2020 IEEE International Conference on Cloud Engineering (IC2E)最新文献

{"title":"Characterizing Public Cloud Resource Contention to Support Virtual Machine Co-residency Prediction","authors":"Xinlei Han, Raymond Schooley, Delvin Mackenzie, O. David, W. Lloyd","doi":"10.1109/IC2E48712.2020.00024","DOIUrl":"https://doi.org/10.1109/IC2E48712.2020.00024","url":null,"abstract":"Hypervisors used to implement virtual machines (VMs) for infrastructure-as-a-service (IaaS) cloud platforms have undergone continued improvements for the past decade. VM components including CPU, memory, network, and storage I/O have evolved from full software emulation, to paravirtualization, to hardware virtualization. While these innovations have helped reduce performance overhead when simulating a computer, considerable performance loss is still possible in the public cloud from resource contention of co-located VMs. In this paper, we investigate the extent of performance degradation from resource contention by leveraging well-known benchmarks run in parallel across three generations of virtualization hypervisors. Using a Python-based test harness we orchestrate execution of CPU, disk, and network I/O bound benchmarks across up to 48 VMs sharing the same Amazon Web Services dedicated host server. We found that executing benchmarks on hosts with many idle Linux VMs produced unexpected performance degradation. As public cloud users are interested in avoiding resource contention from co-located VMs, we next leveraged our dedicated host performance measurements as independent variables to train models to predict the number of co-resident VMs. We evaluated multiple linear regression and random forest models using test data from independent benchmark runs across 96 vCPU dedicated hosts running up to 48 x 2 vCPU VMs where we controlled VM placements. Multiple linear regression over normalized data achieved R2=.942, with mean absolute error of VM co-residency predictions of ±1.61 VMs. We then leveraged our models to infer VM co-residency among a set of 50 VMs on the public cloud, where co-location data is unavailable. Here models cannot be independently verified, but results suggest the relative occupancy level of public cloud hosts enabling users to infer when their VMs reside on busy hosts. Our results characterize how recent hypervisor and hardware advancements are addressing resource contention, while demonstrating the potential to leverage co-located benchmarks for VM co-residency prediction in a public cloud.","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130040071","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":"Data Flow Dependent Component Placement of Data Processing Cloud Applications","authors":"Michael Zimmermann, Uwe Breitenbücher, Kálmán Képes, F. Leymann, Benjamin Weder","doi":"10.1109/IC2E48712.2020.00016","DOIUrl":"https://doi.org/10.1109/IC2E48712.2020.00016","url":null,"abstract":"With the ongoing advances in the area of cloud computing, Internet of Things, Industry 4.0, and the increasing prevalence of cyber-physical systems and devices equipped with sensors, the amount of data generated every second is rising steadily. Thereby, the gathering of data and the creation of added value from this data is getting easier and easier. However, the increasing volume of data stored in the cloud leads to new challenges. Analytics software and scalable platforms are required to evaluate the data distributed all over the internet. But with distributed applications and large data sets to be handled, the network becomes a bottleneck. Therefore, in this work, we present an approach to automatically improve the deployment of such applications regarding the placement of data processing components dependent on the data flow of the application. To show the practical feasibility of our approach, we implemented a prototype based on the open-source ecosystem OpenTOSCA. Moreover, we evaluated our prototype using various scenarios.","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"68 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114006639","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":"IoTWC: Analytic Hierarchy Process Based Internet of Things Workflow Composition System","authors":"Yinhao Li, D. Jha, G. Aujla, G. Morgan, Albert Y. Zomaya, R. Ranjan","doi":"10.1109/IC2E48712.2020.00007","DOIUrl":"https://doi.org/10.1109/IC2E48712.2020.00007","url":null,"abstract":"Internet of Things (IoT) allows the creation of virtually endless connections into a global array of distributed intelligence. However, the design, development, and deployment of IoT applications are complex and complicated due to various unwarranted challenges. For instance, addressing the IoT application users’ subjective and objective opinions with IoT workflow instances remains a challenge for the design of a more holistic approach. Moreover, the complexity of IoT applications increased exponentially due to the heterogeneous nature of the Edge/Cloud services, utilised with the aim of lowering latency in data transformation and increase re-usability. Hence, in this