Ah-Q: Quantifying and Handling the Interference within a Datacenter from a System Perspective

Interference among applications frequently occurs in a datacenter and significantly influences the cost-efficiency and the user experience. However, it is challenging for us to quantify the exact intensity of the interference that occurred in the overall system of a datacenter, because there are many concurrent applications in a datacenter, and their type can be either latency-critical (LC) and best-effort (BE). To address this issue, we present the Ah-Q which includes a theory and a strategy.

First, we propose the "system entropy" (E S ) theory to holistically and analytically quantify the interference in a datacenter to address this vital issue. The interference is caused by the scarcity of resources or/and the irrationality of scheduling. As more appropriate scheduling can compensate for resource scarcity, we derive the concept of "resource equivalence" to quantify the effectiveness of a resource scheduling strategy. We evaluate different resource scheduling strategies to validate the correctness and effectiveness of the proposed theory.

Moreover, using the theory to eliminate interference, we propose a new resource scheduling strategy; i.e., ARQ, which dynamically allocates the isolated resources and the shared resources to simultaneously harvest the benefits of isolation and sharing. Our results show that compared to the state-of-theart strategies (PARTIES and CLITE), ARQ is more effective to reduce the tail latency of the LC applications and to increase the IPC of the BE applications. Compared with PARTIES and CLITE, ARQ increases the yield (the ratio of satisfactory LC applications) by 25% and 20%, respectively; when the load is low, ARQ increases IPC of BE applications by 63.8% and 37.1%, respectively; ARQ reduces E S by 36.4% and 33.3%, respectively. The effectiveness of ARQ has saved resources significantly to achieve the same satisfactory overall user experience.