Abstract. Workload placement on servers has been traditionally driven by mainly performance objectives. In this work, we investigate the design, implementation, and evaluation of a power-aware application placement controller in the context of an environment with heterogeneous virtualized server clusters. The placement component of the application management middleware takes into account the power and migration costs in addition to the performance benefit while placing the application containers on the physical servers. The contribution of this work is two-fold: first, we present multiple ways to capture the cost-aware application placement problem that may be applied to various settings. For each formulation, we provide details on the kind of information required to solve the problems, the model assumptions, and the practicality of the assumptions on real servers. In the second part of our study, we present the pMapper architecture and placement algorithms to solve one practical formulation of the problem: minimizing power subject to a fixed performance requirement. We present comprehensive theoretical and experimental evidence to establish the efficacy of pMapper.

High Performance Computing applications and platforms have been typically designed without regard to power consumption. With increased awareness of energy cost, power management is now an issue even for compute-intensive server clusters. In this work, we investigate the use of power management techniques for high performance applications on modern power-efficient servers with virtualization support. We consider power management techniques such as dynamic consolidation and usage of dynamic power range enabled by low power states on servers.We identify application performance isolation and virtualization overhead with multiple virtual machines as the key bottlenecks for server consolidation. We perform a comprehensive experimental study to identify the scenarios where applications are isolated from each other. We also establish that the power consumed by HPC applications may be application dependent, non-linear and have a large dynamic range. We show that for HPC applications, working set size is a key parameter to take care of while placing applications on virtualized servers. We use the insights obtained from our experimental study to present a framework and methodology for power-aware application placement for HPC applications.

Renewed focus on virtualization technologies and increased awareness about management and power costs of running under-utilized servers has spurred interest in consolidating existing applications on fewer number of servers in the data center. The ability to migrate virtual machines dynamically between physical servers in real-time has also added a dynamic aspect to consolidation. However, there is a lack of planning tools that can analyze historical data collected from an existing environment and compute the potential benefits of server consolidation especially in the dynamic setting. In this paper we describe such a consolidation recommendation tool, called ReCon. Recon takes static and dynamic costs of given servers, the costs of VM migration, the historical resource consumption data from the existing environment and provides an optimal dynamic plan of VM to physical server mapping over time. We also present the results of applying the tool on historical data obtained from a large production environment.

Abstract. Key challenges in managing an I/T environment for e-business lie in the area of root cause analysis, proactive problem prediction, and automated problem remediation. Our approach as reported in this paper, utilizes two important concepts: dependency graphs and dynamic runtime performance characteristics of resources that comprise an I/T environment to design algorithms for rapid root cause identification in case of problems. In the event of a reported problem, our approach uses the dependency information and the behavior models to narrow down the root cause to a small set of resources that can be individually tested, thus facilitating quick remediation and thus leading to reduced administrative costs.

Power-aware dynamic application placement can address underutilization of servers as well as the rising energy costs in a data center.