paper, we present an IoT workflow composition system (IoTWC) to allow IoT users to pipeline their workflows with proposed IoT workflow activity abstract patterns. IoTWC leverages the analytic hierarchy process (AHP) to compose the multi-level IoT workflow that satisfies the requirements of any IoT application. Moreover, the users are befitted with recommended IoT workflow configurations using an AHP based multi-level composition framework. The proposed IoTWC is validated on a user case study to evaluate the coverage of IoT workflow activity abstract patterns and a real-world scenario for smart buildings. The comprehensive analysis shows the effectiveness of IoTWC in terms of IoT workflow abstraction and composition.","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127167224","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":"EdgeSum: Edge-Based Video Summarization with Dash Cams","authors":"Jayden King, L. Huang, Di Wu, Yipeng Zhou, Young Choon Lee","doi":"10.1109/IC2E48712.2020.00011","DOIUrl":"https://doi.org/10.1109/IC2E48712.2020.00011","url":null,"abstract":"With billions of Internet of Things (IoT) devices, such as sensors, security cameras, and dash cams, generating huge amounts of data and transferring it to the cloud, it creates a network bottleneck with the increase of latency and bandwidth usage. Edge computing (EC) as an emerging technology is able to lighten the burden by bringing computational processes to the network edge close to data sources. According to Cisco [1], 75% of generated data consuming network bandwidth is video data. Traditionally video data is handled in the cloud due to its requirements of large storage space and high computational capacity. Dash cams are becoming prevalent as more drivers include them in their vehicles for surveillance or future incident investigation purposes. They are one representative type of IoT device that constantly generates large amounts of data. With such small storage space, the loop mechanism is a common implementation which allows the device to ‘override’ older video files when it has reached maximum storage capacity. In this paper, we design EdgeSum as an edge-based video summarization framework that utilizes mobile devices in the form of edge servers to summarize/compress video data of dash cams before uploading to the cloud for further processing and archiving purposes. The results support the feasibility of the framework in real-world practical applications including vehicles in driving mode, vehicles in parked mode, and surveillance applications. Based on the results, the framework delivers satisfactory performance in reducing latency and bandwidth usage by compressing the video data through summarization technique.","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130033013","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":"Realising Edge Analytics for Early Prediction of Readmission: A Case Study","authors":"Yucen Nan, Wei Li, Feng Lu, Flávia Coimbra Delicato, Albert Y. Zomaya","doi":"10.1109/IC2E48712.2020.00017","DOIUrl":"https://doi.org/10.1109/IC2E48712.2020.00017","url":null,"abstract":"The post-discharge support is increasingly suggested for stroke patients to be discharged earlier and start rehabilitation at home. Considering that stroke patients usually have a high chance of recurrence, a good prognostic program is essential to improve diagnostic capabilities while reducing readmission rate to further save medical sources. In this context, various machine learning methods have been leveraged to obtain diagnostic findings and guide further treatments. However, those approaches mainly focus on performing analysis using a single data source obtained from the hospital, which could ignore the information complementarity between different groups of features and several subtle and discrete differences of physical interpretation among them. In this paper, we propose an Edge-based system design for post-stroke surveillance and warning prediction, called PSMART (Post-Stroke Mobile Auxiliary Rudiment Treatment), for processing enriched pathogenic factors of ischemic stroke from multi-sensors (views) to make readmission warning predictions. Our approach can considerably enrich the distinctive features from raw data, as well as exploit the consistency and complementary proprieties of different views, leading to better learning results. We evaluate the performance of the proposed approach on a real-world dataset, and the accuracy can reach up to 98.98%. Moreover, experiment results also show that our proposed approach can provide better accuracy when compared to the single-view ones.","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133935733","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":"Facilitating plausible deniability for cloud providers regarding tenants’ activities using trusted execution","authors":"Dan O'Keeffe, Thomas Pasquier, Asma Vranaki, D. Eyers","doi":"10.1109/IC2E48712.2020.00013","DOIUrl":"https://doi.org/10.1109/IC2E48712.2020.00013","url":null,"abstract":"A cloud provider that can technically determine