Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing最新文献_第2页

Performance Study of Mixed Reality for Edge Computing 边缘计算混合现实性能研究

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368816

Klervie Toczé, J. Lindqvist, S. Nadjm-Tehrani

引用次数: 13

ATLAS 阿特拉斯

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368802

Daniel Rammer, Sangmi Lee Pallickara, S. Pallickara

引用次数: 4

EFPO EFPO

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368818

Josip Zilic, Atakan Aral, Ivona Brandic

{"title":"EFPO","authors":"Josip Zilic, Atakan Aral, Ivona Brandic","doi":"10.1145/3344341.3368818","DOIUrl":"https://doi.org/10.1145/3344341.3368818","url":null,"abstract":"Many researchers focus on offloading issues and challenges to improve energy efficiency and reduce application response time by employing multi-objective offloading frameworks but without considering offloading failures. Edge Computing, due to distributed architecture that contains diverse resource and reliability characteristics, is prone to server and network failures that can postpone or prevent offloading thus affecting the overall system performance. In this study, we propose a novel solution to model the energy consumption of mobile device and application response time assuming the resource and reliability diversity of the Edge Computing system. The model adopts the Markov Decision Process (MDP), which provides a formal framework for capturing stochastic and non-deterministic behavior of Edge offloading. We propose the Energy Efficient and Failure Predictive Edge Offloading (EFPO) framework based on a model checking solution called Value Iteration Algorithm (VIA). EFPO determines the feasible offloading decision policy, which should yield a near-optimal system performance. Evaluation is performed by offloading various mobile applications modeled as Directed Acyclic Graphs (DAG). Failures are emulated from the failure trace data set from Pacific Northwest National Laboratory. Results show that the proposed EFPO framework yields better time performance between 12% - 57% and better energy efficiency between 15% - 51% when comparing to other offloading decision policies from the literature.","PeriodicalId":261870,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127514228","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: 8

SecHadoop: A Privacy Preserving Hadoop SecHadoop:一个保护隐私的Hadoop

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368819

R. Shyamasundar, Swatish Satheesan, Deepali Mittal, Aakash Chaudhary

{"title":"SecHadoop: A Privacy Preserving Hadoop","authors":"R. Shyamasundar, Swatish Satheesan, Deepali Mittal, Aakash Chaudhary","doi":"10.1145/3344341.3368819","DOIUrl":"https://doi.org/10.1145/3344341.3368819","url":null,"abstract":"With the generation of vast amounts of data, there has been a tremendous need for processing the same in an economical way. MapReduce paradigm provides an economical processing of huge datasets in an effective way. Hadoop is a framework for managing huge amounts of data, and facilitates parallel computations on data using commodity hardware, through an integration of MapReduce paradigm with the HDFS file system. Due to intrinsic data divisions during parallel processing, there is a possibility of data leaks. Thus, in the context of Hadoop, if processing has to keep the privacy invariant over the computation, it is necessary to guarantee privacy not only of the MapReduce process but also assure that the HDFS file system does leak any information. The focus of our work is on data security and privacy in such cloud environments. Our main thrust is to preserve data confidentiality and privacy as per specifications notwithstanding data divisions or scheduling for fault tolerance. We realise privacy invariance on Hadoop by monitoring the information flow from subjects to objects created in Hadoop using the readers writers flow model (RWFM). In this paper, we describe the design, implementation and performance of a security enhanced Hadoop, called SecHadoop. We illustrate our approach with various case studies corresponding to infection of map/reduce tasks, failure of nodes etc., and demonstrate how end-to-end security of programs is realised. It is further shown that the overall overhead is less than 5% on single/multi-node setup.","PeriodicalId":261870,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121141276","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

Developing GDPR Compliant User Data Policies for Internet of Things 制定符合GDPR的物联网用户数据政策

