2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)最新文献_第3页

Ensemble-Based Network Edge Processing 基于集成的网络边缘处理

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/UCC.2018.00022

I. Petri, A. Zamani, Daniel Balouek-Thomert, O. Rana, Y. Rezgui, M. Parashar

{"title":"Ensemble-Based Network Edge Processing","authors":"I. Petri, A. Zamani, Daniel Balouek-Thomert, O. Rana, Y. Rezgui, M. Parashar","doi":"10.1109/UCC.2018.00022","DOIUrl":"https://doi.org/10.1109/UCC.2018.00022","url":null,"abstract":"Estimating energy costs for an industrial process can be computationally intensive and time consuming, especially as it can involve data collection from different (distributed) monitoring sensors. Industrial processes have an implicit complexity involving the use of multiple appliances (devices/ sub-systems) attached to operation schedules, electrical capacity and optimisation setpoints which need to be determined for achieving operational cost objectives. Addressing the complexity associated with an industrial workflow (i.e. range and type of tasks) leads to increased requirements on the computing infrastructure. Such requirements can include achieving execution performance targets per processing unit within a particular size of infrastructure i.e. processing & data storage nodes to complete a computational analysis task within a specific deadline. The use of ensemblebased edge processing is identifed to meet these Quality of Service targets, whereby edge nodes can be used to distribute the computational load across a distributed infrastructure. Rather than relying on a single edge node, we propose the combined use of an ensemble of such nodes to overcome processing, data privacy/ security and reliability constraints. We propose an ensemble-based network processing model to facilitate distributed execution of energy simulations tasks within an industrial process. A scenario based on energy profiling within a fisheries plant is used to illustrate the use of an edge ensemble. The suggested approach is however general in scope and can be used in other similar application domains.","PeriodicalId":288232,"journal":{"name":"2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114468620","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

Quantized BvND: A Better Solution for Optical and Hybrid Switching in Data Center Networks 量化BvND:数据中心网络中光交换和混合交换的更好解决方案

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/UCC.2018.00032

Liang Liu, Jun Xu, L. Fortnow

{"title":"Quantized BvND: A Better Solution for Optical and Hybrid Switching in Data Center Networks","authors":"Liang Liu, Jun Xu, L. Fortnow","doi":"10.1109/UCC.2018.00032","DOIUrl":"https://doi.org/10.1109/UCC.2018.00032","url":null,"abstract":"Data center network continues to grow relentlessly in the amount of data traffic it has to \"switch\" between its server racks. A traditional data center switching architecture, consisting of a network of commodity packet switches (viewed as a giant packet switch), cannot scale with this growing switching demand. Adding an optical switch, which has a much higher bandwidth than the packet switch but incurs a nontrivial reconfiguration delay, to a data center network has been proposed as a costeffective approach to boosting its switching capacity. However, to effectively do so, we need to meticulously schedule the optical switch. In fact, we are dealing with two very different scheduling problems here, namely hybrid switching and standalone optical switching, depending on whether or not there is effective cooperation between the optical switch and the packet switch during their respective scheduling processes. In this work, we propose a solution that performs better than the respective state of art solutions for both scheduling problems. Our solution outperforms by a wide margin all existing optical switching solutions in terms of throughput, yet its computational complexity is comparable to those of others. Our solution also has the best properties of both Eclipse and Solstice, the state of the art hybrid switching solutions. Eclipse and Solstice have different advantages: Eclipse has better throughput performance but incurs a much higher computationally complexity than Solstice. Our solution gets the better of both worlds: it delivers almost the same throughput performance as Eclipse, yet incurs a similar computational complexity as Solstice.","PeriodicalId":288232,"journal":{"name":"2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131757040","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

Energy-Efficient and SLA-Aware Virtual Machine Selection Algorithm for Dynamic Resource Allocation in Cloud Data Centers 基于sla的云数据中心动态资源分配节能虚拟机选择算法

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/UCC.2018.00019

Seyedhamid Mashhadi Moghaddam, Sareh Fotuhi Piraghaj, M. O'Sullivan, C. Walker, C. P. Unsworth

