The looming threat of rising sea levels and frequent heat waves has prompted nations in the Asia-Pacific region to strengthen their sustainability commitments in recent years.
In the Philippines, the Department of Energy is targeting the rollout of 2.45 million electric vehicles from 2023 to 2028 as part of efforts to lessen carbon footprint and combat climate change. Meanwhile, four Philippine cities have also pledged to reduce carbon emissions and achieve net zero in localities by 2050 or earlier.
Industries are heeding the call to climate action, and telecommunications companies are at the forefront of this green revolution. In Singapore, Singtel has already inked partnerships with energy players such as Medco Power to reach operational net-zero emissions in its data centers by 2028. Meanwhile, some of the Philippines’ largest network operators, Globe and PLDT, are leveraging innovative network solutions to improve energy efficiency and keep up with evolving sustainability regulations such as the Energy Efficiency and Conservation Act.
As the spotlight turns toward asset optimization, telcos across the region are working to increase the traffic capacity of their network within their existing infrastructure. They are also embracing new approaches that incorporate practical applications of artificial intelligence (AI) and machine learning (ML) to boost operational efficiency, meet the demands of a dynamic digital landscape, and stay firm to their sustainability commitments.
Network transformation offers a pathway to reduce emissions. Ongoing technological advancements are bolstering network efficiencies and decreasing power per bit to keep energy consumption and associated emissions in check. The “less is more” strategy is being increasingly adopted by telcos as they shift their focus toward deploying equipment that consumes less space, uses less raw materials, and generates less waste.
Also minimized is the downstream and upstream impact of materials and waste generated by networks. Given their mandate to modernize network infrastructure, telcos are increasingly replacing outdated, energy-intensive hardware in fixed and mobile networks with more sustainable equipment and architectures. This not only delivers significantly improved performance but also operates within a much smaller power and space envelope. The outcome is a more efficient network with substantially reduced power consumption.
Beyond moving on from legacy hardware, a greater opportunity for network transformation lies in embracing a software-led transition driven by AI and ML. With virtualized services such as remote management and troubleshooting, operators can help reduce “truck rolls”—eliminating the need for physical installations and repairs.
Leveraging cloud-native network functions also allows telcos to host and manage network functions in the cloud instead of relying on multiple physical network devices. This helps eliminate power-hungry devices and reduce carbon emissions throughout the network. Meanwhile, combining open application programming interfaces with dynamic inventory capabilities enhances telcos’ ability to spot equipment requiring decommissioning or replacement and select the best traffic route.
With the help of intelligent automation, telcos can improve visibility over existing network assets. These technologies can enhance telcos’ traffic engineering capabilities and enable them to scale network capacity in line with fluctuating demands and sudden spikes in traffic—ultimately maximizing bandwidth use without over-provisioning resources.
Ongoing innovation drives telco sustainability, but this potential may be left unrealized without the right underlying network infrastructure. This means going beyond static hardware upgrades and transitioning toward using predictive intelligence to optimize the use of existing assets in a more sustainable and cost-effective manner.
Autonomous networks, which can run without human intervention, have been gaining momentum over the years. But while the autonomous network can configure, monitor, and maintain itself independently, eliminating all human intervention from network management and operations isn’t realistic—nor does it come without risks. Time, field experience, and trust in new capabilities will determine how, when, and where network operators will implement autonomous networks.
Instead of building networks that are simply automated, operators can look toward building networks that are adaptive, and can proactively adjust to changes instead of merely reacting to them. Embracing adaptive networking drives the programmability, intelligence, openness, scale, and security needed to run networks effectively and sustainably. This is key to streamlining operations—delivering unified visibility, analytics, and performance monitoring across a multilayer network, and fully leveraging the capabilities of AI and ML in optimizing wavelengths or reducing energy consumption.
Matthew Vesperman is vice president (Asia Pacific) of Ciena.