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The authors declare that they have no competing interests.
YL carried out the TerraFly deployment pattern studies and v-TerraFly design, participated in the sequence experiment and drafted much of the manuscript. MZ participated the system design, advised and designed most of the experiment and analyzed the experiments results. LW participated in the VM node deployment and helped drafting the manuscript. NR developed the TerraFly technology and major algorithms, designed system layers design and partook in writing the manuscript. All authors read and approved the final manuscript.
GIS application hosts are becoming more and more complicated. Theses hosts’ management is becoming more time consuming and less reliabale decreases with the increase in complexity of GIS applications. The resource management of GIS applications is becoming increasingly important in order to deliver to the user the desired Quality of Service. Map systems often serve dynamic web workloads and involve multiple CPU- and I/O-intensive tiers, which makes it challenging to meet the response time targets of map requests while using the resources efficiently. This paper proposes a virtualized web map service system, v-TerraFly, and its autonomic resource management in order to address this challenge. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. Specifically, this paper proposes new techniques to predict the demand of map workloads online and optimize resource allocations considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak-load-based resource allocation.