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、以服务器为中心的传统模式，转向更灵活的"解耦"数据中心模式。此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，并选择既能优化当前工作负载请求、又能兼顾未来请求的资源。鉴于解耦数据中心受限于可用网络资源这一严重瓶颈，本文提出一种基于启发式的方法RISA，该算法显著降低了解耦数据中心中工作负载分配的网络开销。与现有最优方案相比，RISA使光学组件的功耗降低33%，平均CPU-RAM往返延迟降低50%。此外，RISA在执行时间方面的表现也显著优于现有最优方案。