Type-aware task placement in geo-distributed data centers with low OPEX using data center resizing

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the type-aware task placement problem over geo-distributed data centers. With the consideration of the diversity of user requests and server clusters in modern data centers, we formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an efficient solution is designed for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the greedy algorithm and much approaches the optimal results. © 2014 IEEE.

Sensor placement and measurement of wind for water quality studies in urban reservoirs

We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.

Developing a collaborative model of industry feedback for work placement of business students

Work-integrated learning (WIL) is a signature feature of study in many higher education institutions. In business degrees, industry feedback is recognized as an integral part of the assessment of WIL, yet the role played by industry in appraising student performance in the workplace has not been clearly defined. Based on interviews with industry supervisors and academic mentors, this paper addresses the integration of academic and industry supervisor assessment practices designed to maximize student learning outcomes and capture the depth of the learning experiences during a work placement. A model of industry feedback was developed to incorporate planned assessment practices that achieve the learning outcomes agreed to at the start of the placement by all stakeholders: the student, the academic mentor and the industry supervisor.

Supporting culturally and linguistically diverse students during clinical placement: strategies from both sides of the table

Background: Increasing proportions of Culturally and Linguistically Diverse (CALD) students within health professional courses at universities creates challenges in delivering inclusive training and education. Clinical placements are a core component of most health care degrees as they allow for applied learning opportunities. A research gap has been identified in regard to understanding challenges and strategies for CALD students in health professional placements.

Methods: A key stakeholder approach was used to examine barriers and enablers experienced by CALD students in clinical placement. Semi-structured focus groups with healthcare students (n = 13) and clinical placement supervisors (n = 12) were employed. The focus groups were analysed using open coding and thematic analysis.

Results: Three main barrier areas were identified: placement planning and preparation; teaching, assessment and feedback; and cultural and language issues. Potential solutions included addressing placement planning and preparation barriers, appropriate student placement preparation, pre-placement identification of higher risk CALD students, and diversity training for supervisors. For the barrier of teaching, assessment & feedback, addressing strategies were to: adapt student caseloads, encourage regular casual supervisor-student conversations, develop supportive placement delivery modes and structures, set expectations early, model the constructive feedback process, use visual aids, and tailor the learning environment to individual student needs. The enablers for cultural & language issues were to: build language and practical approaches for communication, raise awareness of the healthcare system (how it interacts with healthcare professions and how patients access it), and initiate mentoring programs.

Conclusions: The findings suggest that teaching and learning strategies should be student-centred, aiming to promote awareness of difference and its impacts then develop appropriate responses by both student and teacher. Universities and partnering agencies, such as clinical training providers, need to provide an inclusive learning environment for students from multiple cultural backgrounds.

Exploring the impact of clinical placement models on undergraduate midwifery students

BACKGROUND: Learning undertaken through clinical placements provides up to 50% of the educational experience for students in pre-registration midwifery courses. However little is known about of the impact various models of clinical placement have on the learning experiences of undergraduate midwifery students. Two clinical placement models have been employed for undergraduate midwifery students at Monash University, including the block placement model and the continuous two days per week model. OBJECTIVE: This project sought to explore the learning experiences of students in these two models of placement. METHOD: Focus groups were held on two campuses with a total of 17 students from different cohorts and programs. DISCUSSION: No one type of placement was favoured over another both had benefits and disadvantages. Further, this study found that regardless of program and clinical placement model the major learning impact for students was related to the midwife they worked with each day on placement rather than to the model. CONCLUSION: No one type of placement was favoured over another both had benefits and disadvantages. Further, this study found that regardless of program and clinical placement model the major learning impact for students was related to the midwife they worked with each day on placement rather than to the model.

Joint optimization of rule placement and traffic engineering for QoS provisioning in software defined network

Software-Defined Network (SDN) is a promising network paradigm that separates the control plane and data plane in the network. It has shown great advantages in simplifying network management such that new functions can be easily supported without physical access to the network switches. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules for high-speed packet processing in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we propose a rule multiplexing scheme, in which the same set of rules deployed on each node apply to the whole flow of a session going through but towards different paths. Based on this scheme, we study the rule placement problem with the objective of minimizing rule space occupation for multiple unicast sessions under QoS constraints. We formulate the optimization problem jointly considering routing engineering and rule placement under both existing and our rule multiplexing schemes. Via an extensive review of the state-of-the-art work, to the best of our knowledge, we are the first to study the non-routing-rule placement problem. Finally, extensive simulations are conducted to show that our proposals significantly outperform existing solutions.

What factors contribute to failure of fieldwork placement? Perspectives of supervisors and university fieldwork educators

Failing a fieldwork placement can be a challenging and emotional experience. This study aimed to explore the perspectives of placement supervisors and university fieldwork educators (UFEs) of the factors that contribute to occupational therapy students failing placement. Twenty placement supervisors and four UFEs participated, and completed single instance surveys and interviews. Failure of a placement was noted to be a stressful experience for all stakeholders. Reasons identified for student failure included poor communication and reflection skills; non-disclosure of health issues and an inability to accept feedback. Placement supervisors highlighted that although failing a student was difficult, there was a need to uphold the values and standards of the profession. Strategies to facilitate placement success were identified.

Optimal VM placement in data centres with architectural and resource constraints

Recent advance in virtualisation technology enables service provisioning in a flexible way by consolidating several virtual machines (VMs) into a single physical machine (PM). The inter-VM communications are inevitable when a group of VMs in a data centre provide services in a collaborative manner. With the increasing demands of such intra-data-centre traffics, it becomes essential to study the VM-to-PM placement such that the aggregated communication cost within a data centre is minimised. Such optimisation problem is proved NP-hard and formulated as an integer programming with quadratic constraints in this paper. Different from existing work, our formulation takes into consideration of data-centre architecture, inter-VM traffic pattern, and resource capacity of PMs. Furthermore, a heuristic algorithm is proposed and its high efficiency is extensively validated.