Disaggregated Data Centers: Challenges and Tradeoffs Disaggregated data centers (DCs) can offer high flexibility for resource allocation; hence, their resource utilization can be significantly improved. However, communications between different types of resources, in particular between CPU and memory, in fully disaggregated DCs face severe problems in terms of stringent requirements for ultralow latency and ultrahigh transmission bandwidth. Optical fiber communication is promising to provide high capacity and low latency, but it is still challenging for the stateoftheart optical technologies to meet the requirements of fully disaggregated DCs. In this article, different levels of resource disaggregation are investigated. For fully disaggregated DCs, two architectural options are presented, where optical interconnects are necessary for the CPUmemory communications. We review the stateoftheart optical transmission and switching technologies, and analyze pros and cons of their applicability in the disaggregated DCs. The results reveal that resource disaggregation does improve the resource utilization in DCs. However, the bandwidth provided by the stateoftheart technologies is not always sufficient for fully disaggregated DCs. It calls for further advances in optical communications to fully utilize the advantages of fully disaggregated DCs.

Machine Learning Assisted Optical Network Resource Scheduling in Data Center Networks Parallel computing allows us to process incredible amounts of data in a timely manner by distributing the workload across multiple nodes and executing computation simultaneously. However, the performance of this parallelism usually suffers from network bottleneck. In optical switching enabled data center networks (DCNs), to satisfy the complex and timevarying bandwidth demands from the parallel computing, it is critical to fully exploit the flexibility of optical networks and meanwhile reasonably schedule the optical resources. Considering that the traffic flows generated by different applications in DCNs usually exhibit different statistical or correlative features, it is promising to schedule the optical resources with the assistance of machine learning. In this paper, we introduce a framework called intelligent optical resources scheduling system, and discuss how this framework can assist resource scheduling based on machine learning approaches. We also present our recent simulation results to verify the performance of the framework.