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

Message from the UCC and BDCAT General Chairs UCC和BDCAT总主席致辞

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/ucc56403.2022.00005

引用次数: 0

An Evaluation of Modeling Options for Cloud-native Application Architectures to Enable Quality Investigations 对云原生应用架构建模选项的评估，以实现质量调查

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/ucc56403.2022.00053

Karolin Durr, Robin Lichtenthaler

引用次数: 1

SI22: A dataset for analysis of DoS attack on the Cloud SI22:用于分析云上DoS攻击的数据集

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00065

Salih Ismail, H. R. Hassen, Mike Just, Hind Zantout

{"title":"SI22: A dataset for analysis of DoS attack on the Cloud","authors":"Salih Ismail, H. R. Hassen, Mike Just, Hind Zantout","doi":"10.1109/UCC56403.2022.00065","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00065","url":null,"abstract":"Distributed Denial of Service (DDoS) is an attack that aims to render a system unusable by targeting it with massive amounts of traffic. The literature contains several datasets that could be used to quantify the effectiveness of such attacks. These datasets contain captured network traffic and measure the success of an attack by the amount of traffic it generated. However, the amount of traffic is not the only metric that should be able to measure a DDoS attack. To handle the attack, the victim would be affected in other facets like memory, processing, and others. Furthermore, the traditional DDoS dataset is quite generic and insights gained from them cannot necessarily be applicable to cloud computing.In this paper, we propose a new DDoS dataset that looks at the actual impact on a victim that resides in the Cloud. We observed more than 230 performance indicators that measure how the key victim’s resources, RAM, CPU, network, and disk are affected during the attacks. We methodically captured the dataset and have broken them down into different scenarios that could help us better study DDoS attacks in the Cloud and DDoS attacks in general. The features of our dataset and grouped into seven categories which could help us further comprehend the granularity of these attacks.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"21 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":"114869251","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

Resolution Matters: Revisiting Prediction-Based Job Co-location in Public Clouds 解决问题:重新审视公共云中基于预测的工作协同定位

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00029

Justin Kur, Jingshu Chen, Ji Xue, Jun Huang

引用次数: 0

TinyTricia – A Space-Optimized Patricia Trie For Transparent Access to Edge Computing Services TinyTricia -一个空间优化的Patricia Trie，用于透明访问边缘计算服务

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00060

Josef Hammer, H. Hellwagner

{"title":"TinyTricia – A Space-Optimized Patricia Trie For Transparent Access to Edge Computing Services","authors":"Josef Hammer, H. Hellwagner","doi":"10.1109/UCC56403.2022.00060","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00060","url":null,"abstract":"Multi-access Edge Computing (MEC) is an essential piece of 5G telecommunication systems to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking and virtual IP+port addresses for registered edge services. A core component of our architecture is a Patricia Trie, which stores all our virtual IP+port addresses. Unfortunately, most implementations of Patricia Tries are not geared toward use cases with millions of keys where a low memory footprint becomes essential. In this paper, we present TinyTric$dot{w}$, a space-efficient open-source implementation of a Patricia Trie for keys up to 256 bits. TinyTricia can keep track of up to half a billion (229) keys $leq$57 bits and up to a quarter of a billion (228-1) keys $leq$256 bits with tiny memory requirements. In the latter case, each key can have a data value of any type. Keys $leq$57 bits require only 8 to 16 bytes per key (i.e., only 0 to 8 bytes overhead per 57-bit key); for keys $geq$58 bits, add the key size (in whole bytes) to these values. Thus, for 16.78 million (224)57-bit keys, our solution requires between 128 and 256 MiB of memory. Unlike some other spaceefficient implementations, TinyTriia allows adding and removing keys at runtime.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"41 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":"115870582","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

Performance of Java in Function-as-a-Service Computing Java在函数即服务计算中的性能

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00047

Qinzhe Wu, L. John

{"title":"Performance of Java in Function-as-a-Service Computing","authors":"Qinzhe Wu, L. John","doi":"10.1109/UCC56403.2022.00047","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00047","url":null,"abstract":"One of the newest forms of serverless computing is Function-as-a-Service (FaaS). FaaS provides a framework to execute modular pieces of code in response to events (e.g., clicking a link in a web application). The FaaS platform takes care of provisioning and managing servers, allowing the developers to focus on their business logic. Additionally, all resource management is event-driven, and developers are only charged for the execution time of their functions. Despite so many apparent benefits, there are some concerns regarding the performance of FaaS. Past work has shown that cold starts typically have a negative effect on response latency (e.g., the initialization could add more than 10× execution time to short Python FaaS functions). However, the magnitude of the slowdown is subject to varying from language to language. This paper investigates how containerization and cold starts impact the performance of Java FaaS functions, and compares with the findings from the prior Python study.We find that containerization overhead slows Java FaaS functions from native execution by 4.42× on average (geometrical mean), ranging from 1.69× up to 15.43×. Comparing with Python in warm containers, Java has more overhead on three of the functions, but faster on the other functions (up to 27.08× faster). The container initialization time for Java is consistently less than half that of Python. However, Java has the additional overhead due to Java Virtual Machine (JVM) warmup which contributes varying amount of latency to the execution depending on the Java function properties. Overall, Java has about 2.60×(2.65×) speedup across seven FaaS functions over Python in cold (warm) start scenarios, respectively.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"57 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":"134165822","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

