ARMS-CC@PODC最新文献

Healthcare Sensor Data Management on the Cloud 云上的医疗传感器数据管理

ARMS-CC@PODC Pub Date : 2017-07-28 DOI: 10.1145/3110355.3110359

Pelagia Tsiachri Renta, Stelios Sotiriadis, E. Petrakis

{"title":"Healthcare Sensor Data Management on the Cloud","authors":"Pelagia Tsiachri Renta, Stelios Sotiriadis, E. Petrakis","doi":"10.1145/3110355.3110359","DOIUrl":"https://doi.org/10.1145/3110355.3110359","url":null,"abstract":"The quality of medical services can be significantly improved by supporting health care procedures with new technologies such as Cloud computing and Internet of Things (IoTs). The need to monitor patient's health remotely and in real time becomes more and more a vital requirement, especially for chronic patients and elderly. In this work, we focus on the management of health care related data stored on the Cloud produced by Bluetooth low energy devices. We present a Cloud based IoT Management System that collects vital user data (e.g. cardiac pulse rate and blood oxygen saturation) on real time. Our solution enables sensor data collection and processing fast and efficient, while users such as medical personnel can subscribe to patient's data and get notifications. The system is designed based on microservices and includes a notification service for both health care providers and patients minimizing the risk of late response to emergency conditions. Alerts are produced according to predefined rules and on patient specific reaction plans. We present an experimental study where we evaluate our system based on real world sensors, while we generate a synthetic dataset for simulating thousands of users. The results are prosperous, as the system responds close to real time even under heavy loads binding to the limits of the web server that receives the service request. The heaviest workload simulates 2000 user requests (while 80 are executed concurrently) is completed in less than 13 seconds when the system deployed in a virtual machine of 2GB RAM, 1 VCPU and 20GB Disk.","PeriodicalId":309271,"journal":{"name":"ARMS-CC@PODC","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125687738","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: 16

A Distributed and Fault Tolerant Robotic Localisation and Mapping in Network Edge 网络边缘的分布式容错机器人定位与映射

ARMS-CC@PODC Pub Date : 2017-07-28 DOI: 10.1145/3110355.3110357

S. Biswas, Swarnava Dey, Rimita Lahiri, A. Mukherjee

{"title":"A Distributed and Fault Tolerant Robotic Localisation and Mapping in Network Edge","authors":"S. Biswas, Swarnava Dey, Rimita Lahiri, A. Mukherjee","doi":"10.1145/3110355.3110357","DOIUrl":"https://doi.org/10.1145/3110355.3110357","url":null,"abstract":"Of late, Cloud Robotics paradigm is being used to augment low-end robots with enhanced sensor data processing, storage and communication capabilities. In an era, where costly specialized hardware are being replaced by commodity hardware, software reliability within Cloud Robotic middleware will allow distributed execution on lightweight, low-cost robots and network edge devices. However, successful functioning of multi-robot systems in critical missions requires resilience in the middleware such that the overall functionity degrades gracefully during hardware or network failures. In the current work, reliable distributed execution capability is added to a well known robotic localization and mapping task such that data transfer between participating nodes is minimized and the application degrades gracefully in case of failure of participating robots. To ensure fault tolerance, an execution model based on the failure probabilities of individual robots and their components is proposed. A lightweight timeseries analysis scheme is presented enabling the robots to find their individual failure probabilities and use that to enhance system reliability in a distributed manner. Both the distribution and predictive recovery schemes are evaluated using standard datasets on virtual machines running robotic middleware.","PeriodicalId":309271,"journal":{"name":"ARMS-CC@PODC","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123196892","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

An RLS Memory-based Mechanism for the Automatic Adaptation of VMs on Cloud Environments 基于RLS内存的云环境下虚拟机自动适配机制

ARMS-CC@PODC Pub Date : 2017-07-28 DOI: 10.1145/3110355.3110358

Carlos Ruiz, H. Duran-Limon, N. Parlavantzas

引用次数: 2

Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption 对OpenCL、OpenACC、OpenMP和CUDA进行基准测试:编程效率、性能和能耗

ARMS-CC@PODC Pub Date : 2017-04-18 DOI: 10.1145/3110355.3110356

Suejb Memeti, Lu Li, Sabri Pllana, J. Kolodziej, C. Kessler

{"title":"Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption","authors":"Suejb Memeti, Lu Li, Sabri Pllana, J. Kolodziej, C. Kessler","doi":"10.1145/3110355.3110356","DOIUrl":"https://doi.org/10.1145/3110355.3110356","url":null,"abstract":"Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comprise general purpose CPUs and accelerators (such as, GPU, or Intel Xeon Phi) that provide high performance with suitable energy-consumption characteristics. However, exploiting the available performance of heterogeneous architectures may be challenging. There are various parallel programming frameworks (such as, OpenMP, OpenCL, OpenACC, CUDA) and selecting the one that is suitable for a target context is not straightforward. In this paper, we study empirically the characteristics of OpenMP, OpenACC, OpenCL, and CUDA with respect to programming productivity, performance, and energy. To evaluate the programming productivity we use our homegrown tool CodeStat, which enables us to determine the percentage of code lines required to parallelize the code using a specific framework. We use our tools MeterPU and x-MeterPU to evaluate the energy consumption and the performance. Experiments are conducted using the industry-standard SPEC benchmark suite and the Rodinia benchmark suite for accelerated computing on heterogeneous systems that combine Intel Xeon E5 Processors with a GPU accelerator or an Intel Xeon Phi co-processor.","PeriodicalId":309271,"journal":{"name":"ARMS-CC@PODC","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114967196","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: 84

An Eye on the Elephant in the Wild: A Performance Evaluation of Hadoop's Schedulers Under Failures 关注野外的大象:失败情况下Hadoop调度器的性能评估

ARMS-CC@PODC Pub Date : 2015-07-20 DOI: 10.1007/978-3-319-28448-4_11

Shadi Ibrahim, Tran Anh Phuong, Gabriel Antoniu

引用次数: 3

Implementing the Cloud Software to Data Approach for OpenStack Environments OpenStack环境下软件到数据的云化实现

ARMS-CC@PODC Pub Date : 2015-07-20 DOI: 10.1007/978-3-319-28448-4_8

Lenos Vakanas, Stelios Sotiriadis, E. Petrakis

引用次数: 7

Partitioning Graph Databases by Using Access Patterns 使用访问模式对图数据库进行分区

ARMS-CC@PODC Pub Date : 2015-07-20 DOI: 10.1007/978-3-319-28448-4_12

V. Tüfekçi, C. Özturan