating data. They need constant connectivity. Edge computing enables these vehicles to communicate between themselves and data centers with low latency, enabling these vehicles to learn and make decisions on the fly.
Edge computing helps organizations improve their energy consumption management. IoT devices and sensors linked to an edge platform can be used to keep track of energy usage and carry out real-time analysis of consumption.
By shifting data processing and storage closer to equipment, IoT devices and sensors can monitor equipment health with low latency and carry out real-time analytics. This enables manufacturers to analyze their production lines and identify changes before the occurrence of faults.
Challenges of Edge and Cloud
Cloud and edge computing both experience a dynamic level of challenges, regardless of the numerous benefits they present. Cloud computing faces the likelihood of higher latency in contrast to edge computing as data has to travel to and from a cloud server. Additionally, since data has to move through the Internet, it risks the possibility of interception by threat actors. Finally, cloud computing requires a constant Internet connection.
Edge computing increases the network bandwidth requirements as enterprises move data and compute to the edge. Edge computing accelerates the need for greater bandwidth across a network. Furthermore, having compute at both the edge and the core means that application data navigates the network in every direction, handling access rights and sharing data – which potentially creates both security and latency issues.
Future of Cloud vs. Edge
As the cloud and the edge continue to prove they can complement each other, they are set to impact the future of network infrastructure, specifically because they are both indispensable to initiatives such as the rollout of 5G.
The use of both edge and cloud computing will continue to open up further avenues for revenues while growing verticals like Industry 4.0. Hyperscale providers will increasingly leverage hybrid cloud and edge models as distributed edge and cloud architectures further improve data processing speeds, lower latency and enable the growth of autonomous devices of all kinds.
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