2014 IEEE 7th International Conference on Cloud Computing最新文献_第9页

Energy and Performance-Aware Task Scheduling in a Mobile Cloud Computing Environment 移动云计算环境下的能源和性能感知任务调度

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.35

X. Lin, Yanzhi Wang, Q. Xie, Massoud Pedram

{"title":"Energy and Performance-Aware Task Scheduling in a Mobile Cloud Computing Environment","authors":"X. Lin, Yanzhi Wang, Q. Xie, Massoud Pedram","doi":"10.1109/CLOUD.2014.35","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.35","url":null,"abstract":"Mobile cloud computing (MCC) offers significant opportunities in performance enhancement and energy saving in mobile, battery-powered devices. An application running on a mobile device can be represented by a task graph. This work investigates the problem of scheduling tasks (which belong to the same or possibly different applications) in an MCC environment. More precisely, the scheduling problem involves the following steps: (i) determining the tasks to be offloaded on to the cloud, (ii) mapping the remaining tasks onto (potentially heterogeneous) cores in the mobile device, and (iii) scheduling all tasks on the cores (for in-house tasks) or the wireless communication channels (for offloaded tasks) such that the task-precedence requirements and the application completion time constraint are satisfied while the total energy dissipation in the mobile device is minimized. A novel algorithm is presented, which starts from a minimal-delay scheduling solution and subsequently performs energy reduction by migrating tasks among the local cores or between the local cores and the cloud. A linear-time rescheduling algorithm is proposed for the task migration. Simulation results show that the proposed algorithm can achieve a maximum energy reduction by a factor of 3.1 compared with the baseline algorithm.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114025623","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: 42

Image Transfer and Storage Cost Aware Brokering Strategies for Multiple Clouds 多云环境下图像传输和存储成本敏感的代理策略

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.103

J. L. Lucas-Simarro, R. Moreno-Vozmediano, F. Desprez, Jonathan Rouzaud-Cornabas

{"title":"Image Transfer and Storage Cost Aware Brokering Strategies for Multiple Clouds","authors":"J. L. Lucas-Simarro, R. Moreno-Vozmediano, F. Desprez, Jonathan Rouzaud-Cornabas","doi":"10.1109/CLOUD.2014.103","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.103","url":null,"abstract":"Nowadays, Clouds are used to host a large range of services. But between different Cloud Service Providers, the pricing model and the price of individual resources can be very different. Furthermore hosting a service in one Cloud is the major cause of service outage. To increase resiliency and minimize the monetary cost of running a service, it becomes mandatory to span it between different Clouds. Moreover, due to dynamicity of both the service and Clouds, it could be required to migrate a service at run time. Accordingly, this ability must be integrated into the multi-Cloud resource manager, i.e. the Cloud broker. But, when migrating a VM to a new Cloud Service Provider, the VM disk image has to be migrated too. Accordingly, data storage and transfer must be taken into account when choosing if and where an application will be migrated. In this paper, we extend a cost-optimization algorithm to take into account storage costs to approximate the optimal placement of a service. The data storage management consists in taking two decisions: the location of the upload of an image, and keep it on-line during the experiment lifetime or delete it when unused. Based on our experimentations, we show that the storage cost of VM disk image must not be neglected as it was done in previous works. Moreover, we show that using the accurate combinations of storage policies can dramatically reduce the storage cost (from 90% to 14% of the total bill).","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"468 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124383893","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

Scalability and Robustness of Time-Series Databases for Cloud-Native Monitoring of Industrial Processes 用于工业过程云原生监测的时间序列数据库的可扩展性和鲁棒性

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.86

Thomas Goldschmidt, A. Jansen, H. Koziolek, Jens Doppelhamer, Hongyu Pei Breivold

引用次数: 34

Fast Server Deprovisioning through Scatter-Gather Live Migration of Virtual Machines 通过散聚式虚拟机热迁移实现快速服务器资源分配

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.58

Umesh Deshpande, Yang You, Danny Chan, Nilton Bila, Kartik Gopalan

{"title":"Fast Server Deprovisioning through Scatter-Gather Live Migration of Virtual Machines","authors":"Umesh Deshpande, Yang You, Danny Chan, Nilton Bila, Kartik Gopalan","doi":"10.1109/CLOUD.2014.58","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.58","url":null,"abstract":"Traditional metrics for live migration of virtual machines (VM) include total migration time, downtime, network overhead, and application degradation. In this paper, we introduce a new metric, \"eviction time\", defined as the time to evict the entire state of a VM from the source host. Eviction time determines how quickly the source host can be taken offline, or the freed resources re-purposed for other VMs. In traditional approaches for live VM migration, such as pre-copy and post-copy, eviction time is equal to the total migration time, because the source and destination hosts are coupled for the duration of the migration. Eviction time increases if the destination host is slow to receive the incoming VM, such as due to insufficient memory or network bandwidth, thus tying up the source host. We present a new approach, called \"Scatter-Gather\" live migration, which reduces the eviction time when the destination host is resource constrained. The key idea is to decouple the source and the destination hosts. The source scatters the VM's memory state quickly to multiple intermediaries (hosts or middleboxes) in the cluster. Concurrently, the destination gathers the VM's memory from the intermediaries using a variant of post-copy VM migration. We have implemented a prototype of Scatter-Gather in the KVM/QEMU platform. In our evaluations, Scatter-Gather reduces the VM eviction time by up to a factor of 6 while maintaining comparable total migration time against traditional pre-copy and post-copy for a resource constrained destination.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115923892","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: 31

