Organisations in Asia-Pacific are seeking out edge computing in search of faster response and cost savings, but they also have concerns about security and latency when large volumes of data are processed on such platforms.
A primary, and often cited, benefit of edge deployments are the rapid response times that would not be possible if data is sent back to a centralised network for processing.
Taiwan’s Taoyuan City, for instance, turned to edge technology in rolling out smart streetlights in its Qingpu district, using HPE’s Edgeline EL10 Internet of Things (IoT) Gateway.
The Taiwanese city has ambitions of becoming a smart city and is looking to deploy and integrate multi-sensor information from edge products into a centralised platform to deliver better citizen services.
“Certain citizen intelligence applications and services require an almost immediate response time [and] this cannot be achieved if data needs to be transmitted back to a centralised cloud for processing,” a spokesperson for Taoyuan City Government’s Public Works Department told ZDNet.
For applications that operate in an outdoor environment, network connectivity also might be affected by external factors such as weather and road construction, the spokesperson explained, noting that edge computing powered by machine learning algorithms were able to mitigate disruptions in network transmissions.
In addition, processing data via edge technology reduced the amount of information that had to be transmitted over a network, offering cost savings in network and cloud storage, she said.
To address customer concerns about outdoor or physical attributes, vendors such as HPE have designed their products to withstand various external factors such as dirt, humidity, temperatures, and vibration.
Jason Tan, HPE’s Asia-Pacific general manager of its IoT enterprise solution group, said the vendor’s edge products were built to operate in environments with temperatures as high as 70 degrees Celsius as well as operate “fan-less”, which provides more flexibility in site deployment.
When asked about the initial concerns that the Taoyuan government may experience when deploying the edge technology, the spokesperson pointed to the need to closely monitor such systems.
“Intelligent edge solutions typically require massive data processing and network connectivity. Hence, ensuring regular system updates as well as stability of the various decentralised devices is critical,” she said.
“Furthermore, as citizens increasingly rely more on such services, we need to ensure the data collected from multiple sensor devices is stored properly and securely.”
According to Zhen Ke, principal engineer of Alibaba Cloud’s IoT business unit, customer concerns about the accuracy of edge computing and latency of the cloud network supporting such devices were not uncommon.
As each node operates independently, data disparity and ensuring data is properly synchronised have been cited as potential challenges with regards to edge computing.
Alibaba addressed such concerns by adopting an integrated approach, instead of treating each node as an independent and isolated function, Zhen said.
“While we are empowering the edge, data will still be fed back to the cloud to ensure data consistency and synchronisation. This [will allow users] to tap cloud’s scalability and flexibility to better address dynamic needs,” he said, adding that Alibaba also leveraged AI and machine learning to enhance the entire compute process.
Tan noted that HPE’s edge systems supported unmodified enterprise software from its partner community, including Citrix, SAP, GE Digital, and Microsoft. This meant that enterprise customers could use the same application stacks at the edge, in datacentres, as well as cloud.
“[It] simplifies the sharing of critical data and insights from the edge across locations to enable data correlation, deep learning, and process coordination,” he said. “For instance, selected predictive maintenance data from several oil rigs can be aggregated and analysed in a central location to enable intelligent maintenance scheduling across oil rigs.”
He added that the emergence of blockchain technology also paved the away for distributed learning capabilities on edge computing platforms, thereby enabling each node to process their learning and decision making using blockchain and ensure data integrity and consistency.
Key considerations before going to the edge
Taoyuan City’s streetlight management edge deployment is still currently in its pilot phase and the government has plans to deploy more streetlights over the next few phrases of the project, according to the spokesperson.
She noted that the city government is hoping to introduce more innovative services by analysing the data collected in the deployment, spanning parameters such as air quality, climate indicators, and image analysis processing.
In deciding the volume and type of data that should and should not be analysed at the edge, she said the Taoyuan government assessed the network transmission bandwidth of the field device as well as the data management centre.
It also considered the immediacy of the application service, whether it required real-time processing and feedback, and whether edge computing could support the required speed and security, she noted.
She added that, compared to traditional datacentres, outdoor environments are harsher and edge deployments in such situations would need to consider factors such as weather, dust conditions, temperature as well as stability of power supply to the device.
“At the same time, the solution is deployed over a large number of streetlights, which limits resources in terms of processing power and configuration,” she said. “Hence, the ability to analyse the smallest function and need is an important consideration when designing an edge computing deployment.”
Alibaba’s Zhen also noted that edge computing is restricted by its physical limitations of requiring space to house the hardware. Apart from relying on a robust cloud to provide the computing resources required for more intensive processing and analysis, he added that AI is essential to enhance such deployments.
“Edge computing is for business applications requiring speed in processing, response, and action, and AI plays an integral role here,” he said. “Data can typically be analysed at the edge for faster responses and quicker actions, whereas for AI training and analysis, the large volume of data will usually be processed at the cloud.”
Alibaba last month announced a partnership with Intel to jointly develop “data-centric” computing products, including a Joint Edge Computing Platform, which features the chipmaker’s software, hardware, and AI technologies as well as Alibaba Cloud’s IoT offerings.
China’s Chongqing Refine-YuMei Die Casting (YuMei) was the first customer to deploy the new Alibaba-Intel edge product, using the platform to identify defects while parts were cast rather than have to wait until the end of the manufacturing line before they were manually inspected.
Bridging the gap between the edge and the cloud in order to bring accelerated vision features and AI performance to the edge of the Internet of Things.
Just as cloud computing seemed to be settling down into a standardized set of platforms, the drive for service differentiation results in new use cases for a faster, more flexible premium service tier. But will those use cases make sense in practice?
The future of edge computing and facial recognition (TechRepublic)
Edge computing will improve industrial processes in manufacturing, and enable facial recognition in retail environments and hotels.
IT resources are aggressively being centralized in the cloud, but some cutting edge technologies will need to balance cloud with localized computing power. That’s where edge computing comes in.