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Delay-aware Resource Allocation for Data Analysis in Cloud-Edge System 16

Delay-aware Resource Allocation for Data Analysis in Cloud-E...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Li, Xin Lian, Zhen Qin, Xiaolin Abawajy, Jemal Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (纸本)9781728111414

there is a strong need for data analysis in information systems to support various services. Traditional cloud data centers provide powerful ability to conduct data analysis jobs. However, the data transmission consumes a large amount of time and leads to a long service delay. the QoS (Quality of Service) caused by long service delay is unacceptable for real-time services or applications. the collaboration with edge computing is an opportunity for service delay reduction. In this paper, we investigate the task placement problem for reducing service delay in cloud-edge system. We use the W-DAG (Weighted Directed Acyclic Graph) to model the data-intensive service or business logic. We analyze the data and resource requirements for the tasks, which constitute the integrated service, and make resource allocation between cloud data center and edge nodes. then, we propose the task placement algorithm to achieve shorter service delay. the core idea is to make a tradeoff between data transmission time and data analysis time. the simulation results show that our algorithm has significant performance improvement on service delay reduction.

关键词： delay-aware data-driven edge computing resource allocation task placement

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Efficient VM Selection Heuristics for Dynamic VM Consolidation in Cloud Datacenters 16

Efficient VM Selection Heuristics for Dynamic VM Consolidati...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Qaiser, Hammad Ur Rehman Shu, Gao Wuhan Univ Technol Sch Comp Sci & Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781728111414

Dynamic Consolidation of Virtual Machines (VMs) in a cloud data center requires live migration of VMs from over-utilized hosts. A primary but relatively an ignored part of consolidation process is the efficient selection of Virtual Machines from an over-utilized host for migration. Two VM selection policies, threshold Based Selection (TBS) and Capacity Based Selection (CBS), have been proposed in this paper. these policies are based on the idea of simultaneously minimizing multiple factors that contribute to the degradation of the quality of service due to consolidation. three degrading factors considered in the policies are, the time duration of VM migrations, time duration hosts remain over-utilized and the total number of migrations required for consolidation. Cost functions, involving these degrading factors, have been provided which formed the bases for TBS and CBS. TBS is an efficient VM selection mechanism that focuses more on the time duration of VM migrations and the total number of migrations required for the consolidation process as a trade-off between the three degrading factors. On the other hand, CBS is another efficient mechanism with more emphasis on reducing the time duration for which hosts remain over-utilized. Experiment results obtained by using Cloudsim simulating toolkit have shown that our proposed policies outperformed conventional VM selection policies like MMT, MU, and RC on indicators such as energy consumption, SLA violations, and overall performance efficiency.

关键词： Virtual Machine Consolidation Virtual Machine Selection Policies Energy Efficient computing Cloud computing Efficient Resource Management System

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Human Activity Recognition Using Federated Learning 16

Human Activity Recognition Using Federated Learning

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Sozinov, Konstantin Vlassov, Vladimir Girdzijauskas, Sarunas KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781728111414

State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mitigate aforementioned issues, federated learning can be employed to train a generic classifier by combining multiple local models trained on data originating from multiple clients. In this work we evaluate federated learning to train a human activity recognition classifier and compare its performance to centralized learning by building two models, namely a deep neural network and a softmax regression trained on both synthetic and real-world datasets. We study communication costs as well as the influence of erroneous clients with corrupted data in federated learning setting. We have found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models. In our experiments federated learning achieved an accuracy of up to 89 % compared to 93 % in centralized training for the deep neural network. the global model trained with federated learning on skewed datasets achieves accuracy comparable to centralized learning. Furthermore, we identified an important issue of clients with corrupted data and proposed a federated learning algorithm that identifies and rejects erroneous clients. Lastly, we have identified a trade-off between communication cost and the complexity of a model. We show that more complex models such as deep neural network require more communication in federated learning settings for human activity recognition compared to less complex models, such as multinomial logistic regression.

