Mining repeated patterns (often called motifs) in CPU utilization of computers (also called CPU host load) is of fundamental importance. Many recently emerging applications running on high performance computing systems rely on motif discovery for various purposes, including efficient task scheduling, energy saving, etc. In this paper, we propose an efficient motif discovery framework for CPU host load. The framework is elaborately designed to take into account the important properties in host load data. The framework benefits from its ability of on-line discovery and the adaptivity to work with massive data. The experiments are conducted in this paper and the experimental results show that the proposed method is effective and efficient. B Ligang He

Investigating the pattern of host load in computing systems is very useful for discovering the data features and predicting the host load in the future. Since the host load can be regarded as the time series data, this paper proposes a pattern discovery framework for host load data by applying time series analysis methods. In the proposed framework, the effective data representation, data segmentation and feature extraction methods are designed based on the characteristics of the host load data. The DBSCAN clustering algorithm is then adopted in the pattern discovery framework to find the patterns in the host load. The extensive experiments have been conducted in this paper to verify the effectiveness of the proposed framework.

Summary Cloud computing emerges as one of the most important technologies for interconnecting people and building the so‐called Internet of People (IoP). In such a cloud‐based IoP, the virtualization technique provides the key supporting environments for running the IoP jobs such as performing data analysis and mining personal information. Nowadays, energy consumption in such a system is a critical metric to measure the sustainability and eco‐friendliness of the system. This paper develops three power‐aware scheduling strategies in virtualized systems managed by Xen, which is a popular virtualization technique. These three strategies are the Least performance Loss Scheduling strategy, the No performance Loss Scheduling strategy, and the Best Frequency Match scheduling strategy. These power‐aware strategies are developed by identifying the limitation of Xen in scaling the CPU frequency and aim to reduce the energy waste without sacrificing the jobs running performance in the computing systems virtualized by Xen. Least performance Loss Scheduling works by re‐arranging the execution order of the virtual machines (VMs). No performance Loss Scheduling works by setting a proper initial CPU frequency for running the VMs. Best Frequency Match reduces energy waste and performance loss by allowing the VMs to jump the queue so that the VM that is put into execution best matches the current CPU frequency. Scheduling for both single core and multicore processors is considered in this paper. The evaluation experiments have been conducted, and the results show that compared with the original scheduling strategy in Xen, the developed power‐aware scheduling algorithm is able to reduce energy consumption without reducing the performance for the jobs running in Xen. Copyright © 2016 John Wiley & Sons, Ltd.