Pub Date : 2020-01-01DOI: 10.4018/ijghpc.2020010101
Salma Azzouzi, Sara Hsaini, M. E. H. Charaf
Conformance testing may be seen as mean to execute an IUT (implementation under test) by carrying out test cases in order to observe whether the behavior of the IUT is conforming to its specifications. However, the development of distributed testing frameworks is more complex and the implementation of the parallel testing components (PTCs) should take into consideration the mechanisms and functions required to support interaction during PTC communication. In this article, the authors present another way to control the test execution of PTCs by introducing synchronization messages into the local test sequences. Then, they suggest an agent-based simulation to implement synchronized local test sequences and resolve the problem of control and synchronization.
{"title":"A Synchronized Test Control Execution Model of Distributed Systems","authors":"Salma Azzouzi, Sara Hsaini, M. E. H. Charaf","doi":"10.4018/ijghpc.2020010101","DOIUrl":"https://doi.org/10.4018/ijghpc.2020010101","url":null,"abstract":"Conformance testing may be seen as mean to execute an IUT (implementation under test) by carrying out test cases in order to observe whether the behavior of the IUT is conforming to its specifications. However, the development of distributed testing frameworks is more complex and the implementation of the parallel testing components (PTCs) should take into consideration the mechanisms and functions required to support interaction during PTC communication. In this article, the authors present another way to control the test execution of PTCs by introducing synchronization messages into the local test sequences. Then, they suggest an agent-based simulation to implement synchronized local test sequences and resolve the problem of control and synchronization.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"14 1","pages":"1-17"},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85235198","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 : 2019-10-01DOI: 10.4018/ijghpc.2019100103
Cynthia Jayapal, N. SarojaM., P. Sultana, S. Jayavel
Humans can be adversely affected by exposure to air pollutants in ambient air. Hence, health-based standards and objectives for a number of pollutants in the air are set by each country. Detection and measurement of contents of the atmosphere are becoming increasingly important. Careful planning of measurements is essential. One of the major factors that influence the representativeness of data collected is the location of monitoring stations. The planning and setting up of a monitoring station are complex and incurs a huge expenditure. An IoT-based real time air pollution monitoring system is proposed to monitor the pollution levels of various pollutants in Coimbatore city. The geographical area is classified as industrial, residential and traffic zones. This article proposes an IoT system that could be deployed at any location and store the measured value in a cloud database, perform pollution analysis, and display the pollution level at any given location.
{"title":"IoT-Based Real Time Air Pollution Monitoring System","authors":"Cynthia Jayapal, N. SarojaM., P. Sultana, S. Jayavel","doi":"10.4018/ijghpc.2019100103","DOIUrl":"https://doi.org/10.4018/ijghpc.2019100103","url":null,"abstract":"Humans can be adversely affected by exposure to air pollutants in ambient air. Hence, health-based standards and objectives for a number of pollutants in the air are set by each country. Detection and measurement of contents of the atmosphere are becoming increasingly important. Careful planning of measurements is essential. One of the major factors that influence the representativeness of data collected is the location of monitoring stations. The planning and setting up of a monitoring station are complex and incurs a huge expenditure. An IoT-based real time air pollution monitoring system is proposed to monitor the pollution levels of various pollutants in Coimbatore city. The geographical area is classified as industrial, residential and traffic zones. This article proposes an IoT system that could be deployed at any location and store the measured value in a cloud database, perform pollution analysis, and display the pollution level at any given location.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"76 1","pages":"28-41"},"PeriodicalIF":1.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83771002","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 : 2019-10-01DOI: 10.4018/ijghpc.2019100104
Sivashanmugam G., SP Shantharajah, N. Iyengar
Artificial intelligence changes the art of solving the computational problems from defined computing structures into disorganized computing structures with the interference of naturally-inspired activities. On this basis, many algorithms were proposed and applied successfully, those results give complete as well as partial solutions for their applications. In this article, the authors consider the same phenomena and the investigation area is a load balancer. The legitimate aim is to bring a zero-tolerance load balancer by applying artificial intelligence techniques. For this, the authors introduced an algorithm named the Eagle Fly algorithm, which is a natural inspired algorithm, completely based on eagle characteristic behavior. From this, the authors examine how tasks are fetched, computed, and server on-demands are supported. This article proves performance metrics received from eagle fly algorithm is good and the results were compared with other existing natural inspired algorithms.
