“I really wish I had a dedicated Linux computer to run computer vision algorithms on,” said my fiancée a couple of weeks ago. If you were there you would have been blinded by the metaphorical light bulb that lit over my head. You see, just the week before, my friend and co-worker had ordered an old, decommissioned (complete with “non-classified” stickers!) Apple Xserve off of eBay for merely $40. Like my fiancée, he wanted to have a machine for a special purpose: test compilations of open source software on a big-endian architecture. I was quite envious that he was able to hack on such cool hardware for such a cheap price. But, I wasn’t yet ready to bring out my wallet. I couldn’t justify indulging a new hobby without good reason—I was stuck waiting for just the right impetus. I didn’t wait long. My fiancée’s wish became my command!
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.
Deduplication is a critical technology for modern production and research systems. In many domains, such as cloud computing, it is often taken for granted . Deduplication magnifies the amount of data you can store in memory , on disk , and in transmission across the network . It comes at the cost of more CPU cycles, and potentially more IO operations at origin and destination storage backends. Microsoft , IBM , EMC , Riverbed , Oracle , NetApp , and other companies tout deduplication as a major feature and differentiator across the computing industry. So what exactly is deduplication?
One of the most basic philosophical questions stems from attempting to identify oneself, with the first step of proving you actually exist. René Descartes provides a proof with
Cogito ergo sum
meaning, “I think, therefore I am.” The intuition is that the mere fact of thinking forms a proof that you exist. But who or what are you exactly? What identifies you? How can we definitively prove you are what you claim to be? Who you claim to be? The problem of identity is an incredibly hard one—how do you know a letter in the mail is from the person that signed it? How do you know a text was written by the owner of a certain phone? How do you know an email comes from the person that owns an email address? This is a fundamental problem that faces the fields of computer science and cryptography, and it is incredibly hard to solve.