Approximate Algorithms for Document Placement in Distributed Web Servers
IEEE Transactions on Parallel and Distributed Systems
Online solutions for scalable file server systems
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Comparison and analysis of ten static heuristics-based Internet data replication techniques
Journal of Parallel and Distributed Computing
Online Balancing Two Independent Criteria
NPC '08 Proceedings of the IFIP International Conference on Network and Parallel Computing
Bicriteria p-Hub Location Problems and Evolutionary Algorithms
INFORMS Journal on Computing
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Computer Communications
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Given the increasing traffic on the World Wide Web (Web), it is difficult for a single popular Web server to handle the demand from its many clients. By clustering a group of Web servers, it is possible to reduce the origin Web server's load significantly and reduce user's response time when accessing a Web document. A fundamental question is how to allocate Web documents among these servers in order to achieve load balancing? In this paper, we are given a collection of documents to be stored on a cluster of Web servers. Each of the servers is associated with resource limits in its memory and its number of HTTP connections. Each document has an associated size and access cost. The problem is to allocate the documents among the servers so that no server's memory size is exceeded, and the load is balanced as equally as possible. In this paper, we show that most simple formulations of this problem are NP-hard, we establish lower bounds on the value of the optimal load, and we show that if there are no memory constraints for all the servers, then there is an allocation algorithm, that is within a factor 2 of the optimal solution. We show that if all servers have the same number of HTTP connections and the same memory size, then a feasible allocation is achieved within a factor 4 of the optimal solution using at most 4 times the optimal memory size. We also provide improved approximation results for the case where documents are relatively small.