Planning the Unplannable: IT Management of Anytime-Anywhere Learning

Learning and instruction are changing in higher education, owing largely to the proliferation of consumer technologies
on today’s campuses.

A growing number of institutions are moving away from the traditional instructor-focused teaching model to a new student-centric approach that favors a more personalized, collaborative, anytime-anywhere—from any device—learning experience. As these colleges and universities move forward, what technologies and policies should they have in place to support their academic needs?

Today’s students don’t want to be tethered—to a device or a classroom. How does virtualization and cloud computing support anytime-anywhere- any-device learning?

learning should be geared toward removing barriers that impede access. This statement is the same as what we said 10 years ago—
the barriers however, have changed. The explosion of mobile devices has created the latest barrier to access. Students do not want their access to be limited to a specific time, to any one device, or to a specific place. The use of cloud computing and virtualization helps to break down barriers of device and place, while time is more closely tied with course design.

Educators are deploying mobile strategies to enable students to learn on the go and when they are able. This type of dynamic learning environment requires robust support to allow students to be able to access learning apps and data from their mobile devices on-the-go.

Virtualization and cloud computing allow campuses to focus resources on core competencies and evaluate how they can enhance those competencies by sharing resources and optimizing service delivery models to ensure
their offerings align to rapidly evolving campus needs.

What policies and technologies do campuses need to have in place to protect both their network and users’ privacy in today’s open, collaborative learning environment?

Big Data – UNDERSTANDING THE FLAVORS OF HPC

 

IT’S IMPORTANT TO recognize that not all high-performance computing systems work the same way. Indeed, choosing between a cloud-based or in-house HPC solution may well depend on the kind of processing work that needs to be done. Dennis Gannon, director of cloud research strategy for the Microsoft Research Connections team, analyzed the work performed by about 90 research groups that were given access to Microsoft Azure cloud resources over the last two years. He concluded that four major architectural differences between cloud clusters and supercomputers—machines running thousands, even tens of thousands of processors—determine which types of high-performance computing should be done where:

Each server in a data center hosts virtual machines, and the cloud runs a fabric scheduler, which manages sets of VMs across the servers. This means that if a VM fails, it can be started up again elsewhere. But it can be inefficient and time- consuming to deploy VMs for each server when setting up batch applications common to HPC.
Data in data centers is stored and distributed over many, many disks. Data is not stored on the local disks of supercomputers, but on network storage.

Clouds are perfect for large-data collaboration and data analytics like MapReduce (a strategy for dividing a problem into hundreds or thousands of smaller problems that are processed in parallel and then gathered, or reduced, into one answer to the original question).

THE GROWTH IN THE VOLUME of the world’s data is currently outpacing Moore’s Law,

Hadoop distribution, dubbed the Cloudera Distribution Including Apache Hadoop (CDH), is an example of data-manage- ment platform that combines a number of components, including support for the Hive and Pig languages; the HBase database for random, real-time read/write access; the Apache ZooKeeper coor- dination service; the Flume service for collecting and aggregating log and event data; Sqoop for relational database integration; the Mahout library of machine learning algorithms; and the Oozie server-based workflow engine, among others.

The sheer volume of data is not why most customers turn to Hadoop. Instead, it’s the flexibility the platform provides.

Hadoop is just one of the technologies emerging to support Big Data analytics, according to James Kobielus, IBM’s Big Data evan- gelist. NoSQL, which is a class of non-relational database-manage- ment systems, is often used to characterize key value stores and other approaches to analytics, much of it focused on unstructured content. New social graph analysis tools are used on many of the new event-based sources to analyze relationships and enable cus- tomer segmentation by degrees of influence. And so-called semantic web analysis (which leverages the Resource Description Framework specification) is critical for many text analytics applications.

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transistors on integrated circuits doubles approximately every two years. If this is indicating the computer chi innovations then it is not keeping up with the rate at which data is being created.

At its core, Hadoop is a combination of Google’s MapReduce and the Hadoop Distributed File System (HDFS). MapReduce is a programming model for processing and generating large data sets. It supports parallel computations on so-called unreliable computer clusters. HDFS is designed to scale to petabytes of storage and to run on top of the file systems of the underlying operating system. Yahoo released to developers the source code for its internal distribution of Hadoop in 2009.

“It was essentially a storage engine and a data-processing engine combined,” explains Zedlewski. “But Hadoop today is really a constellation of about 16 to 17 open source projects, all building on top of that original project, extending its usefulness in all kinds of different directions.”

Business Platform Definitions

 

  1. A set of business and technology building blocks that serve as the foundation for building complementary products and services.
  2. A set of resources used in common across a product family that are also subject to network effects.
  3. An open standard, facilitating 3rd party participation, together with a contractual or reputational governance model.
  4. A business ecosystem that matches buyers with suppliers, who transact directly with each other using system resources.

 

Platforms are economically important and widely observed in modern economies. For example HMOs match patients and physicians. Real estate and auction networks match buyers and sellers. Airline reservation systems match travelers to airline flights. However, thanks largely to technology, platforms are becoming much more prevalent. New platforms are being created e.g. DropBox, Yammer, and KickStarter. Traditional businesses are being reconceived as platforms e.g. U.S. Postal Service, newspapers (Huffington Post). Retail electric markets are evolving into platforms that match consumers with specific power producers, allowing them to express their preferences for cheaper coal or more costly renewable power. In creating strategies for platform markets, managers have typically relied on assumptions and paradigms that apply to businesses without network effects. As a result, they have made decisions in pricing, supply chains, product design, and strategy that are inappropriate for the economics of their changing industries.

 

providing a set of theory, frameworks, and tools to analyze and manage existing businesses and to develop launch strategies for new ventures. The diversity of industry coverage will provide a strong tool kit for students from diverse backgrounds such as finance, marketing, and operations.

 

Thousands of firms, from Facebook to Salesforce, now operate as open ecosystems that match buyers and sellers, gain value and market share from network effects, and harness their users to innovate. Drawing on cases from social media, entrepreneurship, enterprise software, mobile services, healthcare, and consumer products, students will analyze and learn to negotiate platform startup, convert existing businesses, and make vital decisions on issues of openness, cannibalization, and competition. Students will interact with execs of major firms such as Cisco and SAP and with startups. They will learn to apply concepts from two sided networks, industrial organization, information asymmetry, pricing, intellectual property, and game theory to real problems.