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Insights

July 19, 2023

Towards a Zero Touch Coherent Network

Telecommunication service providers face a critical challenge: how to incorporate affordable and compact coherent pluggables…

Telecommunication service providers face a critical challenge: how to incorporate affordable and compact coherent pluggables into their networks while ensuring optimal performance and coverage across most network links.

Automation will be pivotal in achieving affordable and sustainable networks. Software defined networks (SDNs) facilitate network function virtualization (NFV), empowering operators to implement various functions for network management and orchestration. By incorporating an artificial intelligence (AI) layer for management and orchestration with the SDN/NFV framework, operators can unlock even greater benefits, as depicted in the diagram below.

carrier-network — Figure 1: Example of a carrier network with different applications (mobile, home, office) automated via SDN control and AI management.

Nevertheless, achieving a fully automated network requires interfacing with the physical layer of the network. This requires intelligent, coherent pluggables capable of adapting to diverse network requirements.

Zero Touch Networks and the Physical Layer

Telecom and datacom providers aiming to achieve market leadership must scale their operations while efficiently and dynamically allocating existing network resources. SDNs offer a pathway to accomplish this by decoupling switching hardware from software, thereby enabling the virtualization of network functions through a centralized controller unit. This centralized management and orchestration (MANO) layer can implement network functions that switches alone cannot handle, enabling intelligent and dynamic allocation of network resources. This enhanced flexibility and optimization yield improved network outcomes for operators.

Figure 2: Comparing a traditional network approach (left) with an SDN/NFC approach (right).

However, the forthcoming 5G networks will introduce a multitude of devices, software applications, and technologies. Managing these new devices and use cases necessitates self-managed, touchless automated networks. Realizing the full potential of network automation requires the flow of sensor and control data across all OSI model layers, including the physical layer.

As networks grow larger and more complex, MANO software necessitates greater degrees of freedom and adjustability. Next-generation MANO software must optimize both the physical and network layers to achieve the best network fit. Attaining this objective demands intelligent optical equipment and components that can be diagnosed and managed remotely from the MANO layer. This is where smart pluggable transceivers with reconfigurable DSPs come into play.

The Role of Forward Error Correction

Forward error correction (FEC) implemented by DSPs serves as a crucial component in coherent communication systems. FEC enhances the tolerance of coherent links to noise, enabling longer reach and higher capacity. Thanks to FEC, coherent links can handle bit error rates that are a million times higher than those of typical direct detect links. In simpler terms, FEC algorithms allow the DSP to enhance link performance without necessitating hardware changes. This enhancement can be compared to image processing algorithms improving the quality of images produced by phone cameras.

network link with FEC — Figure 3: Simplified diagram of a network link with forward error correction (FEC). The FEC encoder adds a series of redundant bits (called overhead) to the transmitted data stream. The receiver can use this overhead to check for errors without asking the transmitter to resend the data.

When coherent transmission technology emerged, all FEC algorithms were proprietary, guarded closely by equipment and component manufacturers due to their competitive advantage. Consequently, coherent transceivers from different vendors were incompatible, and network deployment required reliance on a single vendor.

However, as data center providers pushed for deeper disaggregation in communication networks, the need for interoperability in coherent transceivers became evident, leading to the standardization of FEC algorithms. The OIF 400ZR standard for data center interconnects adopted a public algorithm called concatenated FEC (CFEC). In contrast, some 400ZR+ MSA standards employ open FEC (oFEC), which provides greater reach at the expense of additional bandwidth and energy consumption. For the longest link lengths (500+ kilometers), proprietary FECs are still necessary for 400G transmission. Nevertheless, public FEC standards have achieved interoperability for a significant portion of the 400G transceiver market.

The Promise of the Smart Transceiver

The realization of a smart coherent pluggable capable of addressing various applications—data centers, carrier networks, SDNs—relies on an equally intelligent and adaptable DSP. The DSP must be reconfigurable through software to adapt to diverse network conditions and use cases.

For instance, a smart DSP could switch between different FEC algorithms to match network performance and use case requirements. Consider the scenario of upgrading a 650-kilometer-long metro link running at 100 Gbps with open FEC to achieve a capacity of 400 Gbps. Open FEC might struggle to deliver the required link performance. However, if the DSP can be reconfigured to employ a proprietary FEC standard, the transceiver would be capable of handling this upgraded link.

