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This paper addresses the resonance problem in a parallel-inverter-based grid-connected system. Harmonic interactions between inverters and the grid exhibit various number of complicated characteristics that threatens the system stability and power quality. In this paper, a method of harmonic resonance analysis is proposed to investigate the resonance problem in a multi-inverter grid-connected system based on resonance modal analysis (RMA) and participation factor (PF) calculations. Initially, an impedance-based analytical approach is employed and expanded to an electricity distribution network that is dominated by multiple inverters with LCL filters and proportional resonant controllers. Then, the RMA is introduced to investigate the resonant interactions among inverters and the grid. Specifically, harmonic resonance characteristics are addressed through transfer functions and the proposed method considering variation of inverter number, different inverter combinations, and critical system components. Finally, time domain simulations on the PSCAD/EMTDC platform are presented. The case study results validate the effectiveness of the proposed method.

Data analytics applications are well-suited for a cloud environment. In this paper we examine the problem of provisioning resources in a public cloud to execute data analytic workloads. The goal of our provisioning method is to determine the most cost-effective configuration for a given data analytic workload. Provisioning a workload in a public cloud environment faces several challenges: it is difficult to develop accurate performance prediction models using standard methods; the space of possible configurations is very large so exact solutions cannot be efficiently determined, and the mix and intensity of query classes in a workload vary dynamically over time. We provide a formulation of the provisioning problem and then define a framework to solve the problem. Our framework contains a cost model to predict the cost of executing a workload on a configuration and a method of selecting configurations. The cost model balances resource costs and penalties from SLAs. The specific resource demands and frequencies are accounted for by queueing network models of the Virtual Machines (VMs), which are used to predict performance. We evaluate our approach experimentally using sample data analytic workloads on Amazon EC2.

The promise of "infinite" resources given by the cloud computing paradigm has led to recent interest in exploiting clouds for large-scale data-intensive computing. Given this supposedly infinite resource set, we need a management function that regulates application workload on these resources. This doctoral research focuses on two aspects of workload management, namely scheduling and provisioning. We propose a novel framework for workload execution and resource provisioning, and associated models, algorithms, and protocols.