What is edge computing?

Edge computing processes data closer to the source rather than relying on a centralized data-processing warehouse. This concept departs from traditional cloud computing, where data is sent to distant servers for processing and storage. Edge computing involves using Internet of Things (IoT) devices and other technologies to handle data at the network edge to reduce latency and improve speed.

An edge device is typically a sensor, a mobile device, or a local server. These devices process data locally to provide more expedient real-time analytics and insights. This local processing is essential for applications requiring immediate data analysis, such as autonomous vehicles, industrial automation, or real-time healthcare monitoring functions.

Edge computing vs. cloud computing

There is a difference between edge computing and cloud computing. The two serve different purposes. Cloud computing is frequently used to handle large-scale data processing and storage, benefiting tasks that aren't time-sensitive. However, edge computing is more efficient for applications requiring immediate data analysis, such as real-time surveillance analysis or IoT devices. The latter approach reduces latency by processing data locally and sending only essential information to the cloud.

While cloud computing centralizes data processing and storage in distant data centers, edge computing distributes these functions closer to the data source. This distribution reduces latency and bandwidth, offering faster data processing capabilities. However, cloud computing still plays an important role in storing large datasets and providing extensive computational power when needed.

Then, there’s the concept of “fog computing,” which acts as an intermediary architectural layer between the cloud and edge devices, giving edge computing more features. Fog computing provides additional processing power and storage for more sophisticated analytics closer to the network edge. This layered approach allows for better load management and real-time data processing, which is essential for applications such as smart grids and connected vehicles.

Benefits of edge computing

Edge computing offers several other benefits, including reducing bandwidth by decreasing the amount of data sent through the network and reducing latency by processing data locally.  

Scalability and flexibility

Edge computing provides scalable and flexible solutions that adapt to the specific needs of different applications and environments. Businesses deploy edge devices as needed, scaling operations up or down without the significant infrastructure changes traditional cloud computing requires. Still, edge environments need to catch up to the cloud space with regard to containerization and orchestration (i.e., Kubernetes). Orchestration tools are still in the maturing stage at the edge.

Security and privacy

Some consider edge computing a more secure approach to data management because it keeps sensitive data closer to its source. Data is processed locally, reducing the risk of exposure during transmission to central data centers. This localized processing also helps comply with data sovereignty regulations that require data to remain within specific geographical boundaries.

IoT data management

Managing IoT data is challenging due to the sheer volume generated by connected devices. Edge computing addresses this by processing and filtering data at the source, sending only necessary information to the cloud. This approach reduces data traffic and ensures IoT systems can respond quickly to changing conditions, which is vital for applications such as augmented reality, telemedicine, and industrial automation.

Challenges in edge computing

However, edge computing has several challenges to ensure its success. One of the primary concerns is data sovereignty, where data must be processed and stored according to the legal and regulatory requirements of the region in which it is collected. This can lead to complications in managing data across different jurisdictions, requiring planning and compliance with standards.

Edge security is another issue. While edge computing can be deemed more secure because data isn’t transmitted as much, processing data closer to the source gives hackers more areas to attack, exposing potential vulnerabilities in the network. Ensuring robust security measures are in place is essential to protect sensitive information from breaches and unauthorized access.

Scalability poses a challenge as well. As the number of edge devices grows, managing and scaling these networks efficiently becomes harder, especially because management tools need to accommodate the dynamic nature of edge networks. 

Moreover, integrating cloud with edge systems can be difficult. Balancing the distribution of workloads between edge and cloud environments is a juggling act to optimize performance and resource use without degrading latency or processing speed.

Opportunities in edge computing

Despite the challenges, edge computing offers numerous opportunities for innovation and growth. 

In addition to improved speed by reducing latency, edge computing’s support for data sovereignty means businesses can potentially have access to new markets previously restricted by data governance issues.

While unrestrained growth of edge devices is an issue, deploying microservices and containerized applications at the edge lets businesses dynamically scale resources according to demand. 

Advancements in artificial intelligence (AI) and machine learning (ML) are promising in terms of edge security. These technologies detect and respond to threats more swiftly, making edge networks more secure.

Real-world applications of edge computing

Edge computing transforms various industries by enabling real-time data processing closer to the source. In manufacturing, edge computing helps predictive maintenance and quality control by analyzing data from IoT sensors on the factory floor. Energy companies use edge computing for smart grid management, optimizing energy distribution and reducing outages by processing data locally. In healthcare, edge computing supports telemedicine and patient monitoring by ensuring quick data processing and reducing latency for time-sensitive applications.

Transportation and logistics benefit from edge computing through real-time tracking and fleet management. Autonomous vehicles rely on edge computing to process data from sensors and cameras for quick decision-making without relying on distant data centers. In retail, edge computing improves customer experiences by delivering personalized services and managing inventory in real time, allowing retailers to respond swiftly to demand changes.

Agriculture employs edge computing for precision farming, using real-time data from sensors and drones to optimize irrigation, pest control, and crop monitoring. This localized data processing helps farmers increase yield and waste fewer resources. 

Smart cities incorporate edge computing to manage traffic flow, enhance public safety by analyzing surveillance cameras, and improve waste management through real-time data analysis.