关键词： Federated Learning Human Activity Recognition Privacy distributed Machine Learning

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Automatic Energy Efficient HPC Programming: A Case Study 16

Automatic Energy Efficient HPC Programming: A Case Study

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Rais, Issam Coullon, Helene Lefevr, Laurent Perez, Christian INRIA IMT Atlantique LS2N UBL F-44307 Nantes France Univ Claude Bernard Lyon 1 Univ Lyon CNRS INRIAENS LyonLIP Lyon France

ISBN: (纸本)9781728111414

Energy consumption is one of the major challenges of modern datacenters and supercomputers. By applying Green Programming techniques, developers have to iteratively implement and test new versions of their software, thus evaluating the impact of each code version on their energy, power and performance objectives. this approach is manual and can be long, challenging and complicated, especially for High Performance computing applications. In this paper, we formally introduces the definition of the Code Version Variability (CVV) leverage and present a first approach to automate Green Programming (i.e., CVV usage) by studying the specific use-case of an HPC stencil-based numerical code, used in production. this approach is based on the automatic generation of code versions thanks to a Domain Specific Language (DSL), and on the automatic choice of code version through a set of actors. Moreover, a real case study is introduced and evaluated though a set of benchmarks to show that several trade-offs are introduced by CVV1. Finally, different kinds of production scenarios are evaluated through simulation to illustrate possible benefits of applying various actors on top of the CVV automation.

关键词： Code version variability leverage green programming automation energy efficiency power capping high performance computing DSL shallow-water equations

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Tromino: Demand and DRF Aware Multi-Tenant Queue Manager for Apache Mesos Cluster 11

Tromino: Demand and DRF Aware Multi-Tenant Queue Manager for...

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11th IEEE/ACM international conference on Utility and Cloud computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Saha, Pankaj Beltre, Angel Govindaraju, Madhusudhan SUNY Binghamton Cloud & Big Data Lab Binghamton NY 13902 USA

ISBN: (纸本)9781538655047

Apache Mesos, a two-level resource scheduler, provides resource sharing across multiple users in a multi-tenant cluster environment. Computational resources (i.e., CPU, memory, disk, etc.) are distributed according to the Dominant Resource Fairness (DRF) policy. Mesos frameworks (users) receive resources based on their current usage and are responsible for scheduling their tasks within the allocation. We have observed that multiple frameworks can cause fairness imbalance in a multiuser environment. For example, a greedy framework consuming more than its fair share of resources can deny resource fairness to others. the user with the least Dominant Share is considered first by the DRF module to get its resource allocation. However, the default DRF implementation, in Apache Mesos' Master allocation module, does not consider the overall resource demands of the tasks in the queue for each user/framework. this lack of awareness can result in users without any pending task receiving more resource offers while users with a queue of pending tasks starve due to their high dominant shares. In a multi-tenant environment, the characteristics of frameworks and workloads must be understood by cluster managers to be able to define fairness based on not only resource share but also resource demand and queue wait time. We have developed a policy driven queue manager, Tromino, for an Apache Mesos cluster where tasks for individual frameworks can be scheduled based on each framework's overall resource demands and current resource consumption. Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue. In the best case, Tromino can significantly reduce the average waiting time of a framework by using the proposed Demand-DRF aware policy.

关键词： Task analysis Resource management Cloud computing Processor scheduling Magnetosphere Ion radiation effects

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the 5th international workshop on social web for disaster management (SWDM'18): Collective sensing, trust, and resilience in global crises 18

The 5th international workshop on social web for disaster ma...

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11th ACM international conference on Web Search and Data Mining, WSDM 2018

作者： Lin, Yu-Ru Castillo, Carlos Yin, Jie School of Computing and Information University of Pittsburgh PA United States Universitat Pompeu Fabra Barcelona Catalonia Spain CSIRO Epping NSW Australia

ISBN: (纸本)9781450355810

During large-scale emergencies such as natural and man-made disasters, a massive amount of information is posted by the public in social media. Collecting, aggregating, and presenting this information to stakeholders can be extremely challenging, particularly if an understanding of the "big picture" is sought. this international workshop, the fifth in the series, is a key venue for researchers and practitioners to discuss research challenges and technical issues around the usage of social media in disaster management. Workshop's website: https://***/site/swdm2018/. © 2018 Copyright held by the owner/author(s).

关键词： Social networking (online)

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Data Locality Aware Algorithm for Task Execution on distributed, Cloud Based Environments 11th

Data Locality Aware Algorithm for Task Execution on Distribu...

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11th international conference on Complex, Intelligent, and Software Intensive Systems (CISIS)

作者： Bica, Mihai Gorgan, Dorian Tech Univ Cluj Napoca Cluj Napoca Romania

ISBN: (纸本)9783319615660;9783319615653

A solution that proactively analyzes the shape of the operator graph of a task based cloud application is studied in this paper. Based on the analysis of the execution graph and operator metadata, the nodes of the execution graph are properly clustered so that highly connected operators are scheduled on the same or nearby computing resources. Two graph partitioning algorithms are studied, implemented and compared. the graph partitioning efficiency is visually analyzed and compared by using existing graph visualization software.

