2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)最新文献

{"title":"Estimating the End-to-End Energy Consumption of Low-Bandwidth IoT Applications for WiFi Devices","authors":"Loic Guegan, Anne-Cécile Orgerie","doi":"10.1109/CloudCom.2019.00049","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00049","url":null,"abstract":"Information and Communication Technology takes a growing part in the worldwide energy consumption. One of the root causes of this increase lies in the multiplication of connected devices. Each object of the Internet-of-Things often does not consume much energy by itself. Yet, their number and the infrastructures they require to properly work have leverage. In this paper, we combine simulations and real measurements to study the energy impact of IoT devices. In particular, we analyze the energy consumption of Cloud and telecommunication infrastructures induced by the utilization of connected devices, and we propose an end-to-end energy consumption model for these devices.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121431688","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":"Social Network Public Opinion Research Based on S-SEIR Epidemic Model","authors":"Min Fang, Linna Li, Liu Yang","doi":"10.1109/CloudCom.2019.00064","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00064","url":null,"abstract":"This paper proposes an S-SEIR-based social network public opinion prediction model. The model takes into account the derivative nature of information in the micro-blog network, as well as the unique propagation rules and textual sentiments in micro-blog. The S-SEIR model of microblog information propagation based on user text sentiment constructed. The factors affecting information dissemination in microblog were studied. The simulation results show that the emotion of the text can directly affect the propagation behavior of the node. Besides, considering the emotional factors of the text can also significantly improve the accuracy of public opinion trend prediction.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115302592","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":"Delta Encoding Overhead Analysis of Cloud Storage Systems Using Client-Side Encryption","authors":"Eric Henziger, Niklas Carlsson","doi":"10.1109/CloudCom.2019.00036","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00036","url":null,"abstract":"With client-side encryption (CSE), a user's data is encrypted before being transferred to a cloud provider. This ensures that only the intended user has access to the information, but complicates effective file synchronization (between different devices and the cloud). Motivated by prior findings that empirically show that the largest performance differences between popular CSE services (CSEs) and non-CSEs typically are related to the implementation of delta encoding solutions to reduce bandwidth usage, in this paper, we evaluate and provide insights into the practical CSE-related delta encoding overheads. First, we use targeted experiments to demonstrate the delta encoding problem associated with CSE and to compare the practical overhead differences associated with three example services implementing delta encoding. Second, we develop an analytic cost model and use it to show that a simple threshold-based CSE policy can reduce the bandwidth and storage usage seen by the best CSE considered here, that such a policy has a provable worst-case overhead within a factor two of the best non-CSE, and typically performs much better. The results are highly encouraging, and show that it is possible to provide CSE at limited additional overhead compared to non-CSE services.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121001027","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":"MovCloud: A Cloud-Enabled Framework to Analyse Movement Behaviors","authors":"Shreya Ghosh, S. Ghosh, R. Buyya","doi":"10.1109/CloudCom.2019.00043","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00043","url":null,"abstract":"Understanding human interests and intents from movement data are fundamental challenges for any location-based service. With the pervasiveness of sensor embedded smartphones and wireless networks and communication, the availability of spatio-temporal mobility trace (timestamped location information) is increasingly growing. Analysing these huge amount of mobility data is another major concern. This paper proposes a cloud-based framework named MovCloud to efficiently manage and analyse mobility data. Specifically, the framework presents a hierarchical indexing schema to store trajectory data in different spatio-temporal resolution, clusters the trajectories based on semantic movement behaviour instead of only raw latitude, longitude point and resolves mobility queries using MapReduce paradigm. MovCloud is implemented over Google Cloud Platform (GCP) and an extensive set of experiments on real-life data yield the effectiveness of the proposed framework. MovCloud has achieved ~ 28% better clustering accuracy and also executed three times faster than the baseline methods.