Team Primacy Concept-based employee evaluation and job performance

This qualitative case study explored how employees learn from Team Primacy Concept (TPC)-based employee evaluation and how they apply the knowledge in their job performance. Kolb's experiential learning model (1974) served as a conceptual framework for the study to reveal the process of how employees learn from TPC evaluation, namely, how they experience, reflect, conceptualize and act on performance feedback. TPC based evaluation is a form of multirater evaluation that consists of three components: self-feedback, supervisor's feedback, and peer feedback. The distinctive characteristic of TPC based evaluation is the team evaluation component during which the employee's professional performance is discussed by one's peers in a face-to-face team setting, while other forms of multirater evaluation are usually conducted in a confidential and anonymous manner.^ Case study formed the methodological framework. The case was the Southeastern Virginia (SEVA) region of the Institute for Family Centered Services, and the participants were eight employees of the SEVA region. Findings showed that the evaluation process was anxiety producing for employees, especially the process of peer evaluation in a team setting. Preparation was found to be an important phase of TPC evaluation. Overall, the positive feedback delivered in a team setting made team members feel acknowledged. The study participants felt that honesty in providing feedback and openness to hearing challenges were significant prerequisites to the TPC evaluation process. Further, in the planning phase, employees strove to develop goals for themselves that were meaningful. Also, the catalyst for feedback implementation appeared to stem from one's accountability to self and to the client or community. Generally, the participants identified a number of performance improvement goals that they attained during their employment with IFCS, which were supported by their developmental plans.^ In conclusion, the study identified the process by which employees learned from TPC-based employee evaluation and the ways in which they used the knowledge to improve their job performance. Specifically, the study examined how participants felt and what they thought about TPC-based feedback, in what ways they reflected and made meaning of the feedback, and how they used the feedback to improve their job performance.^

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.