{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:27:02Z","timestamp":1740137222402,"version":"3.37.3"},"reference-count":58,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2018,11,4]],"date-time":"2018-11-04T00:00:00Z","timestamp":1541289600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871308","91538102 and 41271400"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transactions in GIS"],"published-print":{"date-parts":[[2018,12]]},"abstract":"Abstract<\/jats:title>Remote geovisualization has gained momentum to support large\u2010scale geospatial data analysis and complex decision\u2010making over the last few years. Cloud computing, due to its capabilities to deliver on\u2010demand computing resources, has been embraced to develop and deploy interactive and scalable remote geovisualization applications. However, current cloud computing frameworks do not offer a versatile resource management scheme that is readily applicable for online remote visualization services, which usually require maintaining a satisfactory service level over time under dynamic workloads. To address this gap, we propose an automatic cloud resource management approach based on a bi\u2010level scheduling and horizontal scaling scheme to exploit cloud resources efficiently. At the lower level, a dynamic task\u2010scheduling scheme using collaborative filtering techniques is proposed to allocate virtual cloud resources to execute sub\u2010tasks. The scheduling scheme considers spatio\u2010temporal patterns presented in visualization views. At the upper level, reinforcement learning is adopted to perform resource auto\u2010scaling based on a reward function that integrates three different facets, namely: time cost, resource cost, and service stability. The original reinforcement learning algorithm is improved in two main aspects: (1) considering the delay of resource provisioning that is common in cloud environments; and (2) using online Gaussian estimation to estimate Q values. Task scheduling and auto\u2010scaling interact with each other and are integrated to deliver a comprehensive and responsive resource management solution. Experimental results demonstrate that our approach outperforms several existing cloud resource management methods. The proposed approach is also applicable for other interactive visualization applications, which have similar workload characteristics and performance requirements as interactive remote geovisualization.<\/jats:p>","DOI":"10.1111\/tgis.12479","type":"journal-article","created":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T02:31:36Z","timestamp":1541385096000},"page":"1437-1461","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic cloud resource management for interactive remote geovisualization"],"prefix":"10.1111","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0683-4669","authenticated-orcid":false,"given":"Tong","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing Wuhan University China"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography 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