How edge computing can improve sustainability

Conventional computing methods, which rely on centralized data centers, consume substantial energy to run the systems and require significant water usage for hardware cooling. This traditional approach results in the emission of a significant amount of CO2 and generates electronic waste, presenting recycling challenges for manufacturers.

According to the International Energy Agency (IEA), data centers and data transmission networks accounted for 1 percent of energy-related greenhouse gas emissions. In 2020, these methods contributed to approximately 330 million metric tons of CO2 equivalent emissions, including embodied emissions.

The rising energy demand and greenhouse gas emissions underscore the urgency of adopting sustainable practices through digital technologies.

1. Reducing energy consumption

Centralized data centers are typically designed to maintain continuous operation, necessitating a constant energy supply. In contrast, the decentralized approach of utilizing resource-efficient, power-efficient edge IoT devices significantly lowers energy requirements when compared to centralized systems.

Furthermore, as mentioned earlier, data transmission networks contribute significantly to CO2 emissions. To address this issue, edge computing strategically positions computation closer to the data source, reducing the need for data to traverse long distances to reach a centralized hub.

Manufacturers of edge devices often opt for GPUs to handle resource-intensive edge workloads instead of CPUs, thereby reducing the carbon footprint of data centers. Due to the parallelism in GPUs, certain applications can run faster than when using CPUs, leading to overall energy savings.

Edge data centers also leverage renewable energy sources for powering various components, including lighting, cooling, and ventilation.

2. Optimized for efficiency

Edge computing adopts a distributed computing approach that enhances sustainability through efficient resource utilization. These remotely deployed edge devices are intentionally designed with limited resources and integrated AI algorithms tailored to handle specific edge applications. This local data processing capability increases the burden on network infrastructure and enhances overall resource efficiency.

Likewise, this approach reduces the volume of data transmitted over the network, thus freeing up bandwidth. The greater the amount of data transmitted over the network at any given time, the slower the network speed becomes. Therefore, it becomes more practical to work with data at the edge and only transmit data to the cloud when post-analytical processing is required.

3. Real-time remote monitoring

Centralized cloud servers often incur substantial delays in analyzing and making decisions due to the necessity of transmitting extensive streams of continuous data generated at the edge. This process can be both time-consuming and resource-intensive.

Therefore, it becomes crucial to leverage real-time data analysis and monitoring capabilities provided by the multitude of edge devices deployed in remote locations. This approach enhances the feedback loop and facilitates quicker responses, particularly for mission-critical applications.

Edge computing plays an important role in reducing latency by processing data at the edge, resulting in more efficient services across various industries, including oil and gas, energy, transportation, and healthcare.

For instance, in the context of oil and gas companies, real-time remote monitoring ensures efficient operations without the risk of leaks or spills, which can have significant financial and environmental ramifications.

4. Edge network security

Given that edge devices continuously generate data, safeguarding this data, which often contains sensitive information crucial to organizations in various sectors, is of paramount importance. Centralized storage servers can introduce potential vulnerabilities owing to insecure communication channels and less effective cloud-based security measures.

Edge computing offers an alternative approach to data security by distributing data storage and processing throughout the edge network. Data is processed on various edge devices that are deployed, thereby minimizing the impact in the event of a compromised edge device.

These edge devices can also implement edge security solutions, which can be directly deployed on the edge servers of IIoT gateways. This approach proves highly efficient, especially in areas with intermittent internet connectivity.

The end goal

Achieving sustainability in data centers has always posed a significant challenge for service providers and infrastructure owners. The introduction of edge computing solutions has prompted companies to reevaluate their sustainability strategies, considering the option of relocating applications to the edge to enhance efficiency and innovation in their product offerings.

In the industry’s recommendations, edge data center providers are encouraged to draw lessons from hyperscale cloud data centers, particularly in optimizing cooling systems. This can be achieved by implementing measures such as segregating hot and cold aisles, which results in reduced energy consumption. Additionally, taking advantage of modern chip technologies, such as sleep mode, can substantially decrease energy usage.

In the end, as cloud data centers struggle to meet sustainability targets, the shift toward edge computing solutions becomes imperative. Running workloads at the edge not only proves energy-efficient but also enhances security.

Article Topics

edge computing  |  EDGE Data Centers  |  energy  |  sustainability