2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)最新文献_第6页

A Proactive Cloud Application Auto-Scaler using Reinforcement Learning 使用强化学习的主动云应用程序自动扩展器

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00040

Albin Heimerson, Johan Eker, Karl-Erik Årzén

引用次数: 0

Cloud Storage and Processing Service Selection considering Tiered Pricing and Data Regulations 考虑分层定价和数据规则的云存储和处理服务选择

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00020

Pankaj Sahu, S. Roy, M. Gharote, Sutapa Mondal, S. Lodha

引用次数: 2

FLBCPS: Federated Learning based Secured Computation Offloading in Blockchain-Assisted Cyber-Physical Systems FLBCPS:区块链辅助网络物理系统中基于联邦学习的安全计算卸载

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00071

Prakhar Consul, Ishan Budhiraja, Rajat Chaudhary, Deepak Garg

{"title":"FLBCPS: Federated Learning based Secured Computation Offloading in Blockchain-Assisted Cyber-Physical Systems","authors":"Prakhar Consul, Ishan Budhiraja, Rajat Chaudhary, Deepak Garg","doi":"10.1109/UCC56403.2022.00071","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00071","url":null,"abstract":"Mobile-edge computing (MEC) is a in demand method for improving the quality of computation experience on mobile devices (MD) since it helps MD’s to offload computing activities to MEC servers, which provide strong computing capabilities. However, there are certain unresolved concerns in present computation-offloading works: 1) safety issue; 2) joint computation offloading; and 3) flexible optimization. To solve safety and privacy concerns, we use Federated Learning-based blockchain technology, which provides data accuracy and irreversibility in MEC systems. Federated Learning (FL) is a promising technique towards effective machine learning while protecting privacy in dispersed situations such as the Internet of Things (IoT) and MEC. FL’s effectiveness is dependent on a network of participant nodes contributing their data and computational resources to the collective training of a globally model. As a result, preventing malicious nodes from interfering with model training while rewarding trustworthy nodes to assist to the learning process is critical for improved FL security and performance. We created an efficient resource allocation technique that optimizes computational offloading using a Blockchain-based Federated Learning (FL) method in order to add to the literature. The experimental findings show’s that the recommended FLBCPS technique improve system latency while maintaining consensus security.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114906923","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

TAROT: Spatio-Temporal Function Placement for Serverless Smart City Applications TAROT:无服务器智慧城市应用的时空功能布局

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00013

Vincenzo De Maio, David Bermbach, I. Brandić

{"title":"TAROT: Spatio-Temporal Function Placement for Serverless Smart City Applications","authors":"Vincenzo De Maio, David Bermbach, I. Brandić","doi":"10.1109/UCC56403.2022.00013","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00013","url":null,"abstract":"Emerging smart city applications (i.e., traffic management, smart tourism) have to (i) process data coming from different IoT devices and (ii) deliver results of data processing to various user devices (e.g., smart vehicles or smartphone) while considering applications’ latency constraints. Serverless edge computing has proven to be very effective for latency-aware processing of IoT data, since it allows application developers to define data processing logic in terms of functions which react to data events. However, data processing functions should be dynamically placed and migrated while considering IoT data sources’ location and user devices’ mobility to minimize end-to-end latency. Unfortunately, current serverless computing solutions do not support mobility-aware placement of functions. In this paper, we propose dynamic function placement based on user devices’ mobility to address latency requirements of smart city applications. We consider serverless smart city applications, since this computational model allows to model application as a function execution in response to specific events, which makes it suitable for event-driven applications typical of smart city and IoT. First, we identify the parameters affecting end-to-end latency of serverless smart cities’ applications. Then, based on our findings, we design TAROT, a latency-aware function placement method based on data-driven mobility predictions. Results show improvements up to 46% for average end-to-end latency in comparison to state-of-the-art solutions.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122907477","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: 2

Logical Optimisation and Cost Modelling of Stream-Processing Programs Written in a Purely-Functional Framework 用纯功能框架编写的流处理程序的逻辑优化和成本建模

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00048

Jonathan Dowland, P. Watson, Adam Cattermole

引用次数: 0

Proactive Autonomic Cloud Application Management 主动自主的云应用管理

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00021

M. Rózanska, G. Horn

引用次数: 0

CrossFit: Fine-grained Benchmarking of Serverless Application Performance across Cloud Providers CrossFit:跨云提供商的无服务器应用程序性能的细粒度基准测试