	400ZR	Open ZR+	Proprietary Long Haul
Target Application	Edge data center interconnect	Metro, Regional data center interconnect	Long-Haul Carrier
Target Reach @ 400G	120km	500km	1000 km
Form Factor	QSFP-DD/OSFP	QSFP-DD/OSFP	QSFP-DD/OSFP
FEC	CFEC	oFEC	Proprietary
Standards / MSA	OIF	OpenZR+ MSA	Proprietary

Table 1: Summary of different use cases and standards for a 400G pluggable transceiver.

Reconfigurable DSPs also prove beneficial in auto-configuring links to address specific network conditions, particularly in brownfield links. For example, if the link possesses high-quality fiber, the DSP could be reconfigured to transmit at a higher baud rate. Conversely, if the fiber quality is poor, the DSP could scale down the baud rate to mitigate bit errors. Furthermore, if the smart pluggable detects a relatively short fiber length, it could reduce laser transmitter power or DSP power consumption to conserve energy.

Takeaways

Smart coherent access pluggables would greatly simplify network upgrades. The DSP to match this pluggable should be able to use different error correction levels to handle different reach and future zero-touch network requirements.

The DSP can not only introduce software corrections but also effectuate optical hardware adjustments (output power, amplifier control) to adapt to different noise scenarios. Through these adaptations, the next generation of pluggable transceivers will proficiently handle the telecom carrier and data center use cases presented to them.

Tags: 100G ZR, adaptable, AI, artificial intelligence, automation, Coherent pluggables, cost, data layer, DSPs, FEC, forward error correction, network layer, Network management and orchestration, network requirements, OPEX, physical layer, programmability, Proprietary, reach, reconfigurable, remote, SDNs, Service Providers, smart, Smart pluggable transceivers, software defined, Software-Defined Networks, standardized, standards, telecommunication, versatile, virtualization

Insights

March 29, 2023

What DSPs Does the Cloud Edge Need?

By storing and processing data closer to the end user and reducing latency, smaller data…

By storing and processing data closer to the end user and reducing latency, smaller data centers on the network edge significantly impact how networks are designed and implemented. These benefits are causing the global market for edge data centers to explode, with PWC predicting that it will nearly triple from $4 billion in 2017 to $13.5 billion in 2024. Various trends are driving the rise of the edge cloud: 5G networks and the Internet of Things (IoT), augmented and virtual reality applications, network function virtualization, and content delivery networks.

Several of these applications require lower latencies than before, and centralized cloud computing cannot deliver those data packets quickly enough. As shown in Table 1, a data center on a town or suburb aggregation point could halve the latency compared to a centralized hyperscale data center. Enterprises with their own data center on-premises can reduce latencies by 12 to 30 times compared to hyperscale data centers.

Type of Edge		Datacenter	Location	Number of DCs per 10M people	Average Latency	Size
On-premises edge		Enterprise site	Businesses	NA	2-5 ms	1 rack max
Network (Mobile)	Tower edge	Tower	Nationwide	3000	10 ms	2 racks max
	Outer edge	Aggregation points	Town	150	30 ms	2-6 racks
	Inner edge	Core	Major city	10	40 ms	10+ racks
Regional edge		Regional	Major city	100	50 ms	100+ racks
Not edge		Hyperscale	State/national	1	60+ ms	5000+ racks

Table 1: Types of edge data centers and their characteristics. Source: STL Partners

This situation leads to hyperscale data center providers cooperating with telecom operators to install their servers in the existing carrier infrastructure. For example, Amazon Web Services (AWS) is implementing edge technology in carrier networks and company premises (e.g., AWS Wavelength, AWS Outposts). Google and Microsoft have strategies and products that are very similar. In this context, edge computing poses a few problems for telecom providers. They must manage hundreds or thousands of new nodes that will be hard to control and maintain.

These conditions mean that optical transceivers for these networks, and thus their digital signal processors (DSPs), must have flexible and low power consumption and smart features that allow them to adapt to different network conditions.

Using Adaptable Power Settings

Reducing power consumption in the cloud edge is not just about reducing the maximum power consumption of transceivers. Transceivers and DSPs must also be smart and decide whether to operate on low- or high-power mode depending on the optical link budget and fiber length. For example, if the transceiver must operate at its maximum capacity, a programmable interface can be controlled remotely to set the amplifiers at maximum power. However, if the operator uses the transceiver for just half of the maximum capacity, the transceiver can operate with lower power on the amplifiers. The transceiver uses energy more efficiently and sustainably by adapting to these circumstances.

