Upstream: smooth weighted round-robin balancing.
For edge case weights like { 5, 1, 1 } we now produce { a, a, b, a, c, a, a }
sequence instead of { c, b, a, a, a, a, a } produced previously.
Algorithm is as follows: on each peer selection we increase current_weight
of each eligible peer by its weight, select peer with greatest current_weight
and reduce its current_weight by total number of weight points distributed
among peers.
In case of { 5, 1, 1 } weights this gives the following sequence of
current_weight's:
a b c
0 0 0 (initial state)
5 1 1 (a selected)
-2 1 1
3 2 2 (a selected)
-4 2 2
1 3 3 (b selected)
1 -4 3
6 -3 4 (a selected)
-1 -3 4
4 -2 5 (c selected)
4 -2 -2
9 -1 -1 (a selected)
2 -1 -1
7 0 0 (a selected)
0 0 0
LVS
1 2 3 4 5 6 7 8 91011121314151617181920
Supposing that there is a server setS={S0, S1, …, Sn-1};
W(Si) indicates the weight of Si;
i indicates the server selected last time, and i is initialized with -1;
cw is the current weight in scheduling, and cw is initialized with zero;
max(S) is the maximum weight of all the servers in S;
gcd(S) is the greatest common divisor of all server weights in S;while(true){i=(i + 1) mod n;if(i==0){cw= cw - gcd(S);if(cw <=0){cw= max(S);if(cw==0)return NULL;}}if(W(Si) >= cw)return Si;}