Communication and cooling aware job allocation in data centers for communication-intensive workloads

• Published in: Journal of parallel and distributed computing 2016-10, Vol.96, p.181-193
• Main Authors: Meng, Jie, Llamosí, Eduard, Kaplan, Fulya, Zhang, Chulian, Sheng, Jiayi, Herbordt, Martin, Schirner, Gunar, Coskun, Ayse K.
• Format: Article
• Language: English
• Subjects: Allocations; Communication Pattern; Cooling; Cooling management; Data centers; Energy consumption; High-performance computing; Job allocation; Optimization; Policies; Task mapping; Tasks; Workload
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2016.05.016

Energy consumption is an increasingly important concern in data centers. Today, nearly half of the energy in data centers is consumed by the cooling infrastructure. Existing policies on thermally-aware workload allocation do not consider applications that include many tasks (or threads) running on a large set of nodes with significant communication among the tasks. Such jobs, however, constitute most of the cycles in high performance computing (HPC) domain, and have started to appear in other data centers as well. Job allocation strongly affects the performance of such communication-intensive applications. Communication-aware job allocation methods exist, but they focus solely on performance and do not consider cooling energy. This paper proposes a novel job allocation methodology to jointly minimize communication cost and cooling energy consumption in data centers. We formulate and solve the joint optimization problem using binary quadratic programming. Our joint optimization algorithm reduces cooling energy by 16.4% on average with only a 2.66% average increase in application running time compared to solely performance-aware allocations. To further optimize the communication cost, we develop a Charm++ based framework that extracts the communication behavior of applications. We then integrate our job allocation policy with recursive coordinate bisection (RCB) based task mapping method to place highly-communicating tasks in close proximity. Experimental results show that task mapping further decreases the communication cost by up to 20.9% compared to assuming all-to-all communication, a popular assumption in much of the prior work. •We jointly optimize the cooling and communication costs via job allocation.•Our joint allocation strategy saves 16.4% cooling energy on average.•We design a framework to extract the communication patterns of HPC applications.•Combining joint allocation with task mapping reduces communication costs by 20.9%.