It has been a week since my last post :). Well, I was pretty occupied. Dealing with deadlines, and impromptu soccer + Paris trip :p. Well, back to business now. Here is my latest slide for EEDC assignment. It discusses about determining data center location, based on this paper, Intelligent Placement of Datacenters for Internet Services, by I. Goiri et al. It is pretty interesting because this kind of stuff is usually confidential information for the “juggernaut” of internet (Google, Facebook etc).
I’m planning to cover Network Virtualization topic for my next project in EEDC course. Main inspiration comes from this article from Technology Review, about Nicira. And here is my plan for the task that I present to our professor.
Network Virtualization is the next trend in Virtualization after OS virtualization. It allows user to easily reconfigure a network configuration in a cloud computing environment as well as increase security of the network. Continue reading Rise of Network Virtualization
I read an article titled “Architecting Cloud Scale Identity Fabric” about concept of Identity Management of a Service as part of EEDC assignment. This article is written by Eric Olden, from Symplified. The main thing about this article is about the need of a service to manage user identity in the cloud. Well, I think this diagram from Symplified website is worth more than thousand words:
CAP theorem is widely used in Distributed Database System(DDBS) design. In a nutshell, it says that in designing modern DDBS, we only can choose two properties out of three properties that are crucial for DDBS. The aforementioned properties are Consistency (C), Availability (A) and Partition Tolerance (P). And this diagram below summarize the available combination of CAP:
Well, I would like to write something about our Decentralized System (DS) project and what we are going to do in this project. Since it is the first post about the big picture of our DS project in this blog, I named it “Pilot” :p. Our group is G007, which consists of Julia, Diego, Enkhjin and myself.
When I was looking for example of P2P distributed storage system, I came across video from Google Tech Talk about Wuala. Wuala is an example of distributed peer-to-peer storage system. It used to be startup company, but LaCiE bought it in 2009. It allows you to store your data in the cloud, set up online back up and access your files, share your files with your friends easily, and access them from other computer.
This time, our group needed to prepare presentation about Apache Flume for EEDC homework. Flume is intended to solve challenges in safely transferring huge set of data from a node (example: log files in company web servers) to data store (example: HDFS, Hives, HBase, Cassandra etc etc).
Well, for a simple system with relatively small data set, we usually customize our own solution to do this job, such as to create some script to transfer the log to database. However, this kind of ad-hoc solution is difficult to make it scalable because usually it is created very tailored into our system. It sometimes suffers from problem in manageability, especially when the original programmer or engineer who created the system left the company. It is also often difficult to extend and, furthermore it may have problem in reliability due to some bugs during the implementation.
Well, it’s not really about relationship status :p.. since “they” are referring to distributed systems 😀 #geek
But it is about my latest review for a paper titled titled “Exploiting Availability Prediction in Distributed Systems“, by James W. Mickens and Brian D Noble. As we all know, availability is one of the important properties of distributed systems. Availability is concerned with the capability of a distributed system to serving its client properly although there are some component failures inside the system.They argued that availability modeling is crucial to (generally) make the system better, in term of resource efficiency and understanding system-wide phenomenon. Therefore, they propose a new way to predict availability, and they applied the predictor to three case studies. They found that their predictor works well under test data and they successfully shows that good predictor can improve the systems. Continue reading How available are they?