Pelagia Tsiachri Renta, Stelios Sotiriadis, E. Petrakis
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.
{"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}
S. Biswas, Swarnava Dey, Rimita Lahiri, A. Mukherjee
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.
{"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}
One key factor for Cloud computing success is the resource flexibility it provides. Because of this characteristic, academia and industry have focused their efforts on making efficient use of cloud computational resources without having to sacrifice performance. One way to achieve this purpose is through the automatic adaptation of the computational capabilities of VMs according to their resource utilization and performance. In this paper we present the design and preliminary results of our resource adaptation solution, which proactively adapts VMs (memory-based vertical scaling) to maintain an expected performance. Our solution targets multi-tier applications deployed on Cloud environments, and its core resides in RLS-based resource and performance predictors. Our results show that our solution, when compared with VMs with larger and permanently allocated computational resources, is able to maintain expected performance while reducing resource waste.
{"title":"An RLS Memory-based Mechanism for the Automatic Adaptation of VMs on Cloud Environments","authors":"Carlos Ruiz, H. Duran-Limon, N. Parlavantzas","doi":"10.1145/3110355.3110358","DOIUrl":"https://doi.org/10.1145/3110355.3110358","url":null,"abstract":"One key factor for Cloud computing success is the resource flexibility it provides. Because of this characteristic, academia and industry have focused their efforts on making efficient use of cloud computational resources without having to sacrifice performance. One way to achieve this purpose is through the automatic adaptation of the computational capabilities of VMs according to their resource utilization and performance. In this paper we present the design and preliminary results of our resource adaptation solution, which proactively adapts VMs (memory-based vertical scaling) to maintain an expected performance. Our solution targets multi-tier applications deployed on Cloud environments, and its core resides in RLS-based resource and performance predictors. Our results show that our solution, when compared with VMs with larger and permanently allocated computational resources, is able to maintain expected performance while reducing resource waste.","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":"122875505","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}
Suejb Memeti, Lu Li, Sabri Pllana, J. Kolodziej, C. Kessler
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.
{"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}
Pub Date : 2015-07-20DOI: 10.1007/978-3-319-28448-4_11
Shadi Ibrahim, Tran Anh Phuong, Gabriel Antoniu
{"title":"An Eye on the Elephant in the Wild: A Performance Evaluation of Hadoop's Schedulers Under Failures","authors":"Shadi Ibrahim, Tran Anh Phuong, Gabriel Antoniu","doi":"10.1007/978-3-319-28448-4_11","DOIUrl":"https://doi.org/10.1007/978-3-319-28448-4_11","url":null,"abstract":"","PeriodicalId":309271,"journal":{"name":"ARMS-CC@PODC","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121783936","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}
Pub Date : 2015-07-20DOI: 10.1007/978-3-319-28448-4_8
Lenos Vakanas, Stelios Sotiriadis, E. Petrakis
{"title":"Implementing the Cloud Software to Data Approach for OpenStack Environments","authors":"Lenos Vakanas, Stelios Sotiriadis, E. Petrakis","doi":"10.1007/978-3-319-28448-4_8","DOIUrl":"https://doi.org/10.1007/978-3-319-28448-4_8","url":null,"abstract":"","PeriodicalId":309271,"journal":{"name":"ARMS-CC@PODC","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132462189","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}
Pub Date : 2015-07-20DOI: 10.1007/978-3-319-28448-4_12
V. Tüfekçi, C. Özturan
{"title":"Partitioning Graph Databases by Using Access Patterns","authors":"V. Tüfekçi, C. Özturan","doi":"10.1007/978-3-319-28448-4_12","DOIUrl":"https://doi.org/10.1007/978-3-319-28448-4_12","url":null,"abstract":"","PeriodicalId":309271,"journal":{"name":"ARMS-CC@PODC","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116753073","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}
Pub Date : 2015-07-20DOI: 10.1007/978-3-319-28448-4_7
Catalin Negru, M. Mocanu, V. Cristea
{"title":"Impact of Virtual Machines Heterogeneity on Data Center Power Consumption in Data-Intensive Applications","authors":"Catalin Negru, M. Mocanu, V. Cristea","doi":"10.1007/978-3-319-28448-4_7","DOIUrl":"https://doi.org/10.1007/978-3-319-28448-4_7","url":null,"abstract":"","PeriodicalId":309271,"journal":{"name":"ARMS-CC@PODC","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133720191","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}
Pub Date : 2015-07-20DOI: 10.1007/978-3-319-28448-4_3
A. Bhattacharya, A. Banerjee, Pradipta De
{"title":"Parametric Analysis of Mobile Cloud Computing Frameworks Using Simulation Modeling","authors":"A. Bhattacharya, A. Banerjee, Pradipta De","doi":"10.1007/978-3-319-28448-4_3","DOIUrl":"https://doi.org/10.1007/978-3-319-28448-4_3","url":null,"abstract":"","PeriodicalId":309271,"journal":{"name":"ARMS-CC@PODC","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131934983","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}
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