2022 IEEE International Conference on Cloud Engineering (IC2E)最新文献

Privacy-Preserving Storage in the Fog 雾中的隐私保护存储

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00022

Michael Fabsich, Dominik Kaaser, Vasileios Karagiannis, Stefan Schulte

引用次数: 0

UnifiedNetManagement: Unified Network Management for Heterogeneous Edge Enterprise Network UnifiedNetManagement:异构边缘企业网络的统一网络管理

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00028

C. Chen, Uyen Chau, A. Mercian, Faraz Ahmed, P. Sharma

{"title":"UnifiedNetManagement: Unified Network Management for Heterogeneous Edge Enterprise Network","authors":"C. Chen, Uyen Chau, A. Mercian, Faraz Ahmed, P. Sharma","doi":"10.1109/IC2E55432.2022.00028","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00028","url":null,"abstract":"The data explosion over the past few years has made it necessary to connect edge networks to the more powerful cloud infrastructure. Given the heterogeneity, dis-aggregation, and varied functionalities of the edge, it makes it more challenging to monitor and manage the edge using a single control plane. This is especially relevant to Edge Enterprise Network, which spans across Edge Access Networks, Wireless Access Networks, Sensor networks etc. This data explosion at the edge has also necessitated the edge Enterprise Network to support varied functionality, causing it to comprise of multiple modules to provide best performance. This renders the edge-Network cumbersome to manage and very susceptible to faults. Consequently, the highly increasing need to embrace cloud-native technology for Edge computation has become significant for service and application providers to deploy high computation functionalities closer to end users. This motivates us to build an intelligent framework to maintain and manage Edge Enterprise Networks. In this paper, we present “UnifiedNetManagement”, our Hybrid Cloud solution, that integrates any edge network to the cloud using an event-driven Workflow Manager to provide monitoring, scheduling, and troubleshooting capabilities for a smart edge network management system. We observe close to 2x performance improvement over traditional disaggregated network management.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124689891","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: 0

Message from the Technical Program Chairs: IC2E 2022 技术项目主席致辞:IC2E 2022

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/ic2e55432.2022.00005

引用次数: 0

Is Ant Colony System better than FFD for VM placement in a heterogeneous cluster? 在异构集群中，蚁群系统比FFD更好吗?

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00038

Seungjun Lee, Minjoong Jeong, Sangyoon Oh

引用次数: 0

MicroBlind: Flexible and Secure File System Middleware for Application Sandboxes MicroBlind:应用程序沙箱的灵活和安全的文件系统中间件

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00031

Saketh Maddamsetty, Ayush Tharwani, Debadatta Mishra

{"title":"MicroBlind: Flexible and Secure File System Middleware for Application Sandboxes","authors":"Saketh Maddamsetty, Ayush Tharwani, Debadatta Mishra","doi":"10.1109/IC2E55432.2022.00031","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00031","url":null,"abstract":"Virtual machine (VM) based application sandboxes leverage strong isolation guarantees of virtualization techniques to address several security issues through effective containment of malware. Specifically, in end-user physical hosts, potentially vulnerable applications can be isolated from each other (and the host) using VM based sandboxes. However, sharing data across applications executing within different sandboxes is a non-trivial requirement for end-user systems because at the end of the day, all applications are used by the end-user owning the device. Existing file sharing techniques compromise the security or efficiency, especially considering lack of technical expertise of many end-users in the contemporary times. In this paper, we propose MicroBlind, a security hardened file sharing framework for virtualized sandboxes to support efficient data sharing across different application sandboxes. MicroBlind enables a simple file sharing management API for end users where the end user can orchestrate file sharing across different VM sandboxes in a secure manner. To demonstrate the efficacy of MicroBlind, we perform comprehensive empirical analysis against existing data sharing techniques (augmented for the sandboxing setup) and show that MicroBlind provides improved security and efficiency.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131460918","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: 0

Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services 面向人工智能驱动的物联网服务的能耗和碳足迹测试

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00011

Demetris Trihinas, L. Thamsen, Jossekin Beilharz, Moysis Symeonides

引用次数: 2

Integration of C-V2X Into a Hybrid Testbed to Co-Simulate ITS Applications and Scenarios 将C-V2X集成到混合测试平台中，共同模拟ITS应用和场景

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00009

Paul Geppert, Jossekin Beilharz

{"title":"Integration of C-V2X Into a Hybrid Testbed to Co-Simulate ITS Applications and Scenarios","authors":"Paul Geppert, Jossekin Beilharz","doi":"10.1109/IC2E55432.2022.00009","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00009","url":null,"abstract":"Distributed systems pervade the everyday life and become more relevant for efficiency and comfort, but also safety. Their development is challenged by increasing heterogeneity of hardware, software, and the increased need for interaction and compatibility. For instance, for vehicular communication in Intelligent Transport Systems (ITSs), WiFi and cellular based technologies (ITS-G5 and C-V2X) compete and co-exist. We ad-dress end-to-end testing of such highly connected applications in combination with hardware and complex environments. Related testbeds cover many aspects but lack support to integrate virtual nodes, physical nodes, and co-simulations all together. This work contributes 1) the integration of Cellular-Vehicle-to-Everything (C-V2X) simulation using ns-3 into the hybrid co-simulation framework Marvis; 2) the use of C-V2X on a software-defined radio (SDR) for ITS scenarios in Marvis; 3) an integration of the aforementioned with the competing V2X standard ITS-5G. The evaluation of this open source implementation is based on a currently relevant use case of the DiAK project, a digitized railroad crossing, in which both simulated and physical C-V2X communication are included in the scenario. The measured values recorded in the process show that Marvis can be used for developing and analyzing realistic ITS simulations. The necessary adjustments to the ns-3 C-V2X simulator increase the message transmission latency by only 0.8 ms (or 1.4 %) and can therefore be neglected.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128617721","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: 0