{"title":"Energy-Efficient and SLA-Aware Virtual Machine Selection Algorithm for Dynamic Resource Allocation in Cloud Data Centers","authors":"Seyedhamid Mashhadi Moghaddam, Sareh Fotuhi Piraghaj, M. O'Sullivan, C. Walker, C. P. Unsworth","doi":"10.1109/UCC.2018.00019","DOIUrl":"https://doi.org/10.1109/UCC.2018.00019","url":null,"abstract":"Energy consumption constitutes a significant proportion of data centers' operational costs. Furthermore, the establishment of large scale Cloud data centers due to the fast growth of utility-based IT services made the energy usage of data centers a concern. Cloud data centers use load balancing algorithms to allocate their physical resources (CPU, memory, hard disk, network bandwidth) efficiently on demand and hence optimize their energy consumption. In the load balancing process, some Virtual Machines (VMs) are selected from over-or under-utilized physical hosts and these VMs are migrated, while live and running, to other hosts. This live migration can result in Service Level Agreement Violations (SLAVs) and consequently low Quality of Service (QoS). Thus, in this paper, we propose an energy aware VM selection policy to minimize the number of migrations and consequently decrease SLAVs. Load balancing has three stages: a) Detecting over-and under-utilized hosts; b) Selecting one or more VMs for migration from those hosts; c) Finding destination hosts for the selected VMs. The focus of this research is on the VM selection stage of CPU load balancing. Our proposed VM selection algorithm considers CPU utilization of the VMs on each host and any linear correlation between the CPU usage of the VMs. The algorithm was evaluated on two different real Cloud data sets provided by the CoMon project and Google. Its performance was compared to our benchmark policy that only considers minimum migration time for VM selection. The results showed that our proposed algorithm decreases SLAVs by 66%, ESV (SLAVs × energy consumption) by 64% and the number of \"re over-utilized\" hosts by 81% when the CPU usage of VMs in a data set are highly correlated (e.g., as in the Google data set).","PeriodicalId":288232,"journal":{"name":"2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127975747","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: 12

Publisher's Information 出版商的信息

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/ucc.2018.00037

引用次数: 0

Scheduling Scientific Workflows on Clouds Using a Task Duplication Approach 使用任务复制方法在云上调度科学工作流

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/UCC.2018.00017

T. Genez, R. Sakellariou, L. Bittencourt, E. Madeira, T. Braun

{"title":"Scheduling Scientific Workflows on Clouds Using a Task Duplication Approach","authors":"T. Genez, R. Sakellariou, L. Bittencourt, E. Madeira, T. Braun","doi":"10.1109/UCC.2018.00017","DOIUrl":"https://doi.org/10.1109/UCC.2018.00017","url":null,"abstract":"By renting pay-as-you-go cloud resources (e.g., virtual machines) to do science, the data transfers required during the execution of data-intensive scientific workflows may be remarkably costly not only regarding the workflow execution time (makespan) but also regarding money. As such transfers are prone to delays, they may jeopardise the makespan, stretch the period of resource rentals and, as a result, compromise budgets. In this paper, we explore the possibility of trading some communication for computation during the scheduling production, aiming to schedule a workflow by duplicating some computation of its tasks on which other dependent-tasks critically depend upon to lessen communication between them. This paper explores this premise by enhancing the Heterogeneous Earliest Finish Time (HEFT) algorithm and the Lookahead variant of HEFT. The proposed approach is evaluated using simulation and synthetic data from four real-world scientific workflow applications. Our proposal, which is based on task duplication, can effectively reduce the size of data transfers, which, in turn, contributes to shortening the rental duration of the resources, in addition to minimising network traffic within the cloud.","PeriodicalId":288232,"journal":{"name":"2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116022911","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

Analyzing, Modeling, and Provisioning QoS for NVMe SSDs NVMe ssd的QoS分析、建模和发放

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/UCC.2018.00033

Shashank Gugnani, Xiaoyi Lu, D. Panda

{"title":"Analyzing, Modeling, and Provisioning QoS for NVMe SSDs","authors":"Shashank Gugnani, Xiaoyi Lu, D. Panda","doi":"10.1109/UCC.2018.00033","DOIUrl":"https://doi.org/10.1109/UCC.2018.00033","url":null,"abstract":"NVMe-based SSDs are in huge demand for Big Data analytics owing to their extremely low latency and high throughput for both read and write operations. Their inherent parallelism in request processing makes them ideal to be used in virtualized environments, where sharing of resources is a given. Given the shared resource-driven ideology of cloud environments, it is imperative to design middleware which can provide some guarantee of service to applications. In this paper, we show how such QoS can be provided for NVMe SSDs in virtualized environments. Our contributions are threefold: (1) design of accurate NVMe emulation mechanisms in QEMU to provide QoS schemes, (2) theoretical modeling of arbitration mechanisms for assisting in SLA provisioning, and (3) proposing designs in Intel SPDK to seamlessly use the hardware-based QoS provided by NVMe. We provide a complete case for our designs and validate them through thorough experimental evaluation. We show that Deficit Round Robin (DRR) as a hardware-based arbitration scheme is more suited for providing bandwidth guarantees for NVMe SSDs. Our evaluations show that by combining our proposed QoS-aware NVMe emulator in QEMU and enhanced SPDK runtime, we can achieve I/O bandwidth SLA guarantees in an application oblivious manner.","PeriodicalId":288232,"journal":{"name":"2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127656079","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

UPSARA: A Model-Driven Approach for Performance Analysis of Cloud-Hosted Applications UPSARA:用于云托管应用程序性能分析的模型驱动方法

