Abstract
Consolidation of multiple applications on a single Physical Machine (PM) within a cloud data center can increase utilization, minimize energy consumption, and reduce operational costs. However, these benefits come at the cost of increasing the complexity of the scheduling problem.In this paper, we present a topology-aware resource management framework. As part of this framework, we introduce a Reconsolidating PlaceMent scheduler (RPM) that provides and maintains durable allocations with low maintenance costs for data centers with dynamic workloads. We focus on workloads featuring both short-lived batch jobs and latency-sensitive services such as interactive web applications. The scheduler assigns resources to Virtual Machines (VMs) and maintains packing efficiency while taking into account migration costs, topological constraints, and the risk of resource contention, as well as the variability of the background load and its complementarity to the new VM.We evaluate the model by simulating a data center with over 65,000 PMs, structured as a three-level multi-rooted tree topology. We investigate trade-offs between factors that affect the durability and operational cost of maintaining a near-optimal packing. The results show that the proposed scheduler can scale to the number of PMs in the simulation and maintain efficient utilization with low migration costs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.