IEEE Cloud Computing最新文献_第9页

Performance Profiling of Load Balancing Algorithms in a Cloud Architecture 云架构下负载均衡算法的性能分析

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00020

Samirah Salifu, Nathan Turlington, Michael Galloway

引用次数: 0

Sovereign Data Exchange in Cloud-Connected IoT using International Data Spaces 使用国际数据空间的云连接物联网中的主权数据交换

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00010

Haydar Qarawlus, Malte Hellmeier, Johannes Pieperbeck, Ronja Quensel, Steffen Biehs, Marc Peschke

引用次数: 1

When Edge Computing Meets Compact Data Structures 当边缘计算遇到紧凑的数据结构

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00013

Zheng Li, Diego Seco, José Fuentes-Sepúlveda

{"title":"When Edge Computing Meets Compact Data Structures","authors":"Zheng Li, Diego Seco, José Fuentes-Sepúlveda","doi":"10.1109/IEEECloudSummit52029.2021.00013","DOIUrl":"https://doi.org/10.1109/IEEECloudSummit52029.2021.00013","url":null,"abstract":"Edge computing enables data processing and storage closer to where the data are created. Given the largely distributed compute environment and the significantly dispersed data distribution, there are increasing demands of data sharing and collaborative processing on the edge. Since data shuffling can dominate the overall execution time of collaborative processing jobs, considering the limited power supply and bandwidth resource in edge environments, it is crucial and valuable to reduce the communication overhead across edge devices. Compared with data compression, compact data structures (CDS) seem to be more suitable in this case, for the capability of allowing data to be queried, navigated, and manipulated directly in a compact form. However, the relevant work about applying CDS to edge computing generally focuses on the intuitive benefit from reduced data size, while few discussions about the challenges are given, not to mention empirical investigations into real-world edge use cases. This research highlights the challenges, opportunities, and potential scenarios of CDS implementation in edge computing. Driven by the use case of shuffling-intensive data analytics, we proposed a three-layer architecture for CDS-aided data processing and particularly studied the feasibility and efficiency of the CDS layer. We expect this research to foster conjoint research efforts on CDS-aided edge data analytics and to make wider practical impacts.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"61 1","pages":"29-34"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83793033","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

Preproduction Deploys: Cloud-Native Integration Testing 预生产部署:云原生集成测试

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00015

J. Carroll, Pankaj Anand, David Guo

{"title":"Preproduction Deploys: Cloud-Native Integration Testing","authors":"J. Carroll, Pankaj Anand, David Guo","doi":"10.1109/IEEECloudSummit52029.2021.00015","DOIUrl":"https://doi.org/10.1109/IEEECloudSummit52029.2021.00015","url":null,"abstract":"The microservice architecture for cloud-based systems is extended to not only require each loosely coupled component to be independently deployable, but also to provide independent routing for each component. This supports canary deployments, green/blue deployments and roll-back. Both ad hoc and system integration test traffic can be directed to components before they are released to production traffic. Front-end code is included in this architecture by using server-side rendering of JS bundles. Environments for integration testing are created with preproduction deploys side by side with production deploys using appropriate levels of isolation. After a successful integration test run, preproduction components are known to work with production precisely as it is. For isolation, test traffic uses staging databases that are copied daily from the production databases, omitting sensitive data. Safety and security concerns are dealt with in a targeted fashion, not monolithically. This architecture scales well with organization size; is more effective for integration testing; and is better aligned with agile business practices than traditional approaches.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"286 2 1","pages":"41-48"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72904798","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

Managing Big Data Stream Pipelines Using Graphical Service Mesh Tools 使用图形服务网格工具管理大数据流管道

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00014

M. Faizan, C. Prehofer

引用次数: 0

Image Disguising for Protecting Data and Model Confidentiality in Outsourced Deep Learning 外包深度学习中保护数据和模型机密性的图像伪装

