What is edge computing, and why does it matter?

Stemming from the increased usage and reliance on interconnected devices, edge computing provides a solution for applications that require real-time computing power.

Edge computing is the dissemination of the computation from data centers to the edge of the network. It leverages smart objects, mobile phones, and other internet-connected devices to perform tasks normally performed in the cloud. Like any material, data takes time to travel—the larger the amount of data, the longer it takes to arrive at its destination. With edge computing, the information that is collected and distributed has a shorter distance to travel compared to data stored in the cloud.

The benefits of integrating an edge data center into a company’s system make it hard to ignore the importance and impact of edge computing. From boosting network performance, increasing speeds for end-users, and providing a solution to scalability issues, edge computing solutions help optimize the management and processing speed of high volumes of data.

Compared to data stored on cloud architecture, local intelligence is a key benefit to edge computing. Edge solutions do not rely on network connectivity or human problem-solving to fix an issue. As a result, data is distributed within the system redundantly, allowing for the infrastructure to self-heal and continue running applications as normal.

Issues surrounding scalability are also mitigated through integrating an edge computing system. As organizations grow, their data needs grow along with them and accurately predicting this growth can be near impossible.

Traditionally, organizations have relied on purpose-built data centers and the manual management of systems. This conventional approach leads to increased difficulty in managing different locations, and understanding future needs as companies experience growth.

Edge computing introduces the needed automation for many different locations of an organization to run smoothly through integrating a sealed system that automatically checks error rates, internal drive temperatures, and other essential factors.

Computing, storage and analytics capabilities are being leveraged across smaller devices that can be located closer to end users. Edge data centers and IoT devices allow companies to expand their edge network’s capabilities as they grow, making scalability more affordable and a much smoother process.

Edge computing allows businesses and organizations to overcome the scalability and network performance challenges, while benefiting from higher security and improved ease in problem-solving.

When adopting new technologies—such as edge computing—security should always top of mind. According to Juniper Research, the average cost of a data breach is $150 million. So, it comes as no surprise that companies invest time, financial resources, and energy into mitigating security risks.

The introduction of the 5G infrastructure will usher in a strong wave of reliance for organizations on IoT devices, exposing them to an increased vulnerability of cyber attacks. As security breaches continue to increase, organizations are growing more concerned over these threats which can be extremely costly and time consuming for companies.

A key security risk businesses face is increased exposure to attacks resulting from the manipulation of devices within an edge network. For example, cyber criminals have the ability to install a bot or backdoor to intercept or divert data. Additionally, with 5G networks expected to become the foundation of many IT applications, the integrity and availability of those networks will become a major security concern and challenge for many businesses.

By using the proper network segmentation techniques, edge computing can minimise risks by localising any data breaches or cyber attacks to just one point on the network. For example, affected areas resulting from a cyber breach can effectively isolate without shutting the whole network down.

Because edge computing allows organizations to use the proper network security, it eliminates single weak points making them much less vulnerable.

While most Internet of Things devices currently rely on cloud computing, more and more manufacturers and application developers are starting to realize the power of doing the computing on the device itself. The ability to do advanced, on-device processing and computing is where edge computing and the Internet of Things meet.

Edge computing also addresses the challenges that the Internet of Things is currently facing like network congestion and latency.

The growth of IoT devices is nothing short of explosive—this new generation of cellular technology would be unable to deliver on its promises without ultra-low latency and high levels of connectivity provided by edge computing. As the Internet of Things and IoT devices continue to proliferate, edge computing is quickly becoming a best practice.

The need to bring data processing closer to the end-user to reduce network latency and improve user experience has become the cornerstone of the industrial IoT. Edge computing makes room for growth in IoT applications, especially those that rely on AI and machine learning.

The growth and expansion of IoT is directly correlated with the growth in edge data centers which are expected to become more commonplace. In fact, according to Global Market Insights, the edge data center market is expected to reach $16 billion by 2025.

With any new technology, some hurdles can arise during implementation—the same is true for edge computing. Organizations must have a clear understanding of their own requirements and any issues that may arise during the planning, design, and implementation stages.

