An Adaptive Load Balancing Algorithm Based on Discrete Uniform Distribution

2010 ◽  
Vol 108-111 ◽  
pp. 1392-1396
Author(s):  
Hai Sheng Li ◽  
Hong Yan Shi
Keyword(s):  
Uniform Distribution ◽  
Web Sites ◽  
Service Availability ◽  
Dynamic Algorithm ◽  
Multiple Servers ◽  
Discrete Uniform Distribution ◽  
Load Balancing Algorithm ◽  
Discrete Uniform ◽  
Load Balancer ◽  
Better Than

Centralized cluster system is widely used by many web sites to increase service availability and balancing workload among multiple servers. In order to reduce the possibility which balancer becomes performance bottleneck in clusters, a dynamic adaptive weighted load-balanced algorithm is presented in this paper. The objective of the algorithm is that every working server sends a corresponding ask signals at different rates to balancer based on the discrete uniform distribution principle and the balancer assigning user requests according to an ask queue which consists of these signals and achieves the dynamic negative feedback on workload. Experiment results show that the algorithm is better than the static algorithm, as compared with the dynamic algorithm, it can also reduce the burden on load balancer.

Calculus meets the birthday problem

2016 ◽  
Vol 100 (547) ◽  
pp. 86-92
Author(s):  
Dale K. Hathaway ◽  
Joshua Barks
Keyword(s):  
Uniform Distribution ◽  
The Other ◽  
Birthday Problem ◽  
Discrete Uniform Distribution ◽  
Discrete Uniform ◽  
Birth Time ◽  
Better Than

What is the likelihood P(n, T) that at least two people in a gathering of n people are born within a given time T of each other? In particular, for T = 24 hours what is the smallest n for which P(n, T) is at least 50%? The well-known classic version of the birthday problem asks for the smallest number of people needed to give a better than 50% chance of at least one birthday match with the assumptions that the birthdays are independently selected from a discrete uniform distribution over 365 days. Using the calculus tool of the limit, we refine two characterisations for P(n, T) and show that they give consistent results with each other and with the classic birthday problem as well. At first thought P(n, 24 hours) should answer the classic birthday question. Yet consider this experiment: suppose Andrea was born at noon on June 1, then potential matching birth times (for the other n - 1 people) with her birth time extend from noon on May 31 to noon on June 2, a period of 48 hours! What this period suggests is that P(n, 24hours) exceeds the classic answer. But, by how much? Read on.

Sign in / Sign up

Export Citation Format

Share Document