2023 IEEE Cloud Summit最新文献

Job Recommendation Service for GPU Sharing in Kubernetes Kubernetes中GPU共享的作业推荐服务

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00008

Aritra Ray, Kyle Lafata, Zhaobo Zhang, Ying Xiong, K. Chakrabarty

引用次数: 0

Microservice Debugging with Checkpoint-Restart 使用检查点重新启动的微服务调试

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00016

X. Merino, Carlos E. Otero

引用次数: 0

Copyright Page 版权页

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/cloudsummit57601.2023.00003

引用次数: 0

EL2W: Extended Layer 2 Services for Bare-Metal Provisioning Over WAN EL2W:通过WAN进行裸金属供应的扩展第二层服务

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00017

T. Hacker, Deepika Kaushal, Zhiwei Chu

{"title":"EL2W: Extended Layer 2 Services for Bare-Metal Provisioning Over WAN","authors":"T. Hacker, Deepika Kaushal, Zhiwei Chu","doi":"10.1109/CloudSummit57601.2023.00017","DOIUrl":"https://doi.org/10.1109/CloudSummit57601.2023.00017","url":null,"abstract":"Researchers are embracing deep learning in various interdisciplinary research domains, recognizing undeniable benefits offered by deep neural networks. However, in order to meet the substantial computational demands for processing deep learning models, researchers extensively rely on cloud servers. Nevertheless, the shared nature of cloud servers encourages research labs and facilities to establish private clouds, ensuring exclusive access to computational resources and safeguarding data privacy. Creating a private cloud from bare metal presents challenges with existing provisioning solutions. These solutions not only come with a set of complex installation and configuration steps but are also limited to a constrained local Ethernet broadcast domain for network loading, which may pose unforeseen difficulties and risks for researchers who do not specialize in computing. To address these issues, this paper introduces EL2W, Extended Layer 2 services to Wide area networks (WAN), a novel approach we developed following Infrastructure-as-Code (IaC) principles. EL2W aims to help automate the system installation procedure by reducing the repetitive configurations and setups using Infrastructure-as-Code based scripts and codes. In addition, EL2W can securely expand an Ethernet network’s logical and functional extent beyond the current physical limitations of Ethernet layer 2 networks. We describe the implementation and architecture of a remote bare metal provisioning system built upon secure extended layer 2 networks. Experimental results demonstrate the capability of EL2W for establishing a secure layer 2 connection to provide essential bare metal provisioning services, as well as the effectiveness of a local proxy cache server to reduce the operating system loading time.","PeriodicalId":185645,"journal":{"name":"2023 IEEE Cloud Summit","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114617274","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

Optimization of Datacenter Selection Policy in Cloud Computing using Differential Evolution Algorithm 基于差分进化算法的云计算数据中心选择策略优化

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00012

Shusmoy Chowdhury, Ajay Katangur

{"title":"Optimization of Datacenter Selection Policy in Cloud Computing using Differential Evolution Algorithm","authors":"Shusmoy Chowdhury, Ajay Katangur","doi":"10.1109/CloudSummit57601.2023.00012","DOIUrl":"https://doi.org/10.1109/CloudSummit57601.2023.00012","url":null,"abstract":"Cloud computing has become one of the most influential inventions in modern-day computer science. It has become one of the integral parts of our day-to-day life. The success of our regular activities relies on the efficiency and effectiveness of the cloud services. The datacenter selection policy is one of the vital elements in cloud computing. The user requests are processed in the datacenter. Therefore, an optimal datacenter ensures user satisfaction with cloud services. In this research, we propose a differential evolution-based datacenter selection policy to choose the optimal datacenter for the userbase. Differential evolution mainly depends on the evolutionary process of mutation, which helps the algorithm explore the whole search space and find the optimal datacenter. We have used the Cloud Analyst simulator to experiment with the correctness and performance of our algorithm. The simulation results demonstrate that the proposed differential evolution algorithm performs better than the existing datacenter selection algorithm, such as the closest datacenter selection policy and optimized response time policy.","PeriodicalId":185645,"journal":{"name":"2023 IEEE Cloud Summit","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114302685","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 Shareable and Reproducible Cloud Computing Experiments 迈向可共享和可复制的云计算实验

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00022

T. Malik, S. Khan

引用次数: 0

CDFMR: A Distributed Statistical Analysis of Stock Market Data using MapReduce with Cumulative Distribution Function CDFMR:基于累积分布函数MapReduce的股票市场数据分布式统计分析

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00019

Devendra Dahiphale, Abhijeet Wadkar, K. Joshi

{"title":"CDFMR: A Distributed Statistical Analysis of Stock Market Data using MapReduce with Cumulative Distribution Function","authors":"Devendra Dahiphale, Abhijeet Wadkar, K. Joshi","doi":"10.1109/CloudSummit57601.2023.00019","DOIUrl":"https://doi.org/10.1109/CloudSummit57601.2023.00019","url":null,"abstract":"The stock market generates massive data daily on top of a deluge of historical data. Investors and traders look to stock market data analysis for assurance in their investments, a prime indicator of our global economy. This has led to immense popularity in the topic, and consequently, much research has been done on stock market predictions and future trends. However, due to the relatively slow electronic trading systems and order processing times, the velocity of data, the variety of data, and social factors, there is a need for gaining speed, control, and continuity in data processing (real-time stream processing) considering the amount of data that is being produced daily. Unfortunately, processing this massive amount of data on a single node is inefficient, time-consuming, and unsuitable for real-time processing. Recently, there have been many advancements in Big Data processing technologies such as Hadoop, Cloud MapReduce, and HBase. This paper proposes a MapReduce algorithm for statistical stock market analysis with a Cumulative Distribution Function (CDF). We also highlight the challenges we faced during this work and their solutions. We further showcase how our algorithm is spanned across multiple functions, which are run using multiple MapReduce jobs in a cascaded fashion.","PeriodicalId":185645,"journal":{"name":"2023 IEEE Cloud Summit","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115562908","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

