jm + via:norman-maurer   2

3 Rules of thumb for Bloom Filters
I often need to do rough back-of-the-envelope reasoning about things, and I find that doing a bit of work to develop an intuition for how a new technique performs is usually worthwhile. So, here are three broad rules of thumb to remember when discussing Bloom filters down the pub:

One byte per item in the input set gives about a 2% false positive rate.

The optimal number of hash functions is about 0.7 times the number of bits per item.

3 - The number of hashes dominates performance.

But see also , (thanks Tony Finch!)
bloom-filters  algorithm  probabilistic  rules  reasoning  via:norman-maurer  false-positives  hashing  coding 
august 2014 by jm
'Join-Idle-Queue: A Novel Load Balancing Algorithm for Dynamically Scalable Web Services' [paper]
We proposed the JIQ algorithms for web server farms that are dynamically scalable. The JIQ algorithms significantly outperform the state-of-the-art SQ(d) algorithm in terms of response time at the servers, while incurring no communication overhead on the critical path. The overall complexity of JIQ is no greater than that of SQ(d).

The extension of the JIQ algorithms proves to be useful at very high load. It will be interesting to acquire a better understanding of the algorithm with a varying reporting threshold. We would also like to understand better the relationship of the reporting frequency to response times, as well as an algorithm to further reduce the complexity of the JIQ-SQ(2) algorithm while maintaining its superior performance.
join-idle-queue  algorithms  scheduling  load-balancing  via:norman-maurer  jiq  microsoft  load-balancers  performance 
august 2014 by jm

Copy this bookmark: