Recent trends see a move away from a fixed-resource server-centric datacenter model to a more adaptable "disaggregated" datacenter model. These disaggregated datacenters can then dynamically group resources to the specific requirements of an incoming workload, thereby improving efficiency. To properly utilize these disaggregated datacenters, workload allocation techniques must examine the current state of the datacenter and choose resources that not only optimize the current workload request, but future ones. Since disaggregated datacenters are severely bottlenecked by the available network resources, our work proposes a heuristic-based approach called RISA, which significantly reduces the network usage of workload allocations in disaggregated datacenters. Compared to the state-of-the-art, RISA reduces the power consumption for optical components by 33% and reduces the average CPU-RAM round-trip latency by 50%. Additionally, RISA significantly outperforms the state-of-the-art in terms of execution time.

翻译：近期趋势显示，数据中心正从固定资源的服务器中心化模型转向更灵活的"解耦"模型。这类解耦数据中心能够根据传入工作负载的具体需求动态组合资源，从而提升效率。为充分利用解耦数据中心，工作负载分配技术需评估数据中心当前状态，并选择能同时优化当前及未来工作负载请求的资源。鉴于网络资源是解耦数据中心的主要瓶颈，本文提出一种基于启发式的RISA调度算法，该算法能显著降低解耦数据中心工作负载分配中的网络资源使用量。与现有最优方案相比，RISA将光学组件的功耗降低33%，并使CPU与内存间的平均往返延迟减少50%。此外，RISA在执行时间方面亦显著优于现有最优方案。