2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)最新文献_第7页

Optimized Damage Assessment in Large Datasets in Cloud 云环境下大型数据集的优化损伤评估

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00067

B. Panda, Shruthi Ramakrishnan

引用次数: 0

DEMon: Decentralized Monitoring for Highly Volatile Edge Environments DEMon:高度易变边缘环境的分散监控

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00026

Shashikant Ilager, Jakob Fahringer, Samuel Carlos de Lima Dias, I. Brandić

{"title":"DEMon: Decentralized Monitoring for Highly Volatile Edge Environments","authors":"Shashikant Ilager, Jakob Fahringer, Samuel Carlos de Lima Dias, I. Brandić","doi":"10.1109/UCC56403.2022.00026","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00026","url":null,"abstract":"Monitoring systems play an essential role in efficiently managing resources and application workloads by collecting, storing, and providing requisite information about the state of the resources. However, traditional monitoring systems that collect and store the data in centralized remote servers are infeasible for Edge environments. The centralized architecture increases the communication latency for information storage and retrieval and creates a failure bottleneck. In addition, the Edge resources are arbitrarily (de)provisioned, which creates further challenges for providing quick and trustworthy data. Thus, it is crucial to design and build a monitoring system that is fast, reliable, and trustworthy for such volatile Edge computing systems. Therefore, we propose a Decentralised Edge Monitoring (DEMon) framework, a decentralized, self-adaptive monitoring for highly volatile Edge environments. DEMon, at the core, leverages the stochastic Gossip communication protocol and develops techniques for efficient information dissemination, communication, and retrieval, avoiding a single point of failure and ensuring fast and trustworthy data access. We implement it as a lightweight and portable container-based monitoring system and evaluate it through empirical experiments. The results show that DEMon efficiently decimates and retrieves the monitoring information, addressing the abovementioned challenges.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"16 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":"123683695","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 DML-ICC Workshop Chairs 来自DML-ICC研讨会主席的信息

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/ucc56403.2022.00077

引用次数: 0

Open Source Compiling for V1Model RMT Switch: Making Data Center Networking Innovation Accessible V1Model RMT交换机的开源编译:使数据中心网络创新易于访问

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-11-21 DOI: 10.1109/UCC56403.2022.00024

Debobroto Das Robin, J. Khan

{"title":"Open Source Compiling for V1Model RMT Switch: Making Data Center Networking Innovation Accessible","authors":"Debobroto Das Robin, J. Khan","doi":"10.1109/UCC56403.2022.00024","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00024","url":null,"abstract":"Very few of the innovations in deep networking have seen data center scale implementation. Because, the Data Center network’s extreme scale performance requires hardware implementation, which is only accessible to a few. However, the emergence of reconfigurable match-action table (RMT) paradigm-based switches have finally opened up the development life cycle of data plane devices. The P4 language is the dominant language choice for programming these devices. Now, Network operators can implement the desired feature over white box RMT switches. The process involves an innovator writing new algorithms in the P4 language and getting them compiled for the target hardware. However, there is still a roadblock. After designing an algorithm, the P4 program’s compilation technology is not fully open-source. Thus, it is very difficult for an average researcher to get deep insight into the performance of his/her innovation when executed at the silicon level. There is no open-source compiler backend available for this purpose. Proprietary compiler backends provided by different hardware vendors are available for this purpose. However, they are closed-source and do not provide access to the internal mapping mechanisms. Which inhibits experimenting with new mapping algorithms and innovative instruction sets for reconfigurable match-action table architecture. This paper describes our work toward an open-source compiler backend for compiling P416 targeted for the V1Model architecture-based programmable switches..","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129506599","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

Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge Edge- multiai:对延迟敏感的边缘深度学习应用的多租户

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-11-14 DOI: 10.1109/UCC56403.2022.00012

S. Zobaed, Ali Mokhtari, J. Champati, M. Kourouma, M. Salehi

{"title":"Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge","authors":"S. Zobaed, Ali Mokhtari, J. Champati, M. Kourouma, M. Salehi","doi":"10.1109/UCC56403.2022.00012","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00012","url":null,"abstract":"Smart IoT-based systems often desire continuous execution of multiple latency-sensitive Deep Learning (DL) applications. The edge servers serve as the cornerstone of such IoT based systems, however, their resource limitations hamper the continuous execution of multiple (multi-tenant) DL applications. The challenge is that, DL applications function based on bulky “neural network (NN) models” that cannot be simultaneously maintained in the limited memory space of the edge. Accordingly, the main contribution of this research is to overcome the memory contention challenge, thereby, meeting the latency constraints of the DL applications without compromising their inference accuracy. We propose an efficient NN model management framework, called Edge-MultiAI, that ushers the NN models of the DL applications into the edge memory such that the degree of multi-tenancy and the number of warm-starts are maximized. Edge-MultiAI leverages NN model compression techniques, such as model quantization, and dynamically loads NN models for DL applications to stimulate multi-tenancy on the edge server. We also devise a model management heuristic for Edge-MultiAI, called iWS-BFE, that functions based on the Bayesian theory to predict the inference requests for multi-tenant applications, and uses it to choose the appropriate NN models for loading, hence, increasing the number of warm-start inferences. We evaluate the efficacy and robustness of Edge-MultiAI under various configurations. The results reveal that Edge-MultiAI can stimulate the degree of multi-tenancy on the edge by at least 2× and increase the number of warm-starts by ≈60% without any major loss on the inference accuracy of the applications.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129732619","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: 6

