The dawn of edge computing in business has finally begun. The increase in the creation of data and usage of digital devices contribute to the need for spiking edge computing devices. Applications of edge computing are prevalent and more critical to the success of business across various industries. Unfortunately, edge computing carries a lot of pain points as well. They create management challenges that are not found with data centers or cloud computing. However, these disadvantages can be addressed by implying some countermeasures.
Edge computing is a physical compute infrastructure that is positioned in the spectrum between the device and the hyper-scale cloud, supporting various applications. Edge computing devices bring processing capabilities closer to the end-user of data which eliminates the journey to the cloud data center and reduces latency. It brings memory and computing out of the traditional data center to a close as close to the location where it is created. According to Gartner, the edge computing market will reach US$13 billion by 2022. It is also expected that by 2025, nearly 75% of the data gathered will be processed out of the cloud, mostly using edge computing devices. However, delivering effective edge computing in business to hundreds or thousands of locations comes with a number of challenges. They vary depending upon their functionalities, but in common characteristics, they carry three important setbacks that need to be addressed. Fortunately, there are available remedies that could address the disadvantages and fill the void.
Employing edge computing devices at every end doesn't mean that everything is under surveillance and control. Despite employing edge computing in business, organizations have to assign monitors at every edge location to understand the health and status of each IT component from physical access to the power and cooling to the servers and network devices. Henceforth, the companies can't get an overview of the edge infrastructure, environment, and how it is impacting the edge components. To address the challenge, businesses are adopting intelligent software to manage the highly heterogeneous environment. The software takes care of overviewing the routine process of edge computing devices like collecting, unifying, and processing data from a wide range of sources. With the help of intelligent software, businesses get an opportunity to simplify the monitoring process and nullify the need for human interaction.
Security is one of the major concerns that are making a big impact on people's lives today. Even though when we can argue that adopting the applications of edge computing comes with a handful of benefits, it still has certain setbacks on security grounds. Edge computing in business plays a pivotal role in increasing the number of data processing locations to streamline analytics and the decision-making process. Unfortunately, this is opening the door to security issues and cyberattacks. Besides, standalone data processing equipment and devices risk becoming attack vectors. Fortunately, we can get security solution providers who could leverage a unified view of physical, network, access, and data with automated responses. Businesses can also embrace security service that that rolls out full security without needing the client's involvement.
Edge computing in business experiences the same trouble that every other technology undergoes. Disruptive trends come with a lot of responsibilities like application update, debug-ability in case of problems, and deployment of additional devices. Edge computing devices are no different. They too need to undergo all these steps every now and then. Henceforth, to deploy and manage the solutions on regular basis, businesses have to recruit a skilled professional who can take care of the job. However, remote management options are available for companies to carry out these tasks from a distant place. For this, the applications of edge computing need to be highly sophisticated and should be able to provide a range of features such as data caching in case of lost connections, raw data stream processing to filter, analyze relevant data, message brokering for event-based applications, etc.
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