Efficient state estimators for load control policies in scalable Web server clusters

Replication of information across a server cluster provides a promising way to support popular Web sites. However, a Web server cluster requires some mechanism for directing requests to the best server. One common approach is to use the Domain Name Server (DNS) as a centralized scheduler. However, address caching mechanisms and the non-uniformity of the load from different client domains complicate the load balancing issue and make existing scheduling algorithms for traditional distributed systems not applicable to Web server clusters. In this paper, we consider the theoretical DNS policies that require some system state information. We extend them to realistic situations where state information needs to be estimated with low computation and communication overhead. We show that, by incorporating these estimators into the DNS policies, load balancing improves substantially, even if the DNS control is limited to a small portion of client requests. 1

Efficient state estimators for load control policies in scalable web server clusters

Replication of information across a server cluster provides a promising way to support popular Web sites. However, a Web server cluster requires some mechanism for directing requests to the best server. One common approach is to use the Domain Name Server (DNS) as a centralized scheduler. However, address caching mechanisms and the non-uniformity of the load from different client domains complicate the load balancing issue and make existing scheduling algorithms for traditional distributed systems not applicable to Web server clusters. In this paper, we consider the theoretical DNS policies that require some system state information. We extend them to realistic situations where state information needs to be estimated with low computation and communication overhead. We show that, by incorporating these estimators into the DNS policies, load balancing improves substantially, even if the DNS control is limited to a small portion of client requests

Efficient State Estimators for Load Control Policies in Scalable Web Server Clusters

Replication of information across a server cluster provides a promising way to support popular Web sites. However, a Web server cluster requires some mechanism for directing requests to the best server. One common approach is to use the Domain Name Server (DNS) as a centralized scheduler. However, address caching mechanisms and the non-uniformity of the load from different client domains complicate the load balancing issue and make existing scheduling algorithms for traditional distributed systems not applicable to Web server clusters. In this paper, we consider the theoretical DNS policies that require some system state information. We extend them to realistic situations where state information needs to be estimated with low computation and communication overhead. We show that, by incorporating these estimators into the DNS policies, load balancing improves substantially, even if the DNS control is limited to a small portion of client requests. 1. Introduction With the rapid growth o..

Effective task assignment strategies for distributed systems under highly variable workloads

Heavy-tailed workload distributions are commonly experienced in many areas of distributed computing. Such workloads are highly variable, where a small number of very large tasks make up a large proportion of the workload, making the load very hard to distribute effectively. Traditional task assignment policies are ineffective under these conditions as they were formulated based on the assumption of an exponentially distributed workload. Size-based task assignment policies have been proposed to handle heavy-tailed workloads, but their applications are limited by their static nature and assumption of prior knowledge of a task's service requirement. This thesis analyses existing approaches to load distribution under heavy-tailed workloads, and presents a new generalised task assignment policy that significantly improves performance for many distributed applications, by intelligently addressing the negative effects on performance that highly variable workloads cause. Many problems associated with the modelling and optimisations of systems under highly variable workloads were then addressed by a novel technique that approximated these workloads with simpler mathematical representations, without losing any of their pertinent original properties. Finally, we obtain advance queuing metrics (such as the variance of key measurements like waiting time and slowdown that are difficult to obtain analytically) through rigorous simulation