Edge computing's ability to process data on-site reduces bandwidth usage and enhances data privacy, making it an attractive solution for industries dealing with sensitive information. However, deploying edge computing at scale requires addressing challenges such as network reliability, security, and interoperability among diverse devices and systems.

The business case for edge computing

“It is clear we are entering a new era, driven by one of the most exciting technologies the world has ever seen: artificial intelligence,” said Antonio Neri, president and CEO of Hewlett Packard Enterprise. “It will affect every industry, as well as every level of computing, from the smallest IoT device to the largest exascale supercomputer. And it will require that enterprises be edge-centric, cloud-enabled, and data-driven – characteristics I asserted would define the enterprise of the future.” Consider the benefits of edge computing:

• Happier customers: Edge computing can overcome latency and bandwidth
  problems, which can lead to a better customer experience. For example, a customer enters a favorite retail store and is immediately offered a coupon for a product that will complement a recent purchase. That interactive, real-time involvement with the customer is seen as a way to compete with online retailers and enhance customer loyalty.

• More innovation: “The decentralization of AI through edge AI represents a profound shift in the technological landscape,” according to the Deloitte report, The Future of Edge AI. “As this paradigm evolves, it will likely open new pathways for innovation and reshape how businesses operate across all sectors, making AI an integral and transformative component of our digital lives.“Edge AI” means where, what, and how AI computations are conducted, Deloitte reports. Businesses boost innovation by combining edge AI and edge computing with technologies such as augmented and virtual reality and smart factories.

• Smarter and faster decisions: Businesses will need real-time access to key metrics to compete more effectively, implement new strategies, and pivot quickly to meet customer demands. Businesses can partner with other organizations to deliver what the customer needs at the moment, such as a recommendation for a protein smoothie from a nearby shop after working out at a gym, for example.

“For years, I have said the world would be hybrid – and that prediction has come true as businesses seek to blend the best of on-premises and public-cloud resources,” Neri said. “It’s a powerful mix, but it’s hard to get it right. Organizations need a unified experience across all workloads that is open and secure, as well as sustainable.”

Pushing infrastructure to the edge

As more companies turn to digital business, enterprises will need to expand to the edge. Gartner predicts that 75% of enterprise-managed data in 2025 will be created and processed outside the data center or cloud, moving to edge locations.

The evolution will require some new thinking and new strategies.

Among strategies to meet the challenges, Gartner recommends in its Market Guide for Edge Computing Platforms:

• Take a strategic view with edge computing platform (ECP) technologies: Work across the organization to establish an edge computing strategy to identify the current and long-term use cases the enterprise might want to deploy. Research what leading edge enterprises in your industry are already doing, and look to adjacent industries for future applications. Choose an ECP that makes it easier for you to add these applications in the future.

• Take a strategic view with edge use cases: Today, your edge computing use cases might be focused on data collection, but as digital data at the edge grows, the evolution to edge AI, edge GenAI, and composite AI at the edge is inevitable. Choose an ECP that makes it easier for you to evolve on your data journey.

• Take a strategic view with ECP vendors: The ECP is an important point of vendor lock-in, as well as concentration risk. Vendors can use the platform to evolve and grow in directions that are beneficial to them but may not be ideal for your enterprise. There will be a shakeout of ECP vendors, so choose vendors that make sense with your use cases and vertical industry but are viable long-term. ECPs are a critical component of your digital transformation. Take charge of your own digital transformation strategy, and don’t necessarily follow the vendor’s platform strategy by default.

“Edge computing requirements are driven by business needs, and business units often lead,” writes Thomas Bittman, Gartner vice president and distinguished analyst.

Aerospike and edge computing

Aerospike delivers real-time data processing with sub-millisecond latency for both reads and writes, even in resource-constrained edge environments. This performance makes it ideal for applications requiring immediate insights and actions. Here’s why. 

Real-time processing and low latency

Aerospike delivers real-time data processing with sub-millisecond latency, making it useful for applications requiring immediate insights and actions. Whether it's augmenting a customer's experience in a retail environment or providing real-time analytics in telecommunications, Aerospike ensures data is processed quickly and efficiently.

Scalability and efficiency

Aerospike’s Hybrid Memory Architecture lets it manage large data sets with fewer servers, reducing both cost and carbon footprint. This efficiency is crucial for edge computing, where resources can be limited, and scalability is key to handling fluctuating demands.

Enhanced security and privacy

With edge computing, data is processed closer to where it is generated, reducing the need to transmit sensitive information across networks. Aerospike supports this model by allowing secure, localized data processing, which enhances privacy. Edge security is further bolstered by Aerospike’s robust data encryption and privacy controls, which lower the risk of data breaches even in distributed environments.

Flexibility and adaptability

Aerospike’s flexibility suits edge computing use cases, from IoT applications to augmented reality. Its ability to integrate with existing infrastructure and support distributed architectures makes it a versatile choice for businesses looking to expand their edge capabilities. Moreover, Aerospike’s compatibility with public and private edge offerings lets organizations tailor solutions to their specific needs, adapting to regulatory and cultural requirements.

Exploring edge technologies with Aerospike

Aerospike's real-time processing capabilities, efficiency, security, and flexibility position it as a leading solution for edge computing. As businesses continue to use edge technology for new opportunities and efficiencies, Aerospike provides the robust infrastructure needed to support and advance these initiatives.