{"title":"Avian Based Intelligent Algorithm to Provide Zero Tolerance Load Balancer for Cloud Based Computing Platforms","authors":"Sivashanmugam G., SP Shantharajah, N. Iyengar","doi":"10.4018/ijghpc.2019100104","DOIUrl":"https://doi.org/10.4018/ijghpc.2019100104","url":null,"abstract":"Artificial intelligence changes the art of solving the computational problems from defined computing structures into disorganized computing structures with the interference of naturally-inspired activities. On this basis, many algorithms were proposed and applied successfully, those results give complete as well as partial solutions for their applications. In this article, the authors consider the same phenomena and the investigation area is a load balancer. The legitimate aim is to bring a zero-tolerance load balancer by applying artificial intelligence techniques. For this, the authors introduced an algorithm named the Eagle Fly algorithm, which is a natural inspired algorithm, completely based on eagle characteristic behavior. From this, the authors examine how tasks are fetched, computed, and server on-demands are supported. This article proves performance metrics received from eagle fly algorithm is good and the results were compared with other existing natural inspired algorithms.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"80 1","pages":"42-67"},"PeriodicalIF":1.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83845424","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 : 2019-10-01DOI: 10.4018/ijghpc.2019100101
Anishchandran Chathalingath, A. Manoharan
Fast and efficient tridiagonal solvers are highly appreciated in scientific and engineering domain, but challenging optimization task for computer engineers. The state-of-the-art developments in multi-core computing paves the way to meet this challenge to an extent. The technical advances in multi-core computing provide opportunities to exploit lower levels of parallelism and concurrency for inherently sequential algorithms. In this article, the authors present an optimal performance pipelined parallel variant of the conventional Tridiagonal Matrix Algorithm (TDMA), aka the Thomas algorithm, on a multi-core CPU platform. The implementation, analysis and performance comparison of the proposed pipelined parallel TDMA and the conventional version are performed on an Intel SIMD multi-core architecture. The results are compared in terms of elapsed time, speedup, cache miss rate. For a system of ‘n' linear equations where n = 2^36 in presented pipelined parallel TDMA achieves speedup of 1.294X with a parallel efficiency of 43% initially and inclines towards linear speed up as the system grows.
{"title":"Performance Optimization of Tridiagonal Matrix Algorithm [TDMA] on Multicore Architectures: Computational Framework and Mathematical Modelling","authors":"Anishchandran Chathalingath, A. Manoharan","doi":"10.4018/ijghpc.2019100101","DOIUrl":"https://doi.org/10.4018/ijghpc.2019100101","url":null,"abstract":"Fast and efficient tridiagonal solvers are highly appreciated in scientific and engineering domain, but challenging optimization task for computer engineers. The state-of-the-art developments in multi-core computing paves the way to meet this challenge to an extent. The technical advances in multi-core computing provide opportunities to exploit lower levels of parallelism and concurrency for inherently sequential algorithms. In this article, the authors present an optimal performance pipelined parallel variant of the conventional Tridiagonal Matrix Algorithm (TDMA), aka the Thomas algorithm, on a multi-core CPU platform. The implementation, analysis and performance comparison of the proposed pipelined parallel TDMA and the conventional version are performed on an Intel SIMD multi-core architecture. The results are compared in terms of elapsed time, speedup, cache miss rate. For a system of ‘n' linear equations where n = 2^36 in presented pipelined parallel TDMA achieves speedup of 1.294X with a parallel efficiency of 43% initially and inclines towards linear speed up as the system grows.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"32 1","pages":"1-12"},"PeriodicalIF":1.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78501985","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 : 2019-07-01DOI: 10.4018/IJGHPC.2019070104
Ruby Durairaj, J. Jeyachidra
A critical factor of underwater sensor networks (UWSN) is to maintain energy consumption at minimum, as immediate battery replacement is difficult. This is achieved by reducing duplication of data with similarity functions. The construction of optimal clustering is to avoid data loss. In this article, similarity function-based data aggregation with a Semaphore process is applied to UWSN to retain the energy level at an advantage. Sensor nodes (SNs) are clustered in a Date Palm Tree approach. The Minkowski Distance model is used in Data Aggregation Nodes (DANs) to check similar measures of readings collected from cluster members. The Semaphore concept is executed in all DANs and cluster heads (CHs) to enhance network life and regulate excessive exploitation of energy levels of the SN, DANs, and CHs. The message queue (MQ) can be used to allow the packets transferred from the DANs to the cluster heads (CHs). The proposed algorithm SBDA with similarity measures would result in better link quality, reduction in redundancy, data delay, and would control the consumption of energy.