tenants’ operations may be compelled to disclose such activities by law enforcement agencies (LEAs). The situation gets even more complex when multiple LEAs across different jurisdictions are involved, e.g., because of the distributed locations of cloud servers and data storage. Yet cloud providers typically do not need or want to know about their tenants’ activities, other than measuring how such activities incur expenses for using cloud resources.Thus mechanisms should be developed for cloud providers to have sufficient plausible deniability with regards to the processing being carried out by tenants on their platform, in jurisdictions that permit cloud providers to avoid liabilities in this way. Symmetrically, such mechanisms could protect tenants from legal over-reach, for example, when the country in which the cloud provider is incorporated could force disclosure of the processing carried out by cloud tenants.But to what extent can cloud providers acquire plausible deniability? Current discussions regarding risk have focused on data confidentiality and integrity. We argue that processing operations can equally reveal sensitive information—such as trade secrets and business processes—and that for some classes of application both data protection and algorithm protection are necessary.In this paper, we examine the legal and technical motivations for achieving plausible deniability in cloud interactions. We demonstrate the likely performance overhead of using containers secured with technologies such as Intel SGX. Further, we examine the current limitations of our proposed plausible deniability mechanisms, and outline a potential approach for enabling lawful access to enclaves subject to appropriate judicial oversight.","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131552874","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":"CloudPush: Smart Delivery of Push Notification to Secure Multi-User Support for IoT Devices","authors":"Md. Shamsul Arifin Mozumder, Muhammad Abdullah Adnan","doi":"10.1109/IC2E48712.2020.00008","DOIUrl":"https://doi.org/10.1109/IC2E48712.2020.00008","url":null,"abstract":"Internet of things (IoT) with a cloud server has become popular nowadays and it’s going to be used in almost every aspect of human life. All devices will be connected to the internet and can communicate with each other where cloud plays an import role in the IoT environment. However, often cloud-enabled IoT environments have potential security risks and do not have multi-user support. In this paper, we discuss an IoT push messaging framework named CloudPush framework consisting of a client application, IoT devices, and a cloud system. In this framework, IoT devices can work on an ad hoc network and send event notifications to the client applications through the cloud. We show that CloudPush framework has vulnerabilities while maintaining multiple user accounts between a client application and IoT device in the cloud. The client application can receive unintended and unauthorized notification messages due to the lack of managing multiple accounts properly in the cloud server. To ensure stability in this framework while sending push notifications through the cloud by IoT devices, we discuss potential vulnerabilities and their solutions in this paper. We demonstrate that the aggregated throughput of CloudPush framework is 12-15% better than IoTivity framework even though IoTivity does not support multi-user for an IoT resource and a client application. If IoT device’s events are sent to multiple client applications i.e. events are distributed among client applications, then the throughput of CloudPush framework increases to 12-25% compared with the IoTivity framework because the CloudPush framework runs optimized searching algorithm in cloud and scales event notifications in both cloud server and cloud push service layer. For a secured multi-user support, notification message data is encrypted that makes the CloudPush system 3-5% slower but still, it performs 9-12% better than the IoTivity framework.","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122497420","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":"IC2E 2020 Breaker Page","authors":"","doi":"10.1109/ic2e48712.2020.00003","DOIUrl":"https://doi.org/10.1109/ic2e48712.2020.00003","url":null,"abstract":"","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128753333","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":"IC2E 2020 Commentary","authors":"","doi":"10.1109/ic2e48712.2020.00002","DOIUrl":"https://doi.org/10.1109/ic2e48712.2020.00002","url":null,"abstract":"","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993452","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":"IC2E 2020 TOC","authors":"","doi":"10.1109/ic2e48712.2020.00004","DOIUrl":"https://doi.org/10.1109/ic2e48712.2020.00004","url":null,"abstract":"","PeriodicalId":173494,"journal":{"name":"2020 IEEE International Conference on Cloud Engineering (IC2E)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121449453","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}