Exploring the Design Space for Optimizations with Apache Aurora and Mesos

Citations Over TimeTop 10% of 2016 papers

Abstract

Cloud infrastructures increasingly include a heterogeneous mix of components in terms of performance, power, and energy usage. As the size of cloud infrastructures grows, power consumption becomes a significant constraint. We use Apache Mesos and Apache Aurora, which provide massive scalability to Web-scale applications, to demonstrate how a policy driven approach involving bin-packing workloads according to their power profiles, instead of the default allocation by Mesos and Aurora, can effectively reduce the peak-power and energy usage as well as the node utilization, when workloads are co-scheduled. Our experimental results show reductions of 11% in peak power, 86% for total energy usage, and an increase in utilization of 148% for memory and 8% CPU for the different policies.

Related Papers

• → Two-Dimensional Finite Bin-Packing Algorithms(1987)342 cited
• → Identify Patterns in Online Bin Packing Problem: An Adaptive Pattern-Based Algorithm(2022)10 cited
• → A new algorithm for the Bin-packing problem with fragile objects(2016)4 cited
• → A Hybrid PSO-LS approach for solving the Two-Dimensional Bin Packing Problem with weight capacities constraint(2019)2 cited
• → Dynamic dual bin packing using fuzzy objectives(2002)5 cited