Fiber monitoring is also an essential variable in this equation. A smart DSP could change its modulation scheme or lower the power of its semiconductor optical amplifier (SOA) if telemetry data indicates a good quality fiber. Conversely, if the fiber quality is poor, the transceiver can transmit with a more limited modulation scheme or higher power to reduce bit errors. If the smart pluggable detects that the fiber length is relatively short, the laser transmitter power or the DSP power consumption could be scaled down to save energy.

The Importance of a Co-Design Philosophy for DSPs

Transceiver developers often source their DSP, laser, and optical engine from different suppliers, so all these chips are designed separately. This setup reduces the time to market and simplifies the research and design processes, but it comes with performance and power consumption trade-offs.

In such cases, the DSP is like a Swiss army knife: a jack of all trades designed for different kinds of PIC but a master of none. Given the ever-increasing demand for capacity and the need for sustainability both as financial and social responsibility, transceiver developers increasingly need a steak knife rather than a Swiss army knife.

As we explained in a previous article about fit-for-platform DSPs, a transceiver optical engine designed on the indium phosphide platform could be designed to run at a voltage compatible with the DSP’s signal output. This way, the optimized DSP could drive the PIC directly without needing a separate analog driver, doing away with a significant power conversion overhead compared to a silicon photonics setup, as shown in the figure below.

Figure 1: Comparison between the drive power and power consumption of three different PIC + DSP pairings: standard DSP with a silicon PIC, standard DSP with an InP PIC, and an optimized DSP with an InP.

Scaling the Edge Cloud with Automation

With the rise of edge data centers, telecom providers must manage hundreds or thousands of new nodes that will take much work to control and maintain. Furthermore, providers also need a flexible network with pay-as-you-go scalability that can handle future capacity needs. Automation is vital to achieving such flexibility and scalability.

Automation potential improves further by combining artificial intelligence with the software-defined networks (SDNs) framework that virtualizes and centralizes network functions. This creates an automated and centralized management layer that can allocate resources efficiently and dynamically. For example, the AI network controller can take telemetry data from the whole network to decide where to route traffic and adjust power levels, reducing power consumption.

Figure 2: Comparing a traditional network approach (left) with an SDN/NFC approach (right).

In this context, smart digital signal processors (DSPs) and transceivers can give the AI controller more degrees of freedom to optimize the network. They could provide more telemetry to the AI controller so that it makes better decisions. The AI management layer can then remotely control programmable interfaces in the transceiver and DSP so that the optical links can adjust the varying network conditions. If you want to know more about these topics, you can read last week’s article about transceivers in the age of AI.

Takeaways

Cloud-native applications require edge data centers that handle increased traffic and lower network latency. However, their implementation came with the challenges of more data center interconnects and a massive increase in nodes to manage. Scaling edge data center networks will require greater automation and more flexible power management, and smarter DSPs and transceivers will be vital to enable these goals.

Co-design approaches can optimize the interfacing of the DSP with the optical engine, making the transceiver more power efficient. Further power consumption gains can also be achieved with smarter DSPs and transceivers that provide telemetry data to centralized AI controllers. These smart network components can then adjust their power output based on the decisions and instructions of the AI controller.

Tags: 5G, AI, artificial intelligence, Augmented Reality, automation, cloud edge, Co-Design Philosophy, data centers, Delivery Networks, DSPs, Edge Cloud, Fiber Monitoring, Hyperscale Data Center, Internet of Things, IoT, latency, Network Function Virtualization, optical transceivers, power consumption, Software-Defined Networks, Telecom Operators, Virtual Reality

Insights

March 22, 2023

Transceivers in the Age of AI

Artificial intelligence (AI) will have a significant role in making optical networks more scalable, affordable,…

Artificial intelligence (AI) will have a significant role in making optical networks more scalable, affordable, and sustainable. It can gather information from devices across the optical network to identify patterns and make decisions independently without human input. By synergizing with other technologies, such as network function virtualization (NFV), AI can become a centralized management and orchestration network layer. Such a setup can fully automate network provisioning, diagnostics, and management, as shown in the diagram below.

Figure 1: Example of a carrier network with different applications (mobile, home, office) automated via NFV control and AI management.

However, artificial intelligence and machine learning algorithms are data-hungry. To work optimally, they need information from all network layers and ever-faster data centers to process it quickly. Pluggable optical transceivers thus need to become smarter, relaying more information back to the AI central unit, and faster, enabling increased AI processing.

