S. A. Javadi, Piyush Shyam Banginwar, Vaishali Chanana, Rashmi Narvekar, Mitesh Kumar Savita, Anshul Gandhi
{"title":"Improving server utilization via resource-adaptive batch VMs: poster","authors":"S. A. Javadi, Piyush Shyam Banginwar, Vaishali Chanana, Rashmi Narvekar, Mitesh Kumar Savita, Anshul Gandhi","doi":"10.1145/3155016.3155025","DOIUrl":"https://doi.org/10.1145/3155016.3155025","url":null,"abstract":"Public cloud data centers often suffer from low resource utilization [1]. To increase utilization, recent works have proposed running batch workloads next to customer VMs to leverage idle resources [6]. While effective, the key challenge here is interference - the performance degradation of the colocated customer VMs due to resource contention with batch workload VMs at the underlying host server [5].","PeriodicalId":201544,"journal":{"name":"Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference: Posters and Demos","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124991697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pezhman Nasirifard, Aleksander Slominski, Vinod Muthusamy, Vatche Isahagian, H. Jacobsen
{"title":"A serverless topic-based and content-based pub/sub broker: demo","authors":"Pezhman Nasirifard, Aleksander Slominski, Vinod Muthusamy, Vatche Isahagian, H. Jacobsen","doi":"10.1145/3155016.3155024","DOIUrl":"https://doi.org/10.1145/3155016.3155024","url":null,"abstract":"Building scalable, highly available publish/subscribe (pub/sub) systems can require sophisticated algorithms and a tremendous amount of engineering effort. This paper demonstrates a way to build a pub/sub broker on top of the OpenWhisk serverless platform that performs topic-based and content-based matching. This approach radically simplifies the design and significantly reduces the amount of code while still achieving scalability targets. Furthermore, we present a publisher/subscriber client application to interact with the broker as well as an evaluator application that enforces heavy workload on the broker to measure the scalability and latency of the pub/sub system and discover the potential bottlenecks.","PeriodicalId":201544,"journal":{"name":"Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference: Posters and Demos","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114885463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
So-Jung Park, Jungho Lee, So-Young Jun, Kang-Min Kim, SangKeun Lee
{"title":"MoCA+: incorporating user modeling into mobile contextual advertising: demo","authors":"So-Jung Park, Jungho Lee, So-Young Jun, Kang-Min Kim, SangKeun Lee","doi":"10.1145/3155016.3155022","DOIUrl":"https://doi.org/10.1145/3155016.3155022","url":null,"abstract":"In-app advertising has become a significant source of revenue for mobile apps. Mobile contextual advertising is one of the recent approaches to improve the effectiveness of in-app advertising, which seeks to target an app page content that a user is viewing. Typically, mobile contextual advertising is based on the cloud-based architecture, which may cause many privacy concerns, because in-device user data inevitably sends to ad servers. In our previous work [3], we developed a novel mobile contextual advertising platform, called MoCA, which was designed to improve the relevance of in-app ads in a privacy protecting manner. However, MoCA does not explicitly model user interests.","PeriodicalId":201544,"journal":{"name":"Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference: Posters and Demos","volume":"91 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129278165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}