IEEE Cloud Computing最新文献_第5页

vTPM-SM: An Application Scheme of SM2/SM3/SM4 Algorithms Based on Trusted Computing in Cloud Environment 基于可信计算的SM2/SM3/SM4算法在云环境中的应用方案

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00058

Min Zhou, Shuhua Ruan, Junwei Liu, Xingshu Chen, Miaomiao Yang, Qixu Wang

{"title":"vTPM-SM: An Application Scheme of SM2/SM3/SM4 Algorithms Based on Trusted Computing in Cloud Environment","authors":"Min Zhou, Shuhua Ruan, Junwei Liu, Xingshu Chen, Miaomiao Yang, Qixu Wang","doi":"10.1109/CLOUD55607.2022.00058","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00058","url":null,"abstract":"Numbers of applications and businesses are hosted on cloud computing platforms, and it is essential for cloud tenants to protect their data through encryption or other methods. When tenants use encryption algorithms provided by software, they are bound to face the defect that keys are not protected by hardware. Trusted computing technology can securely store the key in the hardware device. However, the hardware TPM cannot provide services for multiple VMs simultaneously. The virtual trusted computing technology virtualizes the TPM and can assign vTPM to each VM. Currently, vTPM only supports RSA, ECDSA, SHA256, and AES algorithms, et al. Relevant studies have shown that SM2/SM3/SM4 algorithms are more secure than ECDSA/SHA256/AES. In order to cope with the limitations of the cryptographic algorithms supported by vTPM, we design the vTPM-SM scheme to provide a secure and reliable SM2/SM3/SM4 algorithm application method for cloud environments. Experiments show that vTPM-SM can effectively realize the VM using Chinese commercial cryptographic algorithms through vTPM. Compared with the existing scheme, using SM2/SM3/SM4 algorithm reduces the time overhead by about 31.6%, 83.3% and 15.5%, respectively.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"140 1","pages":"351-356"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79828479","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

Bayesian Uncertainty Modelling for Cloud Workload Prediction 云工作负荷预测的贝叶斯不确定性建模

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00018

Andrea Rossi, Andrea Visentin, S. Prestwich, Kenneth N. Brown

引用次数: 3

An Approximation Algorithm for Minimizing the Cloud Carbon Footprint through Workload Scheduling 通过工作负载调度最小化云碳足迹的近似算法

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00075

Tayebeh Bahreini, A. Tantawi, A. Youssef

引用次数: 1

Application Deployment Strategies for Reducing the Cold Start Delay of AWS Lambda 减少AWS Lambda冷启动延迟的应用部署策略

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00016

Jaime Dantas, Hamzeh Khazaei, Marin Litoiu

{"title":"Application Deployment Strategies for Reducing the Cold Start Delay of AWS Lambda","authors":"Jaime Dantas, Hamzeh Khazaei, Marin Litoiu","doi":"10.1109/CLOUD55607.2022.00016","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00016","url":null,"abstract":"Serverless computing has emerged in recent years as the new computing paradigm adopted by key players in the industry for software development. This new paradigm has seen rapid growth in adoption due to its unique billing model and scaling characteristics. Public cloud providers such as Amazon Web Services (AWS) offer several configurations and language runtimes for their serverless functions. Although extensively explored by the research community, this field still lacks current studies that address the many challenges developers face when leveraging serverless functions for real-world applications. One of these challenges that are often overseen by many programmers is the cold start problem which is present in any serverless application. For this reason, we propose the first study to characterize the underlying cold start impacts caused by the choice of language runtime, application size, memory size and deployment type on AWS Lambda. In this paper, we analyze the performance of the container-based deployment and ZIP-based deployment of AWS Lambda using a variety of language runtimes and applications running with different function configurations; then we propose guidelines for developers and cloud managers to consider when deploying/managing the workloads on the cloud.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"383 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77758967","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

Towards More Effective and Explainable Fault Management Using Cross-Layer Service Topology 利用跨层业务拓扑实现更有效和可解释的故障管理

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00026

D. Mathews, Mudit Verma, J. Lakshmi, P. Aggarwal

引用次数: 0

Dynamic Energy and Expenditure Aware Data Replication Strategy 动态能量和支出感知数据复制策略

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00027

Morgan Séguéla, R. Mokadem, J. Pierson

引用次数: 1

DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting DeTrust-FL:分散信任环境下的隐私保护联邦学习

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00065

Runhua Xu, N. Baracaldo, Yi Zhou, Ali Anwar, S. Kadhe, Heiko Ludwig

{"title":"DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting","authors":"Runhua Xu, N. Baracaldo, Yi Zhou, Ali Anwar, S. Kadhe, Heiko Ludwig","doi":"10.1109/CLOUD55607.2022.00065","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00065","url":null,"abstract":"Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated learning typically requires the utilization of multi-party computation techniques to provide strong privacy guarantees by ensuring that an untrusted or curious aggregator cannot obtain isolated replies from parties involved in the training process, thereby preventing potential inference attacks. Until recently, it was thought that some of these secure aggregation techniques were sufficient to fully protect against inference attacks coming from a curious aggregator. However, recent research has demonstrated that a curious aggregator can successfully launch a disaggregation attack to learn information about model updates of a target party. This paper presents DeTrust-FL, an efficient privacy-preserving federated learning framework for addressing the lack of transparency that enables isolation attacks, such as disaggregation attacks, during secure aggregation by assuring that parties’ model updates are included in the aggregated model in a private and secure manner. DeTrust-FL proposes a decentralized trust consensus mechanism and incorporates a recently proposed decentralized functional encryption scheme in which all parties agree on a participation matrix before collaboratively generating decryption key fragments, thereby gaining control and trust over the secure aggregation process in a decentralized setting. Our experimental evaluation demonstrates that DeTrust-FL outperforms state-of-the-art FE-based secure multi-party aggregation solutions in terms of training time and reduces the volume of data transferred. In contrast to existing approaches, this is achieved without creating any trust dependency on external trusted entities.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"22 1","pages":"417-426"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81216528","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

