Model-driven disaster management

Disaster management (DM) is a challenging domain to model because of the variety of dynamic characteristics attached to the domain. Metamodeling is a model-driven approach that describes how semantic domain models can be built into an artifact called a Metamodel. By collecting all the domain concepts and partitioning the domain problems into sub-domain-problems, a metamodel can produce a domain-specific language. This paper presents a Disaster Management Metamodel that can serve as a representational layer of DM expertise. This metamodel leads to better knowledge sharing and facilitates combining and matching different DM activities to best manage the disaster on hand.     

Model-driven multidimensional modeling of secure data warehouses

Data Warehouses (DW), Multidimensional (MD) databases, and On-Line Analytical Processing (OLAP) applications provide companies with many years of historical information for the decision-making process. Owing to the relevant information managed by these systems, they should provide strong security and confidentiality measures from the early stages of a DW project in the MD modeling and enforce them. In the last years, there have been some proposals to accomplish the MD modeling at the conceptual level. Nevertheless, none of them considers security measures as an important element in their models, and therefore, they do not allow us to specify confidentiality constraints to be enforced by the applications that will use these MD models. In this paper, we present an Access Control and Audit (ACA) model for the conceptual MD modeling. Then, we extend the Unified Modeling Language (UML) with this ACA model, representing the security information (gathered in the ACA model) in the conceptual MD modeling, thereby allowing us to obtain secure MD models. Moreover, we use the OSCL (Object Security Constraint Language) to specify our ACA model constraints, avoiding in this way an arbitrary use of them. Furthermore, we align our approach with the Model-Driven Architecture, the Model-Driven Security and the Model-Driven Data Warehouse, offering a proposal highly compatible with the more recent technologies.     

GPU-aware resource management in heterogeneous cloud data centers

The Journal of Supercomputing - The power of rapid scalability and easy maintainability of cloud services is driving many high-performance computing applications from company server racks into...     

Energy-aware application performance management in virtualized data centers

Both performance and energy cost are important concerns for current data center operators. Traditionally, however, IT and mechanical engineers have separately optimized the cyber and physical aspects of data center operations. This paper considers both of these aspects with the eventual goal of developing performance and power management techniques that operate holistically to control the entire cyber-physical complex of data center installations. Toward this end, we propose a balance of payments model for holistic power and performance management. As an example of coordinated cyber-physical system management, the energy-aware cyber-physical system (EaCPS) uses an application controller on the cyber side to guarantee application performance, and on the physical side, it utilizes electric current-aware capacity management (CACM) to smartly place executables to reduce the energy consumption of each chassis present in a data center rack. A web application, representative of a multi-tier web site, is used to evaluate the performance of the controller on the cyber side, the CACM control on the physical side, and the holistic EaCPS methods in a mid-size instrumented data center. Results indicate that coordinated EaCPS outperforms separate cyber and physical control modules.     

Model-driven design for the visual analysis of heterogeneous data

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.     

模型驱动的预算管理系统设计与实现

针对财务预算的领域概念和复杂需求,将一种模型驱动的开发方式实践于预算管理系统的设计与实现,使开发人员脱离复杂的数据库设计,更加专注领域概念的消化、模型和数据传输对象的创建。给出了预算管理系统的整体说明和框架的总体结构设计,并以开发顺序依次介绍了各层的设计与实现,最后结合系统快速上线的效果说明了这种模型驱动开发方式的可行性、便利性和快捷性。     

Balance of power: dynamic thermal management for Internet data centers

Internet-based applications and their resulting multitier distributed architectures have changed the focus of design for large-scale Internet computing. Internet server applications execute in a horizontally scalable topology across hundreds or thousands of commodity servers in Internet data centers. Increasing scale and power density significantly impacts the data center's thermal properties. Effective thermal management is essential to the robustness of mission-critical applications. Internet service architectures can address multisystem resource management as well as thermal management within data centers.     

