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Cost-Efficient Resource Provisioning in Delay-Sensitive Cooperative Fog computing 24

Cost-Efficient Resource Provisioning in Delay-Sensitive Coop...

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24th IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Lu, Shuaibing Wu, Jie Duan, Yubin Wang, Ning Fang, Zhiyi Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China Temple Univ Ctr Networked Comp Philadelphia PA 19122 USA

ISBN: (纸本)9781538673089

Recently, fog computing has become a highly virtualized platform that provides computation, storage, and networking services between end devices and traditional cloud data centers. In this paper, we address the resource provision (RP) problem for delay-sensitive users in cooperative fog computing. Our objective is to find a feasible provision scheme that minimizes the total monetary cost proportional to the number of fog nodes for network operators under the deadline and capacity constraints by considering the cooperation of fog nodes. We consider two cases of our RP problem: the Unlimited-Processor Fog Nodes (UPFN) case and the Limited-Processor Fog Nodes (LPFN) case. For the UPFN case, each fog node has unlimited processors. the requests on each fog node can be processed in parallel ideally, i.e. with no scheduling delay. the LPFN case corresponds to a more realistic scenario where the scheduling delay is non-eligible. In either case, our RP problem is proven to be NP-hard. For the UPFN case, we propose two greedy algorithms which iteratively remove fog nodes according to their global or local cooperative influences until there is no feasible provision that can guarantee users' deadlines. For the LPFN case, it is not trivial to check the existence of a feasible provision due to the interactive influence on the scheduling delay for requests. We find a near-optimal solution with bound 8/3 OPT + epsilon(2)/8m alpha using the continuous congestion game and check the feasibility, where m is the number of fog nodes and a is a constant value related to the delay function. Extensive simulations demonstrate the efficiency of our schemes.

关键词： Fog computing delay-sensitive cooperation cost efficiency

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12th international conference on Cloud computing, CLOUD 2019 held as part of the Services conference Federation, SCF 2019

12th International Conference on Cloud Computing, CLOUD 2019...

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12th international conference on Cloud computing, CLOUD 2019 held as part of the Services conference Federation, SCF 2019

ISBN: (纸本)9783030235017

the proceedings contain 24 papers. the special focus in this conference is on Cloud computing. the topics include: Exploiting the spam correlations in scalable online social spam detection;dynamic network anomaly detection system by using deep learning techniques;heterogeneity-aware data placement in hybrid clouds;towards automated configuration of cloud storage gateways: A data driven approach;the case for physical memory pools: A vision paper;a parallel algorithm for bayesian text classification based on noise elimination and dimension reduction in spark computing environment;on the optimal number of computational resources in MapReduce;class indistinguishability for outsourcing equality conjunction search;a hybrid approach for synchronizing clocks in distributed systems;a Method and tool for automated induction of relations from quantitative performance logs;JCallGraph: Tracing microservices in very large scale container cloud platforms;an overview of cloud computing testing research;a robust multi-terminal support method based on tele-immersion multimedia technology;CMonitor: A monitoring and alarming platform for container-based clouds;CPR: Client-side processing of range predicates;systematic construction, execution, and reproduction of complex performance benchmarks;multiple workflow scheduling with offloading tasks to edge cloud;min-edge P-cycles: An efficient approach for computing P-cycles in optical data center networks;toward accurate and efficient emulation of public blockchains in the cloud;Teleportation of VM disk images over WAN;live migration of virtual machines in openstack: A perspective from reliability evaluation;an approach to failure prediction in cluster by self-updating cause-and-effect graph.

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Modeling Application Performance in Docker Containers using Machine Learning Techniques 24

Modeling Application Performance in Docker Containers using ...

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24th IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Ye, Kejiang Kou, Yanmin Lu, Chengzhi Wang, Yang Xu, Cheng-Zhong Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China

ISBN: (纸本)9781538673089

Docker container is experiencing a rapid development with the support from industry like Google and is being widely used in large scale production cloud environments. However the performance of applications running in Docker containers is still not clear due to the complex relationship between container resource allocation and application performance. In this paper, we first study the impact of key parameters in container resource allocation that affect the performance of containerized applications. then, we present modeling techniques over CPU, memory and I/O resources to characterize the performance of applications running in containers. To address this multi-dimensional modeling problem, we propose three machine learning techniques, i.e. Linear Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN). We implement and evaluate the modeling techniques for four complex benchmark workloads from Spark. Experimental results demonstrate the proposed models can achieve as low as 2.27% prediction error, with an average of 10.13% for most applications. Furthermore, the prediction accuracy of SVM and ANN models are substantially better than LR based approaches, with 48.13% and 29.30% improvement.

关键词： Container Cloud computing Performance Modeling ANN

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cuBool: Bit-Parallel Boolean Matrix Factorization on CUDA-enabled Accelerators 24

cuBool: Bit-Parallel Boolean Matrix Factorization on CUDA-en...

