2015 International Conference on Cloud and Autonomic Computing最新文献_第2页

End-to-End QoS Prediction Model of Vertically Composed Cloud Services via Tensor Factorization 基于张量分解的垂直组合云服务端到端QoS预测模型

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.29

Raed Karim, Chen Ding, A. Miri, Md Shahinur Rahman

{"title":"End-to-End QoS Prediction Model of Vertically Composed Cloud Services via Tensor Factorization","authors":"Raed Karim, Chen Ding, A. Miri, Md Shahinur Rahman","doi":"10.1109/ICCAC.2015.29","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.29","url":null,"abstract":"The rapid growth of published cloud services in the Internet makes the service selection and recommendation a challenging task for both users and service providers. Services' QoS properties such as response time and throughput are often used to select the best of functionally equivalent services. In cloud environment, software services collaborate with other complementary services to provide complete solutions to end users. The service selection is done based on QoS requirements submitted by end users. Software providers alone cannot guarantee users' QoS requirements. These requirements must be end-to-end, representing all collaborating services in a solution. In this paper, we propose an end-to-end QoS prediction model for vertically composed services which are composed of three types of cloud services: software (SaaS), infrastructure (IaaS) and data (DaaS). It exploits historical QoS values and cloud services and users information to predict unknown end-to-end QoS values of composite services. The experiments demonstrate that our proposed model outperforms other prediction models in terms of the prediction accuracy. We also study the impact of different parameters on the prediction results. In the experiments, we used real cloud services' QoS data collected using our developed QoS monitoring and collecting system.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133486904","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}

引用次数: 6

Toward Autonomic Cloud: Automatic Anomaly Detection and Resolution 走向自治云:自动异常检测和解决

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.32

R. Ahad, Eric S. Chan, Adriano Santos

引用次数: 12

Auto-Scoring of Personalised News in the Real-Time Web: Challenges, Overview and Evaluation of the State-of-the-Art Solutions 实时网络中个性化新闻的自动评分:最先进解决方案的挑战，概述和评估

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.9

Paris Carbone, Vladimir Vlassov

引用次数: 1

SCOUT: A Monitor and Profiler of Grid Resources for Large-Scale Scientific Computing SCOUT:用于大规模科学计算的网格资源监视器和分析器

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.39

Md. Azam Hossain, Hieu Trong Vu, Jik-Soo Kim, Myungho Lee, Soonwook Hwang

{"title":"SCOUT: A Monitor and Profiler of Grid Resources for Large-Scale Scientific Computing","authors":"Md. Azam Hossain, Hieu Trong Vu, Jik-Soo Kim, Myungho Lee, Soonwook Hwang","doi":"10.1109/ICCAC.2015.39","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.39","url":null,"abstract":"Computational Grids consist of heterogeneous collections of geographically distributed computing resources and have supported numerous scientific applications that require substantial amounts of computing power and storage space. From the point of view of scientists who want to leverage these Grid computing resources, effectively locating appropriate computing resources with minimized allocation overheads is crucial to successfully execute large-scale scientific applications. However, Grid resource availability is highly unstable and current Grid Information Service (GIS) does not provide accurate state information of computing resources. This can make it very difficult for users and systems (Schedulers, Resource brokers) to schedule the jobs in the Grid system and to map tasks on appropriate available resources. In this paper, we present SCOUT system that can provide scientific users with current state information about Grid computing resources including the number of available CPU cores and average response time to get resources allocated. With the help of SCOUT, we can periodically profile resource availability of the Computing Elements (CE) in Grids and monitor their average response time and performance. It provides a mechanism to find out the number of available CPU cores required for the applications to execute their tasks within shortest expected time which can accelerate the productivity of leveraging Grid computing resources for solving complex and challenging scientific problems. We have performed resource profiling based on SCOUT system on two different VO(Virtual Organization)s during one month period and based on that information, we could successfully perform large-scale drug repositioning simulations over 2,000 CPU cores.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134201980","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}

引用次数: 3

NDSS: A Named Data Storage System NDSS:命名数据存储系统

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.12

Shuo Chen, Junwei Cao, Lipeng Zhu

引用次数: 6

Monitoring and Management of Service Level Agreements in Cloud Computing 云计算中服务水平协议的监控与管理

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.28

S. Anithakumari, K. Sekaran

引用次数: 12

CLaaS: Cybersecurity Lab as a Service -- Design, Analysis, and Evaluation 网络安全实验室即服务——设计、分析和评估

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.34

Cihan Tunc, S. Hariri, Fabian De La Peña Montero, Farah Fargo, P. Satam

引用次数: 6

HDFS Heterogeneous Storage Resource Management Based on Data Temperature 基于数据温度的HDFS异构存储资源管理

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.33

Rohith Subramanyam

{"title":"HDFS Heterogeneous Storage Resource Management Based on Data Temperature","authors":"Rohith Subramanyam","doi":"10.1109/ICCAC.2015.33","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.33","url":null,"abstract":"Hadoop has traditionally been used as a large-scale batch processing system. However, interactive applications such as Facebook Messenger are becoming increasingly prominent in the Hadoop world. A key bottleneck in adapting Hadoop to real-time processing is disk data transfer rate. The advent of Solid State Drives (SSDs) holds great promise in this regard as they provide bandwidth on the orders of magnitude better than that of rotating disks. But due to their higher cost per gigabyte, a common approach is to have heterogeneous storage types. This paper presents a Storage Resource Management technique that automatically and dynamically moves data across this tiered storage based on Data Temperature, migrating \"hot\" data towards faster storage and \"cold\" data towards inexpensive archival storage. Thus, the cluster adapts based on the characteristics of the workloads over time to make effective use of the scarce expensive storage. Finally, I evaluate my modified version of the Hadoop Distributed File System (HDFS) against the vanilla version to compare their performances. The results are promising and show an improvement in both read and write performance with a significant improvement in read performance.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121332067","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}

引用次数: 8

Learning-Based Localized Offloading with Resource-Constrained Data Centers 基于学习的资源受限数据中心局部卸载

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.26

Jia Guo, James Bradley Wendt, M. Potkonjak

引用次数: 0

The Unobservability Problem in Clouds 云中的不可观测性问题

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.11

Anshul Gandhi, Parijat Dube, A. Karve, Andrzej Kochut, Harsha Ellanti

{"title":"The Unobservability Problem in Clouds","authors":"Anshul Gandhi, Parijat Dube, A. Karve, Andrzej Kochut, Harsha Ellanti","doi":"10.1109/ICCAC.2015.11","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.11","url":null,"abstract":"The cloud is not transparent. Users of cloud computing cannot control or monitor important information about their VMs or services, such as placement, true resource allocation, virtualization overhead, etc. Likewise, cloud providers cannot obtain important information about their users' deployment such as the application model, the role of each VM, etc. While such information is not required to be revealed, we claim that this lack of information prevents users from fully understanding their resource availability, and limits the feasibility of various performance management solutions. We refer to this lack of information as the \"Unobservability\" problem. In this paper, we describe the unobservability problem and present various use cases from our experience managing a medium-scale cloud deployment with several hundred VMs and experiments on EC2 that highlight the severe impact of unobservability on performance, and the limitations it imposes on users and cloud providers. We show that, interestingly, unobservability often diminishes the potential benefits of cloud computing. To address unobservability, we present and evaluate a practical solution to the unobservability problem that reveals important unobservable information without requiring any instrumentation or changes to the cloud.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129918900","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}

引用次数: 11