Handling heterogeneous workflows in the Cloud while enhancing optimizations and performance 在云中处理异构工作流，同时增强优化和性能

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00021

Emile Cadorel, Hélène Coullon, Jean-Marc Menaud

{"title":"Handling heterogeneous workflows in the Cloud while enhancing optimizations and performance","authors":"Emile Cadorel, Hélène Coullon, Jean-Marc Menaud","doi":"10.1109/CLOUD55607.2022.00021","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00021","url":null,"abstract":"The goal of a workflow engine is to facilitate the writing, the deploying, and the execution of a scientific workflow (i.e., graph of coarse-grain and heterogeneous tasks) on distributed infrastructures. With the democratization of the Cloud paradigm, many workflow engines of the state of the art offer a way to execute workflows on distant data centers by using the Infrastructure-as-a-Service (IaaS) or the Function-as-a-Service (FaaS) services of Cloud providers. Hence, workflow engines can take advantage of the (presumably) infinite resources and the economical model of the Cloud. However, two important limitations lie in this vision of Cloud-oriented workflow engines. First, by using existing services of Cloud providers, and by managing the workflows at the user side, the Cloud providers are unaware of both the workflows and their user needs, and cannot apply specific resource optimizations to their infrastructure. Second, for the same reasons, handling the heterogeneity of tasks (different operating systems) in workflows necessarily degrades either the transparency for the users (who must provision different types of resources), or the completion time performance of the workflows, because of the stacking of virtualization layers. In this paper, we tackle these two limitations by presenting a new Cloud service dedicated to scientific workflows. Unlike existing workflow engines, this service is deployed and managed by the Cloud providers, and enables specific resource optimizations and offers a better control of the heterogeneity of the workflows. We evaluate our new service in comparison to Argo, a well-known workflow engine of the literature based on FaaS services. This evaluation was made on a real distributed experimental platform with a realistic and complex scenario.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"81 1","pages":"49-58"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78327805","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

Sequence Clock: A Dynamic Resource Orchestrator for Serverless Architectures 序列时钟:无服务器架构的动态资源编排器

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00024

Ioannis Fakinos, Achilleas Tzenetopoulos, Dimosthenis Masouros, S. Xydis, D. Soudris

{"title":"Sequence Clock: A Dynamic Resource Orchestrator for Serverless Architectures","authors":"Ioannis Fakinos, Achilleas Tzenetopoulos, Dimosthenis Masouros, S. Xydis, D. Soudris","doi":"10.1109/CLOUD55607.2022.00024","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00024","url":null,"abstract":"Function-as-a-service (FaaS) represents the next frontier in the evolution of cloud computing being an emerging paradigm that removes the burden of configuration and management issues from users. This is achieved by replacing the well-established monolithic approach with graphs of standalone, small, stateless, event-driven components called functions. At the same time, from the cloud providers’ perspective, problems such as availability, load balancing and scalability need to be resolved without being aware of the functionality, behavior or resource requirements of their tenants’ code. However, in this context, functions’ containers coexist with others inside a host of finite resources, where a passive resource allocation technique does not guarantee a well-defined quality of service (QoS) in regards to time latency. In this paper, we present Sequence Clock, an expandable latency targeting tool that actively monitors serverless invocations in a cluster and offers execution of a sequential chain of functions, also known as pipelines or sequences, while achieving the targeted time latency. Two regulation methods were utilized, with one of them achieving up to 82% decrease in the severity of time violations and in some cases even eliminating them completely.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"37 1","pages":"81-90"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78352347","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

Fog Computing out of the Box with FogDEFT Framework: A Case Study 雾计算开箱即用的FogDEFT框架:一个案例研究

IEEE Cloud Computing Pub Date : 2022-07-01 DOI: 10.1109/CLOUD55607.2022.00057

S. Srirama, Suvam Basak

{"title":"Fog Computing out of the Box with FogDEFT Framework: A Case Study","authors":"S. Srirama, Suvam Basak","doi":"10.1109/CLOUD55607.2022.00057","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00057","url":null,"abstract":"Fog computing is the key technology to overcome the limitation of cloud computing in the domain of IoT applications. The service placement in the nearest fog devices drastically reduces the network delay, connectivity, and reliability issues and delivers real-time capabilities as an extension, reduces energy consumption and network overhead in the case of large sensor networks. However, the adoption rate of fog computing is not in proportion with the performance it proposes because, resource constraints, heterogeneity, and lack of standardization, require application-specific proprietary solutions. Therefore, we propose a framework that extends OASIS - Topology and Orchestration Specification for Cloud Applications (TOSCA) standard for modeling IoT applications and uses containerization technology to handle platform independence and interoperability, creating seamless coordination and cooperation across fog devices. The framework abstracts all the heterogeneity and complexities and offers a user-friendly paradigm to model and dynamically deploy fog services, on-demand, on the fly, from a remote system. The framework is demonstrated with a case study of the dynamic deployment of climate control service on the fog prototype.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"32 1","pages":"342-350"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72938696","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