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24th IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Kobus, Robin Lamoth, Adrian Mueller, Andre Hundt, Christian Kramer, Stefan Schmidt, Bertil Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (纸本)9781538673089

Boolean Matrix Factorization (BMF) is a commonly used technique in the field of unsupervised data analytics. the goal is to decompose a ground truth matrix C into a product of two matrices A and B being either an exact or approximate rank k factorization of C. Both exact and approximate factorization are time-consuming tasks due to their combinatorial complexity. In this paper, we introduce a massively parallel implementation of BMF - namely cuBool - in order to significantly speed up factorization of huge Boolean matrices. Our approach is based on alternately adjusting rows and columns of A and B using thousands of lightweight CUDA threads. the massively parallel manipulation of entries enables full usage of all available cores on modern CUDA-enabled GPUs. Additionally, modelling up to 32 consecutive entries of the Boolean matrices A, B and C as 32-bit integer results in fewer data accesses and faster computation of inner products. this bit-parallel approach allows for a significant decrease of memory requirements in contrast to gradient-based continuous updates of entries on dense representations. cuBool is compared to other state-of-the-art matrix factorization algorithms. Experiments on a number of real-world data sets show highly competitive results at only a fraction of computation time. cuBool proves to be a good compromise between low run time and a high-quality factorization for the decomposition of large-scale Boolean matrices. It can freely be accessed under https://***/funatiq/cubool.

关键词： Data milling CUDA matrix factorization massively parallel computing

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ECHO: A Reliable distributed Cellular Core Network for Hyper-scale Public Clouds 18

ECHO: A Reliable Distributed Cellular Core Network for Hyper...

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24th Annual international conference on Mobile computing and Networking (MobiCom)

作者： Binh Nguyen Zhang, Tian Radunovic, Bozidar Stutsman, Ryan Karagiannis, thomas Kocur, Jakub Van der Merwe, Jacobus Univ Utah Salt Lake City UT 84112 USA Microsoft Res Redmond WA USA Core Network Dynam Berlin Germany

ISBN: (纸本)9781450359030

Economies of scale associated with hyper-scale public cloud platforms offer flexibility and cost-effectiveness, resulting in various services and businesses moving to the cloud. One area with little progress in this direction is cellular core networks. A cellular core network manages the state of cellular clients;it is a large distributed state machine with different virtualization challenges compared to typical cloud services. In this paper we present a novel cellular core network architecture, called ECHO, particularly suited to public cloud deployments, where the availability guarantees might be an order of magnitude worse compared to existing (redundant) hardware platforms. We present the design and implementation of our approach and evaluate its functionality on a public cloud platform. Analysis shows ECHO promises higher availability than existing telco solutions.

关键词： reliability distributed systems mobile networks nfv

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An Insurance-based Framework Against Security threat in Mobile Crowdsourcing systems 24

An Insurance-based Framework Against Security Threat in Mobi...

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24th IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Zhao, Rong Jiang, Linshan Zhang, Kaiyue Zhang, Jin Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Nanyang Technol Univ Dept Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781538673089

Mobile crowdsourcing is a popular computing paradigm that enables smart devices to measure and collect various sensing data. When the users participate in the sensing platform, they may face various attacks and fall in a non-secure environment. Under the malicious attack, the sensing data which should be transmitted to the platform may suffer from data loss, which reduces the users' utility and platform's utility. the data loss can also lead to the reduction of the users' participatory motivations because they are not able to earn expected money due to data loss. therefore, the total sensing quality and the social welfare will be affected eventually. To solve this problem, in Ibis paper, we propose a novel insurance-based framework to compensate the data loss due to security threat in mobile crowdsourcing systems. this framework can motivate the users with high-security levels to participate in the crowdsourcing system, thus improves the platform's utility. We formulate our framework as a Stackelberg game, where the platform is a leader and the users are followers. the theoretical analysis shows that the Nash Equilibrium exists in our framework and it can maximize the platform's revenue while considering the users' participatory willingness. Simulation results show that our framework achieves more participators, more platform's utility and social welfare, compared with existing mechanism.

关键词： Crowdsourcing stackelberg game incentive mechanism insurance

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Accelerating Real-Time Tracking Applications over Big Data Stream with Constrained Space 24th

Accelerating Real-Time Tracking Applications over Big Data S...

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24th international conference on Database systems for Advanced Applications (DASFAA)

作者： Wu, Guangjun Yun, Xiaochun Wang, Shupeng Fu, Ge Li, Chao Liu, Yong Li, Binbin Wang, Yong Zhao, Zhihui Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9783030185763;9783030185756

Existing approaches are insufficient to provide real-time results for tracking applications against big and fast data streams. In this paper, we leverage freshness sensitive properties of tracking applications and propose an approximate query answering approach, called FS-Sketch, to accelerating real-time temporal queries over big data streams. FS-Sketch constructs its sketch over high-speed data streams via composed online sampling strategies, including sliding-window sampling and space-constrained sampling. Furthermore, FS-Sketch can compress its sketch into constrained space dynamically via utilizing timedecayed mechanism. We evaluate performance of FS-Sketch using realworld and synthetic datasets. FS-Sketch can respond temporal queries within 2ms from 1.4 billion records with accurate estimates. Meanwhile, FS-Sketch can also outperform the state-of-the-art big data analytical system (Spark) by 5 orders of magnitude on response time when we query over TB-scale real-world datasets.

