Electron cloud buildup and impedance effects on beam dynamics in the future circular e+e− collider and experimental characterization of thin TiZrV vacuum chamber coatings

The Future Circular Collider FCC-ee is a study toward a high luminosity electron-positron collider with a centre-of-mass energy from 91 GeV to 365 GeV. Due to the beam parameters and pipe dimensions, collective effects and electron cloud can be very critical aspects for the machine and can represent the main limitations to its performance. An estimation of the electron cloud build up in the main machine components and an impedance model are required to analyze the induced instabilities and to find solutions for their mitigation. Special attention has been given to the resistive wall impedance associated with a layer of nonevaporable getter (NEG) coating on the vacuum chamber required for electron cloud mitigation. The studies presented in this paper will show that minimizing the thickness of this coating layer is mandatory to increase the single bunch instability thresholds in the proposed lepton collider at 45.6 GeV. For this reason, NEG thin films with thicknesses below 250 nm have been investigated by means of numerical simulations to minimize the resistive wall impedance. In parallel, an extensive measurement campaign was performed at CERN to characterize these thin films, with the purpose of finding the minimum effective thickness satisfying vacuum and electron cloud requirements

First detection and energy measurement of recoil ions following beta decay in a Penning trap with the WITCH experiment

The WITCH experiment (Weak Interaction Trap for CHarged particles) will search for exotic interactions by investigating the beta-neutrino angular correlation via the measurement of the recoil energy spectrum after beta decay. As a first step the recoil ions from the beta-minus decay of 124In stored in a Penning trap have been detected. The evidence for the detection of recoil ions is shown and the properties of the ion cloud that forms the radioactive source for the experiment in the Penning trap are presented.Comment: 9 pages, 6 figures (9 figure files), submitted to European Physical Journal

Energy-Aware Lease Scheduling in Virtualized Data Centers

Energy efficiency has become an important measurement of scheduling algorithms in virtualized data centers. One of the challenges of energy-efficient scheduling algorithms, however, is the trade-off between minimizing energy consumption and satisfying quality of service (e.g. performance, resource availability on time for reservation requests). We consider resource needs in the context of virtualized data centers of a private cloud system, which provides resource leases in terms of virtual machines (VMs) for user applications. In this paper, we propose heuristics for scheduling VMs that address the above challenge. On performance evaluation, simulated results have shown a significant reduction on total energy consumption of our proposed algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling algorithm with the same fulfillment of performance requirements. We also discuss the improvement of energy saving when additionally using migration policies to the above mentioned algorithms.Comment: 10 pages, 2 figures, Proceedings of the Fifth International Conference on High Performance Scientific Computing, March 5-9, 2012, Hanoi, Vietna

A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud

Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page