{"title":"Semaphore Based Data Aggregation and Similarity Findings for Underwater Wireless Sensor Networks","authors":"Ruby Durairaj, J. Jeyachidra","doi":"10.4018/IJGHPC.2019070104","DOIUrl":"https://doi.org/10.4018/IJGHPC.2019070104","url":null,"abstract":"A critical factor of underwater sensor networks (UWSN) is to maintain energy consumption at minimum, as immediate battery replacement is difficult. This is achieved by reducing duplication of data with similarity functions. The construction of optimal clustering is to avoid data loss. In this article, similarity function-based data aggregation with a Semaphore process is applied to UWSN to retain the energy level at an advantage. Sensor nodes (SNs) are clustered in a Date Palm Tree approach. The Minkowski Distance model is used in Data Aggregation Nodes (DANs) to check similar measures of readings collected from cluster members. The Semaphore concept is executed in all DANs and cluster heads (CHs) to enhance network life and regulate excessive exploitation of energy levels of the SN, DANs, and CHs. The message queue (MQ) can be used to allow the packets transferred from the DANs to the cluster heads (CHs). The proposed algorithm SBDA with similarity measures would result in better link quality, reduction in redundancy, data delay, and would control the consumption of energy.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"179 1","pages":"59-76"},"PeriodicalIF":1.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83005278","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 : 2019-07-01DOI: 10.4018/IJGHPC.2019070101
A. Raveendran, E. Sherly
In this article, the authors studied hotspots in cloud data centers, which are caused due to a lack of resources to satisfy the peak immediate requests from clients. The nature of resource utilization in cloud data centers are totally dynamic in context and may lead to hotspots. Hotspots are unfavorable situations which cause SLA violations in some scenarios. Here they use trend aware regression (TAR) methods as a load prediction model and perform linear regression analysis to detect the formation of hotspots in physical servers of cloud data centers. This prediction model provides an alarm period for the cloud administrators either to provide enough resources to avoid hotspot situations or perform interference aware virtual machine migration to balance the load on servers. Here they analyzed the physical server resource utilization model in terms of CPU utilization, memory utilization and network bandwidth utilization. In the TAR model, the authors consider the degree of variation between the current points in the prediction window to forecast the future points. The TAR model provides accurate results in its predictions.
{"title":"TAR Based Hotspot Prediction in Cloud Data Centres","authors":"A. Raveendran, E. Sherly","doi":"10.4018/IJGHPC.2019070101","DOIUrl":"https://doi.org/10.4018/IJGHPC.2019070101","url":null,"abstract":"In this article, the authors studied hotspots in cloud data centers, which are caused due to a lack of resources to satisfy the peak immediate requests from clients. The nature of resource utilization in cloud data centers are totally dynamic in context and may lead to hotspots. Hotspots are unfavorable situations which cause SLA violations in some scenarios. Here they use trend aware regression (TAR) methods as a load prediction model and perform linear regression analysis to detect the formation of hotspots in physical servers of cloud data centers. This prediction model provides an alarm period for the cloud administrators either to provide enough resources to avoid hotspot situations or perform interference aware virtual machine migration to balance the load on servers. Here they analyzed the physical server resource utilization model in terms of CPU utilization, memory utilization and network bandwidth utilization. In the TAR model, the authors consider the degree of variation between the current points in the prediction window to forecast the future points. The TAR model provides accurate results in its predictions.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"16 1","pages":"1-22"},"PeriodicalIF":1.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86987514","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 : 2019-07-01DOI: 10.4018/IJGHPC.2019070103
M. M. Agarwal, M. C. Govil, M. Sinha, Saurabh Gupta
Internet of Things will serve communities across the different domains of life. The resource of embedded devices and objects working under IoT implementation are constrained in wireless networks. Thus, building a scheme to make full use of energy is key issue for such networks. To achieve energy efficiency, an effective Fuzzy-based network data Fusion Light Weight Protocol (FLWP) is proposed in this article. The innovations of FLWP are as follows: 1) the simulated network's data fusion through fuzzy controller and optimize the energy efficiency of smart tech layer of internet of things (Energy IoT); 2) The optimized reactive route is dynamically adjusted based on fuzzy based prediction accurately from the number of routes provided by base protocol. If the selection accuracy is high, the performance enhances the network quality; 3) FLWP takes full advantage of energy to further enhance target tracking performance by properly selecting reactive routes in the network. Authors evaluated the efficiency of FLWP with simulation-based experiments. FLWP scheme improves the energy efficiency.
{"title":"Fuzzy based Data Fusion for Energy Efficient Internet of Things","authors":"M. M. Agarwal, M. C. Govil, M. Sinha, Saurabh Gupta","doi":"10.4018/IJGHPC.2019070103","DOIUrl":"https://doi.org/10.4018/IJGHPC.2019070103","url":null,"abstract":"Internet of Things will serve communities across the different domains of life. The resource of embedded devices and objects working under IoT implementation are constrained in wireless networks. Thus, building a scheme to make full use of energy is key issue for such networks. To achieve energy efficiency, an effective Fuzzy-based network data Fusion Light Weight Protocol (FLWP) is proposed in this article. The innovations of FLWP are as follows: 1) the simulated network's data fusion through fuzzy controller and optimize the energy efficiency of smart tech layer of internet of things (Energy IoT); 2) The optimized reactive route is dynamically adjusted based on fuzzy based prediction accurately from the number of routes provided by base protocol. If the selection accuracy is high, the performance enhances the network quality; 3) FLWP takes full advantage of energy to further enhance target tracking performance by properly selecting reactive routes in the network. Authors evaluated the efficiency of FLWP with simulation-based experiments. FLWP scheme improves the energy efficiency.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"381 1","pages":"46-58"},"PeriodicalIF":1.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78010974","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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