An inter-cell resource usage analysis of large-scale datacentre trace logs 大规模数据中心跟踪日志的单元间资源使用分析

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00054

Zekun Sun, John Panneerselvam, Lu Liu, Yao Lu, Wan Tang

引用次数: 0

Blockchain and Federated-Learning empowered secure and trustworthy vehicular traffic 区块链和联邦学习为安全可靠的车辆交通提供了支持

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00061

Banhirup Sengupta, Souvik Sengupta, Susham Nandi, Anthony Simonet-Boulogne

{"title":"Blockchain and Federated-Learning empowered secure and trustworthy vehicular traffic","authors":"Banhirup Sengupta, Souvik Sengupta, Susham Nandi, Anthony Simonet-Boulogne","doi":"10.1109/UCC56403.2022.00061","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00061","url":null,"abstract":"Emergence of the autonomous and connected vehicles and modern vehicular networks improved the quality of the traditional transportation system. However, because of the increased usage of software and the development of wireless interfaces, vehicular networks, autonomous vehicles, and the overall transportation infrastructure are vulnerable to cyberattacks. Intrusion detection mechanisms (IDM) can be easily tailored in response to the increasing attack surface. Deep learning algorithms have made tremendous progress in detecting such malicious attack traffic. On the other hand, Existing IDM requires network devices with high computational capabilities to continuously train and update complicated network models, which limits intrusion detection systems’ efficiency and defence potential due to restricted resources and late model updates. Therefore to address this issue, this paper proposes a cooperative intrusion detection mechanism that distributes the training model across dispersed edge devices (such as linked automobiles, autonomous vehicles and roadside units (RSU)). Furthermore, we used the distributed federated learning approach to limit the centralised server’s operating functionalities during the model training phase. Signficantly adopting the federated learning mechanism helps to improve the overall data privacy in the transportation system. Notably, we used blockchain technology to ensure the authenticity and security of the aggregated training model. This paper examines typical attacks and demonstrates that the suggested solution preserves cooperative privacy for vehicular traffic systems while lowering computing costs for training the deep learning model to develop the autonomous, intelligent, distributed intrusion detection mechanism.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"57 4 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":"134561421","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

Runtime Microservice Self-distribution for Fine-grain Resource Allocation 用于细粒度资源分配的运行时微服务自分布

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00043

Renato S. Dias, Roberto Rodrigues Filho, L. Bittencourt, F. Costa

{"title":"Runtime Microservice Self-distribution for Fine-grain Resource Allocation","authors":"Renato S. Dias, Roberto Rodrigues Filho, L. Bittencourt, F. Costa","doi":"10.1109/UCC56403.2022.00043","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00043","url":null,"abstract":"The development of systems using microservices as buildingblocks have gained! major popularity in the industry in the past few years. Widely used services, such as Netflix and Uber, have been built entirely as microservice architectures. Due to the modularity and self-containedness of microservices, coupled with the use of elastic deployment infrastructures, a number of tools to assist the scalability of such systems have been created. However, these tools are limited to act at a fixed granularity, being able to replicate, relocate and provide access to extra resources only at the level of the entire microservice, even when only one of its parts actually demands more resources. In this paper, we propose the use of the concepts of adaptive component models, emergent microservices, and self-distributing systems to explicitly define the internal micro-architecture of microservices. In this approach, a microservice is built as a dynamic configuration of components, which can be seamlessly adapted and distributed on top of an elastic cloud infrastructure by the underlying platform. We evaluate the benefits of the approach by exploring different scenarios that entail the use of dynamic adaptation and self-distribution to perform vertical and horizontal scaling of microservices at a fine granularity. We analyze the involved tradeoffs and discuss how the approach can be further explored.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"51 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":"132237930","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

Geofence-Based Service Discovery in the Computing Continuum 计算连续体中基于地理位置的服务发现

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00035

Kurt Horvath, Dragi Kimovski, C. Uran, R.-C. Prodan, H. Wöllik

引用次数: 0