Assessment of relationship between management policies and transfer of skills: A case of public secondary school heads in Nyeri County, Kenya

Despite heavy investments in training and development, low-quality education standards and challenges in management practices have been a sticky problem in the Kenyan education sector. Organization Theory guided the main aim of this study in establishing the relationship between management policies and transfer of skills for public secondary school headteachers in schools management. The study adopted a descriptive research design. Questionnaires were used to collect quantitative and qualitative data. Data were analyzed through descriptive and inferential statistics. The study population comprised 226 headteachers.  A mixture of stratified random sampling and census study was adopted in sampling 69 respondents. The results established that management policies explained a 23.4% variation in the transfer of skills for school heads. ?1 = 0.536 and p-value = 0.000 (p<0.05) indicated that management policies had positive and significant effects on transfer of skills. The study established that unsupportive policies like the Students Council, two centers of power, strict policies, and lack of inclusivity in policy formulation adversely affected school heads in implementing learned skills. The study recommends fair representation of all categories of education stakeholders in policies review and formulation to alleviate challenges associated with management policies to enhance the transfer of skills. The Government should address the issue of two centers of power in the public basic education sector. The paper sought to call the attention of the professional development trainers to exercise follow-ups to assess post-training behavioral outcomes in the context of organizational climate

Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 201

Improving data center efficiency through smart grid integration and intelligent analytics

The ever-increasing growth of the demand in IT computing, storage and large-scale cloud services leads to the proliferation of data centers that consist of (tens of) thousands of servers. As a result, data centers are now among the largest electricity consumers worldwide. Data center energy and resource efficiency has started to receive significant attention due to its economical, environmental, and performance impacts. In tandem, facing increasing challenges in stabilizing the power grids due to growing needs of intermittent renewable energy integration, power market operators have started to offer a number of demand response (DR) opportunities for energy consumers (such as data centers) to receive credits by modulating their power consumption dynamically following specific requirements. This dissertation claims that data centers have strong capabilities to emerge as major enablers of substantial electricity integration from renewables. The participation of data centers into emerging DR, such as regulation service reserves (RSRs), enables the growth of the data center in a sustainable, environmentally neutral, or even beneficial way, while also significantly reducing data center electricity costs. In this dissertation, we first model data center participation in DR, and then propose runtime policies to dynamically modulate data center power in response to independent system operator (ISO) requests, leveraging advanced server power and workload management techniques. We also propose energy and reserve bidding strategies to minimize the data center energy cost. Our results demonstrate that a typical data center can achieve up to 44% monetary savings in its electricity cost with RSR provision, dramatically surpassing savings achieved by traditional energy management strategies. In addition, we investigate the capabilities and benefits of various types of energy storage devices (ESDs) in DR. Finally, we demonstrate RSR provision in practice on a real server. In addition to its contributions on improving data center energy efficiency, this dissertation also proposes a novel method to address data center management efficiency. We propose an intelligent system analytics approach, "discovery by example", which leverages fingerprinting and machine learning methods to automatically discover software and system changes. Our approach eases runtime data center introspection and reduces the cost of system management.2018-11-04T00:00:00

Towards Mitigating Co-incident Peak Power Consumption and Managing Energy Utilization in Heterogeneous Clusters

As data centers continue to grow in scale, the resource management software needs to work closely with the hardware infrastructure to provide high utilization, performance, fault tolerance, and high availability. Apache Mesos has emerged as a leader in this space, providing an abstraction over the entire cluster, data center, or cloud to present a uniform view of all the resources. In addition, frameworks built on Mesos such as Apache Aurora, developed within Twitter and later contributed to the Apache Software Foundation, allow massive job submissions with heterogeneous resource requirements. The availability of such tools in the Open Source space, with proven record of large-scale production use, make them suitable for research on how they can be adapted for use in campus-clusters and emerging cloud infrastructures for different workloads in both academia and industry. As data centers run these workloads and strive to maintain high utilization of their components, they suffer a significant cost in terms of energy and power consumption. To address this cost we have developed our own framework, Electron, for use with Mesos. Electron is designed to be configurable with heuristic-driven power capping policies along with different scheduling policies such as Bin Packing and First Fit. We characterize the performance of Electron, in comparison with the widely used Aurora framework. On average, our experiments show that Electron can reduce the 95th percentile of CPU and DRAM power usage by 27.89%, total energy consumption by 19.15%, average power consumption by 27.90%, and max peak power usage by 16.91%, while maintaining a similar makespan when compared to Aurora using the proper combination of power capping and scheduling policies

Empirical characterization and modeling of power consumption and energy aware scheduling in data centers

