Load Balancing with Memory

A standard load balancing model considers placing Ò balls into Ò bins by choosing possible locations for each ball independently and uniformly at random and sequentially placing each in the least loaded of its chosen bins. It is well known that allowing just a small amount of choice ( ¾) greatly improves performance over random placement ( ½). In this paper, we show that similar performance gains occur by introducing memory. We focus on the situation where each time a ball is placed, the least loaded of that ball’s choices after placement is remembered and used as one of the possible choices for the next ball. For example, we show that when each ball gets just one random choice, but can also choose the best of the last ball’s choices, the maximum number of balls in a bin is ÐÓ ÐÓ Ò ¾ÐÓ · Ç´½µ with high probability, where ´½·Ô µ ¾is the golden ratio. The asymptotic performance is therefore better with one random choice and one choice from memory than with two...

• Type: Conference
• Year: 2002
• Where: FOCS
• Authors: Michael Mitzenmacher, Balaji Prabhakar, Devavrat Shah
• Keywords: Ball’s Choices, FOCS 2002, Load Balancing Model, Random Choices, Theoretical Computer Science