2022 IEEE International Conference on Cloud Engineering (IC2E)最新文献_第2页

Decentralized Computation Market for Stream Processing Applications 流处理应用的分散计算市场

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00012

Scott Eisele, Michael Wilbur, Taha Eghtesad, Kevin Silvergold, Fred Eisele, Ayan Mukhopadhyay, Aron Laszka, Abhishek Dubey

{"title":"Decentralized Computation Market for Stream Processing Applications","authors":"Scott Eisele, Michael Wilbur, Taha Eghtesad, Kevin Silvergold, Fred Eisele, Ayan Mukhopadhyay, Aron Laszka, Abhishek Dubey","doi":"10.1109/IC2E55432.2022.00012","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00012","url":null,"abstract":"While cloud computing is the current standard for outsourcing computation, it can be prohibitively expensive for cities and infrastructure operators to deploy services. At the same time, there are underutilized computing resources within cities and local edge-computing deployments. Using these slack resources may enable significantly lower pricing than comparable cloud computing; such resources would incur minimal marginal expenditure since their deployment and operation are mostly sunk costs. However, there are challenges associated with using these resources. First, they are not effectively aggregated or provisioned. Second, there is a lack of trust between customers and suppliers of computing resources, given that they are distinct stakeholders and behave according to their own interests. Third, delays in processing inputs may diminish the value of the applications. To resolve these chal-lenges, we introduce an architecture combining a distributed trusted computing mechanism, such as a blockchain, with an efficient messaging system like Apache Pulsar. Using this architecture, we design a decentralized computation market where customers and suppliers make offers to deploy and host applications. The proposed architecture can be realized using any trusted computing mechanism that supports smart contracts, and any messaging framework with the necessary features. This combination ensures that the market is robust without incurring the input processing delays that limit other blockchain based solutions. We evaluate the market protocol using game-theoretic analysis to show that deviation from the protocol is discouraged. Finally, we assess the performance of a prototype implementation based on experiments with a streaming computer-vision application.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114230065","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

Pay-as-you-Train: Efficient ways of Serverless Training 按培训付费:无服务器培训的有效方式

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00020

Dheeraj Chahal, Mayank Mishra, S. Palepu, R. Singh, Rekha Singhal

{"title":"Pay-as-you-Train: Efficient ways of Serverless Training","authors":"Dheeraj Chahal, Mayank Mishra, S. Palepu, R. Singh, Rekha Singhal","doi":"10.1109/IC2E55432.2022.00020","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00020","url":null,"abstract":"Serverless (FaaS) architecture is emerging as a paradigm of choice for many application types, including event triggered, query processing, and machine learning (ML). The use of serverless platforms for ML inference is well known, but its applicability for model training is still under exploration. This paper presents an efficient “pay-as-you-train” methodology for training large deep learning models using serverless cloud services for compute and data management. Serverless compute (such as AWS Lambda) and serverless data management systems (such as AWS key-value store DynamoDB) impose restrictions on the computing time and size of the allowed data objects respectively. We present a novel approach for training deep learning models, which overcomes the limitations imposed by the underlying serverless platforms. We also present an analytical model to study the performance and cost involved in training using different data management services (such as AWS object storage S3, in-memory Memcached, and DynamoDB) as a communication channel with serverless platforms. Additionally, we compare the performance and cost of these services available on cloud. Our optimization techniques improve the performance and hence the cost of training by a factor of 1.2x to 5.5x with these services.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123277687","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

TriggerBench: A Performance Benchmark for Serverless Function Triggers TriggerBench:无服务器功能触发器的性能基准

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00018

Joel Scheuner, M. Bertilsson, O. Grönqvist, Henrik Tao, Henrik Lagergren, Jan-Philipp Steghöfer, P. Leitner

