IOWN Technology Director, IOWN Development Office, R&D Planning Department, NTT
Masahisa Kawashima is currently leading NTT's R&D of Innovative Optical and Wireless Network (IOWN) as the IOWN Technology Director. He is also serving as the Chair of the Technology Working Group at IOWN Global Forum. He has been working as a bridge between technologies and businesses... Read More →
MUFG Bank and NTTDATA worked with the IOWN Global Forum to create use cases and Reference Implementation Models demonstrating how APN and Optical DC networks can transform financial systems. A white paper was published detailing these innovations and their applications. MUFG Bank, NTTDATA, and NTT West tested their ideas through pilot experiments, yielding two major outcomes: real-time database synchronization reduces reliance on complex backup frameworks, and virtual instances enable seamless Data Center transitions without downtime, enhancing efficiency. This allows financial institutions to meet evolving customer demands more effectively. Research is ongoing to integrate Optical Network technology with OCP hardware to address latency, bandwidth, and prioritization issues for NIC-to-NIC communication. By combining software, hardware, and networks, NTTDATA aims to create smarter, more resilient financial systems.
NTT is considering Data-Centric Infrastructure (DCI) using IOWN technologies. DCI processes data efficiently by combining geographically dispersed resources. To achieve this, we’re verifying Composable Disaggregated Infrastructure (CDI) – a flexible hardware solution – and considering a multi-vendor approach. CDI consists of servers, PCIe expansion boxes, and switches, enabling software-controlled allocation of accelerators for optimal performance. Utilizing multi-vendor CDI requires an interface like OFA Sunfish to reduce operational costs. Our verification has revealed challenges in the physical operation of CDI and implementing a multi-vendor configuration. These include increased cabling costs, racking limitations, and inconsistencies in product functionalities and procedures requiring careful configuration management. This session will share these challenges and proposed solutions.
The proposed Layer 2 transparent network, bridging VM and Container networks, is a software-defined network for AI services deployment. The cloud provider offers a tenant-aware and transparent combining VM and Container network into the same network domain. The benefits of this network are to provide the full Layer 2 network and to reduce communication overhead in the multi-tenant cloud system. The tenant can deploy its services in VMs and Containers. The communications among VMs and containers are in the same Layer 2 domain. It could reduce routing efforts and isolate the network traffic among different tenants.
Inference processing of large language models (LLMs) is computationally intensive, and efficient management and reuse of intermediate data, known as KV Cache, are crucial for performance improvement. In this presentation, we propose a novel architecture leveraging NTT's innovative photonics-based networking technology, "IOWN APN (All-Photonics Network)," to enable low-latency, high-bandwidth sharing of large-scale KV Cache among geographically distributed data centers. By exploiting the unique capabilities of IOWN APN, the proposed KV Cache sharing system significantly enhances inference throughput and improves power efficiency, paving the way for reduced environmental impact and more sustainable operational models for LLM inference. Through this presentation, we aim to engage with the OCP community to discuss the potential for wide-area distributed AI computing based on open standards.
The introduction of optical-circuit-switches (OCSs) has been considered as key to cost-effectively scale the AI interconnect infrastructure. However, current AI interconnect is realized by vendor-proprietary hardware and software solutions and we thus lack the interoperability and openness in this domain. This could lead to increase both capital and operational expenditure for GPU service providers. Recently, IOWN Global Forum started an activity on defining a reference implementation model for the AI interconnect infrastructure. Among several study items on that activity, this presentation introduces an open network controller framework for managing the AI interconnect with multi-vendor OCSs.