While the advantages associated with it are massive, the edge computing model does face obstacles that businesses must address, and ultimately avoid.

A distributed edge system can be more complex to manage than a centralised cloud architecture. It is good practice to centralize where you can and distribute infrastructure only where you must. Businesses that put too much processing on edge devices soon find that the latency and speed issues they were looking to solve with edge computing come back and, in some instances, make matters worse.

Complexity at the edge should be avoided at all costs, as the more complexity you have, the harder it is to govern security, scalability, management, and maintenance of edge devices. Complexity can be reduced by leveraging unified control planes and a repeatable consistent edge design.

Edge devices should be purpose-built and only do the minimum of what’s needed to collect, process, and transmit data, as well as respond to issues that need immediate attention. Organizations should look for simple solutions that are easy to manage. Otherwise, they will likely need to increase spending on deploying their IT staff to handle any operational issues that will inevitably occur when edge solutions are overly complex.

As more data is stored at the edge and more computing happens remotely, edge computing demands a shift in network bandwidth. Traditionally, businesses have allocated a higher bandwidth to data centers and a lower bandwidth to endpoints. Now, organizations are challenged with balancing more bandwidth across the network when moving IoT devices from the core to the periphery.

When deployed successfully, certain data is processed locally without being sent to the cloud so less bandwidth will be required. With the ever-increasing number of IoT devices all generating live data, bandwidth savings could be considerable with edge computing.

Machine learning models that are trained in the public cloud can then be deployed at the edge for inferencing. However, there is always a delicate balance of sizing your computing resources at the edge versus your network capacity. It is also important to discriminate what type of data to keep or discard at the edge to manage the storage and transfer of large sets of data.

As mentioned previously, edge computing does offer some mitigation to security concerns. But an edge disrupted environment can also create new security threats. The IoT and network-connected devices increase the opportunity for security risks to access sensitive information.

Because data processing is taking place at the edges of the network, risks of identity theft and cyber security breaches are present. However, an edge solution’s infrastructure can reduce the amount of data exposed to cyber attacks. Edge networks can be secured by using the correct hardening policies, ensuring the proper threat detection exists, and encrypting the data at rest and in transit. Data can be protected on local drives before being moved back to the microdata center.

Across almost every sector, businesses are trying to leverage the benefits of edge computing but continue to face on-going challenges when managing it. Edge solutions often require customization for clients to meet their specific needs, as there is no standardized, one-size-fits-all approach. No two organizations are the same, and every IT strategy should be unique based on business objectives.

Despite the increased benefits they see when having a centrally focused system of local infrastructure, managing edge solutions is the biggest challenge for organizations. Shifting to a centrally managed and deployed system encourages businesses to escalate their management concerns to a selected partner with the skills and expertise to supervise the edge networks. This partner supervision can, in turn, deliver the advantages of being closer to the edge and support long term business objectives.

Businesses should consider working with a trusted IT partner to supervise edge networks and deliver its benefits to support long term business objectives. An effective edge computing model should address network security risks, management complexities, the limitations of latency and bandwidth, and maximise the true value of a business’ technology investments.

We are now operating in a data environment, where data is the moving force of modern society and has revolutionised the business landscape as we know it.

Data infrastructure is as important as physical infrastructure in today’s business environment—a strong data infrastructure will increase interoperability and collaboration, efficiency and productivity. Data As Infrastructure™ recognizes that increased digitization has transformed data into a critical infrastructure at an organizational and societal level.

Put simply, Data As Infrastructure™ takes a data-first approach—an organization must design their infrastructure around how they are capturing, managing and using their data, instead of trying to fit their data into a technology infrastructure. Data As Infrastructure™ is the baseline condition for a healthy, progressive society, and a competitive global economy.

Edge computing is the latest evolution of a strong data structure. Every day, there are more powerful devices in the hands of consumers and on the edge, making it more convenient for businesses to rely on edge computing.

Whether it is optimising business operations, improving user experience, enhancing existing offerings or pioneering new ones, edge computing promises to affect every aspect of business. This will bring new challenges, but organizations that use these emerging technologies properly will see a marked, long term benefit to their businesses.