Faster Transceivers for the Age of AI

Optical transceivers are crucial in developing better AI systems by facilitating the rapid, reliable data transmission these systems need to do their jobs. High-speed, high-bandwidth connections are essential to interconnect data centers and supercomputers that host AI systems and allow them to analyze a massive volume of data.

In addition, optical transceivers are essential for facilitating the development of artificial intelligence-based edge computing, which entails relocating compute resources to the network’s periphery. This is essential for facilitating the quick processing of data from Internet-of-Things (IoT) devices like sensors and cameras, which helps minimize latency and increase reaction times.

400 Gbps links are becoming the standard across data center interconnects, but providers are already considering the next steps. LightCounting forecasts significant growth in the shipments of dense-wavelength division multiplexing (DWDM) ports with data rates of 600G, 800G, and beyond in the next five years. We discuss these solutions in greater detail in our article about the roadmap to 800G and beyond.

Figure 2: Shipments of high-speed DWDM ports by data rate (historical data and forecast). Source: LightCounting Optical Communications Market Forecast, April 2022.

Coherent Modules Need to Provide More Telemetry Data

Mobile networks now and in the future will consist of a massive number of devices, software applications, and technologies. Self-managed, zero-touch automated networks will be required to handle all these new devices and use cases. Realizing this full network automation requires two vital components.

Artificial intelligence and machine learning algorithms for comprehensive network automation: For instance, AI in network management can drastically cut the energy usage of future telecom networks.
Sensor and control data flow across all network model layers, including the physical layer: As networks grow in size and complexity, the management and orchestration (MANO) software needs more degrees of freedom and dials to turn.

These goals require smart optical equipment and components that provide comprehensive telemetry data about their status and the fiber they are connected to. The AI-controlled centralized management and orchestration layer can then use this data for remote management and diagnostics. We discuss this topic further in our previous article on remote provisioning, diagnostics, and management.

For example, a smart optical transceiver that fits this centralized AI-management model should relay data to the AI controller about fiber conditions. Such monitoring is not just limited to finding major faults or cuts in the fiber but also smaller degradations or delays in the fiber that stem from age, increased stress in the link due to increased traffic, and nonlinear optical effects. A transceiver that could relay all this data allows the AI controller to make better decisions about how to route traffic through the network.

A Smart Transceiver to Rule All Network Links

After relaying data to the AI management system, a smart pluggable transceiver must also switch parameters to adapt to different use cases and instructions given by the controller.

Let’s look at an example of forward error correction (FEC). FEC makes the coherent link much more tolerant to noise than a direct detect system and enables much longer reach and higher capacity. In other words, FEC algorithms allow the DSP to enhance the link performance without changing the hardware. This enhancement is analogous to imaging cameras: image processing algorithms allow the lenses inside your phone camera to produce a higher-quality image.

Figure 1 Simplified diagram of a network link with Forward Error Correction (FEC). — Figure 3: Simplified diagram of a network link with forward error correction (FEC). The FEC encoder adds redundant bits (overhead) to the transmitted data stream. The receiver can use this overhead to check for errors without asking the transmitter to resend the data.

A smart transceiver and DSP could switch among different FEC algorithms to adapt to network performance and use cases. Let’s look at the case of upgrading a long metro link of 650km running at 100 Gbps with open FEC. The operator needs to increase that link capacity to 400 Gbps, but open FEC could struggle to provide the necessary link performance. However, if the transceiver can be remotely reconfigured to use a proprietary FEC standard, the transceiver will be able to handle this upgraded link.

Reconfigurable transceivers can also be beneficial to auto-configure links to deal with specific network conditions, especially in brownfield links. Let’s return to the fiber monitoring subject we discussed in the previous section. A transceiver can change its modulation scheme or lower the power of its semiconductor optical amplifier (SOA) if telemetry data indicates a good quality fiber. Conversely, if the fiber quality is poor, the transceiver can transmit with a more limited modulation scheme or higher power to reduce bit errors. If the smart pluggable detects that the fiber length is relatively short, the laser transmitter power or the DSP power consumption could be scaled down to save energy.

Takeaways

Optical networks will need artificial intelligence and machine learning to scale more efficiently and affordably to handle the increased traffic and connected devices. Conversely, AI systems will also need faster pluggables than before to acquire data and make decisions more quickly. Pluggables that fit this new AI era must be fast, smart, and adapt to multiple use cases and conditions. They will need to scale up to speeds beyond 400G and relay monitoring data back to the AI management layer in the central office. The AI management layer can then program transceiver interfaces from this telemetry data to change parameters and optimize the network.