Energy-efficient management is key in modern data centers in order to reduce operational cost and environmental contamination. Energy management and renewable energy utilization are strategies to optimize energy consumption in high-performance computing. In any case, understanding the power consumption behavior of physical servers in datacenter is fundamental to implement energy-aware policies effectively. These policies should deal with possible performance degradation of applications to ensure quality of service. This thesis presents an empirical evaluation of power consumption for scientific computing applications in multicore systems. Three types of applications are studied, in single and combined executions on Intel and AMD servers, for evaluating the overall power consumption of each application. The main results indicate that power consumption behavior has a strong dependency with the type of application. Additional performance analysis shows that the best load of the server regarding energy efficiency depends on the type of the applications, with efficiency decreasing in heavily loaded situations. These results allow formulating models to characterize applications according to power consumption, efficiency, and resource sharing, which provide useful information for resource management and scheduling policies. Several scheduling strategies are evaluated using the proposed energy model over realistic scientific computing workloads. Results confirm that strategies that maximize host utilization provide the best energy efficiency.Agencia Nacional de Investigación e Innovación FSE_1_2017_1_14478

Cloud Workload Allocation Approaches for Quality of Service Guarantee and Cybersecurity Risk Management

It has become a dominant trend in industry to adopt cloud computing --thanks to its unique advantages in flexibility, scalability, elasticity and cost efficiency -- for providing online cloud services over the Internet using large-scale data centers. In the meantime, the relentless increase in demand for affordable and high-quality cloud-based services, for individuals and businesses, has led to tremendously high power consumption and operating expense and thus has posed pressing challenges on cloud service providers in finding efficient resource allocation policies. Allowing several services or Virtual Machines (VMs) to commonly share the cloud\u27s infrastructure enables cloud providers to optimize resource usage, power consumption, and operating expense. However, servers sharing among users and VMs causes performance degradation and results in cybersecurity risks. Consequently, how to develop efficient and effective resource management policies to make the appropriate decisions to optimize the trade-offs among resource usage, service quality, and cybersecurity loss plays a vital role in the sustainable future of cloud computing. In this dissertation, we focus on cloud workload allocation problems for resource optimization subject to Quality of Service (QoS) guarantee and cybersecurity risk constraints. To facilitate our research, we first develop a cloud computing prototype that we utilize to empirically validate the performance of different proposed cloud resource management schemes under a close to practical, but also isolated and well-controlled, environment. We then focus our research on the resource management policies for real-time cloud services with QoS guarantee. Based on queuing model with reneging, we establish and formally prove a series of fundamental principles, between service timing characteristics and their resource demands, and based on which we develop several novel resource management algorithms that statically guarantee the QoS requirements for cloud users. We then study the problem of mitigating cybersecurity risk and loss in cloud data centers via cloud resource management. We employ game theory to model the VM-to-VM interdependent cybersecurity risks in cloud clusters. We then conduct a thorough analysis based on our game-theory-based model and develop several algorithms for cybersecurity risk management. Specifically, we start our cybersecurity research from a simple case with only two types of VMs and next extend it to a more general case with an arbitrary number of VM types. Our intensive numerical and experimental results show that our proposed algorithms can significantly outperform the existing methodologies for large-scale cloud data centers in terms of resource usage, cybersecurity loss, and computational effectiveness

Modeling and optimization of high-performance many-core systems for energy-efficient and reliable computing

Thesis (Ph.D.)--Boston UniversityMany-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems. We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies. Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy

Improving efficiency and resilience in large-scale computing systems through analytics and data-driven management

Applications running in large-scale computing systems such as high performance computing (HPC) or cloud data centers are essential to many aspects of modern society, from weather forecasting to financial services. As the number and size of data centers increase with the growing computing demand, scalable and efficient management becomes crucial. However, data center management is a challenging task due to the complex interactions between applications, middleware, and hardware layers such as processors, network, and cooling units. This thesis claims that to improve robustness and efficiency of large-scale computing systems, significantly higher levels of automated support than what is available in today's systems are needed, and this automation should leverage the data continuously collected from various system layers. Towards this claim, we propose novel methodologies to automatically diagnose the root causes of performance and configuration problems and to improve efficiency through data-driven system management. We first propose a framework to diagnose software and hardware anomalies that cause undesired performance variations in large-scale computing systems. We show that by training machine learning models on resource usage and performance data collected from servers, our approach successfully diagnoses 98% of the injected anomalies at runtime in real-world HPC clusters with negligible computational overhead. We then introduce an analytics framework to address another major source of performance anomalies in cloud data centers: software misconfigurations. Our framework discovers and extracts configuration information from cloud instances such as containers or virtual machines. This is the first framework to provide comprehensive visibility into software configurations in multi-tenant cloud platforms, enabling systematic analysis for validating the correctness of software configurations. This thesis also contributes to the design of robust and efficient system management methods that leverage continuously monitored resource usage data. To improve performance under power constraints, we propose a workload- and cooling-aware power budgeting algorithm that distributes the available power among servers and cooling units in a data center, achieving up to 21% improvement in throughput per Watt compared to the state-of-the-art. Additionally, we design a network- and communication-aware HPC workload placement policy that reduces communication overhead by up to 30% in terms of hop-bytes compared to existing policies.2019-07-02T00:00:00