关键词： Graph partitioning distributed systems Application execution

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Cold Start Influencing Factors in Function as a Service 11

Cold Start Influencing Factors in Function as a Service

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11th IEEE/ACM international conference on Utility and Cloud computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Manner, Johannes Endress, Martin Heckel, Tobias Wirtz, Guido Univ Bamberg Distributed Syst Grp Bamberg Germany

ISBN: (纸本)9781728103594

Function as a Service (FaaS) is a young and rapidly evolving cloud paradigm. Due to its hardware abstraction, inherent virtualization problems come into play and need an assessment from the FaaS point of view. Especially avoidance of idling and scaling on demand cause a lot of container starts and as a consequence a lot of cold starts for FaaS users. the aim of this paper is to address the cold start problem in a benchmark and investigate influential factors on the duration of the perceived cold start. We conducted a benchmark on AWS Lambda and Microsoft Azure Functions with 49500 cloud function executions. Formulated as hypotheses, the influence of the chosen programming language, platform, memory size for the cloud function, and size of the deployed artifact are the dimensions of our benchmark. Cold starts on the platform as well as the cold starts for users were measured and compared to each other. Our results show that there is an enormous difference for the overhead the user perceives compared to the billed duration. In our benchmark, the average cold start overheads on the user's side ranged from 300ms to 24s for the chosen configurations.

关键词： Serverless computing Function as a Service FaaS Cloud Functions Cold Start Benchmarking

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Cloud computing on Cooperative Cars (C4S) An Architecture to Support Navigation-as-a-Service 11

Cloud Computing on Cooperative Cars (C4S) An Architecture to...

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11th IEEE international conference on Cloud computing (CLOUD) Part of the IEEE World Congress on Services

作者： Ansari, Keyvan Univ Sunshine Coast Sippy Downs Qld Australia

ISBN: (纸本)9781538672358

this paper offers a cloud computing architecture supporting Navigation-as-a-Service (NAVaaS) for Cooperative Intelligent Transportation Systems (C-ITS) to ensure interoperability among C-ITS users, i.e., users of single-and multi-Global Navigation Satellite Systems (GNSS) receivers. the proposed architecture, namely, Cloud computing on Cooperative Cars (C4S) is a tailored design for connected vehicles using Dedicated Short-Range Communications (DSRC) or cellular connections. C4S is a scalable Everything-as-a-Service (XaaS) architecture using Mobile Cloud computing (MCC) for sharing resources, platforms, software, data and communications as services. these shared infrastructure, platform, software and communications services are distributed across clusters over a network of servers and clients complementing Vehicular Ad-hoc Networks (VANETs). C4S sits above the VANET layer of C-ITS to improve transportation in terms of safety, reliability, productivity and resilience, and environmental protection. To this end, C4S establishes itself over two levels of MCC: (1) DSRC-based local fogs and (2) the Internet-based remote cloud. this architecture made an array of novel cloud-based navigation services possible that supports VANETs. the mechanism, capacity and applications of NAVaaS are verified through a describing function model and a series of experimental studies.

关键词： cloud computing cooperative intelligent transportation systems (C-ITS) dedicated short-range communications (DSRC) Everything-as-a-Service (XaaS) fog computing Navigation-as-a-Service (NAVaaS) single- and multi-GNSS interoperability vehicle-to-infrastructure (V2I) vehicle-to-vehicle (V2V) vehicular ad-hoc networks (VANETs)

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Towards Osmotic computing: Looking at Basic Principles and Technologies 11th

Towards Osmotic Computing: Looking at Basic Principles and T...

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11th international conference on Complex, Intelligent, and Software Intensive Systems (CISIS)

作者： Villari, Massimo Celesti, Antonio Fazio, Maria Univ Messina Dept Ingn Messina Italy

ISBN: (纸本)9783319615660;9783319615653

Osmotic computing is becoming the new paradigm in the area of computing. this paper shows how it can represents the glue of recent topics including Cloud, Edge and Fog computing, and Internet of things (IoT). Osmotic computing introduces elements allowing to treat computation, networking, storage, data transfer and management among Cloud and IoT devices in Edge computing layers in a more harmonized fashion. In particular, we discuss how it can enable an abstraction of services that could bring into a new Software Defined of Everything era.

关键词： Internet of things

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