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Feature Articles: Disaggregated Computing Will Change the World

Vol. 19, No. 7, pp. 52–57, July 2021.

Disaggregated Computing, the Basis of IOWN

Akira Okada, Seiji Kihara, and Yoshikatsu Okazaki


To achieve IOWN (the Innovative Optical and Wireless Network), we need advanced computer systems that can efficiently process huge amounts of data compared with current capabilities. To meet this demand, NTT is studying an innovative computer architecture, called disaggregated computing, that makes maximum use of photonics-electronics convergence technology. This article describes the overall outline and basic concept of this new computer architecture.

Keywords: IOWN (Innovative Optical and Wireless Network), disaggregated computing, photonics-electronics convergence technology


1. Introduction

Almost everything is connected to networks, and a vast amount of data from them is drastically changing social, economic, and cultural activities. By interconnecting a huge amount of data, it is important to create new value that cannot be obtained only from individual data in such a smart society. To achieve this, not only a broadband network, which transfers data at unprecedented high speed, but also an information processing system with a high processing capacity that is beyond today’s technologies is required.

To achieve such a smart society, NTT has proposed the Innovative Optical and Wireless Network (IOWN) [1]. The IOWN Global Forum was founded in 2020 [2] to accelerate innovation of a new communication infrastructure to meet our future data and computing requirements through the development of technologies, frameworks, and specifications. IOWN is a broad vision that includes innovative networks and computing systems boosted by photonics technologies and services provided using them.

2. Computing power for IOWN

Figure 1 shows the conceptual diagram of IOWN. It consists of the following elements. The first is the All-Photonics Network (APN), which uses photonics-electronics convergence technology to provide significantly higher bandwidth and lower latency to the network. The second is Digital Twin Computing (DTC) for reproducing the real world in digital space on the basis of a large amount of sensing data from the real world. The third is Cognitive Foundation (CF) for integrated control from the transmission layer to application layer.

Fig. 1. Conceptual diagram of IOWN.

The APN, DTC, and CF require high processing power. In the IOWN era, the network functions implemented by dedicated network nodes, such as routers and mobile base stations, will be implemented as software. Therefore, the network nodes comprising the APN will be computers with high processing power such as high packet-processing rate and huge scheduling capacity. In DTC, computers must process a large amount of data collected from data sources, such as sensors and video cameras, with high definition and granularity. Therefore, they require much higher processing capacity than modern computer systems. CF needs to provide total control and management capabilities for the APN and DTC, such as proper control of computing resources and integrated control of computing resources and networks. To do this, it is necessary to aggregate and analyze a large number of network and computing requests and allocate appropriate bandwidth and wavelength as well as computing resources in a highly real-time manner. This requires quite high processing power that has not been available.

Achieving this high processing power with conventional computer architecture requires a large number of servers and consumes significant electric power. To make IOWN a reality, a computer with extremely high performance per unit power is indispensable to obtain the required high processing power without increasing the environmental load. NTT, through three laboratories, i.e., Software Innovation Center, Network Service Systems Laboratories, and Device Technology Laboratories, has begun developing a computer architecture called disaggregated computing, which uses photonics-electronics convergence technology to solve this problem.

3. Details of disaggregated computing

Figure 2 shows the concept of disaggregated computing. This new architecture combines a physical configuration (hardware architecture), logical configuration (software architecture), and control scheme to maximize the high-speed, low-power consumption, and low-loss characteristics of photonics technologies and achieve overwhelmingly high performance compared with current computers. This architecture is a paradigm shift from the conventional server-oriented architecture of connecting a closed “computer” in a box via a network to the architecture of directly connecting resources such as central processing units (CPUs) and memory via optical interconnects and treating them as a computer on a rack-scale by using high-speed and long-reach photonics technologies.

Fig. 2. Concept of disaggregated computing.

The most important key technology of this architecture is the photonics-electronics convergence technology for enabling large-capacity, long-distance transmission, and low power consumption, which is impossible with electric technology alone.

