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Edge Computing and Cloud Computing – Compared and Contrasted

Conventional cloud computing moves all information to the cloud computing center via the network and resolves the computational and storage issues in a centralized manner. However, the advent of Edge Computing will not phase out conventional cloud computing. 

To the contrary, rather than being opposed to each other, Edge Computing and Cloud Computing complement each other. They enable the digital transformation of businesses to a larger degree. Edge Computing is a more sophisticated variant of cloud computing that minimizes latency by bringing the services in closer proximity to the end-users. It reduces the load of a cloud by furnishing resources and services in the Edge network. 

With regards to a few internet services, specific information requires to be returned to the cloud for process upon being processed by Edge computing, like in-depth analysis of data mining and sharing, which needs the collaboration of both concepts.  

The primary differences between Edge computing and Cloud computing are as follows: 

  1. Cloud. The salient feature of cloud computing is that it can undertake processing of a huge amount of information, execute in-depth analysis, in addition to having a critical role in non-real-time information processing, like business decision-making and other domains.
  2. Edge. On the other side of the bargain, we have Edge. Edge concentrates on the local (on-device) and can have a better role in smaller-scale, real-time intelligent analyses, like fulfilling the real-time requirements of distributed services.

At the time of writing this article, Edge computing is needed to share the pressure of the cloud and take control of activities within its domain of the edge. Local information processing minimizes the risk of large-scale information loss. These aspects provide stability to services that consist of connected devices/gadgets in a network. (IoT) 

Fog Computing 101 

Fog Computing was first put forth by Cisco in 2012, 9 years ago, and was termed a “highly virtualized platform that furnishes storage, compute, and network services across end devices and conventional cloud computing data centers, usually, but not exclusively situated at the edge of the network”. 

The OpenFog Consortium defines fog computing as a “system-level horizontal architecture that distributes resources and services of computing, storage, control, and networking anywhere along the range from cloud to things, therefore accelerating the speed of decision-making.” 

Therefore, fog computing is a concept, where a large number of heterogenous, ubiquitous, and geographically distributed gadgets interact with the network to execute storage and processing activities with no interference from third parties. These activities can be for assisting fundamental network functionality or new services and apps that run in isolated settings. 

Fog computing is an intelligent extension of cloud computing targeted at filling the bridge with IoT devices. Hence, Fog computing should be viewed as a substitute to the conventional cloud architecture, but instead as a fresh architecture that combines, IoT, Edge, and Cloud together in a single, meaningful package. 

The Traits of Edge Computing 

Edge computing contains various traits that are similar to cloud computing but expands the cloud by its particular architecture. 

  • Geo-distribution. IoT apps with a basis on sensor networks reap great advantages through the process of information at a local level via edge computing platforms. Big data analytics can be executed swiftly with improved accuracy. Edge systems assist real-time analytics and AI-processing on a huge scale. Example of this include sensor networks to survey the environment, for instance, collision avoidance setups, or pipeline surveillance. 
  • Location awareness. This enables the utilization of technologies like GPS to identify the location of gadgets. Location awareness can be utilized by Edge computing apps and edge-based disaster handling. 
  • Reduced Latency. The reduced latency of Edge Computing helps the users to carry out their resource-heavy and delay-sensitive apps on the edge device. Examples of such apps consist of smart vehicles, remote health surveillance, industrial management systems, and warehouse logistics. 
  • Heterogeneity. This is with regards to the presence of various platforms, architectures, communication, and computing systems utilized by the Edge computing elements.  
  • Bandwidth heavy use cases. A growing amount of data produced by IoT today is bandwidth heavy, particularly the information from surveillance cameras, for example. Putting computational assets in close proximity to the high bandwidth sources means that much lesser information requires to be delivered to the distant cloud data centers. For examples, videos and sensor information from dangerous locations can be processed at a local level to furnish real-time data to responders in public safety deployments. 

Benefits of Edge Computing 

Edge computing systems record and process collected information locally on edge devices without uploads onto a cloud computing platform. This carries with it several critical benefits to reduce the pressure of network bandwidth and other drawbacks of cloud computing. 

  • Performance. Edge computing tech provides quicker information processing and analyses, quick response speed assisting real-time services. It gives end-users with a plethora of quick response services, particularly in the domain of automatic driving intelligent manufacturing, video surveillance, and other location awareness, where swift feedback is crucially important. For instance, Edge computing facilitates real-time computer vision apps. 
  • Privacy/Security. The centralized perspective of conventional cloud computing needs all information to be put on to the cloud for unified process. Hence, cloud-based process holds considerable risk of information loss, and leakage. Edge computing has its foundation on localized information, which sidesteps the risks that are endemic to network transmission. Attempts to compromise by malicious actors and failures only impact local data, not the complete data. On-device computing and AI interference makes it possible to ensure information security and safety. Further, on-device computing facilitates distributed training of AI models, named Federated Learning. 
  • Efficiency. On-device computing minimizes the quantity of information transmitted over the network, minimizes the transmitting costs, the network bandwidth pressures, minimizes energy usage of local resources, and enhances computing efficiency. Edge computing is behind the production of increasingly efficient AI hardware like deep learning accelerators. 
  • Reliability. Edge computing tech furnishes ways to render services with increased stability, robustness, and a high degree of accessibility. The increased reliability of connected on-device systems is particularly vital in mission-critical apps that are prone to network disconnections which might cause disastrous consequences. (Medical surveillance or public transportation systems) 

The Future 

Edge computing visualizes getting services and utilities of cloud computing in closer proximity to the end-user for quicker processing of information intensive applications. As a technology, it is set to complement cloud computing, and it unifies in a harmonious fashion with IoT, and Cloud to form Fog Computing. The future isn’t foggy indeed. 

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