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368812

M. Barati, I. Petri, O. Rana

{"title":"Developing GDPR Compliant User Data Policies for Internet of Things","authors":"M. Barati, I. Petri, O. Rana","doi":"10.1145/3344341.3368812","DOIUrl":"https://doi.org/10.1145/3344341.3368812","url":null,"abstract":"With recent adoption of Internet of Things (IoT) technologies and their use in industry, user data privacy concerns remain a major preoccupation of regulation bodies. The European General Data Protection Regulation (GDPR) enables users to control their data and be informed about any devices involved in collecting and processing this data. The overall objective is to enable individuals to have full rights and control over their data assets and to be able to transfer their data without any unmitigated risk. Blockchains provide the benefits of a distributed ledger that can securely manage digital transactions -- where the centralisation of data is eliminated. Blockchains have recently entered as an enabling technology into the IoT market, and used in a variety of different application areas. Blockchains enable the implementation of a more trusted system capable of processing operations between IoT services and sources of data. In smart buildings, for example, Blockchains support the formation of smart contracts as a means to give transactional capabilities to IoT devices, allowing users to keep data ownership and privacy using an immutable dataset. We describe how Blockchain technology can be used to develop an audit trail of data generated in IoT devices, enabling GDPR rules to be verified on such a trail. We describe how to translate a set of such rules into smart contracts to protect personal data in a transparent and automatic way.","PeriodicalId":261870,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121866398","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: 15

High Performance Dynamic Graph Model for Consistent Data Integration 一致数据集成的高性能动态图模型

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368806

Bilal Arshad, A. Anjum

{"title":"High Performance Dynamic Graph Model for Consistent Data Integration","authors":"Bilal Arshad, A. Anjum","doi":"10.1145/3344341.3368806","DOIUrl":"https://doi.org/10.1145/3344341.3368806","url":null,"abstract":"In a distributed environment, data from heterogeneous sources are brought together in a unified and consistent manner for analytics and insights. Inconsistencies arising due to the dynamic nature of sources such as addition/deletion of column or merging of columns can compromise the consistency of the distributed system. This can lead to the linking of inaccurate records and faulty data entries. Resulting in false reports and erroneous analyses. Furthermore, issues such as performance guarantees and scalability fuel the existing challenges. We have proposed an alternate graph-based approach to integrate data using an in-memory environment. The central idea of the approach is the use of graphs to integrate heterogeneous data sources in a distributed environment. The underlying approach provides both high-performance and scalability to address changes in a dynamic system for data integration. This allows the generation of graphs from individual source data and modifications in a consistent manner so that the state of the overall distributed system always remains coherent. It provides a novel way of combining consistent data integration and performance in a distributed sys-tem. Our system performs better than existing graph systems for dynamic graph evolution ensuring consistency and provides the necessary scalability guarantees as the size of the data increases. Results also show the correctness of the approach when integrating disparate data-sets","PeriodicalId":261870,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126351218","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 General Framework for Privacy-preserving Computation on Cloud Environments 云环境下隐私保护计算的通用框架

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368815

J. Basilakis, B. Javadi

{"title":"A General Framework for Privacy-preserving Computation on Cloud Environments","authors":"J. Basilakis, B. Javadi","doi":"10.1145/3344341.3368815","DOIUrl":"https://doi.org/10.1145/3344341.3368815","url":null,"abstract":"While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that can potentially assure secure processing of sensitive data by third-party cloud vendors. It relies on the fact that computations can occur on encrypted data without the need for decryption, although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. This paper examines a proposed technology platform, known as the Homomorphic Encryption Bus (HEB), that leverages HE with data obfuscation methods over a minimal network interaction model, allowing a uniform, flexible and general approach to cloud-based privacy-preserving system integration. The platform is uniquely designed to overcome barriers limiting the mainstream application of existing Fully Homomorphic Encryption (FHE) schemes in the cloud. A client-server interaction model involving ciphertext decryption on the client end is necessary to achieve resetting of 'noisy' ciphertexts in place of a much more inefficient (server only) recryption procedure. Data perturbation techniques are used to obfuscate intermediate data decrypted on the client-side of ciphertext interactions, in a way that is unintelligible to the client. In addition to efficient noise resetting, interactions involving data perturbations also achieve plaintext (binary to integer-based and vice versa) message space swapping, and conversion of accumulated integer-based encodings to a reduced embedded binary form. There appears to be little existing literature that examines these techniques as a means of broadening HE processing capabilities and practical application over the cloud. Interaction performance is examined in terms of timing and multiplicative circuit depth costs, through a simple equation evaluation and against standard recryption.","PeriodicalId":261870,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116914893","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