Tags: 800G, 800G and beyond, adaptation, affordable, AI, artificial intelligence, automation, CloudComputing, data, DataCenter, EFFECT Photonics, FEC, fiber quality, innovation, integration, laser arrays, machine learning, network conditions, network optimization, Networking, optical transceivers, photonic integration, Photonics, physical layer, programmable interface, scalable, sensor data flow, technology, Telecommunications, telemetry data, terabyte, upgrade, virtualization

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Insights

May 25, 2022

Improving Edge Computing with Coherent Optical Systems on Chip

Smaller data centers placed locally have the potential to minimize latency, overcome inconsistent connections, and…

Smaller data centers placed locally have the potential to minimize latency, overcome inconsistent connections, and store and compute data closer to the end-user. These benefits are causing the global market for edge data centers to explode, with PWC predicting that it will nearly triple from $4 billion in 2017 to $13.5 billion in 2024. Cloud-native applications are driving the construction of edge infrastructure and services. However, they cannot distribute their processing capabilities without considerable investments in real estate, infrastructure deployment, and management.

This situation leads to hyperscalers cooperating with telecom operators to install their servers in the existing carrier infrastructure. For example, Amazon Web Services (AWS) is implementing edge technology in carrier networks and company premises (e.g., AWS Wavelength, AWS Outposts). Google and Microsoft have strategies and products that are very similar. In this context, edge computing poses a few problems for telecom providers too. They must manage hundreds or thousands of new nodes that will be hard to control and maintain.

At EFFECT Photonics, we believe that coherent pluggables with an optical System-on-Chip (SoC) can become vital in addressing these datacom and telecom sector needs and enabling a new generation of distributed data center architectures. Combining the optical SoCs with reconfigurable DSPs and modern network orchestration and automation software will be a key to deploying edge data centers.

Edge data centers are a performance and sustainability imperative

Various trends are driving the rise of the edge cloud:

5G technology and the Internet of Things (IoT): These mobile networks and sensor networks need low-cost computing resources closer to the user to reduce latency and better manage the higher density of connections and data.
Content delivery networks (CDNs): The popularity of CDN services continues to grow, and most web traffic today is served through CDNs, especially for major sites like Facebook, Netflix, and Amazon. By using content delivery servers that are more geographically distributed and closer to the edge and the end user, websites can reduce latency, load times, and bandwidth costs as well as increasing content availability and redundancy.
Software-defined networks (SDN) and Network function virtualization (NFV). The increased use of SDNs and NFV requires more cloud software processing.
Augment and virtual reality applications (AR/VR): Edge data centers can reduce the streaming latency and improve the performance of AR/VR applications.

Several of these applications require lower latencies than before, and centralized cloud computing cannot deliver those data packets quickly enough. As shown in Table 1, a data center on a town or suburb aggregation point could halve the latency compared to a centralized hyperscale data center. Enterprises with their own data center on-premises can reduce latencies by 12 to 30 times compared to hyperscale data centers.

Type of Edge		Data center	Location	Number of DCs per 10M people	Average Latency	Size
On-premises edge		Enterprise site	Businesses	NA	2-5 ms	1 rack max
Network (Mobile)	Tower edge	Tower	Nationwide	3000	10 ms	2 rack max
	Outer edge	Aggrega- tion points	Town	150	30 ms	2-6 rack max
	Inner edge	Core	Major city	10	40 ms	10+ rack max
Regional edge		Regional	Major city	100	50 ms	100+ racks
Not edge		Hyperscale	State/ national	1	60+ ms	5000+ racks

Table 1: Types of edge data centers and their characteristics. Source: STL Partners

Cisco estimates that 85 zettabytes of useful raw data were created in 2021, but only 21 zettabytes were stored and processed in data centers. Edge data centers can help close this gap. For example, industries or cities can use edge data centers to aggregate all the data from their sensors. Instead of sending all this raw sensor data to the core cloud, the edge cloud can process it locally and turn it into a handful of performance indicators. The edge cloud can then relay these indicators to the core, which requires a much lower bandwidth than sending the raw data.

Figure 1 Global data center traffic vs. useable data created per year. Graphic sourced from Cisco Global Cloud Index.