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127014627","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":"Parametric Canonical Correlation Analysis","authors":"Shangyu Chen, Shuo Wang, R. Sinnott","doi":"10.1109/CloudCom.2019.00060","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00060","url":null,"abstract":"Generally, suppose a wave is a linear combination of multiple basis(Not necessarily a sine or cosine waves, it could also be a wavelet, etc.), different types of waves may be similar on some basis, but vary greatly on a certain basis. To address this problem, we introduce a PCCA-based feature extraction method that extends canonical correlation analysis (CCA). The PCCA-based method can train efficient classifiers to rely on only a few samples for periodic signals with support for removing noisy signals. As a demonstration, an efficient system is implemented for the classification of electrocardiogram (ECG) signals by PCCA. The performance is measured using several normal and abnormal ECG signals from the real-world database. These are compared with three commonly-adopted feature extraction techniques using five classes classification tasks related to ECG heartbeats. The AUC(Area under the ROC curve) of the PCCA-based feature extraction technique with two-digits size train dataset for four ECG type-pairs we compared were 0.8805, 0.957, 0.8968 and 1.00 respectively. The experimental results demonstrate that the proposed feature extraction techniques achieve better performance compared to other features extraction techniques with small amount of well-labeled data.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127800545","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":"Generalized Cost-Aware Cloudlet Placement for Vehicular Edge Computing Systems","authors":"Dixit Bhatta, Lena Mashayekhy","doi":"10.1109/CloudCom.2019.00033","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00033","url":null,"abstract":"One of the well-known challenges in Edge Computing is strategic placement of cloudlets. The fundamental goals of this challenge are to minimize the deployment cost of cloudlets and to guarantee minimum latency for users of edge services. However, building cloudlet infrastructure may not be feasible in many situations and areas (e.g., disaster situations, unexpected surge in demand, and remote rural areas). Vehicular edge computing, VEC, introduces mobile cloudlets to augment edge computing capacity, enhance its coverage, and reduce latency significantly. However, efficient cloudlet placement is even more critical in VEC as it is not a long-term decision and needs to be repeated over time. In this paper, we address this challenge by designing a generalized cost-aware cloudlet placement approach that places a set of heterogeneous cloudlets in a region and fully maps user applications to appropriate cloudlets while ensuring their latency requirements. We first formulate the problem as a multi-objective integer programming model in a general deployment scenario. This is a computationally NP-hard problem. To tackle its intractability, we then propose a genetic algorithm-based approach, GACP. We investigate the effectiveness of GACP by performing extensive experiments on multiple deployment scenarios based on New York City OpenData. The results show that GACP obtains close to optimal cost placement in significantly reduced time.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114858666","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":"ProactiveCache: On Reducing Degraded Read Latency of Erasure Coded Cloud Storage","authors":"Rekha Nachiappan, B. Javadi, R. Calheiros, K. Matawie","doi":"10.1109/CloudCom.2019.00041","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00041","url":null,"abstract":"Erasure coding is gaining attraction in cloud storage systems because it improves data reliability with huge cost savings in terms of storage. However, data recovery in erasure codes includes high disk I/O, network traffic and complex decoding that impacts degraded read latency, in case of failures. Data access latency is one of the most important metrics to determine Quality of Service. Reducing degraded latency in erasure coding is vital to improve user performance. To reduce degraded read latency of erasure codes, in this paper, we have proposed a cache based technique called ProactiveCache. This proactively copies objects in failure predicted machine into a cache tier. To deploy ProactiveCache, cloud storage system should employ various failure prediction methods to predict hardware failures. On accurate failure predictions, ProactiveCache eliminates degraded read latency. For evaluation, ProactiveCache is implemented on Ceph object storage. Experimental results show that Proactive-Cache reduces degraded read latency up to 38% and improves throughput by 37%.