Selecting Efficient Cloud Resources for HPC Workloads 为HPC工作负载选择高效的云资源

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368798

Jeferson Rech Brunetta, E. Borin

{"title":"Selecting Efficient Cloud Resources for HPC Workloads","authors":"Jeferson Rech Brunetta, E. Borin","doi":"10.1145/3344341.3368798","DOIUrl":"https://doi.org/10.1145/3344341.3368798","url":null,"abstract":"Constant advances in CPU, storage, and network virtualization are enabling high-performance computing (HPC) applications to be efficiently executed on cloud computing systems. In this computing model, users pay only for what they use, with no need to acquire nor maintain expensive computing infrastructure. Moreover, users have at their disposal multiple kinds of computing resources and are able to assemble computing infrastructures that fit the application needs. Nonetheless, the available computing resources vary in price and performance and selecting the proper resources to execute the applications is of utmost importance to optimize cost and performance. In this work, we discuss the performance and cost implications of selecting different kinds of cloud resources to execute HPC workloads and show that the best resources for executing a given application depend not only on the application itself but also on the input dataset being processed. We also propose a methodology to support the selection of efficient cloud resources for these applications and show that is was able to select the best of 11 different cloud infrastructure configurations to execute 8 different benchmarks by executing just a few seconds of each application on each one of the configurations.","PeriodicalId":261870,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125756618","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: 10

Tensor-Based Resource Utilization Characterization in a Large-Scale Cloud Infrastructure 大规模云基础设施中基于张量的资源利用表征

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368801

W. Dargie

{"title":"Tensor-Based Resource Utilization Characterization in a Large-Scale Cloud Infrastructure","authors":"W. Dargie","doi":"10.1145/3344341.3368801","DOIUrl":"https://doi.org/10.1145/3344341.3368801","url":null,"abstract":"The introduction of virtualization and cloud computing has enabled a large number of containers/virtual machines to share computing resources. Nevertheless, the number and size of data centres are still on the rise, partly on account of an ever increasing amount of generated data and workloads worldwide. On the other hand, independent studies indicate that a large number of servers in contemporary data centres are underutilised. One of the strategies currently adopted by the research community in order to deal with resource inefficiency is dynamic workload consolidation. The idea behind is dynamically balancing the supply of computing, communication, and storage resources with the demand for resources. This entails populating physical servers with an optimal number of complementary workloads. Most existing or proposed approaches employ multi-variate optimisation to achieve this goal but do not easily lend themselves to fast and intuitive solutions. In this paper, we investigate the scope and usefulness of dimensionality reduction techniques (tensor decomposition) to identify execution and resource utilisation patterns in hosted containers/virtual machines. Our analysis is based on two large-scale data centres, one of them hosts 1190 commercial virtual machines on 59 physical computing servers and 29 physical storage servers organised in 9 clusters and the other 44373 containers on 3985 physical servers. Our analysis shows that spatial and temporal patters can be uncovered with tensor decomposition, based on which efficient clustering can be realised.","PeriodicalId":261870,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132706177","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

Privacy-by-Design Distributed Offloading for Vehicular Edge Computing 基于隐私设计的车辆边缘计算分布式卸载

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Pub Date : 2019-12-02 DOI: 10.1145/3344341.3368804

Weibin Ma, Lena Mashayekhy

{"title":"Privacy-by-Design Distributed Offloading for Vehicular Edge Computing","authors":"Weibin Ma, Lena Mashayekhy","doi":"10.1145/3344341.3368804","DOIUrl":"https://doi.org/10.1145/3344341.3368804","url":null,"abstract":"Vehicular Edge Computing (VEC) is a distributed computing paradigm that utilizes smart vehicles (SVs) as computational cloudlets (edge nodes) by virtue of their inherent attributes such as mobility, low operating costs, flexible deployment, and wireless communication ability. VEC extends edge computing services by expanding computing coverage and further improving quality-of-services (QoS) for devices. Due to limited onboard energy and computation capabilities of SV-mounted cloudlets, a single vehicle might not be able to execute a large number of tasks and guarantee their desired QoS. To address this problem, the overloaded vehicle can fulfill its overwhelming workload by offloading its tasks to other available connected vehicles. However, data privacy and accessibility are of critical importance that need to be considered for offloading. In this paper, we propose privacy-by-design offloading solutions for VEC to facilitate latency requirements of user demands and reduce energy consumption of vehicles.We formulate the Data pRotection Offloading Problem (DROP) as an Integer Program and prove its NP-hardness. To provide computationally tractable solutions, we propose three distributed algorithms by leveraging graph theory to solve this problem. We evaluate the performance of our proposed algorithms by extensive experiments and compare them to the optimal results obtained by IBM ILOG CPLEX. The results demonstrate the flexibility, scalability, and cost efficiency of our proposed algorithms in providing practical privacy-by-design offloading solutions enabling edge services along the cloud-to-thing continuum.","PeriodicalId":261870,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131737539","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