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Details of Grant 

EPSRC Reference: EP/T026081/1
Title: OptoCloud: Ultra-fast optically interconnected heterogeneous Data Centers
Principal Investigator: Zervas, Professor G
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Advanced Micro Devices Inc (AMD) Columbia University Finisar
Microsoft National Technical University of Athens Sumitomo Electric Industries, Ltd.
Department: Electronic and Electrical Engineering
Organisation: UCL
Scheme: EPSRC Fellowship
Starts: 01 March 2021 Ends: 28 February 2026 Value (£): 1,120,128
EPSRC Research Topic Classifications:
Networks & Distributed Systems Optical Communications
Optoelect. Devices & Circuits
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
15 Jan 2020 EPSRC ICT Prioritisation Panel January 2020 Announced
25 Feb 2020 ICT Fellowship Interviews 25 February 2020 Announced
Summary on Grant Application Form
The majority of human activities, including transport, Internet, banking, public health and entertainment, depend on Data Centers. Cloud traffic is forecasted to grow exponentially and account for 95% of global traffic. In 2015, the total power consumption of data centers worldwide was higher than the national power consumption of the UK and is predicted to increase up to 15-times by 2030.

Currently, all data center networks are formed based on hierarchical electronic packet switched networks; however, they can't keep up with demand creating a ever increasing gap between data growth and Moore's Law. So, while compute node power, measured in flop/s, has increased by 65 times in the last 18 years, the node communication bandwidth has only increased by 4.8 times and the bytes communicated per flop have decreased 8 times. This creates a computation to communication wall, minimizing data movement and constraining applications to operate locally. In addition, these systems also suffer from very high median latencies, O(100microseconds) (order of 100microseconds), and 99.9-percentile tail latencies, O(100ms), to the detriment of the system and application performance.

The OptoCloud fellowship aims to design and build an energy efficient, cost effective, scalable, single hop, and nanosecond speed optical circuit switched network. This will interconnect heterogeneous systems made of servers, CPUs, accelerators, neuromorphic processors, memory elements, storage to support different parts (rack, end-of-row) and sizes of data centers (small-medium size ~10-100,000 to ~1,000,000 server farm). Crucially, the network aims to offer zero data loss, without in-network a) buffering, b) active switching and routing, and c) network header addressing and processing to minimize complexity, and to consume very low power. Furthermore, the system also will inherently support 1-to-1, 1-to-N, N-to-N and N-to-1 connectivity in a synchronous manner without the need for data replication for multi/broad -casting, currently not possible. This is key to support diverse workloads such as storage caching, large-scale database lookups, training distributed deep neural networks, parallel computing that use communication primitives such as allreduce, broadcast and reduce, gather and scatter, all-to-all among others.

To achieve these, OptoCloud will explore the fundamental challenges of sub-nanosecond optical switching, near receiver-less low-power transceivers and nanosecond scheduling able to reconfigure circuits and shape IT and network topologies every 10s-100s of nanoseconds. It aims to offer orders of magnitude improvement in a) switching, b) scheduling and network topology re-configuration, c) power consumption, d) medium and tail latency and finally e) throughput with zero data loss.

The PI will work with the PDRAs, PhD students, industrial partners (Microsoft, Finisar, Xilinx, Sumitomo Electric), as well as universities (Columbia and National Technical University of Athens) and form a unique compute and optical network ecosystem to methodologically answer fundamental questions while reflecting all necessary requirements on the proposed concepts, and rigorously evaluating developed technologies using industrial driven use case scenarios.

Key Findings
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