Cloud and Edge Computing: Bandwidth and Cost Optimization

Cloud computing has provided businesses with servers, databases, software, and analytics that are set in the cloud. With the diverse capabilities of cloud computing, numerous platforms and software can be installed into the cloud that can either store data or computes data into insightful analytics. Since 90% of companies have already used cloud computing, it is no surprise that the global cloud computing market will reach $623.3 billion by 2023 with a CAGR of 18%.

The Issue with Cloud Computing

Since most of the computing power and data storage can be sent to the cloud, numerous devices can be connected to the internet. From household appliances such as refrigerators and vacuum cleaners to major transportation vessels such as planes and ships, countless smart devices utilize cloud computing. However, the limitation of cloud computing is the significant amount of latency (delay). This issue compromises different applications like self-driving cars and industrial plants where both create big data and can cause severe accidents if there is a significant delay.

On the other hand, businesses are in a dilemma when deciding to use public or private clouds. Even if public clouds are less expensive than private clouds, public clouds have security and privacy concerns. In fact, in a Forcepoint study cited by Tara Seals (2017), only 32% of cybersecurity professionals have the resources to secure the companies' intellectual property (IP) and other critical business data. It is also daunting that 21% of their critical data is stored in public cloud services.

What Edge Computing Brings to the Table

As big data is being processed and stored, it is important to assist with cloud computing, and at the same time explore what edge computing can offer. Edge computing distributes data computation through the use of on-premise smart devices. It can makes data processing more efficient by reducing the bandwidth use and improving response times, and enables data processing in remote locations as well. Overall, it addresses the afforementioned issues cloud computing has.

Edge computing is not perceived as an alternative but rather as a support to cloud computing. With amounts of data to be processed only growing bigger, an increase in users of edge computing can be expected, with its CAGR predicted at at least 27.3%.

Using edge computing and cloud computing together comes with the challenge of determining which kind of data will be processed in the cloud or the premises. The first factor, as mentioned earlier, is latency. Smart sensors used in the manufacturing process can send the data to the cloud, but every millisecond is important. If the monitoring system fails to identify the faults of a motor, mechanical failure can occur and unplanned downtime costs $260 thousand per hour. Other factors to consider when managing data include the amount of data that needs to be processed and matters of privacy. Consumer devices like mobile phones, smart speakers, smart appliances, and home security cameras must ensure that personal data is kept private. Lastly, Augmented reality (AR), virtual reality (VR), and mixed reality will have current bandwidth requirements at 2 megabytes per second (Mbps) that will grow to 50 Mbps. As a result, edge computing will be necessary for processing some of the data to assist cloud computing.

Challenges Ahead

Even if implementing cloud computing and edge computing together is the main challenge, there are other issues that management has to address. For example, the AI must learn and improve at each interaction for it to prescribe. Stephen Blum states “For the AI to learn in real-time, the matrix (AI brain) must allow training while also answering to your requests. Additionally, data learned must be synchronized with peering edges” (Overby, 2020). In addition, Edge AI requires big data for it to function well. McCarthy from International Data Corporation says “Many companies do not meet the minimum requirements, whether it be in terms of volume of historical data or the right kind of data to achieve the desired outcome.” There are a lot of challenges and issues that need to be dealt with when it comes to balancing cloud computing and edge computing. However, if implemented well, countless applications can be designed and enhanced, such as virtual reality, healthcare monitoring, machine health monitoring, and autonomous driving.

In summary:

  • Despite having a huge market, cloud computing comes with some issues like privacy, limited bandwidth, and latency

  • Edge computing can address the disadvantages of cloud computing so when made to work side-by-side, they can complement each other well

  • Implementing cloud computing and edge computing can be a challenge especially since both have different requirements and thresholds to be met