关键词： Approximate answering Big data query Data streams distributed computing Networks

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REOH: Using Probabilistic Network for Runtime Energy Optimization of Heterogeneous systems 24

REOH: Using Probabilistic Network for Runtime Energy Optimiz...

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24th IEEE international conference on Parallel and distributed systems, ICPADS 2018

作者： Tran, Vi Ngoc-Nha Oines, Tommy Horsch, Alexander Ha, Phuong Hoai Department of Computer Science UiT The Arctic University of Norway Norway

ISBN: (纸本)9781538673089

Significant efforts have been devoted to choosing the best configuration of a computing system to run an application energy efficiently. However, available tuning approaches mainly focus on homogeneous systems and are inextensible for heterogeneous systems which include several components (e.g., CPU s, G PU s) with different architectures. this study proposes a holistic tuning approach called REOH using probabilistic network to predict the most energy-efficient configuration (i.e., which platform and its setting) of a heterogeneous system for running a given application. Based on the computation and communication patterns from Berkeley dwarfs, we conduct experiments to devise the training set including 7074 data samples covering varying application patterns and characteristics. Validating the REOH approach on heterogeneous systems including CPUs and GPUs shows that the energy consumption by the REOH approach is close to the optimal energy consumption by the Brute Force approach while saving 17 % of sampling runs compared to the previous (homogeneous) approach using probabilistic network. Based on the REOH approach, we develop an open-source energy-optimizing runtime framework for selecting an energy efficient configuration of a heterogeneous system for a given application at runtime. © 2018 IEEE.

关键词： Energy utilization

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User Experience-Enhanced and Energy-Efficient Task Scheduling on Heterogeneous Multi-Core Mobile systems 24

User Experience-Enhanced and Energy-Efficient Task Schedulin...

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24th IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Huang, Yanting Liu, Weichen Li, Mengquan Chen, Peng Yang, Lei Xiao, Chunhua Ye, Yaoyao Chongqing Univ Coll Comp Sci Chongqing Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Shanghai Jiao Tong Univ Dept Micronano Elect Shanghai Peoples R China

ISBN: (纸本)9781538673089

Heterogeneous Multi-Core Mobile systems has been widely used to improve performance. However, it faces with the challenge of tradeoff between energy saving and user experience. ARM *** architecture, a heterogeneous computing architecture, is a power-optimization technology. In most *** devices, however, it still cannot achieve excellent user experience and higher energy saving. In this paper, we propose an improved task scheduling (UCES-GTS) by introducing the concept of user-centric task on *** mobile device. In order to enhance user experience, the response time of user centric tasks is shortened with reducing slack time of them properly. We then present a detailed algorithm to compute appropriate frequency and allocate the CPU resources to each task. the experimental evaluation results show that our improved global task scheduling model can achieve 17% and 8% energy saving average compared with the clustered switching scheduling and the original global task scheduling respectively. And the response time of user-centric tasks can decrease 27% average, which means excellent user experience.

关键词： Heterogeneous Multi-Core Mobile systems Energy Consumption User experience Task Scheduling big.LITTLE architecture

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Blockchain and IoT Data Analytics for Fine-Grained Transportation Insurance 24

Blockchain and IoT Data Analytics for Fine-Grained Transport...

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24th IEEE international conference on Parallel and distributed systems, ICPADS 2018

作者： Li, Zengxiang Xiao, Zhe Xu, Quanqing Sotthiwat, Ekanut Mong Goh, Rick Siow Liang, Xueping Institute of High Performance Computing ASTAR Singapore Big Data Experience Center Computer Engineering Department King Mongkut's University of Technology Thonburi Thailand Old Dominion University NorfolkVA United States

ISBN: (纸本)9781538673089

Innovations such as the Cloud, Internet of things (IoT), and data analytics have already dramatically altered the customer experience in many, if not all, industries. Blockchain, as another emerging technology, is expected to be the next generation infrastructure to established trusted multiparty collaborations. In this paper, we investigated the convergence of aforementioned technologies, by presenting a prototype of fine-grained transportation insurance. Insurance premium were assessed based on vehicle's usage and driver's behavior, which were deduced from streaming IoT data collected from mobile sensors. this incentive mechanism promotes fairness among drivers and encourages safer driving style. the prototype takes advantage of both private blockchain (e.g., high transaction rate in Hyperledger) and public blockchain (e.g., inbuilt cryptocurrency). Besides system architecture and implementation details, preliminary performance evaluations are presented and discussed. © 2018 IEEE.

关键词： Insurance

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