{"title":"TriggerBench: A Performance Benchmark for Serverless Function Triggers","authors":"Joel Scheuner, M. Bertilsson, O. Grönqvist, Henrik Tao, Henrik Lagergren, Jan-Philipp Steghöfer, P. Leitner","doi":"10.1109/IC2E55432.2022.00018","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00018","url":null,"abstract":"Serverless computing offers a scalable event-based paradigm for deploying managed cloud-native applications. Function triggers are essential building blocks in serverless, as they initiate any function execution. However, function triggering is insufficiently studied and inherently hard to measure given the distributed, ephemeral, and asynchronous nature of event-based function coordination. To address this gap, we present TriggerBench, a cross-provider benchmark for evaluating serverless function triggers based on distributed tracing. We evaluate the trigger latency (i.e., time to transition between two functions) of eight types of triggers in Microsoft Azure and three in AWS. Our results show that all triggers suffer from long tail latency, storage triggers introduce variable multi-second delays, and HTTP triggers are most suitable for interactive applications. Our insights can guide developers in choosing optimal event or messaging triggers for latency-sensitive applications. Researchers can extend TriggerBench to study the latency, scalability, and reliability of further trigger types and cloud providers.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123975298","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: 4

Understanding Software Security Vulnerabilities in Cloud Server Systems 了解云服务器系统中的软件安全漏洞

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00033

Olufogorehan Tunde-Onadele, Yuhang Lin, Xiaohui Gu, Jingzhu He

{"title":"Understanding Software Security Vulnerabilities in Cloud Server Systems","authors":"Olufogorehan Tunde-Onadele, Yuhang Lin, Xiaohui Gu, Jingzhu He","doi":"10.1109/IC2E55432.2022.00033","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00033","url":null,"abstract":"Cloud systems have been widely adopted by many real world production applications. Thus, security vulnerabilities in those cloud systems can cause serious widespread impact. Although previous intrusion detection systems can detect security attacks, understanding the underlying software defects that cause those security vulnerabilities is little studied. In this paper, we conduct a systematic study over 110 software security vulnera-bilities in 13 popular cloud server systems. To understand the underlying vulnerabilities, we answer the following questions: 1) what are the root causes of those security vulnerabilities? 2) what threat impact do those vulnerable code have? 3) how do developers patch those vulnerable code? Our results show that the vulnerable code of the studied security vulnerabilities comprise five common categories: 1) improper execution restrictions, 2) improper permission checks, 3) improper resource path-name checks, 4) improper sensitive data handling, and 5) improper synchronization handling. We further extract principal vulnerable code patterns from those common vulnerability categories.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115256884","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

PACED: Provenance-based Automated Container Escape Detection 基于来源的自动容器逃逸检测

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00035

Mashal Abbas, Shahpar Khan, Abdul Monum, Fareed Zaffar, Rashid Tahir, D. Eyers, Hassaan Irshad, Ashish Gehani, V. Yegneswaran, Thomas Pasquier

{"title":"PACED: Provenance-based Automated Container Escape Detection","authors":"Mashal Abbas, Shahpar Khan, Abdul Monum, Fareed Zaffar, Rashid Tahir, D. Eyers, Hassaan Irshad, Ashish Gehani, V. Yegneswaran, Thomas Pasquier","doi":"10.1109/IC2E55432.2022.00035","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00035","url":null,"abstract":"The security of container-based microservices relies heavily on the isolation of operating system resources that is provided by namespaces. However, vulnerabilities exist in the isolation of containers that may be exploited by attackers to gain access to the host. These are commonly referred to as container escape attacks. While prior work has identified vulnerabilities in namespace isolation, no general container escape detection and warning system has been presented. We present Paced, a novel, realtime system to detect container-escape attacks. We define what constitutes a cross-namespace event and how such events can be used to detect a container escape attack. We develop a provenance-based approach to isolate cross-namespace events and propose a rule—privileged_flow—to detect attacks on Docker and Kubernetes environments. We evaluate our detection method on a suite of contemporary CVEs with container escape exploits, bad container configurations, and benchmarks. Paced achieves near-perfect accuracy with no false negatives. We release our implementation and datasets as free, open-source software.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121899776","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

Guarding Against Universal Adversarial Perturbations in Data-driven Cloud/Edge Services 防范数据驱动的云/边缘服务中的普遍对抗性扰动

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00032

Xingyu Zhou, Robert Canady, Yi Li, S. Bao, Yogesh D. Barve, D. Balasubramanian, A. Gokhale

