With the advent of the paradigm of the Internet of Things, many computing elements need many modifications to promote Quality of Service (QoS). Quality of Service is a pillar that promotes real-time reaction to time-critical tasks. Any impediments to QoS should be resolved and handled. In 2012, fog computing was implemented to enhance QoS in current systems in a bid to tackle QoS problems encountered by using cloud computing alone. Currently, the primary focus in fog computing is now on enhancing QoS. The primary goal of this study is, therefore, to critically review and evaluate the literature on the work done to improve elements of QoS in fog computing. This study begins by examining the roots of history, characteristics, and advantages of fog computing. Secondly, it discusses the important elements of QoS parameters. Finally, open problems that still affect fog computing are identified and discussed in order to achieve enhanced QoS.
{"title":"A Review of Quality of Service in Fog Computing for the Internet of Things","authors":"W. T. Vambe, Chii Chang, K. Sibanda","doi":"10.4018/ijfc.2020010102","DOIUrl":"https://doi.org/10.4018/ijfc.2020010102","url":null,"abstract":"With the advent of the paradigm of the Internet of Things, many computing elements need many modifications to promote Quality of Service (QoS). Quality of Service is a pillar that promotes real-time reaction to time-critical tasks. Any impediments to QoS should be resolved and handled. In 2012, fog computing was implemented to enhance QoS in current systems in a bid to tackle QoS problems encountered by using cloud computing alone. Currently, the primary focus in fog computing is now on enhancing QoS. The primary goal of this study is, therefore, to critically review and evaluate the literature on the work done to improve elements of QoS in fog computing. This study begins by examining the roots of history, characteristics, and advantages of fog computing. Secondly, it discusses the important elements of QoS parameters. Finally, open problems that still affect fog computing are identified and discussed in order to achieve enhanced QoS.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123036296","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}
In this article, the researchers have provided a discussion on computation offloading and the importance of docker-based containers, known as light weight virtualization, to improve the performance of edge computing systems. At the end, they have also proposed techniques and a case study for computation offloading and light weight virtualization.
{"title":"Edge Computing: A Review on Computation Offloading and Light Weight Virtualization for IoT Framework","authors":"M. Patel, S. Chaudhary","doi":"10.4018/ijfc.2020010104","DOIUrl":"https://doi.org/10.4018/ijfc.2020010104","url":null,"abstract":"In this article, the researchers have provided a discussion on computation offloading and the importance of docker-based containers, known as light weight virtualization, to improve the performance of edge computing systems. At the end, they have also proposed techniques and a case study for computation offloading and light weight virtualization.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121199635","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}
In this article, the proposed feedback-based resource management approach provides data processing, huge computation, large storage, and networking services between Internet of Things (IoT)-based Cloud data centers and the end-users. The real-time applications of IoT, such as smart city, smart home, health care management systems, traffic management systems, and transportation management systems, require less response time and latency to process the huge amount of data. The proposed feedback-based resource management plan provides a novel resource management technique, consisting of an integrated architecture and maintains the service-level agreement (SLA). It can optimize energy consumption, response time, network bandwidth, security, and reduce latency. The experimental results are tested with the IFogSim tool kit and have proved that the proposed approach is effective and suitable for smart communication in IoT-based cloud.
{"title":"Feedback-Based Resource Utilization for Smart Home Automation in Fog Assistance IoT-Based Cloud","authors":"B. Mallikarjuna","doi":"10.4018/ijfc.2020010103","DOIUrl":"https://doi.org/10.4018/ijfc.2020010103","url":null,"abstract":"In this article, the proposed feedback-based resource management approach provides data processing, huge computation, large storage, and networking services between Internet of Things (IoT)-based Cloud data centers and the end-users. The real-time applications of IoT, such as smart city, smart home, health care management systems, traffic management systems, and transportation management systems, require less response time and latency to process the huge amount of data. The proposed feedback-based resource management plan provides a novel resource management technique, consisting of an integrated architecture and maintains the service-level agreement (SLA). It can optimize energy consumption, response time, network bandwidth, security, and reduce latency. The experimental results are tested with the IFogSim tool kit and have proved that the proposed approach is effective and suitable for smart communication in IoT-based cloud.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122293615","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}
Fog Computing provides resources as a service. Various providers are providing the best form of Quality of Services (QoS) which works in the principal of pay per use. Now it is important to connect the Internet of Things (IoT) services in fog computing. The strategy for choosing a service provider is assessed by which cloud provider provides what.
{"title":"Realm Towards Service Optimization in Fog Computing","authors":"Ashish Tiwari, R. Sharma","doi":"10.4018/IJFC.2019070102","DOIUrl":"https://doi.org/10.4018/IJFC.2019070102","url":null,"abstract":"Fog Computing provides resources as a service. Various providers are providing the best form of Quality of Services (QoS) which works in the principal of pay per use. Now it is important to connect the Internet of Things (IoT) services in fog computing. The strategy for choosing a service provider is assessed by which cloud provider provides what.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129542979","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}
Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.
