{"id":12888,"date":"2014-10-06T01:16:26","date_gmt":"2014-10-05T22:16:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=12888"},"modified":"2014-10-06T01:16:26","modified_gmt":"2014-10-05T22:16:26","slug":"load-balancing-in-data-warehouse-evolution-and-perspectives","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12888","title":{"rendered":"Load Balancing in Data Warehouse &#8211; Evolution and Perspectives"},"content":{"rendered":"<p>The problem of load balancing is one of the crucial features in distributed data warehouse systems. In this article original load balancing algorithms are presented. The Adaptive Load Balancing Algorithms for Queries (ALBQ) and the algorithms that use grammars and learning machines in managing the ETL process. These two algorithms base the load balancing on queries analysis, however the methods of query analysis are quite different. While ALBQ bases on calculation of computing power and available system assets, the gaSQL algorithm includes direct grammar analysis of the SQL query language and its classification using machine learning. The WINE-HYBRIS algorithm that uses the CUDA architecture and Cloud Computing will be presented as a platform for developing the gaSQL algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The problem of load balancing is one of the crucial features in distributed data warehouse systems. In this article original load balancing algorithms are presented. The Adaptive Load Balancing Algorithms for Queries (ALBQ) and the algorithms that use grammars and learning machines in managing the ETL process. These two algorithms base the load balancing on [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,750,1782,14,1025,20,466,253],"class_list":["post-12888","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-cloud","tag-computer-science","tag-cuda","tag-machine-learning","tag-nvidia","tag-nvidia-geforce-9600-gt","tag-nvidia-geforce-gtx-260"],"views":2358,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12888","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=12888"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12888\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12888"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12888"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12888"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}