Top 5 Data Strategies for Efficient Edge Computing Services

The global edge computing market size is anticipated to reach US$43.4 billion by 2027, exhibiting a CAGR of 37.4% over the forecast period.

Edge computing services are the hot cake in the tech world, presently. With a surge in demand of 5G network and IoT across all sectors, edge computing is growing parallel. Though at a nascent stage, the need for this technology has been realized by the tech giants. Edge computing is data-driven and involves the distinction between the data that is being processed centrally to provide information and the data that is being processed at the sidelines for triggering actions to stimulate the automation process. In either case, structured, uncluttered and reliable data is paramount, for edge computing operations to be successful. Any discrepancies in the generation, storage and operation of the data can become deteriorating for edge computing. Thus, it becomes imperative to understand the data strategies that will help in deploying secured and efficient edge computing services.

Understanding Edge Computing `

According to Gartner, Edge computing is an emerging topology-based computing model that enables and optimizes extreme decentralization, placing nodes as close as possible to the sources and sinks of data and content. At the primary level, it is the amalgamation of the computing processes and data storage, where data is stored near the point of operations.

The big tech organizations like Apple, Google and Microsoft are already in the race for gaining momentum in the race of edge computing supremacy. In July this year, Google announced a collaborative project with Orange, the Telco giant, to create a data platform that will bolster the edge-computing offerings to the enterprises. Moreover, it is observed that Apple, Tesla, and Amazon are already leading the world of edge computing.

Gartner has forecasted that by 2022, an estimated 75% of the enterprise-generated data will be created and processed outside the traditional centralized data centre or cloud, whereas the global edge computing market size is anticipated to reach US$43.4 billion by 2027, exhibiting a CAGR of 37.4% over the forecast period. With the growing demand of 5G network amongst telecommunication operators, 5G is expected to act as a catalyst for the market growth.

Strategising Data for Efficient Edge Computing Operations

1. Data Stockpiling- Since edge-computing is associated with 5G network and IoT, by Stockpiling data, the cost of edge-computing operations gets reduced, and the organizations get reliable insights, which is imperative in edge computing operations. This can be especially utilized in manufacturing, where the variety of data can assist in better customer satisfaction, by offering an assortment of products.

2. Integrating Data with Blockchain– It is a known fact that data is significant for edge computing operations. But often this data is encrypted, unreliable and generated from unknown sources, thus increasing the possibility of cyber malware and privacy infringement. By integrating the data, in the blockchain, will enable organizations to retrieve the information about the data source, thus mitigating the cybersecurity threat or privacy threat.

3. By Building data sources near the computation process– One of the classic attributes of edge computing process is storing data in the centralised hubs that reduces the overall latency of the desired output. By establishing the gathered data close to the computational process, will aid to monitor the system's function and identify, as well as to detect the anomaly within the system, so that damage due to the malfunction can be prevented.

4. Context of the data-By understanding the context of the data, and assigning them on different processing locations, it will help the organizations to build an efficient and effective edge computing network.

5. Two-tiered Data Transmission Strategy- By establishing a two-tiered data transmission strategy, the most useful insights are collected from the raw data. This will assist in building a low latency network, and will also keep the new data on fast devices such as solid-state devices, and older data in slow devices. This approach reduces the cost of data storage practices but generates a large volume of data which are costly and are not required immediately.

Conclusion

By engineering a strategic approach, the challenges with edge computing can be quelled. A secure and reliable data is imperative for efficient and secured edge computing operations.