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/UCC.2018.00009

Yogesh D. Barve, Shashank Shekhar, S. Khare, Anirban Bhattacharjee, A. Gokhale

{"title":"UPSARA: A Model-Driven Approach for Performance Analysis of Cloud-Hosted Applications","authors":"Yogesh D. Barve, Shashank Shekhar, S. Khare, Anirban Bhattacharjee, A. Gokhale","doi":"10.1109/UCC.2018.00009","DOIUrl":"https://doi.org/10.1109/UCC.2018.00009","url":null,"abstract":"Accurately analyzing the sources of performance anomalies in cloud-based applications is a hard problem due both to the multi tenant nature of cloud deployment and changing application workloads. To that end many different resource instrumentation and application performance modeling frameworks have been developed in recent years to help in the effective deployment and resource management decisions. Yet, the significant differences among these frameworks in terms of their APIs, their ability to instrument resources at different levels of granularity, and making sense of the collected information make it extremely hard to effectively use these frameworks. Not addressing these complexities can result in operators providing incompatible and incorrect configurations leading to inaccurate diagnosis of performance issues and hence incorrect resource management. To address these challenges, we present UPSARA, a model-driven generative framework that provides an extensible, lightweight and scalable performance monitoring, analysis and testing framework for cloud-hosted applications. UPSARA helps alleviate the accidental complexities in configuring the right resource monitoring and performance testing strategies for the underlying instrumentation frameworks used. We evaluate the effectiveness of UPSARA in the context of representative use cases highlighting its features and benefits.","PeriodicalId":288232,"journal":{"name":"2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132348054","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: 9

A Model Driven Engineering Approach for Flexible and Distributed Monitoring of Cross-Cloud Applications 跨云应用灵活分布式监控的模型驱动工程方法

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/UCC.2018.00012

Dani Baur, F. Griesinger, Giannis Verginadis, V. Stefanidis, Ioannis Patiniotakis

{"title":"A Model Driven Engineering Approach for Flexible and Distributed Monitoring of Cross-Cloud Applications","authors":"Dani Baur, F. Griesinger, Giannis Verginadis, V. Stefanidis, Ioannis Patiniotakis","doi":"10.1109/UCC.2018.00012","DOIUrl":"https://doi.org/10.1109/UCC.2018.00012","url":null,"abstract":"Cloud computing and its computing as a utility paradigm offers on-demand resources, enabling its users to seamlessly adapt applications to the current demand. With its (virtually) unlimited elasticity, managing deployed applications becomes more and more complex raising the need for automation. Such autonomous systems leverage the importance to constantly monitor and analyse the deployed workload and the underlying infrastructure serving as knowledge-base for deriving corrective actions like scaling. Existing monitoring solutions, however are not designed to cope with a frequently changing topology. We propose a monitoring and event processing framework following a model-driven approach, that allows users to express i) the monitoring demand by directly referencing entities of the deployment context, ii) aggregate the monitoring data using mathematical expressions, iii) trigger and process events based on the monitoring data and finally iv) attach scalability rules to those events. We accompany the modelling language with a monitoring orchestration and distributed complex event processing framework, capable of enacting the model in a frequently changing multi-cloud infrastructure, considering cloud-specific aspects like communication costs.","PeriodicalId":288232,"journal":{"name":"2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127736272","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

Confluence: Adaptive Spatiotemporal Data Integration Using Distributed Query Relaxation over Heterogeneous Observational Datasets 融合:在异构观测数据集上使用分布式查询松弛的自适应时空数据集成

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/UCC.2018.00027

Saptashwa Mitra, S. Pallickara

{"title":"Confluence: Adaptive Spatiotemporal Data Integration Using Distributed Query Relaxation over Heterogeneous Observational Datasets","authors":"Saptashwa Mitra, S. Pallickara","doi":"10.1109/UCC.2018.00027","DOIUrl":"https://doi.org/10.1109/UCC.2018.00027","url":null,"abstract":"Combining data from disparate sources enhances the opportunity to explore different aspects of the phenomena under consideration. However, there are several challenges in doing so effectively that include, inter alia, the heterogeneity in data representation and format, collection patterns, and integration of foreign data attributes in a ready-to-use condition. In this study, we have designed a scalable query-oriented distributed data integration framework, Confluence, that also dynamically generates accurate interpolations for the targeted spatiotemporal scopes along with an estimate of the uncertainty involved with such estimation in case of spatiotemporal misalignment of datapoints. Confluence efficiently orchestrates computations to evaluate spatiotemporal query joins and facilitates distributed query evaluations with a dynamic relaxation of query constraints. Query evaluations are locality-aware and we leverage model-based dynamic parameter selection to provide accurate estimation for data points. We have included empirical benchmarks that profile our system in terms of accuracy, latency, and throughput at scale and also demonstrate its improvement in performance in a distributed cloud computing environment over GeoMesa, a Spark-based geospatial analytics framework.","PeriodicalId":288232,"journal":{"name":"2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128729598","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

[Title page i] [标题页i]

2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2018-12-01 DOI: 10.1109/ucc.2018.00001

引用次数: 0