IEEE Cloud Computing Pub Date : 2021-09-01 DOI: 10.1109/CLOUD53861.2021.00020

Sagar Sharma, A. Alam, Keke Chen

引用次数: 7

Para: Harvesting CPU time fragments in Big Data Analytics Para:在大数据分析中获取CPU时间片段

IEEE Cloud Computing Pub Date : 2021-09-01 DOI: 10.1109/CLOUD53861.2021.00081

Yuzhao Wang, Hongliang Qu, Junqing Yu, Zhibin Yu

{"title":"Para: Harvesting CPU time fragments in Big Data Analytics","authors":"Yuzhao Wang, Hongliang Qu, Junqing Yu, Zhibin Yu","doi":"10.1109/CLOUD53861.2021.00081","DOIUrl":"https://doi.org/10.1109/CLOUD53861.2021.00081","url":null,"abstract":"Modern data analytics typically run tasks on statically reserved resources (e.g., CPU and memory), which is prone to over-provision to guarantee the Quality of Service (QoS), leading to a large amount of resource time fragments. As a result, the resource utilization of a data analytics cluster is severely under-utilized. Workload co-location on shared resources has been substantially studied, but they are unaware the sizes of resource time fragments, making them hard to improve the resource utilization and guarantee QoS at the same time. In this paper, we propose Para, an event-driven scheduling mechanism, to harvest the CPU time fragments in co-located big data analytic workloads. Para innovates three techniques: 1) identifying the Idle CPU Time Window (ICTW) associated with each CPU core by capturing the task-switch event; 2) designing a runtime communication mechanism between each task execution of a workload and the underlying resource management system; 3) designing a pull-based scheduler to schedule a workload to run in the ICTW of another workload. We implement Para based on Apache Mesos and Spark. And the experimental results show that Para improves the CPU utilization by 44% and 30% on average relative to the original Mesos and enhanced Mesos under Spark's dynamic mode (MSDM), respectively. Moreover, Para increases the averaged task throughput of Mesos and MSDM by 4.8x and 1.7x, respectively, while guaranteeing the execution time of the primary applications.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"51 1","pages":"625-636"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74962083","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

A system for proactive risk assessment of application changes in cloud operations 对云操作中的应用程序变更进行前瞻性风险评估的系统

IEEE Cloud Computing Pub Date : 2021-09-01 DOI: 10.1109/CLOUD53861.2021.00025

Raghav Batta, L. Shwartz, M. Nidd, A. Azad, H. Kumar

{"title":"A system for proactive risk assessment of application changes in cloud operations","authors":"Raghav Batta, L. Shwartz, M. Nidd, A. Azad, H. Kumar","doi":"10.1109/CLOUD53861.2021.00025","DOIUrl":"https://doi.org/10.1109/CLOUD53861.2021.00025","url":null,"abstract":"Change is one of the biggest contributors to service outages. With more enterprises migrating their applications to cloud and using automated build and deployment the volume and rate of changes has significantly increased. Furthermore, microservice-based architectures have reduced the turnaround time for changes and increased the dependency between services. All of the above make it impossible for the Site Reliability Engineers (SREs) to use the traditional methods of manual risk assessment for changes. In order to mitigate change-induced service failures and ensure continuous improvement for cloud native services, it is critical to have an automated system for assessing the risk of change deployments. In this paper, we present an AI-based system for proactively assessing the risk associated with deployment of application changes in cloud operations. The risk assessment is accompanied with actionable risk explainability. We discuss the usage of this system in two primary scenarios of automated and manual deployment. In automated deployment scenario, our approach is able to alert SREs on 70 % of problematic changes by blocking only 1.5 % of total changes and recommending human intervention. In manual deployment scenario, our approach recommends the SREs to perform extra due diligence for 2.8 % of total changes to capture 84 % of problematic changes.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"16 1","pages":"112-123"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78461191","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

Energy-Aware Learning Agent (EALA) for Disaggregated Cloud Scheduling 分布式云调度的能量感知学习代理(EALA)

IEEE Cloud Computing Pub Date : 2021-09-01 DOI: 10.1109/CLOUD53861.2021.00075

Nicholas Nordlund, V. Vassiliadis, Michele Gazzetti, D. Syrivelis, L. Tassiulas

{"title":"Energy-Aware Learning Agent (EALA) for Disaggregated Cloud Scheduling","authors":"Nicholas Nordlund, V. Vassiliadis, Michele Gazzetti, D. Syrivelis, L. Tassiulas","doi":"10.1109/CLOUD53861.2021.00075","DOIUrl":"https://doi.org/10.1109/CLOUD53861.2021.00075","url":null,"abstract":"Cloud data centers require enormous amounts of energy to run their clusters of computers. There are huge financial and environmental incentives for cloud service providers to increase their energy efficiency without causing significant negative impacts on their customers' qualities of experience. Increasing resource utilization reduces energy consumption by consolidating workloads on fewer machines and allows cloud service providers to turn off inactive devices. While traditional architectures only allow virtual machines (VMs) to use the memory and CPU resources of a single device, VMs in a disaggregated cloud can utilize the small residual capacities of multiple separate devices. Separating VM resources across multiple devices leads to severe fragmentation that eventually negates any positive impact disaggregation has on utilization. To address the fragmentation problem, we present a method of ensuring a cloud operates using the minimal number of devices over time. Here we introduce an Energy-Aware Learning Agent (EALA) that uses reinforcement learning to guarantee the system can meet minimal quality of service requirements and provide energy savings without the need for VM migration. We evaluate the use of EALA guiding the decisions of Best-Fit compared to vanilla Best-Fit using the Google cluster trace. We show that EALA improves utilization by 2% and reduces the number of times that compute nodes switch on and off by 11% compared to vanilla Best-Fit.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"17 1","pages":"578-583"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88837609","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: 3

Konveyor Move2Kube: Automated Replatforming of Applications to Kubernetes Konveyor Move2Kube:自动将应用程序迁移到Kubernetes

IEEE Cloud Computing Pub Date : 2021-09-01 DOI: 10.1109/CLOUD53861.2021.00093

P. V. Seshadri, Harikrishnan Balagopal, Pablo Loyola, Akash Nayak, Chander Govindarajan, Mudit Verma, Ashok Pon Kumar, Amith Singhee

引用次数: 1