A Probabilistic Approach for Secure and Verifiable Computation of kNN Queries in Cloud 云环境下kNN查询安全可验证计算的概率方法

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00009

Gowri Pandian Sundarapandi, S. Bokhary, B. Samanthula, Boxiang Dong

{"title":"A Probabilistic Approach for Secure and Verifiable Computation of kNN Queries in Cloud","authors":"Gowri Pandian Sundarapandi, S. Bokhary, B. Samanthula, Boxiang Dong","doi":"10.1109/CloudSummit57601.2023.00009","DOIUrl":"https://doi.org/10.1109/CloudSummit57601.2023.00009","url":null,"abstract":"The tremendous benefits of outsourcing have influenced many organizations to migrate their data and DBMS functionalities to the cloud infrastructure. Since cloud service providers offer advanced security services, data migration typically boosts the security of business data and operations. Nonetheless, since a cloud is an untrusted third party, it is important for organizations to ensure confidentiality of the outsourced data and integrity of results generated by the cloud. In this paper, we focus on the Secure and Verifiable Computation for k-Nearest Neighbor (SVC-kNN) problem. Existing SVC-kNN solutions delegate the verification task solely to a single semi-trusted party and are unreliable in terms of security as the verification server could be either dishonest or compromised. To address these issues, we propose a novel and probabilistic SVC-kNN protocol that utilizes a randomization technique and additive homomorphic properties under the two-cloud model. Our proposed solution facilitates the clouds to jointly compute verification proofs and allows the end-user to verify the computation results efficiently. Additionally, our solution is highly efficient from the data owner and query issuers’ perspective as most of the critical computations are performed by the clouds. Furthermore, our experimental results using a real dataset clearly demonstrate the effectiveness of our protocol.","PeriodicalId":185645,"journal":{"name":"2023 IEEE Cloud Summit","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121006587","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

QoS Preferences Edge User Allocation Using Reinforcement Learning 基于强化学习的QoS偏好边缘用户分配

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00010

Liang Bao, Suqi Gao, Zelin Li

{"title":"QoS Preferences Edge User Allocation Using Reinforcement Learning","authors":"Liang Bao, Suqi Gao, Zelin Li","doi":"10.1109/CloudSummit57601.2023.00010","DOIUrl":"https://doi.org/10.1109/CloudSummit57601.2023.00010","url":null,"abstract":"The rapid growth of smartphones worldwide has driven the rapid development of 5G technology, mobile terminals, and edge technology. Edge computing servers are often deployed in a distributed manner in a similar manner to mobile base stations, with users located within the server’s coverage area transferring tasks to servers that are relatively close and have more resources. Compared to central cloud computing servers, edge servers have very limited resources, are more dynamic and heterogeneous, and therefore require a fast and efficient resource allocation strategy. The edge user allocation strategy is mostly based on greedy heuristic algorithms, which have efficient allocation results for problems with a single scenario and small scale, but it is difficult to achieve better results under the conditions of complex scenarios with scarce resources. In this paper, we propose a hybrid edge user allocation algorithm with reinforcement learning and greedy algorithms—GreedyRL. The algorithm uses reinforcement learning to interact with the environment through trial and error, iterative updating, learning from past experience to train an intelligent body model, and giving appropriate edge user allocation solutions in the face of dynamic environments. The effectiveness and efficiency of GreedyRL are demonstrated by designing experiments.","PeriodicalId":185645,"journal":{"name":"2023 IEEE Cloud Summit","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122526995","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

Lifespan and energy-oriented load balancing algorithms across sets of nodes in Green Edge Computing 绿色边缘计算中跨节点集的寿命和能量导向负载平衡算法

2023 IEEE Cloud Summit Pub Date : 2023-07-01 DOI: 10.1109/CloudSummit57601.2023.00013

Gabriele Proietti Mattia, R. Beraldi

{"title":"Lifespan and energy-oriented load balancing algorithms across sets of nodes in Green Edge Computing","authors":"Gabriele Proietti Mattia, R. Beraldi","doi":"10.1109/CloudSummit57601.2023.00013","DOIUrl":"https://doi.org/10.1109/CloudSummit57601.2023.00013","url":null,"abstract":"Green-powered edge computing architectures allow bringing computation in areas that are not reached by the power grids. More often, in applications for Precision Agriculture and Smart Cities, we could have a set of nodes that are coupled with an accumulator which is, during the day, re-charged by the energy harvested by small solar panels. With the latest advances in technology, the edge node is generally assimilated to be a low-power Single Board Computer (SBC), and it is able to carry out even relatively demanding tasks. For example, it can run deep learning models to images or video sequences captured in loco by cameras. However, due to the differences in terms of power consumption and weather conditions, each node experiences a different lifespan, some nodes may even shut down prematurely, causing the interruption of the portion of the deployed service. In this paper, we propose three decentralized algorithms that solve the problem by making the nodes cooperatively balance the traffic in order to level and maximize their lifespan. By comparing the approaches in two different experiments by using a cluster of Raspberry Pi 4 we show that our solutions allow to increase the lifespan of the service of 10% on average wrt the case in which no algorithm is applied.","PeriodicalId":185645,"journal":{"name":"2023 IEEE Cloud Summit","volume":"9 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123830512","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