Industrially Relevant Monomers: From Fundamental Kinetics to Application in Controlled Polymerization

Arrhenius parameters of the propagation rate coefficient, kp, of (meth)acrylates are determined (via PLP-SEC) in order to detect global structure-property relationships. Additional physicochemical data is employed in providing a plausible reasoning for the identified trends. The reported kp values are of highest importance for kinetic modeling also of RDRP processes such as ATRP or RAFT. Furthermore, MHKS parameters, temperature dependent densities, viscosities, Tg and dn/dc values are reported

Analysis of the Scope of Dynamic Power Management in Emerging Server Architectures

The architectures of large-scale Internet servers are becoming more complex each year in order to store and process a large amount of Internet data (Big Data) as efficiently as possible. One of the consequences of this continually growing complexity is that individual servers consume a significant amount of data even when they are idle. In this paper we experimentally investigate the scope and usefulness of existing and proposed dynamic power management strategies to manage power at core, socket, and server levels. Our experiment involves four dynamic voltage and frequency scaling policies, three different workloads having different resource consumption statistics, and the activation and deactivation of different sockets (packets) of a multicore, multi-socket server. Moreover, we establish a quantitative relationships between the workload (w) and the estimated power consumption (p) under different power management strategies to make a quantitative comparison of the different strategies and server configurations

Extending the Cutting Stock Problem for Consolidating Services with Stochastic Workloads

Data centres and similar server clusters consume a large amount of energy. However, not all consumed energy produces useful work. Servers consume a disproportional amount of energy when they are idle, underutilised, or overloaded. The effect of these conditions can be minimised by attempting to balance the demand for and the supply of resources through a careful prediction of future workloads and their efficient consolidation. In this paper we extend the cutting stock problem for consolidating workloads having stochastic characteristics. Hence, we employ the aggregate probability density function of co-located and simultaneously executing services to establish valid patterns. A valid pattern is one yielding an overall resource utilisation below a set threshold. We tested the scope and usefulness of our approach on a 16-core server with 29 different benchmarks. The workloads of these benchmarks have been generated based on the CPU utilisation traces of 100 real-world virtual machines which we obtained from a Google data centre hosting more than 32000 virtual machines. Altogether, we considered 600 different consolidation scenarios during our experiment. We compared the performance of our approach-system overload probability, job completion time, and energy consumption-with four existing/proposed scheduling strategies. In each category, our approach incurred a modest penalty with respect to the best performing approach in that category, but overall resulted in a remarkable performance clearly demonstrating its capacity to achieve the best trade-off between resource consumption and performance