2015 International Conference on Cloud and Autonomic Computing最新文献_第4页

An Empirical Evaluation of NVM Express SSD NVM Express固态硬盘的实证评价

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.41

Yongseok Son, Hara Kang, Hyuck Han, H. Yeom

引用次数: 9

DNS-IDS: Securing DNS in the Cloud Era DNS- ids:云时代的DNS安全防护

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.46

P. Satam, H. Alipour, Y. Al-Nashif, S. Hariri

{"title":"DNS-IDS: Securing DNS in the Cloud Era","authors":"P. Satam, H. Alipour, Y. Al-Nashif, S. Hariri","doi":"10.1109/ICCAC.2015.46","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.46","url":null,"abstract":"Recently, there has been a rapid growth in cloud computing due to their ability to offer computing and storage on demand, its elasticity, and significant reduction in operational costs. However, cloud security is a grand obstacle for full deployment and utilization of cloud services. In this paper, we address the security of the DNS protocol that is widely used to translate the cloud domain names to correct IP addresses. The DNS protocol is prone to attacks like cache poisoning attacks and DNS hijacking attacks that can lead to compromising user's cloud accounts and stored information. We present an anomaly based Intrusion Detection System (IDS) for the DNS protocol (DNS-IDS) that models the normal operations of the DNS protocol and accurately detects any abnormal behavior or exploitation of the protocol. The DNS-IDS system operates in two phases, the training phase and the operational phase. In the training phase, we model the normal behavior of the DNS protocol as a finite state machine and we derive the normal temporal statistics of how normal DNS traffic transition within that state machine and store them in a database. To bound the normal event space, we also apply few known DNS attacks (e.g. Cache poisoning) and store the temporal statistics of the abnormal DNS traffic transition in a separate database. Then we develop an anomaly metric for the DNS protocol that is a function of the temporal statistics for both the normal and abnormal transitions of the DNS by applying classification algorithms like the Bagging algorithm. During the operational phase, the anomaly metric is used to detect DNS attacks (both known and novel attacks). We have evaluated our approach against a wide range of DNS attacks (DNS hijacking, Kaminsky attack, amplification attack, Birthday attack, DNS Rebinding attack). Our results show attack detection rate of 97% with very low false positive alarm rate (0.01397%), and round 3% false negatives.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"45 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":"117238248","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: 13

Secure and Efficient Cloud Computing Framework 安全高效的云计算框架

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.45

L. Tawalbeh, Raad S. Al-Qassas, Nour S. Darwazeh, Y. Jararweh, Fahd M. Al-Dosari

{"title":"Secure and Efficient Cloud Computing Framework","authors":"L. Tawalbeh, Raad S. Al-Qassas, Nour S. Darwazeh, Y. Jararweh, Fahd M. Al-Dosari","doi":"10.1109/ICCAC.2015.45","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.45","url":null,"abstract":"Cloud computing is a very useful solution to many individual users and organizations. It can provide many services based on different needs and requirements. However, there are many issues related to the user data that need to be addressed when using cloud computing. Among the most important issues are: data ownership, data privacy, and storage. The users might be satisfied by the services provided by the cloud computing service providers, since they need not worry about the maintenance and storage of their data. On the other hand, they might be worried about unauthorized access to their private data. Some solutions to these issues were proposed in the literature, but they mainly increase the cost and processing time since they depend on encrypting the whole data. In this paper, we are introducing a cloud computing framework that classifies the data based on their importance. In other words, more important data will be encrypted with more secure encryption algorithm and larger key sizes, while less important data might even not be encrypted. This approach is very helpful in reducing the processing cost and complexity of data storage and manipulation since we do not need to apply the same sophisticated encryption techniques to the entire users data. The results of applying the proposed framework show improvement and efficiency over other existing frameworks.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"40 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":"123449739","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: 13

A Sensor-Actuator Model for Data Center Optimization 数据中心优化的传感器-执行器模型

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.13

Jakub Krzywda, Per-Olov Östberg, E. Elmroth

引用次数: 1

Automating Platform Selection for MapReduce Processing in the Cloud 云中MapReduce处理的自动化平台选择

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-01 DOI: 10.1109/ICCAC.2015.15

Zhuoyao Zhang, L. Cherkasova, B. T. Loo

{"title":"Automating Platform Selection for MapReduce Processing in the Cloud","authors":"Zhuoyao Zhang, L. Cherkasova, B. T. Loo","doi":"10.1109/ICCAC.2015.15","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.15","url":null,"abstract":"Cloud computing enables a user to quickly provision any desirable size Hadoop cluster and then pay for the time these resources were used. With the same budget, a user can rent a larger amount of resources and process its scale-out application in a shorter time, or rent a smaller size cluster but pay a for longer processing time. Moreover, there is a variety of different types of VM instances in the Cloud (e.g., small, medium, or large EC2 instances). The capacity differences of the offered VMs are reflected in VM's pricing. Therefore, again for the same price a user can get a variety of \"similar capacity\" Hadoop clusters based on different VM instance types. We observe that performance of MapReduce applications may vary significantly on different platforms. This makes a selection of the best cost/performance platform for a given workload a non-trivial problem, especially when it contains multiple jobs with different platform preferences. In this work1, we design a framework for solving the following problem: given a completion time target for a set of MapReduce jobs, determine a homogeneous or heterogeneous Hadoop cluster configuration (i.e., the number, types of VMs, and the job schedule) for processing these jobs within a given deadline while minimizing the rented infrastructure cost. We generalize the proposed framework to take into account possible node failures and degraded performance goals. Our evaluation study with Amazon EC2 platform reveals that for different workload mixes, an optimized platform choice may result in 45-68% cost savings for achieving the same performance objectives when using different (but seemingly equivalent) choices. Moreover, depending on a workload the heterogeneous solution may outperform the homogeneous cluster solution by 26-42%. We analyze and discuss possible causes for observed performance differences of MapReduce processing on the Amazon EC2 platforms.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115895037","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

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution 自学习云控制器:面向知识进化的模糊q -学习

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-07-02 DOI: 10.1109/ICCAC.2015.35

Pooyan Jamshidi, Amir Molzam Sharifloo, C. Pahl, Andreas Metzger, G. Estrada

引用次数: 70

Towards Data-Driven Autonomics in Data Centers 迈向数据中心的数据驱动自治

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-05-19 DOI: 10.1109/ICCAC.2015.19

A. Sîrbu, Özalp Babaoglu

{"title":"Towards Data-Driven Autonomics in Data Centers","authors":"A. Sîrbu, Özalp Babaoglu","doi":"10.1109/ICCAC.2015.19","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.19","url":null,"abstract":"Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing machine state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114271786","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: 18