In the conventional server-oriented architecture shown in Fig. 2(a), when data needs to be exchanged between servers, data must be transmitted and received by an external network. As network protocols have evolved to have a deep-layered stack to provide service functions such as reachability and path and session management, the overhead of protocol stack increases to communicate between servers.

In disaggregate computing, shown in Fig. 2(b), however, CPUs, graphics processing units (GPUs), field-programmable gate array (FPGAs), and other computing resources are connected via an interconnect (Photonic Fabric) using photonics-electronics convergence technology. This technology eliminates protocol conversion for communication between resources, significantly reducing communication overhead, which is unavoidable in the current architecture. In contrast to the current configuration in which power control and adding hardware resources are done on a per-server basis, disaggregated computing enables power control and adding hardware resources to be done on a per-resource basis, making it possible to provide a more power-efficient and flexible computing environment.

The three key points of disaggregated computing are physical configuration, logical configuration, and function-placement control. The following describes each point in detail.

3.1 Point 1: Physical configuration

Figure 3 outlines the physical configuration of disaggregated computing. For high-speed electrical signals with speeds over 100 Gbit/s per lane, signal attenuation in the transmission path is a critical issue because of the physical principle that the higher the frequency of an electrical signal, the greater the attenuation in the transmission path. Such a high-speed signal exceeding 100 Gbit/s requires a high-power driver circuit and complex circuit that compensates for the signal waveforms degraded by attenuation. Both consume a huge amount of power. Even if these power-hungry circuits are used, only a few tens of centimeters can be transmitted. On the other hand, optical signals have the advantage of being able to transmit high-speed signals farther than electrical signals, with only 0.2 dB of power loss over 1 km of optical fiber transmission.

Fig. 3. Physical configuration of disaggregated computing.

In disaggregated computing, compact photonics-electronics converged devices with high density, wide band, and low power consumption are mounted next to the large-scale integrated circuit (LSI) to immediately convert electrical signals from the LSI into optical signals. Therefore, the distance of the electrical signals can be kept the shortest between the LSI and adjacent photonics-electronics converged devices. By shortening the transmission distance of the high-speed electrical interface from the LSI, the power consumption of the high-speed interface on the LSI can be significantly reduced. NTT Device Technology Laboratories is studying and developing photonics-electronics converged devices for this application, and the details are described in an article in this issue [4].

Figure 4 shows a mock-up of a disaggregated computer. Cards with accelerators, CPUs, and other devices are connected by a backplane with optical wiring. Each card has compact high-density photonics-electronics converged devices mounted next to the LSI. High-speed multi-channel optical signals from the devices are connected to the backplane via optical traces on the card and optical backplane connector.

Fig. 4. Mock-up of the disaggregated computer.

With such a configuration, it is possible to increase the scale of a computer from a box size to a rack-scale, exceeding the limit of the reach of the conventional electric signal. The processing capabilities of the computer can also be increased by adding cards on demand, providing a flexible and efficient system.

3.2 Point 2: Logical configuration

Figure 5 shows the logical configuration of disaggregated computing. Although a CPU has the advantage of executing all types of processing, its versatility makes it less power efficient than accelerators for specific workloads.

Fig. 5. Logical configuration of disaggregated computing.

Therefore, with disaggregated computing, we aim to reduce CPU dependency by using accelerators for specific workloads. Accelerators are generally more power-efficient than CPUs when executing specific processes.

Since a disaggregated computer using optical interconnect can be made scalable compared with a conventional server, more accelerators can be efficiently aggregated into a computer. This enables a heterogeneous accelerator pool with several different accelerators that can offload the workload on the CPU, improving power efficiency.

However, even in this case, if the CPU needs to intervene in data transfer between accelerators, the CPU load will increase and the effect of offloading processing to accelerators will be lost. Therefore, NTT Software Innovation Center is promoting the study of greatly reducing CPU load by memory-centric data transfer without CPU intervention. This accelerator pooling and memory-centric data transfer between accelerators would significantly reduce CPU processing and improve power efficiency, which is described in an article in this issue [5].