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116271075","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":"A Deep Transfer Learning Approach for Seizure Detection Using RGB Features of Epileptic Electroencephalogram Signals","authors":"A. Agrawal, Gopal Chandra Jana, Prachi Gupta","doi":"10.1109/CloudCom.2019.00063","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00063","url":null,"abstract":"This paper demonstrates an approach based on Deep Transfer Learning for the classification for Seizure and Non-seizure Electroencephalogram (EEG) signals. Recognizing seizure signals in intelligent way is quite important in clinical diagnosis of Epileptic seizure. Various traditional and deep machine learning techniques are employed for this purpose. However, the Epileptic seizure prediction and classification performance is not satisfactory over small EEG dataset using traditional approaches. The Transfer learning approach overcomes this by reusing the pre-trained networks such as googlenet, resnet101 and vgg19 trained on large Image database. This experiment has been done in two phases: (1) RGB image dataset generated for the seizure and non-seizure EEG signals data of University of Bonn using a novel preprocessing technique, (2) we configured googlenet, resnet101 and vgg19 trained networks to learn a new pattern or features from the RGB image Dataset and finally, above mentioned networks have been used for the classification. The use of Vgg19 network shows greater accuracy among the three but takes comparatively more prediction time. We will mainly emphasize on the results obtained from the googlenet, since it provides effective accuracy taking less time for prediction. The proposed method achieved an accuracy of above 99% for a smaller number of epochs and maximum accuracy of 100% when we increase number of epochs. Experimental outcomes show the proposed approach using googlent achieved better performance w.r.t to many state-of-the-art classification algorithms even on the small EEG dataset. In addition, classification performance of our proposed approach has compared with different traditional machine learning techniques over the same input data.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123691882","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":"A Resource Design Framework to Realize Intent-Based Cloud Management","authors":"Chaofeng Wu, Shingo Horiuchi, Kenichi Tayama","doi":"10.1109/CloudCom.2019.00018","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00018","url":null,"abstract":"More businesses and organizations are choosing clouds to accommodate their workload, e.g., annalistic departments of enterprises use public/private cloud to deploy the analysis function of data, and telecommunication operators deploy Virtual Network Functions (NFVs) such as virtual Evolved Packet Core (vEPC) on clouds to provide telecommunication services. When utilizing the cloud, the cloud user is concerned about the functionality, reliability, performance, etc. of the cloud-based function, which we call the intent or the service requirements. However, to meet the intent, the user needs to tell the cloud resource orchestrator/controller what kinds of Virtual Machines (VMs) are needed and the amount of computing resources necessary to allocate to each VM, which we call resource requirements. In our preliminary work [1], we addressed this problem and proposed an Intent-Based Cloud Management (IBCM) framework to \"translate\" the cloud user's service requirements into resource requirements. In this work, we have focused on realizing a Resource Design Framework (RDF) for IBCM that decides the needed computing resource amount to fulfill the cloud user' intent/ service requirements. The main contributions of this work are as follows: (1) We have systematically analyzed factors that need to be taken into consideration when designing the resources. (2) On the basis of the analysis, we propose the architecture of RDF that is applicable for various cloud-based applications and scenarios. To the best of knowledge, this is the first work to address this issue. (3) We have validated the proposed RDF in an experimental cloud-based machine learning scenario. The experiment results show our RDF is able to infer the performance and operation state with over 90% precision, thus enabling resources to be precisely designed in accordance with performance requirements and operation policy. (4) In addition to precise resource design, we also proposed a service level agreement (SLA) violation prevention mechanism for RDF and verified its effectiveness.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130267821","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":"Learning Resource Recommendation Based on Generalized Matrix Factorization and Long Short-Term Memory Model","authors":"Tianhang Guo, Yiping Wen, Feiran Wang, Junjie Hou","doi":"10.1109/CloudCom.2019.00040","DOIUrl":"https://doi.org/10.1109/CloudCom.2019.00040","url":null,"abstract":"Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally can't deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named G-LSTM. Our model integrates the Generalized Matrix Factorization (GMF) with Long Short-Term Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy.","PeriodicalId":181972,"journal":{"name":"2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129899065","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}