A Reference Architecture for Datacenter Scheduling: Design, Validation, and Experiments

Abstract

Datacenters act as cloud-infrastructure to stakeholders across industry, government, and academia. To meet growing demand yet operate efficiently, datacenter operators employ increasingly more sophisticated scheduling systems, mechanisms, and policies. Although many scheduling techniques already exist, relatively little research has gone into the abstraction of the scheduling process itself, hampering design, tuning, and comparison of existing techniques. In this work, we propose a reference architecture for datacenter schedulers. The architecture follows five design principles: components with clearly distinct responsibilities, grouping of related components where possible, separation of mechanism from policy, scheduling as complex workflow, and hierarchical multi-scheduler structure. To demonstrate the validity of the reference architecture, we map to it stateof-the-art datacenter schedulers. We find scheduler-stages are commonly underspecified in peer-reviewed publications. Through trace-based simulation and real-world experiments, we show underspecification of scheduler-stages can lead to significant variations in performance.

Capelin: Data-Driven Compute Capacity Procurement for Cloud Datacenters Using Portfolios of Scenarios

“…Live Platform Management (RM&S in Figure 2): We model a workload and resource manager that performs management and control of all clusters and hosts, and is re-sponsible for the lifecycle of submitted VMs, including their placement onto the available resources [3]. The resource manager is configurable and supports various allocation policies, defining the distribution of workloads over resources.…”

“…Without a detailed understanding of the implications of various decisions, the final decision is taken by committee, and it is typically an overprovisioned and conservative approach ( 4 ). In contrast, Capelin adds and semi-automates a data-driven approach to data analysis and decision support ( 2 ), and enables capacity planners to take fine-grained decisions based on curated and greatly reduced data ( 3 ). With such support, even a single capacity planner can make a tailored, fine-grained decision on topology changes to the cloud datacenter ( 4 ).…”

A Holistic Analysis of Datacenter Operations: Resource Usage, Energy, and Workload Characterization -- Extended Technical Report

“…Further, we show the average server power usage normalized to 5,500 Watts which is the maximum the cooling system can handle per rack. The server temperature is normalized to the minimum of the maximum allowed temperatures for the different CPU models, which is the Intel® Xeon® Silver 4110 Processor having a limit of 77 degrees Celsius 3 .…”

“…Datacenter operations: Several articles provide a holistic view of datacenter operations, including job allocation [3], cloud services [37], physical network [32], etc. Different from related work, our article provides a view of the effect of the workload on machine metrics.…”

Intelligent Scheduling of Bag-of-Tasks Applications in the Cloud

“…This inadvertently leads to performance unpredictability. Andreadis et al [2] when proposing a reference architecture for datacenter scheduling pointed out that because of the complex nature of the environment in a datacenter, comparing scheduling heuristics and improving performance is challenging.…”