{"title":"Guarding Against Universal Adversarial Perturbations in Data-driven Cloud/Edge Services","authors":"Xingyu Zhou, Robert Canady, Yi Li, S. Bao, Yogesh D. Barve, D. Balasubramanian, A. Gokhale","doi":"10.1109/IC2E55432.2022.00032","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00032","url":null,"abstract":"Although machine learning (ML)-based models are increasingly being used by cloud-based data-driven services, two key problems exist when used at the edge. First, the size and complexity of these models hampers their deployment at the edge, where heterogeneity of resource types and constraints on resources is the norm. Second, ML models are known to be vulnerable to adversarial perturbations. To address the edge deployment issue, model compression techniques, especially model quantization, have shown significant promise. However, the adversarial robustness of such quantized models remains mostly an open problem. To address this challenge, this paper investigates whether quantized models with different precision levels can be vulnerable to the same universal adversarial perturbation (UAP). Based on these insights, the paper then presents a cloud-native service that generates and distributes adversarially robust compressed models deployable at the edge using a novel, defensive post-training quantization approach. Experimental evaluations reveal that although quantized models are vulnerable to UAPs, post-training quantization on the synthesized, adversarially-trained models are effective against such UAPs. Furthermore, deployments on heterogeneous edge devices with flexible quantization settings are efficient thereby paving the way in realizing adversarially robust data-driven cloud/edge services.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122469432","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

Demo Paper: Benchmarking Scalability of Cloud-Native Applications with Theodolite 演示论文:利用Theodolite对云原生应用程序的可扩展性进行基准测试

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00037

S. Henning, W. Hasselbring

引用次数: 3

An End-to-End Framework for Benchmarking Edge-Cloud Cluster Management Techniques 边缘云集群管理技术的端到端基准测试框架

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00010

Philipp Raith, T. Rausch, Paul Prüller, Alireza Furutanpey, S. Dustdar

引用次数: 4

Log-Based CRDT for Edge Applications 基于日志的边缘应用CRDT

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00021

N. Saquib, C. Krintz, R. Wolski

引用次数: 0

4DHI: An index for approximate kNN search of remotely sensed images in Key-Value databases 4DHI:在键值数据库中对遥感图像进行近似kNN搜索的索引

2022 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2022-09-01 DOI: 10.1109/IC2E55432.2022.00025

C. Hewage, A. Vo, Nhien-An Le-Khac, D. Laefer, M. Bertolotto

{"title":"4DHI: An index for approximate kNN search of remotely sensed images in Key-Value databases","authors":"C. Hewage, A. Vo, Nhien-An Le-Khac, D. Laefer, M. Bertolotto","doi":"10.1109/IC2E55432.2022.00025","DOIUrl":"https://doi.org/10.1109/IC2E55432.2022.00025","url":null,"abstract":"State-of-the-art, scalable, indexing techniques in location-based image data retrieval are primarily focused on supporting window and range queries. However, support of these indexes is not well explored when there are multiple spatially similar images to retrieve for a given geographic location. Adoption of existing spatial indexes such as the kD-tree pose major scalability impediments. In response, this work proposes a novel scalable, key-value, database oriented, secondary-memory based, spatial index to retrieve the top $k$ most spatially similar images to a given geographic location. The proposed index introduces a 4-dimensional Hilbert index (4DHI). This space filling curve is implemented atop HBase (a key-value database). Experiments performed on both synthetically generated and real world data demonstrate comparable accuracy with MD-HBase (a state of the art, scalable, multidimensional point data management system) and better performance. Specifically, 4DHI yielded 34% - 39% storage improvements compared to the disk consumption of the original index of MD-HBase. The compactness in 4DHI also yielded up to 3.4 and 4.7 fold gains when retrieving 6400 and 12800 neighbours, respectively; compared to the adoption of original index of MD-HBase for respective neighbour searches. An optimization technique termed “Bounding Box Displacement” (BBD) is introduced to improve the accuracy of the top $k$ approximations in relation to the results of in-memory kD-tree. Finally, a method of reducing row key length is also discussed for the proposed 4DHI to further improve the storage efficiency and scalability in managing large numbers of remotely sensed images.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125469338","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