Cloud computing vs. Edge computing: Comparison & Challenges

Cloud computing is a centralized, distributed, and parallel system that consists of virtualized and organized computers that are dynamically supplied and set or a large number of existing computing resources, resulting in service at the connected device level. It is a computing technology that provides data storage, sharing, and processing services over networks using visualized and scalable resources.

Cloud computing plays an important role in data processing because it provides access-based computing infrastructure oriented to subscription, data, and application services. It has the advantages of flexibility, storage, sharing, and easy accessibility.

Instead of software or storage on local computers, Cloud computing shares the majority of the computer resources. Computer resources are placed in multiple locations to distribute their work, and these computer parts are run simultaneously in a computer group. This method is used to create analytics that runs faster and handles data processing that takes a long time and consumes a lot of power.

Unlike Cloud computing, Edge computing is a decentralized computing service that includes storage, processing, and applications. It occurs at the network’s edge and serves as a link between end-users and cloud data centers. This method reduces the amount of data that must travel over the network while causing minimal delays. Edge computing is thought to improve Cloud computing by performing Data Analytics as close as possible to the data sources.

Many experts believe that Edge computing and Fog computing are interchangeable terms. With minor differences, Edge computing and Fog computing can be used interchangeably. Fog computing is more concerned with infrastructure, whereas both technologies are similar in data analytics. The multi-layer Edge and Fog computing architecture can support fast response times while also providing high computing performance for data processing and analytics. Data processing is distributed across edge devices, with data processing tasks that cannot be handled by edge systems being moved to the cloud. As a result, scalability and efficiency are significantly improved due to reduced computing and routing burdens. This has the added benefit of reducing network traffic.

Edge has several advantages over the cloud, including range, speed, and privacy. But the most striking thing is that operating costs are lower. There are six significant advantages that edge computing has over cloud computing.

  • Greater reach and speed
  • Real-time analytics and increased real-time performance (low-latency)
  • Less risk, more privacy, and more information security
  • Scalability
  • The edge is significantly less expensive
  • Enhanced power-efficiency

Edge vs. Cloud – A comparison

When it comes to storing and processing data, Edge computing and Cloud computing are very similar. The differences between these technologies, on the other hand, are related to physical storage and processing locations, the amount of analyzed data, processing speed, and so on.

Features Cloud computing Edge computing
Scalability Supported Supported
Interoperability Supported. It is a needed functionality when using services from multiple cloud providers to be able to move workloads between them as well as having the ability to mitigate between providers. Supported – The increases interest in IoT development by various vendors and providers and the heterogeneity of edge comput ing requires high interoperability and flexibility
Mobility Not supported. Highly supported
Heterogeneity Not supported. Supported. IoT devices belong to different vendors and providers with different comput ing power, applications and storage resources.
Geographical distribution Cloud is naturally a distributed storage but it does not support the geographical distribution of device. Supported – key characteristic of the deployment of IoT applications based on sensor networks that benefits from edge computing.
Location Awareness Not supported Supported
Performance Congestion or server failures when processing can affect cloud service which can increase the delay. Supports the shortest response time, most efficient processing and smallest network pressure which by all means enhance performance.
QoS Management Supported for non-real time processing. Supported for real-time and provides better QoS (Quality of Service) and lower latency to the end users

Challenges of Edge computing

The development of edge computing technologies is still in its infancy. Frameworks that are still in their infancy, as opposed to Cloud computing frameworks like Microsoft Azure, Amazon Web Service, Google App Engine, and so on, are proof of this. Most current Edge computing frameworks use dedicated physical edge computing servers for computation and storage or simple ports with limited virtualization support.

The main impediment to Edge computing is the limited amount of data available. Because Edge computing technologies have memory limitations, storing a large amount of data is also limited. This technology is used for Micro Data storage in the case of Edge computing devices. The amount of data generated in the Industry 4.0 environment, on the other hand, is constantly increasing.

Edge computing technology must support several types of storage due to data enlargement, ranging from ephemeral at the lowest level to semi-permanent at the highest, covering a wider range of local geographical areas for a longer period. On the other hand, Cloud computing provides global coverage for storing data on a monthly and yearly basis. As a result, Cloud computing solutions are designed for Big Data storage, with data stored in logical pools and users able to access their data from anywhere.

Challenges of Cloud computing

Cloud computing is a technology that major industries are adopting to improve their businesses’ flexibility in terms of data storage, transformation, and exchange, allowing them to improve their profitability, interoperability, capability, scalability, etc. Many existing Cloud computing challenges, on the other hand, have yet to be fully addressed, and new challenges continue to emerge in industry implementations of this technology. In this section, we summarize the challenges in Cloud computing and argue that the implementation of Edge computing solutions can potentially solve those challenges.

The most significant issue is data security when it comes to Cloud computing challenges, as data stored in the cloud is owned by various providers. Because service providers rarely have access to data centers’ physical data protection systems, they must rely on infrastructure providers for complete data security. Even in a virtual private cloud, the service provider can only determine security settings remotely and has no way of knowing whether they are fully implemented. As a result, an unauthorized user can access and misuse data or information. When it comes to sensitive and confidential data, the original data must be kept in password-protected data management systems with security guard services in cloud computing environments. On the other hand, Edge computing technologies provide much higher security for sensitive and confidential data because they are installed in industrial environments, and data is not transferred over the Internet.

Another issue is the time it takes to process and compute information. It manifests itself in a slower response time, which prevents the use of real-time analysis because data processing and computation are done remotely from the data source. Unlike Cloud computing, Edge computing allows processing and computing tasks at the network edge, where data is generated, reducing the distance data must travel on the network while maintaining minimal latency. Edge computing over the Cloud significantly reduces computing and routing burdens, improves efficiency, and lowers network usage.

Even though data storage is a benefit of Cloud computing when dealing with large amounts of data, the yearly costs of data storage are a barrier to implementing this solution. The costs rise even more as the amount of data grows and data analysis and processing, in addition to data storage, become paid requirements. Edge computing solutions are not cheap, to be sure. On the other hand, Edge computing is much more cost-effective than Cloud solutions because it uses less-expensive IoT devices and avoids paying for additional services by shifting endpoint processors and memory capacity to edge gateways.

In terms of costs, Cloud computing technologies necessitate an extra payment for analysis transfer to obtain an offload decision based on Data Analytics. On the other hand, Edge computing technologies do not require analysis transfer due to their physical location. As a result, there are no costs associated with data analysis transfer when using Edge computing. The lack of common standardized IoT protocols reflects the challenge of standardization in Cloud computing and Edge computing. The lack of common standards may result in various issues, including increased insecurity in data transfer to and from the cloud.