Edge data centers therefore allow more sensor data to be aggregated and processed to make systems worldwide smarter and more efficient. The ultimate goal is to create entire “smart cities” that use this sensor data to benefit their inhabitants, businesses, and the environment. Everything from transport networks to water supply and lightning could be improved if we have more sensor data available in the cloud to optimize these processes. Distributing data centers is also vital for future data center architectures. While centralizing processing in hyper-scale data centers made them more energy-efficient, the power grid often limits the potential location of new hyperscale data centers. Thus, the industry may have to take a few steps back and decentralize data processing capacity to cope with the strain of data center clusters on power grids. For example, data centers can be relocated to areas where spare power capacity is available, preferably from nearby renewable energy sources. EFFECT Photonics envisions a system of datacentres with branches in different geographical areas, where data storage and processing are assigned based on local and temporal availability of renewable (wind-, solar-) energy and total energy demand in the area.

Figure 2: High-speed fiber-optic connections allow data processing and storage to move to locations where excess (green) energy is available. If power is needed for other purposes, such as charging electric vehicles, data can be moved elsewhere

Coherent technology simplifies the scaling of edge data center interconnects

As edge data center interconnects became more common, the issue of how to interconnect them became more prominent. Direct detect technology had been the standard in the short-reach data center interconnects. However, reaching the distances greater than 50km and bandwidths over 100Gbps required for modern edge data center interconnects required external amplifiers and dispersion compensators that increased the complexity of network operations. At the same time, advances in electronic and photonic integration allowed longer reach coherent technology to be miniaturized into QSFP-DD and OSFP form factors. This progress allowed the Optical Internetworking Forum (OIF) to create the 400ZR and ZR+ standards for 400G DWDM pluggable modules. With small enough modules to pack a router faceplate densely, the datacom sector could profit from a 400ZR solution for high-capacity data center interconnects of up to 80km. If needed, extended reach 400ZR+ pluggables can cover several hundreds of kilometers. Cignal AI forecasts that 400ZR shipments will dominate in the edge applications, as shown in Figure 3.

Figure 3: Forecast of 100G port equivalents shipped for edge applications. These shipments are overwhelmingly 400ZR standard technology. Source: Cignal AI Transport Applications Report Q42021.

Further improvements in integration can further boost the reach and efficiency of coherent transceivers. For example, by integrating all photonic functions on a single chip, including lasers and optical amplifiers, EFFECT Photonics’ optical System-On-Chip (SoC) technology can achieve higher transmit power levels and longer distances while keeping the smaller QSFP-DD form factor, power consumption, and cost.

Maximizing Edge Computing with Automation

With the rise of edge data centers, telecom providers must manage hundreds or thousands of new nodes that will be hard to control and maintain. Furthermore, providers also need a flexible network with pay-as-you-go scalability that can handle future capacity needs. Fortunately, several new technologies are enabling this scalable and automated network management.

First of all, the rise of self-tuning algorithms has made the installation of new pluggables easier than ever. They eliminate additional installation tasks such as manual tuning and record verification. They are host-agnostic, can plug into any third-party host equipment, and scale as you grow. Standardization also allows modules from different vendors to communicate with each other, avoiding compatibility issues and simplifying upgrade choices. The communication channels used for self-tuning algorithms can also be used for remote diagnostics and management, such as the case of EFFECT Photonics NarroWave technology.

Automation potential improves further by combining artificial intelligence with the software-defined networks (SDNs) framework that virtualizes and centralizes network functions. This creates an automated and centralized management layer that can allocate resources efficiently and dynamically. For example, AI in network management will become a significant factor in reducing the energy consumption of future telecom networks.

Future smart transceivers with reconfigurable digital signal processors (DSPs) can give the AI-controlled management layer even more degrees of freedom to optimize the network. These smart transceivers will relay more device information for diagnosis, and depending on the management layer instructions, they can change their coding schemes to adapt to different network requirements

Takeaways

Cloud-native applications require edge data centers with lower latency, and that better fit the existing power grid. However, their implementation came with the challenges of more data center interconnects and a massive increase in nodes to manage. Fortunately, coherent pluggables with self-tuning can play a vital role in addressing these datacom and telecom sector challenges and enabling a new generation of distributed data center architectures. Combining these pluggables with modern network orchestration and automation software will boost the deployment of edge data centers. EFFECT Photonics believes that with these automation technologies (self-tuning, SDNs, AI), we can reach the goal of a self-managed, zero-touch automated network that can handle the massive scale-up required for 5G networks and edge computing.

Tags: 400ZR, artificial intelligence, cloud, coherent, computing, data centers, DSP, edge, edge data centers, infrastructure, latency, network, network edge, operators, optical system-on-chip, pluggables, self-tuning, services

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