{"id":129,"date":"2021-03-01T07:32:00","date_gmt":"2021-03-01T07:32:00","guid":{"rendered":"https:\/\/c3.itec.aau.at\/?p=129"},"modified":"2021-03-16T07:33:28","modified_gmt":"2021-03-16T07:33:28","slug":"paper-accepted-at-the-5th-ieee-icfec-2021","status":"publish","type":"post","link":"https:\/\/c3.itec.aau.at\/index.php\/paper-accepted-at-the-5th-ieee-icfec-2021\/","title":{"rendered":"Paper accepted at the 5th IEEE ICFEC 2021"},"content":{"rendered":"\n<p>The paper \u201cMultilayer Resource-aware Partitioning for Fog Application Placement\u201d has been accepted for publication at the&nbsp;<a href=\"https:\/\/icfec2021.eeecs.qub.ac.uk\/\">5th IEEE international conference on Fog and Edge computing 2021 (ICFEC 2021)<\/a>&nbsp;, with an acceptance rate of 17% for regular papers.<\/p>\n\n\n\n<p><strong>Authors:<\/strong>&nbsp;Zahra Najafabadi Samani, Nishant Saurabh, Radu Prodan<\/p>\n\n\n\n<p><strong>Abstract:<\/strong>&nbsp;Fog computing emerged as a crucial platform for the deployment of IoT applications. The complexity of such applications requires methods that handle the resource diversity and network structure of Fog devices, while maximizing the service placement and reducing resource wastage. Prior studies in this domain primarily focused on optimizing application-specific requirements and fail to address the network topology combined with the different types of resources encountered in Fog devices. To overcome these problems, we propose a multilayer resource-aware partitioning method to minimize the resource wastage and maximize the service placement and deadline satisfaction rates in a Fog infrastructure with high multi-user application placement requests. Our method represents the heterogeneous Fog resources as a multilayered network graph and partitions them based on network topology and resource features. Afterward, it identifies the appropriate device partitions for placing an application according to its requirements, which need to overlap in the same network topology partition. Simulation results show that our multilayer resource-aware partitioning method is able to place twice as many services, satisfy deadlines for three times as many application requests, and reduce the resource wastage up to 15-32 times compared to two availability and resource-aware state-of-the-art methods.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The paper \u201cMultilayer Resource-aware Partitioning for Fog Application Placement\u201d has been accepted for publication at the&nbsp;5th IEEE international conference on Fog and Edge computing 2021 (ICFEC 2021)&nbsp;, with an acceptance rate of 17% for regular papers. Authors:&nbsp;Zahra Najafabadi Samani, Nishant Saurabh, Radu Prodan Abstract:&nbsp;Fog computing emerged as a crucial platform for the deployment of IoT [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-129","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Paper accepted at the 5th IEEE ICFEC 2021 - C3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/c3.itec.aau.at\/index.php\/paper-accepted-at-the-5th-ieee-icfec-2021\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Paper accepted at the 5th IEEE ICFEC 2021 - C3\" \/>\n<meta property=\"og:description\" content=\"The paper \u201cMultilayer Resource-aware Partitioning for Fog Application Placement\u201d has been accepted for publication at the&nbsp;5th IEEE international conference on Fog and Edge computing 2021 (ICFEC 2021)&nbsp;, with an acceptance rate of 17% for regular papers. 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