基于改进多目标优化算法的分布式数据中心负载调度

在数据中心的运营中运营商需要考虑如何在利润最大化的同时降低碳排放和提升服务质量,这些目标之间的平衡是一个巨大挑战.针对该问题,建立分布式数据中心负载调度的多目标优化模型,提出一种改进拥挤距离和自适应交叉变异的非支配排序遗传算法(ICDA-NSGA-II).在NSGA-II算法的基础上,通过对拥挤距离的改进能够提高算法的开采和勘探能力,引入正态分布交叉(NDX)算子和自适应变异算子增强种群的多样性,从而保证算法能快速、准确地得到Pareto解集.为了显示改进算法的有效性,对基准测试函数进行求解,仿真结果表明,改进算法相比于典型的NSGA-II和MOEA/D具有更快的收敛速度和精度,在分布式数据中心负载调度优化中,能够快速有效地给出满足利润、碳排放和服务质量等目标的Pareto最优解.     

Energy-efficient data centers

Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years. A major component of the today’s ICT is constituted by the data centers which have experienced an unprecedented growth in their size and population, recently. The Internet giants like Google, IBM and Microsoft house large data centers for cloud computing and application hosting. Many studies, on energy consumption of data centers, point out to the need to evolve strategies for energy efficiency. Due to large-scale carbon dioxide ( $\mathrm{CO}_2$ ) emissions, in the process of electricity production, the ICT facilities are indirectly responsible for considerable amounts of green house gas emissions. Heat generated by these densely populated data centers needs large cooling units to keep temperatures within the operational range. These cooling units, obviously, escalate the total energy consumption and have their own carbon footprint. In this survey, we discuss various aspects of the energy efficiency in data centers with the added emphasis on its motivation for data centers. In addition, we discuss various research ideas, industry adopted techniques and the issues that need our immediate attention in the context of energy efficiency in data centers.     

Service level agreement based energy-efficient resource management in cloud data centers

As cloud computing has become a popular computing paradigm, many companies have begun to build increasing numbers of energy hungry data centers for hosting cloud computing applications. Thus, energy consumption is increasingly becoming a critical issue in cloud data centers. In this paper, we propose a dynamic resource management scheme which takes advantage of both dynamic voltage/frequency scaling and server consolidation to achieve energy efficiency and desired service level agreements in cloud data centers. The novelty of the proposed scheme is to integrate timing analysis, queuing theory, integer programming, and control theory techniques. Our experimental results indicate that, compared to a statically provisioned data center that runs at the maximum processor speed without utilizing the sleep state, the proposed resource management scheme can achieve up to 50.3% energy savings while satisfying response-time-based service level agreements with rapidly changing dynamic workloads.     

Utilization-based pricing for power management and profit optimization in data centers

Traditional load balancing approaches may spread the load on more computers as long as the performance in terms of response time or cost is minimized. Nowadays power is a growing cost factor for data centers. In this paper, from the service provider’s point of view, the load balancing decision is made based on whether power consumption can be reduced or more profit can be earned. To achieve this, we design pricing algorithms to influence the load distribution. Both algorithms take into account the utilization of computers besides other factors, such as prices and power costs. In the first algorithm, we design pricing functions with respect to the computer utilization to encourage or discourage resource usage. In the second algorithm, we focus on the profit that a service provider can earn after deducting power cost from its revenue. We formulate this profit optimization problem and derive the optimum price solution.     

Power and performance management for parallel computations in clouds and data centers

We address scheduling independent and precedence constrained parallel tasks on multiple homogeneous processors in a data center with dynamically variable voltage and speed as combinatorial optimization problems. We consider the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint on multiple processors. Our approach is to use level-by-level scheduling algorithms to deal with precedence constraints. We use a simple system partitioning and processor allocation scheme, which always schedules as many parallel tasks as possible for simultaneous execution. We use two heuristic algorithms for scheduling independent parallel tasks in the same level, i.e., SIMPLE and GREEDY. We adopt a two-level energy/time/power allocation scheme, namely, optimal energy/time allocation among levels of tasks and equal power supply to tasks in the same level. Our approach results in significant performance improvement compared with previous algorithms in scheduling independent and precedence constrained parallel tasks.     