3.3 Point 3: Function-placement control

To take full advantage of disaggregated computing with physical and logical configurations, it is important to control the arrangement of the software to make the most of this architecture.

For this purpose, NTT Network Service Systems Laboratories has proposed power-aware dynamic allocation-control technology for optimum utilization of computing resources, such as CPU and various accelerators, on the basis of software characteristics. As shown in Fig. 6, the software is divided into small functions, and devices such as accelerators and CPUs used by each function are dynamically selected by the power monitor & controller to minimize power consumption. It also uses a normally off device to provide event-driven control that turns on only when necessary. Details are described in an article in this issue [6].

Fig. 6. Function-placement control of disaggregated computing.

4. Future plan

NTT laboratories are developing disaggregated computing on the basis of the innovative technologies described above. We will prototype and evaluate trial machines in combination with typical applications, such as image inference, and demonstrate disaggregated computing. In the long term, we will consider the introduction of photonics-electronics convergence technology into LSI chips, the latest research results on optical processing devices, and the development of more advanced architectures.


[1] NTT Technology Report for Smart World 2020,
[2] IOWN Global Forum,
[3] H. Nishi, T. Fujii, N. P. Diamantopoulos, K. Takeda, E. Kanno, T. Kakitsuka, T. Tsuchizawa, H. Fukuda, and S. Matsuo, “Integration of Eight-channel Directly Modulated Membrane-laser Array and SiN AWG Multiplexer on Si,” J. Light. Technol., Vol. 37, No. 2, pp. 266–273, 2019.
[4] T. Sakamoto, N. Sato, and T. Segawa, “Photonics-electronics Convergence Technologies for Disaggregated Computing,” NTT Technical Review, Vol. 19, No. 7, pp. 58–64, July 2021.
[5] T. Ishizaki and Y. Yamabe, “Memory-centric Architecture for Disaggregated Computers,” NTT Technical Review, Vol. 19, No. 7, pp. 65–69, July 2021.
[6] M. Kaneko, "Power-aware Dynamic Allocation-control Technology for Maximizing Power Efficiency in a Photonic Disaggregated Computer," NTT Technical Review, Vol. 19, No. 7, pp. 70–74, July 2021.
Akira Okada
Vice President, Head of NTT Device Technology Laboratories.
He received a B.S. and M.S. in physics in 1988 and 1990, and a Ph.D. in materials science in 1993 from Keio University. He joined NTT in 1993 and conducted research on polymer-based waveguide devices, full-mesh wavelength division multiplexing networks, optical packet switching, and optical modules for access networks. From October 1997 to October 1998, he was a visiting scholar at Stanford University, CA, USA. He is a member of the Institute of Electrical and Electronics Engineers (IEEE), the Institute of Electronics, Information and Communication Engineers (IEICE), and the Japan Society of Applied Physics (JSAP).
Seiji Kihara
Vice President and Head of NTT Software Innovation Center.
He received a B.S. and M.S. in information science from the Tokyo Institute of Technology in 1990 and 1992 and joined NTT in 1992. His current research interests include operating systems, computer networks, and open source software. He is a member of the Information Processing Society of Japan (IPSJ), IEICE, and the Association for Computing Machinery (ACM).
Yoshikatsu Okazaki
Vice President and Head of NTT Network Service Systems Laboratories.
He received a B.E. and M.E. in applied physics from the University of Tokyo in 1989 and 1991. Since he joined NTT in 1991, he has been engaged in research and development on telecom network systems and network management systems. He also worked on network design and planning at NTT WEST. He is currently working on managing development of future network system technologies toward IOWN as the vice president and head of NTT Network Service Systems Laboratories. From 2018 to 2019, he chaired the Technical Committee on Network Systems, IEICE.