Microsoft Enterprise Consortium How To’s for the Classroom

Practical workshop that includes presentations and “hands on” use of examples and exercises utilizing the Microsoft Enterprise Consortium hosted at the University of Arkansas Sam M. Walton College of Business Enterprise Systems. Industry partners of the Enterprise Systems program at the Sam M. Walton College of Business have donated computing systems and multiple, large-scale datasets for instructional use – Sam’s Club (6 tables and over 55 million rows of POS transactions for 18 stores), Dillard’s Department Stores (5 tables with a transactions table of 120 million rows), Tyson Foods, and Wal-Mart RFID data sets. This workshop provides faculty “How To’s” that can be incorporated into a wide variety of courses that include topics on databases, data warehousing, business intelligence and decision making. The Microsoft Enterprise Consortium includes the full SQL Server 2008 Management Studio and Business Intelligence Development Studio. Further, this Consortium includes access to the large datasets referenced above plus a number of datasets corresponding to database and data mining texts

Analisa dan Perancangan Jaringan Private Cloud Computing Berbasis Web Eyeos

— Private Cloud Computing is the model of Cloud Computing that provides a smaller scope to be able to provide certain services to specific users on an Enterprise scale companies. Expension of the company at Sutindo Raya Mulia, PT will add new employees and new computer devices and their software application that has a license. It will lead to the use of base servers load in the company. In the Cloud Computing system base servers, local no longer has to do all the heavy load when used for all resource servers cloud computing will be the one using openstack. Openstack is a cloud OS that manages the resource for function of compute, storage, network, and can be implemented for the characteristics of Infrastructure as a Service (IAAS). OpenStack can be built through the virtual server instance for eyeOS as one of cloud computing services Software As A Service (SAAS). Computer users simply install the browser on their computer can already implement in the company by using the eyeOS

Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management

In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterprise users. By purchasing a cloud server, users do not have to buy a large number of computers, saving computing costs. According to a report by China Economic News Network, the scale of cloud computing in China has reached 209.1 billion yuan. At present, the more mature cloud service providers in China are Ali Cloud, Baidu Cloud, Huawei Cloud and so on. Therefore, this paper proposes an innovative approach to solve complex problems in cloud computing resource scheduling and management using machine learning optimization techniques. Through in-depth study of challenges such as low resource utilization and unbalanced load in the cloud environment, this study proposes a comprehensive solution, including optimization methods such as deep learning and genetic algorithm, to improve system performance and efficiency, and thus bring new breakthroughs and progress in the field of cloud computing resource management.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time

Enabling controlling complex networks with local topological information

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulflling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefned state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efciently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.The work was partially supported by National Science Foundation of China (61603209), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. (61603209 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under Campus for Research Excellence and Technological Enterprise (CREATE) programme)Published versio

Multimedia Streaming through Wireless Networks

An overview of wireless networks, cross-layer optimization techniques, and advances in wireless LAN technologies is presented. This paper presents a scalable and adaptive system-level approach to wireless multimedia in the emerging, Proactive Enterprise computing environment. A Distributed Network Information Base with Service Agents at each node is proposed to enable network-wide, proactive adaptation with adaptive routing and end-to-end Quality of Service (QoS) management. The paper suggests that a combination of technological advancements in emerging wireless networks, node-level cross-layer optimizations, and the proposed distributed cross-node system-level architecture are all required to efficiently scale and adapt wireless multimedia in the current market