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Data-Driven Resource Shaping for Compute Clusters

1 CONTEXT AND PROBLEM STATEMENT Data-center efficiency is a subject that attracted a vast amount of research, which fueled the proliferation of a wide array of resource management tools aiming at an efficient operating point, where cluster resources are fully utilized. However, despite such efforts, data-center resources go often under utilized, as shown in recent traces from large-scale production clusters. In most system implementations, the concept of reservation and allocation coincide, although neither is representative of the true resource utilization a request might induce on the system. Thus, reservation requests are engineered to cope with peak resource demands of an application, which induces poor system utilization. In this work, we study the problem of cluster efficiency by reducing the resource slack – defined as the difference between resource allocation and utilization – induced by reservation-centric application schedulers. We introduce a new mechanism that predicts the resource utilization and dynamically adjusts the resource allocation accordingly.