4/7/14, 10:30am: Mainframe 50th Anniversary
Only the highest scored students have the opportunity to participate in the IBM Master the Mainframe World Championship. In this competition, 43 student contestants from five continents, will compete for the grand prize of mainframe computing, which has 50 years of history behind it. This two-day event will be taking place in New York City, April 7-8, and XRDS will be there covering every detail live for you. Continue reading
In the last decade the Internet has come to dominate how we consume information. News, entertainment, and even education, are often a click away. If you know what you want, a few typed words can lead you to the webpage you seek. But what if your search is less concrete? What if you are looking to find inspiring, new, undiscovered content to consume? You could certainly ask a friend, or you could ask a personal recommendation engine. Continue reading
In my thesis work I’m developing a framework built on top of KVM and QEMU which adds the capability of cloud-wide agentless monitoring. If you’re interested in this line of thinking read on for a high-level introduction and please comment in!
Three properties of the Virtual Machine (VM) abstraction enable and distinguish modern cloud computing: strong isolation, virtualized hardware, and soft-state provisioning. Strong isolation provides isolation between a VM and its host, and between a VM and other VMs executing on the same host. Because of strong isolation, separate entities may share the same host without knowledge of each other in a multi-tenant environment. Virtualized hardware frees a VM from its underlying hardware architecture and devices. This freedom consolidates workloads, now untethered from their hosts, by migrating them as the work intensity varies, and assigning resources only when needed. Soft-state provisioning reduces the time to deploy a running service. Requested resources can tightly match current workloads, and as the demands of the workload change over time, resources are elastically scaled. Continue reading
During recent experiments for a research paper, my research group observed very strange symptoms from our Google Glass. Most of our experiments were done to study the impact of latency on cognitive assistance applications such as programs designed to remind you who is in front of you, or notify you that it is safe to cross the street. We observed a large variation in latency which was unexplainable by the usual culprits such as poorly performing WiFi networks. We had isolated all the possible sources outside of the Google Glass, but the unknown source of latency jitter was still ruining our experimental results. At this point, we knew we had to figure out what was going on inside the Google Glass itself.
As a PhD student who does research on theory and algorithms for massive data analysis, I am interested in exploring current and future challenges in this area, which I’d like to share it here. There are two major points of view when we talk about big data problems:
One is more focused on industry and business aspects of big data, and includes many IT companies who work on analytics. These companies believe that the potential of big data lies in its ability to solve business problems and provide new business opportunities. To get the most from big data investments, they focus on questions which companies would like to answer. They view big data not as a technological problem but as a business solution, and their main goals are to visualize, explore, discover and predict.