Workload-aware Dynamic GPU Resource Management in Component-based Applications 基于组件的应用程序中工作负载感知的动态GPU资源管理

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00030

Hoda Sedighi, Daniel Gehberger, R. Glitho

{"title":"Workload-aware Dynamic GPU Resource Management in Component-based Applications","authors":"Hoda Sedighi, Daniel Gehberger, R. Glitho","doi":"10.1109/IC2E55432.2022.00030","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00030","url":null,"abstract":"In edge and cloud environments, using graphics processing units (GPUs) as a high-speed parallel computing device increases the performance of compute-intensive applications. Nowadays, due to the increase in the volume and complexity of data to be processed, GPUs are more actively used in component-based applications. As a result, the sequence of multiple interdependent components is co-located on the GPU and shares GPU resources. The overall application performance in this kind of application depends on the data transfer overhead and the performance of each component in the sequence. Managing the components' competitive use of shared GPU resources faces various challenges. The lack of a low-overhead and online technique for dynamic GPU resource allocation leads to imbalanced GPU usage and penalizes the overall performance. In this paper, we present efficient GPU memory and resource managers that improve overall system performance by using shared memory and dynamically assigning portions of shared GPU resources. The portions are based on the components' workload and throughput-based performance analyzer while guaranteeing the application's progress. The evaluation results show that our dynamic resource allocation method is able to improve the average performance of the applications with the various number of concurrent components by up to 29.81% over the default GPU concurrent multitasking. We also show that using shared memory results in 2x performance improvements.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122813057","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: 0

CloudBruno: A Low-Overhead Online Workload Prediction Framework for Cloud Computing CloudBruno:用于云计算的低开销在线工作负载预测框架

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00027

V. Jayakumar, Shivani Arbat, I. Kim, Wei Wang

{"title":"CloudBruno: A Low-Overhead Online Workload Prediction Framework for Cloud Computing","authors":"V. Jayakumar, Shivani Arbat, I. Kim, Wei Wang","doi":"10.1109/IC2E55432.2022.00027","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00027","url":null,"abstract":"Accurate prediction of future incoming workloads to cloud applications, such as future user request count, is critical to proactive auto-scaling, and in general, critical to the cost-effectiveness of cloud deployments. However, designing a generic predictive framework that can accurately predict for any types of workloads is difficult, especially when the workload is dynamic and can change to a pattern that has not been observed in the training data sets. However, existing workload prediction solutions typically rely on complex machine learning models, which require comprehensive training data, making it difficult for them to handle dynamic workloads. Moreover, the training of existing workload prediction solutions are also expensive in terms of both time and computing resources. This paper presents a generic and low-cost online workload prediction framework, called Cloud Bruno, which combines the more accurate LSTM models with less expensive but fast SVM models to achieve high accuracy and low training overhead. When compared to existing predictors, CloudBruno had at least 8.8 % lower error than existing deep learning-based predictors for a highly-dynamic workload that does not have comprehensive training data (i.e, has changes unknown to training data). For workloads with comprehensive training data, Cloud Bruno's error was at most 2.5 % higher than optimized deep learning-based predictors. More importantly, Cloud Bruno can effectively execute on a free cloud CPU, allowing it to be used as an online workload predictor without additional cost.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123504500","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: 1

Function Memory Optimization for Heterogeneous Serverless Platforms with CPU Time Accounting 基于CPU时间计费的异构无服务器平台的功能内存优化

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00019

R. Cordingly, Sonia Xu, W. Lloyd

{"title":"Function Memory Optimization for Heterogeneous Serverless Platforms with CPU Time Accounting","authors":"R. Cordingly, Sonia Xu, W. Lloyd","doi":"10.1109/IC2E55432.2022.00019","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00019","url":null,"abstract":"Serverless Function-as-a-Service (FaaS) platforms often abstract the underlying infrastructure configuration into the single option of specifying a function's memory reservation size. This resource abstraction of coupling configurations options (e.g. vCPUs, memory, disk), combined with the lack of profiling, leaves developers to make ad hoc decisions on how to configure functions. Solutions are needed to mitigate exhaustive brute force searches of large parameter input spaces to find optimal configurations which can incur high costs. To address these challenges, we propose CPU Time Accounting Memory Selection (CPU-TAMS). CPU-TAMS is a workload agnostic memory selection method that utilizes CPU time accounting principles and regression modeling to recommend memory settings that reduce function runtime and subsequently, cost. Comparing CPU-TAMS to eight existing selection methods, we find that CPU-TAMS finds maximum value memory settings with only 8% runtime and 5% cost error compared to brute force testing while only requiring a single profiling run to evaluate function resource requirements. We adapt CPU-TAMS for use on four commercial FaaS platforms demonstrating efficacy to optimize function memory configurations where platforms feature heterogeneous infrastructure management policies.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"149 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130803033","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: 7