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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.

In a multicore processor system, running multiple applications on different cores in the same chip could cause resource contention, which leads to performance degradation. Recent studies have shown that job co-scheduling can effectively reduce the contention. However, most existing co-schedulers do not aim to find the optimal co-scheduling solution. It is very useful to know the optimal co-scheduling performance so that the system and scheduler designers can know how much room there is for further performance improvement. Moreover, most co-schedulers only consider serial jobs, and do not take parallel jobs into account. This paper aims to tackle the above issues. In this paper, we first present a new approach to modelling the problem of co-scheduling both parallel and serial jobs. Further, a method is developed to find the optimal co-scheduling solutions. The simulation results show that compare to the method that only considers serial jobs, our developed method to co-schedule parallel jobs can improve the performance by 31% on average.

On-chip cache is often shared between processes that run concurrently on different cores of the same processor. Resource contention of this type causes the performance degradation to the co-running processes. Contention-aware co-scheduling refers to the class of scheduling techniques to reduce the performance degradation. Most existing contention-aware co-schedulers only consider serial jobs. However, there often exist both parallel and serial jobs in computing systems. This thesis aims to tackle these issues. We start with modelling the problem of co-scheduling the mix of serial and parallel jobs as an Integer Programming (IP) problem. Then we construct a co-scheduling graph to model the problem, and a set of algorithms are developed to find both optimal and near-optimal solutions. The results show that the proposed algorithms can find the optimal co-scheduling solution and that the proposed approximation technique is able to find the near optimal solutions. In order to improve the scalability of the algorithms, we use GPU to accelerate the solving process. A graph processing framework, called WolfPath, is proposed in this thesis. By taking advantage of the co-scheduling graph, WolfPath achieves significant performance improvement. Due to the long preprocessing time of WolfPath, we developed WolfGraph, a GPU-based graph processing framework that features minimal preprocessing time and uses the hard disk as a memory extension to solve large-scale graphs on a single machine equipped with a GPU device. Comparing with existing GPU-based graph processing frameworks, WolfGraph can achieve similar execution time but with minimal preprocessing time.