{"title":"Enabling User Driven Web Applications on Remote Computing Resource","authors":"Weijia Xu, Ruizhu Huang, Yige Wang","doi":"10.1109/SERVICES.2018.00038","DOIUrl":"https://doi.org/10.1109/SERVICES.2018.00038","url":null,"abstract":"While CI providers have continued success with the infrastructure-as-a-service model (IaaS), there are increasing demands to offer more user driven service models from domain scientists. We propose a user driven web application that empowers users to run their interactive analytic tasks using CI resources dynamically. Ad-hoc analysis routines can be described with multiple pre-defined task modules in a configuration file that can be shared and re-used. A user can run the web application on CI resource without alleviated privilege or additional service deployment by administrators. The functions and user interface of the web application are automatically initialized based on the configuration file. Therefore, the framework offers a new way for a user to access and utilize remote resources. This new model can effectively reduce the access barrier to remote computing resource offered by CI. In this paper, we describe the proposed architecture of this framework and give a use case example.","PeriodicalId":130225,"journal":{"name":"2018 IEEE World Congress on Services (SERVICES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129503287","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}
F. Yang-Turner, Lawrence Gripper, J. Swann, Trien Do, D. Foster, Denis Volk, Anita Ramanan, Marcus Robinson, T. Peto, D. Crook
{"title":"An Open-Source Azure Solution for Scalable Genomics Workflows","authors":"F. Yang-Turner, Lawrence Gripper, J. Swann, Trien Do, D. Foster, Denis Volk, Anita Ramanan, Marcus Robinson, T. Peto, D. Crook","doi":"10.1109/SERVICES.2018.00033","DOIUrl":"https://doi.org/10.1109/SERVICES.2018.00033","url":null,"abstract":"We present an open-source Azure solution for running scalable genomics workflows. It benefits from state-of-art distributed workflow framework, container and cloud technologies and allows users to create a cluster that is scaled to suit their workload in minutes. We describe the design decisions, solution testing and automation options to support a variety of users for their genomic data analytics. The solution demonstrates a generic and customizable approach to run genomic data analytics workflows on a cloud environment.","PeriodicalId":130225,"journal":{"name":"2018 IEEE World Congress on Services (SERVICES)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127200218","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}
{"title":"On Minimizing the Makespan of a Set of Offline MapReduce Jobs","authors":"Majun He, Wenxia Guo, Houwen Huang, Bo He, Jing Wang, Wenhong Tian","doi":"10.1109/SERVICES.2018.00014","DOIUrl":"https://doi.org/10.1109/SERVICES.2018.00014","url":null,"abstract":"There are quite a few algorithms on minimizing the makespan of a set of offline MapReduce jobs. However, these algorithms are heuristic or suboptimal. The best-known algorithm for minimizing the makespan is 3-approximation by applying Johnson model. In this paper, we evaluate three algorithms in terms of approximation, computational complexity, stability and optimal configuration of the underline cluster for the first time.","PeriodicalId":130225,"journal":{"name":"2018 IEEE World Congress on Services (SERVICES)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127195050","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}
{"title":"Stigmergy-Based QoS Optimisation for Flexible Service Composition in Mobile Communities","authors":"Andrei Palade, S. Clarke","doi":"10.1109/SERVICES.2018.00027","DOIUrl":"https://doi.org/10.1109/SERVICES.2018.00027","url":null,"abstract":"Mobile users can form a service-sharing community within a geographic area by using their mobile devices. Finding Quality of Service (QoS) optimal service compositions in such mobile environments is challenging because of the inherent dynamism in services deployed on mobile devices. Existing service composition proposals for mobile environments either use template-matching composition or require a-priori knowledge about the QoS objectives' weights, which limits the composition flexibility in such environments. This paper introduces a QoS optimisation mechanism for planning-based service composition in mobile environments, where mobile software agents use stigmergic coordination to iteratively explore parts of the distributed service composition space to approximate a set of QoS optimal configurations. We present a mechanism that minimises the exploration of previously identified non-optimal solutions to encourage exploration of different parts of the service space. We evaluate the performance of the proposed approach and compare the results with a baseline variant, a Dijkstra-based, a Greedy and a Random approach. The results show that the proposed approach can achieve higher utility compared to the evaluated proposals at the cost of increased overhead.","PeriodicalId":130225,"journal":{"name":"2018 IEEE World Congress on Services (SERVICES)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114593740","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}
Zujie Ren, Na Yun, Weisong Shi, Youhuizi Li, Jian Wan, Lihua Yu, Xinxin Fan
{"title":"Characterizing the Effectiveness of Query Optimizer in Spark","authors":"Zujie Ren, Na Yun, Weisong Shi, Youhuizi Li, Jian Wan, Lihua Yu, Xinxin Fan","doi":"10.1109/SERVICES.2018.00034","DOIUrl":"https://doi.org/10.1109/SERVICES.2018.00034","url":null,"abstract":"In the big data community, Spark has been widely used for processing interactive queries. Spark employs a query optimizer, called Catalyst, to provides a set of optimization rules and supports Cost-Based Optimization (CBO). In this paper, we investigated the effectiveness of the optimization rules and costbasedoptimization in Catalyst. We conducted comprehensive validation experiments by varying the data volume and cluster scale, and found that the execution time of most TPC-H queries were reduced slightly even when query optimizations are applied. We derived some interesting observations on Catalyst, which can help the community better understand and improve the queryoptimizer of Spark in future.","PeriodicalId":130225,"journal":{"name":"2018 IEEE World Congress on Services (SERVICES)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116366953","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}