This column focuses on challenges of edge computing and explore strategy considerations for implementing and utilizing edge computing in the organization.
Edge Computing Challenges
By shifting computing power and data storage closer to those devices on the network, edge computing has managed to secure benefits such as:
- faster response times,
- improved reliability, and
- superior cost effectiveness for many businesses.
However, along with the benefits, there are some challenges related to edge computing that need to be mitigated. There are two key areas of edge computing that can prove to be challenges.
Security and Privacy
As edge computing tremendously lessens the data exposure at large by distributing storage a different point along the network, it in turn becomes prone to more individual points of attack. The ownership of the generated information at each point and the responsibility when something goes wrong at the edge of the network becomes questionable.
Load Balancing
The growing number of Internet of Things (IoT) devices producing high volumes of data at high speeds creates a breaking point and overloads networks. Transmitting this data for processing requires a lot of power and bandwidth. To keep up with the continuous service, each edge data center must utilize multiple applications and manage the network traffic using load balancing technologies that spread out the data and keep servers from becoming overloaded.
Latency
With the increased number of devices transferring data simultaneously, how will system administrators prioritize the network layout to minimize or eliminate network congestion and lag?
These are the challenges to consider around edge computing, but they should not deter any organization from using it. These challenges can be tackled with a comprehensive strategy…
A Balanced Edge Computing Strategy
Successful integration of edge computing in the organization requires firm strategic planning. The organization’s vision and mission will determine the extent of edge computing usage and the required level of adoption. There are some key components that any sound edge computing strategy should possess:
Clearly Defined Objectives and Requirements
Organizations involving remote sites require real-time monitoring and data analytics, which then may require regional data centers to handle the weight of data transmissions and the streaming of video files. Self-driving cars can generate as much as a gigabyte of data every second and with heavy duty processors giving each car enough computing power to become a mobile data center. But this comes at a great cost. How much of this is cost effective and where to draw the line?
Detailed and Comprehensive Map of the Network Topology
With edge computing, dealing with more than one server is not uncommon. This makes the physical topology of the network more complex. Mapping it out provides clarity on how it will work in practice and reality. This will unlock the power to identify where there may be potential issues with network overload or with the priority of data transmissions. The best way to map a topology is to work backwards from the edge towards the core. Based on the complexity of the network, there may be various links between edge processing units and data sources which need to connect back to the data core. Defining these details elevates value to the strategy. A clearly defined network greatly empowers the edge computing strategy.
Effective Maintenance, Monitoring and Security
Whether the organization uses local devices, localized data centers or regional data centers, their maintenance needs to be defined and scheduled from the start to avoid problems.
Properly developing a sound and balanced strategy for implementing edge computing saves organizations time and cost in the long run. It also helps ensure a strong start foundation towards a successful implementation.
Now, you have begun to grasp the challenges and strategy of Edge Computing.
Coming Up in the next article in this series: Guaranteed Effective Edge Computing Implementation
To be continued….
This quarter’s column is written by Anandhi Sutti with THE MITRE CORPORATION and has over 20 years of experience in Data Management. She has helped public and private sector clients to oversee and strategize the implementation of Data Management, Cloud Computing and Business Intelligence (BI) projects. She has strong expertise in Business Intelligence, Data Analytics, Data Modeling, Data Architecture, Data Catalog, Cloud and Data Strategy along with architecting complex systems and applications using a Software Development Life Cycle (SDLC). Anandhi has a master’s degree in computer applications and bachelor’s degree in mathematics.
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The author’s affiliation with The MITRE Corporation is provided for identification purposes only and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author.’©2022 The MITRE Corporation. ALL RIGHTS RESERVED
