Institut für Informatik und Computational Science
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The Domain Name System belongs to the core services of the Internet infrastructure. Hence, DNS availability and performance is essential for the operation of the Internet and replication as well as load balancing are used for the root and top level name servers.
This paper proposes an architecture for credit based server load balancing (SLB) for DNS. Compared to traditional load balancing algorithms like round robin or least connection, the benefit of credit based SLB is that the load balancer can adapt more easily to heterogeneous load requests and back end server capacities. The challenge of this approach is the definition of a suited credit metric. While this was done before for TCP based services like HTTP, the problem was not solved for UDP based services like DNS.
In the following an approach is presented to define credits also for UDP based services. This UDP/DNS approach is implemented within the credit based SLB implementation salbnet. The presented measurements confirm the benefit of the self-adapting credit based SLB approach. In our experiments, the mean (first) response time dropped significantly compared to weighted round robin (WRR) (from over 4 ms to about 0.6 ms for dynamic pressure relieve (DPR)).
Scheduling performance in computational grid can potentially benefit a lot from accurate execution time estimation for parallel jobs. Most existing approaches for the parallel job execution time estimation, however, require ample past job traces and the explicit correlations between the job execution time and the outer layout parameters such as the consumed processor numbers, the user-estimated execution time and the job ID, which are hard to obtain or reveal. This paper presents and evaluates a novel execution time estimation approach for parallel jobs, the user-behavior clustering for execution time estimation, which can give more accurate execution time estimation for parallel jobs through exploring the job similarity and revealing the user submission patterns. Experiment results show that compared to the state-of-art algorithms, our approach can improve the accuracy of the job execution time estimation up to 5.6 %, meanwhile the time that our approach spends on calculation can be reduced up to 3.8 %.