模型驱动下的形状分布算法

三维模型的特征提取方法中,形状分布算法简单,具有强的不变性和鲁棒性,但其抽样点不具有代表性,影响了系统的检索精度.为增强抽样点的有效性,我们改进抽样策略,首先引入模型复杂度描述参数,扩大抽样点集;然后利用可分离离散小波变换,逐层分解样点集,增强其有效性,并使最终样点数相同;最后使用形状分布算法进行三维模型的特征提取,并应用在三维模型检索上,对比实验结果表明,改进后的算法提高了系统的检索精度.     

Model-driven reverse engineering

Reverse engineering is the process of comprehending software and producing a model of it at a high abstraction level, suitable for documentation, maintenance, or reengineering. But from a manager's viewpoint, there are two painful problems: 1) It's difficult or impossible to predict how much time reverse engineering will require. 2) There are no standards to evaluate the quality of the reverse engineering that the maintenance staff performs. Model-driven reverse engineering can overcome these difficulties. A model is a high-level representation of some aspect of a software system. MDRE uses the features of modeling technology but applies them differently to address the maintenance manager's problems. Our approach to MDRE uses formal specification and automatic code generation to reverse the reverse-engineering process. Models written in a formal specification language called SLANG describe both the application domain and the program being reverse engineered, and interpretations annotate the connections between the two. The ability to generate a similar version of a program gives managers a fixed target for reverse engineering. This, in turn, enables better effort prediction and quality evaluation, reducing development risk.     

数据中心节能算法研究综述

介绍了在数据中心服务器的硬件层、操作系统层、应用程序层以及集群层新涌现的节能技术及算法,给出了数据中心节能的其他研究问题,最后介绍了研究热点以及未来所面临的挑战。     

Energy-saving self-configuring networked data centers

In this paper, we develop the optimal minimum-energy scheduler for the dynamic online joint allocation of the task sizes, computing rates, communication rates and communication powers in virtualized Networked Data Centers (NetDCs) that operates under hard per-job delay-constraints. The referred NetDC’s infrastructure is composed by multiple frequency-scalable Virtual Machines (VMs), that are interconnected by a bandwidth and power-limited switched Local Area Network (LAN). Due to the nonlinear power-vs.-communication rate relationship, the resulting Computing-Communication Optimization Problem (CCOP) is inherently nonconvex. In order to analytically compute the exact solution of the CCOP, we develop a solving approach that relies on the following two main steps: (i) we prove that the CCOP retains a loosely coupled structure, that allows us to perform the lossless decomposition of the CCOP into the cascade of two simpler sub-problems; and, (ii) we prove that the coupling between the aforementioned sub-problems is provided by a (scalar) constraint, that is linear in the offered workload. The resulting optimal scheduler is amenable of scalable and distributed online implementation and its analytical characterization is in closed-form. After numerically testing its actual performance under randomly time-varying synthetically generated and real-world measured workload traces, we compare the obtained performance with the corresponding ones of some state-of-the-art static and sequential schedulers.     

Educational tours of data processing centers

This article presents suggestions for making data processing center tours an educational experience. The article lists three objectives and six steps to follow in preparing for and carrying out the tours.The authors have used the six-step approach repeatedly with considerable success. By following the six steps, tours have been conducted with a minimum of confusion and have met the three objectives.     

云数据中心中一种物理资源利用阈值边界管理策略

提出了云数据中心的一种物理资源利用阈值边界管理策略RUT-MS(physical resource utilization thresholds management strategy)。RUT-MS把虚拟机迁移过程进一步划分为超负载主机检测、虚拟机选择、虚拟机放置第1阶段、低负载主机检测和虚拟机放置第2阶段。使用一种迭代权重线性回归方法来预测物理资源的阈值上限,避免超负载的物理主机数量的增加;采用最小能量消耗策略完成虚拟机选择过程。使用多维物理资源的均方根来确定其资源使用阈值下限,减少低负载主机数量。实验结果表明： RUT-MS物理资源利用阈值边界管理策略使云数据中心的能量消耗和虚拟机迁移次数明显减少,SLA违规率和SLA及能量消耗联合指标只有少量的增加。