Approximate algorithms for document placement in distributed Web servers

Abstract

We study approximate algorithms for placing a set of documents into M distributed Web servers in this paper. We define the load of a server to be the summation of loads induced by all documents stored. The size of a server is defined in a similar manner. We propose five algorithms. Algorithm 1 balances the loads and sizes of the servers by limiting the loads to k l and the sizes to k s times their optimal values, where 1 klÀ1 þ 1 ksÀ1 1. This result improves the bounds on load and size of servers in [10]. Algorithm 2 further reduces the load bound on each server by using partial document replication, and Algorithm 3 by sorting. Algorithm 4 employs both partial replication and sorting. Last, without using sorting and replication, we give Algorithm 5 for the dynamic placement at the cost of a factor Oðlog MÞ in the time-complexity.

Online Balancing Two Independent Criteria

“…It slightly improves the result for the online algorithm in [14], especially for small values of M . This is the best known result which can be generalized for balancing multi-parameters.…”

“…It bounds the load by 4L using at most 4S storage space, where L and S (defined in Section 2) are commonly used as the trivial worst case lower bounds for load and storage space, respectively. In 2005, we proposed some algorithms [14], including an O(log M )-time online algorithm which bounds the load and storage space of each server by k l L and k s S, respectively, where k l > 2, k s > 2, and [2], where k can be any integer from 1 to M . It bounds the load and storage space by…”

“…There are some different types of approximate solutions for load balancing. A common one is to bound the load of each entity by a limit [4,12,14]. Its variant is to set the limit according to the capacity of each individual entity [3].…”

Caching Techniques on CDN Simulated Frameworks

“…The content of the static cache is identified by applying a content replication algorithm. A wide range of content replication algorithms have been proposed in literature [12,19,21,32,37]. Kangasharju et al [12] use four heuristic methods: 1) random, 2) popularity, 3) greedy-single, and finally 4) greedy-global.…”

“…However, the greedy approaches are not feasible to implement on real applications due to their high complexity. Tse [32] study the content placement problem from another point of view. Specifically, the author presents a set of greedy approaches where the placement is occurred by balancing the loads and sizes of the surrogate servers.…”