Misc links edition July 3rd

Nice papers:




LogPlayer: Fault-tolerant Exactly-once Delivery using gRPC Asynchronous Streaming



Interesting posts:






Podcast suggestions: 



Pet peeves:

Lack of participatory and effective leadership across all levels and domains is disconcerting. Rough waters are truer tests of leadership. In calm water, every ship has a good captain.

The immense popularity of ML makes it hard to recruit PhD students in other CS areas.
 
Albert Einstein *(no verified email)*. I wonder what his rate-my-professor ranking is.

Music:



Follow on Twitter: 

Comments

Post a Comment

Popular posts from this blog

The Ben-Or decentralized consensus algorithm

Image
In PODC 1983, Michael Ben-Or published a randomized distributed asynchronous consensus algorithm in a paper titled "Another advantage of free choice (Extended Abstract): Completely asynchronous agreement protocols" . After 15 years, a paper in 1998 provided a correctness proof of Ben-Or's algorithm . Above is the pseudocode for Ben-Or from that paper. From the pseudocode it looks like Ben-Or is a very simple algorithm, but in reality its behavior is not easy to understand and appreciate. But fret not, TLA+ modeling and model checking helps a lot for understanding the behavior of the Ben-Or algorithm. It is fulfilling when you finally understand how the algorithm works, and how safety is always preserved and progress is achieved eventually probabilistically. I had assigned modeling of Ben-Or as the TLA+ project for my distributed systems class . Here I share my PlusCal modeling of Ben-Or, and discuss how this model helps us to understand the algorithm better. Here is the l...
Read more

Paper review. Gray Failure: The Achilles' Heel of Cloud-Scale Systems

Image
This paper (by Peng Huang, Chuanxiong Guo, Lidong Zhou, Jacob R. Lorch, Yingnong Dang, Murali Chintalapati, and Randolph Yao) occurred in HotOS 2017. The paper is an easy read at 5 pages, and considers the fault-tolerance problem of cloud scale systems. Although cloud provides redundancy for masking and replacing failed components, this becomes useful only if those failures can be detected. But some failures that are partial and suble failures remain undetected and these "gray failures" lead to major availability breakdowns and performance anomalies in cloud environments. Examples of gray failures are performance degradation, random packet loss, flaky I/O, memory thrashing/leaking, capacity pressure, and non-fatal exceptions. The paper identifies a key feature of gray failure as differential observability . Consider the setup in Figure 2. Within a system, an observer  gathers information about whether the system is failing or not. Based on the observations, a reactor takes a...
Read more