A Cost-Effective and Reliable Cloud Storage 具有成本效益和可靠性的云存储

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.132

Yongmei Wei, Y. W. Foo

引用次数: 3

A Model Driven Framework for Secure Outsourcing of Computation to the Cloud 一个模型驱动的计算安全外包到云的框架

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.145

M. Nassar, A. Erradi, Farida Sabry, Q. Malluhi

引用次数: 4

Traceability for Adaptive Information Security in the Cloud 云环境中自适应信息安全的可追溯性

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.141

A. Nhlabatsi, T. Tun, N. Khan, Y. Yu, A. Bandara, K. Khan, B. Nuseibeh

引用次数: 2

Optimizing Power and Performance Trade-offs of MapReduce Job Processing with Heterogeneous Multi-core Processors 异构多核处理器优化MapReduce作业处理的功耗和性能权衡

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.41

Feng Yan, L. Cherkasova, Zhuoyao Zhang, E. Smirni

{"title":"Optimizing Power and Performance Trade-offs of MapReduce Job Processing with Heterogeneous Multi-core Processors","authors":"Feng Yan, L. Cherkasova, Zhuoyao Zhang, E. Smirni","doi":"10.1109/CLOUD.2014.41","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.41","url":null,"abstract":"Modern processors are often constrained by a given power budget that forces designers to consider different trade-offs, e.g., to choose between either many slow, power-efficient cores, or fewer faster, power-hungry cores, or to select a combination of them. In this work, we design and evaluate a new Hadoop scheduler, called DyScale, that exploits capabilities offered by heterogeneous cores within a single multi-core processor for achieving a variety of performance objectives. A typical MapReduce workload contains jobs with different performance goals: large, batch jobs that are throughput oriented, and smaller interactive jobs that are response-time sensitive. Heterogeneous multi-core processors enable creating virtual resource pools based on the different core types for multi-class priority scheduling. These virtual Hadoop clusters, based on \"slow\" cores versus \"fast\" cores can effectively support different performance objectives that cannot be achieved in a Hadoop cluster with homogeneous processors. Using detailed measurements and extensive simulation study we argue in favor of heterogeneous multi-core processors as they provide performance means for \"faster\" processing of the small, interactive MapReduce jobs (up to 40% faster), while at the same time offer an improved throughput (up to 40% higher) for large, batch job processing.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125181189","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: 16

CURLA: Cloud-Based Spam URL Analyzer for Very Large Datasets CURLA:基于云的垃圾邮件URL分析器，用于非常大的数据集

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.102

Shams Zawoad, Ragib Hasan, Munirul M. Haque, Gary Warner

{"title":"CURLA: Cloud-Based Spam URL Analyzer for Very Large Datasets","authors":"Shams Zawoad, Ragib Hasan, Munirul M. Haque, Gary Warner","doi":"10.1109/CLOUD.2014.102","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.102","url":null,"abstract":"URL blacklisting is a widely used technique for blocking phishing websites. To prepare an effective blacklist, it is necessary to analyze possible threats and include the identified malicious sites in the blacklist. Spam emails are good source for acquiring suspected phishing websites. However, the number of URLs gathered from spam emails is quite large. Fetching and analyzing the content of this large number of websites are very expensive tasks given limited computing and storage resources. Moreover, a high percentage of URLs extracted from spam emails refer to the same website. Hence, preserving the contents of all the websites causes significant storage waste. To solve the problem of massive computing and storage resource requirements, we propose and develop CURLA - a Cloud-based spam URL Analyzer, built on top of Amazon Elastic Computer Cloud (EC2) and Amazon Simple Queue Service (SQS). CURLA allows processing large number of spam-based URLs in parallel, which reduces the cost of establishing equally capable local infrastructure. Our system builds a database of unique spam-based URLs and accumulates the content of these unique websites in a central repository, which can be later used for phishing or other counterfeit websites detection. We show the effectiveness of our proposed architecture using real-life spam-based URL data.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125380969","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: 4

Deadline-Constrained MapReduce Scheduling Based on Graph Modelling 基于图建模的截止日期约束MapReduce调度

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.63

Chien-Hung Chen, Jenn-Wei Lin, S. Kuo

{"title":"Deadline-Constrained MapReduce Scheduling Based on Graph Modelling","authors":"Chien-Hung Chen, Jenn-Wei Lin, S. Kuo","doi":"10.1109/CLOUD.2014.63","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.63","url":null,"abstract":"MapReduce is a software framework for processing data-intensive applications with a parallel manner in cloud computing systems. There are also an increasing number of MapReduce jobs that require deadline guarantees. The existing deadline-concerning scheduling schemes do not consider the two problems in the MapReduce computing environment: slot performance heterogeneity and job time variation. In this paper, we utilize the Bipartite Graph modeling to propose a new MapReduce Scheduler called the BGMRS. The BGMRS can obtain the optimal solution of the deadline-constrained scheduling problem by transforming the problem into a well-known graph problem: minimum weighted bipartite matching. The BGMRS has the following features. It considers the heterogeneous cloud computing environment, such that the computing resources of some nodes cannot meet the deadlines of some jobs. As the job progresses, the BGMRS can dynamically find different computing resources for running the job without violating the job deadline. This is beneficial in the computing resource utilization. The BGMRS can also trade the data locality off against the deadline to make more jobs with deadline guarantees. If the available computing resources of the system cannot meet all job deadlines, the BGMRS can minimize the number of jobs with the deadline violation. Finally, simulation experiments are performed to demonstrate the effectiveness of the BGMRS in the deadline-constrained scheduling.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114659875","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: 10