{"title":"Resource Provisioning and Scheduling Techniques of IoT Based Applications in Fog Computing","authors":"Rajni Gupta","doi":"10.4018/IJFC.2019070104","DOIUrl":"https://doi.org/10.4018/IJFC.2019070104","url":null,"abstract":"Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128146671","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}
Due to centralized nature for cloud computing and some other reasons, high mobility cannot be supported and low latency requirements for some applications such as Internet of Things (IoT) that require real time and mobility support. To satisfy such requirements new technologies, fog computing is a good solution, where we use edges of network for service provisioning instead of far datacenters allocated in clouds. Low latency response is the most attractive property for fog computing, which is very suitable for IoT multi-billion devices, sensors and actuators generates huge amount of data that need processing and analysis for smart decision generation. The main objective of this article is to show the super ability of fog computing over cloud-only computing. The authors present a patient monitoring system as a case study for simulation; they evaluated the performance of the system using: latency, network usage, power consumption, cost of execution and simulation execution time performance metrics. The results show that the Fog computing is superior over Cloud-only paradigm in all performance measurements.
{"title":"Fog Computing to Serve the Internet of Things Applications: A Patient Monitoring System","authors":"A. Hudaib, Layla Albdour","doi":"10.4018/IJFC.2019070103","DOIUrl":"https://doi.org/10.4018/IJFC.2019070103","url":null,"abstract":"Due to centralized nature for cloud computing and some other reasons, high mobility cannot be supported and low latency requirements for some applications such as Internet of Things (IoT) that require real time and mobility support. To satisfy such requirements new technologies, fog computing is a good solution, where we use edges of network for service provisioning instead of far datacenters allocated in clouds. Low latency response is the most attractive property for fog computing, which is very suitable for IoT multi-billion devices, sensors and actuators generates huge amount of data that need processing and analysis for smart decision generation. The main objective of this article is to show the super ability of fog computing over cloud-only computing. The authors present a patient monitoring system as a case study for simulation; they evaluated the performance of the system using: latency, network usage, power consumption, cost of execution and simulation execution time performance metrics. The results show that the Fog computing is superior over Cloud-only paradigm in all performance measurements.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197125","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}
Jagadish S. Kallimani, Chekuri Sailusha, Pankaj Lathar, K. Srinivasa
The purpose of the smart cities mission is to drive economic growth and improve the quality of life of people by empowering local area development and harnessing technology. All the information gathered is placed across the cloud so that any person of the city can get the information within no time. This helps the citizens to be smart by preserving their precious time and also being healthy. This article mainly discusses about the urban mobility solutions such as traffic management, smart parking, garbage monitoring system and air pollution monitoring system.
{"title":"Development of Community Based Intelligent Modules Using IoT to Make Cities Smarter","authors":"Jagadish S. Kallimani, Chekuri Sailusha, Pankaj Lathar, K. Srinivasa","doi":"10.4018/IJFC.2019070101","DOIUrl":"https://doi.org/10.4018/IJFC.2019070101","url":null,"abstract":"The purpose of the smart cities mission is to drive economic growth and improve the quality of life of people by empowering local area development and harnessing technology. All the information gathered is placed across the cloud so that any person of the city can get the information within no time. This helps the citizens to be smart by preserving their precious time and also being healthy. This article mainly discusses about the urban mobility solutions such as traffic management, smart parking, garbage monitoring system and air pollution monitoring system.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122503190","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 : 2019-01-01DOI: 10.4018/978-1-5225-3142-5.CH002
R. Segall, J. Cook, G. Niu
Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical to application performance in such data-intensive computing systems. However, if existing resource management frameworks in these systems lack the support for storage management, this would cause unpredictable performance degradation when applications are under input/output (I/O) contention. Storage management of data-intensive systems is a challenge. Big Data plays a most major role in storage systems for data-intensive computing. This article deals with these difficulties along with discussion of High Performance Computing (HPC) systems, background for storage systems for data-intensive applications, storage patterns and storage mechanisms for Big Data, the Top 10 Cloud Storage Systems for data-intensive computing in today's world, and the interface between Big Data Intensive Storage and Cloud/Fog Computing. Big Data storage and its server statistics and usage distributions for the Top 500 Supercomputers in the world are also presented graphically and discussed as data-intensive storage components that can be interfaced with Fog-to-cloud interactions and enabling protocols.