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00016

Joel Scheuner, Rui Deng, Jan-Philipp Steghöfer, P. Leitner

引用次数: 2

Deadline Aware Data Offloading in Fog Computing 雾计算中的截止日期感知数据卸载

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00045

Addis Tsega, Ayalew Belay Habtie

{"title":"Deadline Aware Data Offloading in Fog Computing","authors":"Addis Tsega, Ayalew Belay Habtie","doi":"10.1109/UCC56403.2022.00045","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00045","url":null,"abstract":"Fog computing is an extension of cloud computing where services are provided at the edge of a network. With the growth of the Internet of Things (IoT), different applications have emerged. Many of these applications such as the Intelligent Transportation system (ITS), Tactile Internet applications (telerobot and robot surgery) and healthcare emergency application are delay-sensitive and need high reliability. Fog computing becomes an essential and integral part of cloud computing to provide a better quality of service for these applications to fulfill latency requirements and needed reliability. Under the fog computing environment, different resource allocation, task scheduling algorithms, and task offloading techniques are proposed to manage applications. These deployed algorithms and techniques enhance resource utilization, power consumption and overall response time fully or partially. But it is still difficult to accomplish the required latency requirement of applications with these algorithms. To solve this problem, with the use of a design science approach a deadline-aware data offloading model as well as algorithm is proposed and implemented to fog nodes. The proposed algorithm considers the deadline of a task, computation power of fog nodes, task nature and transmission delay during scheduling. After the evaluation of these parameters, the node controller offloads the task at the appropriate fog node. The evaluation of the proposed algorithm is done with a simulator. The effectiveness of the proposed algorithm is evaluated and compared with the existing first come first served (FCFS) and dynamic task offloading algorithms. According to the literature these algorithms are found to be the best algorithms from other scheduling algorithms. The experimental results show that the proposed algorithm achieves 24% and 17% less overall delay time when compared to FCFS and Dynamic Offloading algorithms respectively.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"1159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114161664","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

Online Self-Evolving Anomaly Detection for Reliable Cloud Computing 面向可靠云计算的在线自进化异常检测

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00014

Tianyu Bai, Haili Wang, Jingda Guo, Xu Ma, Mahendra Talasila, Sihai Tang, Song Fu, Qing Yang

{"title":"Online Self-Evolving Anomaly Detection for Reliable Cloud Computing","authors":"Tianyu Bai, Haili Wang, Jingda Guo, Xu Ma, Mahendra Talasila, Sihai Tang, Song Fu, Qing Yang","doi":"10.1109/UCC56403.2022.00014","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00014","url":null,"abstract":"Production cloud computing systems consist of hundreds to thousands of computing and storage nodes. Such a scale, combined with ever-growing system complexity, is causing a key challenge to failure and resource management for dependable cloud computing. Efficient system monitoring and failure detection are crucial for understanding emergent, cloudwide phenomena and intelligently managing cloud resources for system-level dependability assurance and application-level performance assurance. To detect failures, we need to monitor the cloud execution and collect runtime performance data. These data are usually unlabeled at runtime in real-world systems, and thus a prior failure history is not always available. In this paper, we present a self-evolving anomaly detection framework for cloud dependability assurance. Our framework does not require any prior failure history, and it self-evolves by continuously exploring newly verified anomaly records and continuously updating the anomaly detector at runtime without expensive model retraining. A distinct advantage of our framework is that cloud system operators only need to check a small number of detected anomalies (compared with thousands-millions of system/application event records) and their decisions are leveraged to update the detector. Thus, the detector evolves following the upgrade of system hardware, update of software stack, and change of user workloads. Moreover, we design two types of detectors, one for general anomaly detection and the other for type-specific anomaly detection. Leveraging self-evolution and online learning techniques, our detectors can achieve 88.94% of sensitivity and 94.60% of specificity on average, which makes them suitable for real-world deployment.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114477776","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

Educational Data in the Cloud Legal Implications and Technical Recommendations 云中的教育数据的法律含义和技术建议

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00032

Ben Cohen, Ashley Hu, Deisy Patino, Joel Coffman

引用次数: 0