Performance Evaluation of Serverless Edge Computing for Machine Learning Applications 面向机器学习应用的无服务器边缘计算性能评估

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-10-19 DOI: 10.1109/UCC56403.2022.00025

Q. Trieu, B. Javadi, J. Basilakis, A. Toosi

{"title":"Performance Evaluation of Serverless Edge Computing for Machine Learning Applications","authors":"Q. Trieu, B. Javadi, J. Basilakis, A. Toosi","doi":"10.1109/UCC56403.2022.00025","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00025","url":null,"abstract":"Next generation technologies such as smart health-care, self-driving cars, and smart cities require new approaches to deal with the network traffic generated by the Internet of Things (IoT) devices, as well as efficient programming models to deploy machine learning techniques. Serverless edge computing is an emerging computing paradigm from the integration of two recent technologies, edge computing and serverless computing, that can possibly address these challenges. However, there is little work to explore the capability and performance of such a technology. In this paper, a comprehensive performance analysis of a serverless edge computing system using popular open-source frameworks, namely, Kubeless, OpenFaaS, Fission, and funcX is presented. The experiments considered different programming languages, workloads, and the number of concurrent users. The machine learning workloads have been used to evaluate the performance of the system under different working conditions to provide insights into the best practices. The evaluation results revealed some of the current challenges in serverless edge computing and open research opportunities in this emerging technology for machine learning applications.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"96 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016516","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

Peer-to-Peer Energy Trading meets IOTA: Toward a Scalable, Low-Cost, and Efficient Trading System 点对点能源交易与IOTA:迈向可扩展，低成本和高效的交易系统

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-10-12 DOI: 10.1109/UCC56403.2022.00069

Conor Mullaney, Adnan Aijaz, Nathan Sealey, Ben Holden

引用次数: 2

Supporting Multi-Cloud in Serverless Computing 在无服务器计算中支持多云

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-09-19 DOI: 10.1109/UCC56403.2022.00051

Haidong Zhao, Zakaria Benomar, Tobias Pfandzelter, N. Georgantas

{"title":"Supporting Multi-Cloud in Serverless Computing","authors":"Haidong Zhao, Zakaria Benomar, Tobias Pfandzelter, N. Georgantas","doi":"10.1109/UCC56403.2022.00051","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00051","url":null,"abstract":"Serverless computing is a widely adopted cloud execution model composed of Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) offerings. The increased level of abstraction makes vendor lock-in inherent to serverless computing, raising more concerns than previous cloud paradigms. Multicloud serverless is a promising emerging approach against vendor lock-in, yet multiple challenges must be overcome to tap its potential. First, we need to be aware of both the performance and cost of each FaaS provider. Second, a multi-cloud architecture needs to be proposed before deploying a multi-cloud workflow. Domain-specific serverless offerings must then be integrated into the multi-cloud architecture to improve performance and/or save costs. Finally, we require workload portability support for serverless multi-cloud. In this paper, we present a multi-cloud library for crossserverless offerings. We develop an analysis system to support comparison among public FaaS providers in terms of performance and cost. Moreover, we present how to alleviate data gravity with domain-specific serverless offerings. Finally, we deploy workloads on these architectures to evaluate several public FaaS offerings.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133875634","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

FedComm: Understanding Communication Protocols for Edge-based Federated Learning 联邦通信:理解基于边缘的联邦学习的通信协议

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-08-18 DOI: 10.1109/UCC56403.2022.00018

Gary Cleland, Di Wu, R. Ullah, B. Varghese

{"title":"FedComm: Understanding Communication Protocols for Edge-based Federated Learning","authors":"Gary Cleland, Di Wu, R. Ullah, B. Varghese","doi":"10.1109/UCC56403.2022.00018","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00018","url":null,"abstract":"Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This exchange incurs a communication overhead and impacts the performance of FL training. There is limited understanding of how communication protocols specifically contribute to the performance of FL. Such an understanding is essential for selecting the right communication protocol when designing an FL system. This paper presents FedComm, a benchmarking methodology to quantify the impact of optimized application layer protocols, namely Message Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and ZeroMQ Message Transport Protocol (ZMTP), and non-optimized application layer protocols, namely as TCP and UDP, on the performance of FL. FedComm measures the overall performance of FL in terms of communication time and accuracy under varying computational and network stress and packet loss rates. Experiments on a lab-based testbed demonstrate that TCP outperforms UDP as a non-optimized application layer protocol with higher accuracy and shorter communication times for 4G and Wi-Fi networks. optimized application layer protocols such as AMQP, MQTT, and ZMTP outperformed nonoptimized application layer protocols in most network conditions, resulting in a 2. 5x reduction in communication time compared to TCP while maintaining accuracy. The experimental results enable us to highlight a number of open research issues for further investigation. FedComm is available for download from https://github.com/qub-blesson/edComm.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116171932","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