{"title":"Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing","authors":"R. Segall, J. Cook, G. Niu","doi":"10.4018/978-1-5225-3142-5.CH002","DOIUrl":"https://doi.org/10.4018/978-1-5225-3142-5.CH002","url":null,"abstract":"Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical to application performance in such data-intensive computing systems. However, if existing resource management frameworks in these systems lack the support for storage management, this would cause unpredictable performance degradation when applications are under input/output (I/O) contention. Storage management of data-intensive systems is a challenge. Big Data plays a most major role in storage systems for data-intensive computing. This article deals with these difficulties along with discussion of High Performance Computing (HPC) systems, background for storage systems for data-intensive applications, storage patterns and storage mechanisms for Big Data, the Top 10 Cloud Storage Systems for data-intensive computing in today's world, and the interface between Big Data Intensive Storage and Cloud/Fog Computing. Big Data storage and its server statistics and usage distributions for the Top 500 Supercomputers in the world are also presented graphically and discussed as data-intensive storage components that can be interfaced with Fog-to-cloud interactions and enabling protocols.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122116576","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}
With the advancements in science and technology, data is being generated at a staggering rate. The raw data generated is generally of high value and may conceal important information with the potential to solve several real-world problems. In order to extract this information, the raw data available must be processed and analysed efficiently. It has however been observed, that such raw data is generated at a rate faster than it can be processed by traditional methods. This has led to the emergence of the popular parallel processing programming model – MapReduce. In this study, the authors perform a comparative analysis of two popular data processing engines – Apache Flink and Hadoop MapReduce. The analysis is based on the parameters of scalability, reliability and efficiency. The results reveal that Flink unambiguously outperformance Hadoop's MapReduce. Flink's edge over MapReduce can be attributed to following features – Active Memory Management, Dataflow Pipelining and an Inline Optimizer. It can be concluded that as the complexity and magnitude of real time raw data is continuously increasing, it is essential to explore newer platforms that are adequately and efficiently capable of processing such data.
{"title":"A Study on the Performance and Scalability of Apache Flink Over Hadoop MapReduce","authors":"Pankaj Lathar, K. Srinivasa","doi":"10.4018/IJFC.2019010103","DOIUrl":"https://doi.org/10.4018/IJFC.2019010103","url":null,"abstract":"With the advancements in science and technology, data is being generated at a staggering rate. The raw data generated is generally of high value and may conceal important information with the potential to solve several real-world problems. In order to extract this information, the raw data available must be processed and analysed efficiently. It has however been observed, that such raw data is generated at a rate faster than it can be processed by traditional methods. This has led to the emergence of the popular parallel processing programming model – MapReduce. In this study, the authors perform a comparative analysis of two popular data processing engines – Apache Flink and Hadoop MapReduce. The analysis is based on the parameters of scalability, reliability and efficiency. The results reveal that Flink unambiguously outperformance Hadoop's MapReduce. Flink's edge over MapReduce can be attributed to following features – Active Memory Management, Dataflow Pipelining and an Inline Optimizer. It can be concluded that as the complexity and magnitude of real time raw data is continuously increasing, it is essential to explore newer platforms that are adequately and efficiently capable of processing such data.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115639868","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}
N. Sudhakar Yadav, K. G. Srinivasa, B. Eswara Reddy
A software framework is a reusable design that requires various software components to function almost out of the box. To specify a framework, the creator must specify the different components that form the framework and how to instantiate them. Also, the communication interfaces between these various components must be defined. In this article, the authors propose such a framework based on the internet of things (IoT) for developing applications for handling emergencies of some kind. This article demonstrates the usage of the framework by explaining various applications such as tracking the status of autistic students, analytics on medical records to detect and mitigate chronic heart diseases in the Indian demographic, prediction of Parkinson's disease, determining the type of disease that corresponds to the dermatology field, and health monitoring and management using internet of things (IoT) sensing.
{"title":"An IoT-Based Framework for Health Monitoring Systems: A Case Study Approach","authors":"N. Sudhakar Yadav, K. G. Srinivasa, B. Eswara Reddy","doi":"10.4018/IJFC.2019010102","DOIUrl":"https://doi.org/10.4018/IJFC.2019010102","url":null,"abstract":"A software framework is a reusable design that requires various software components to function almost out of the box. To specify a framework, the creator must specify the different components that form the framework and how to instantiate them. Also, the communication interfaces between these various components must be defined. In this article, the authors propose such a framework based on the internet of things (IoT) for developing applications for handling emergencies of some kind. This article demonstrates the usage of the framework by explaining various applications such as tracking the status of autistic students, analytics on medical records to detect and mitigate chronic heart diseases in the Indian demographic, prediction of Parkinson's disease, determining the type of disease that corresponds to the dermatology field, and health monitoring and management using internet of things (IoT) sensing.","PeriodicalId":218786,"journal":{"name":"Int. J. Fog Comput.","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127631515","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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