Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Optimal static load balancing in distributed computer systems
Journal of the ACM (JACM)
Optimal allocation of file servers in a local network environment
IEEE Transactions on Software Engineering
A vertex-allocation theorem for resources in queuing networks
Journal of the ACM (JACM)
Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
Optimal allocation of multiple class resources in computer systems
SIGMETRICS '88 Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Generative networkload models for a single server environment
SIGMETRICS '94 Proceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Optimal Selection of CPU Speed, Device Capacities, and File Assignments
Journal of the ACM (JACM)
A General Model for Optimal Static Load Balancing in Star Network Configurations
Performance '84 Proceedings of the Tenth International Symposium on Computer Performance Modelling, Measurement and Evaluation
Hi-index | 0.24 |
The transparent way in which the remote resources are shared with improved performance justify the rapid replacement of single Large Computer Systems by Local Area Networks (LANs). Information (data) sharing is one of the major goals of LANs and file servers are one of the means of supporting data sharing in LAN environments. A typical LAN might have one or more file servers and in a network consisting of multiple file servers making effective use of them is an important design issue. One of the means of effectively utilizing the file servers is to restrict the number of clients using the server (Client Server Allocation Problem). The existing techniques for solving the client server allocation problem either treat the clients on the network as statistically identical or treat them as entirely independent. However, the evaluation studies conducted in the context of computer networks show that clients on a network are neither identical nor entirely independent, but can be grouped into different classes based on their workload generation pattern. Thus, an algorithm which solves the client server allocation problem taking care of the workload generation pattern is required. In this paper, we propose a new iterative algorithm for solving such problems based on the concept of demand inflation. The time and space complexities of the proposed algorithm are compared with that of the existing algorithms. The application of the proposed algorithm is illustrated with an example.