Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847723
Sultan S. Alshamrani, D. Kowalski, L. Gąsieniec
Cloud computing systems are often seen as dynamic pools of Virtual Machines (VM) installed on provider side physical machines to be offered to Cloud users. Cloud customers could use these Virtual Machines as services, platforms or as a whole infrastructure. However, in practice the infrastructure of a computing Cloud includes several levels, such as virtual gateways, virtual clusters and virtual nodes. In this paper, we pursue a study of the impact of a hierarchical structure, formed of three levels, on the process of monitoring the system with the main goal of discovering symptoms of malicious behaviors in Clouds. We address in this paper two major questions. First question refers to optimize the number of clusters in the hierarchical structure to guarantee efficient monitoring. The second question, posed in some previous papers in this area, concerns efficient distributed implementation of the monitoring process; Namely, how to choose locally the next VM to be visited by a Forensic Virtual Machines (FVM) in a light and local way.
{"title":"The impact of hierarchical structure on efficiency of Cloud monitoring","authors":"Sultan S. Alshamrani, D. Kowalski, L. Gąsieniec","doi":"10.1109/CLOUDTECH.2016.7847723","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847723","url":null,"abstract":"Cloud computing systems are often seen as dynamic pools of Virtual Machines (VM) installed on provider side physical machines to be offered to Cloud users. Cloud customers could use these Virtual Machines as services, platforms or as a whole infrastructure. However, in practice the infrastructure of a computing Cloud includes several levels, such as virtual gateways, virtual clusters and virtual nodes. In this paper, we pursue a study of the impact of a hierarchical structure, formed of three levels, on the process of monitoring the system with the main goal of discovering symptoms of malicious behaviors in Clouds. We address in this paper two major questions. First question refers to optimize the number of clusters in the hierarchical structure to guarantee efficient monitoring. The second question, posed in some previous papers in this area, concerns efficient distributed implementation of the monitoring process; Namely, how to choose locally the next VM to be visited by a Forensic Virtual Machines (FVM) in a light and local way.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116858874","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 : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847721
Z. Bouzidi, L. Terrissa, Ahmed Lahmadi, N. Zerhouni, R. Gouriveau
Prognostic and health management (PHM) systems are designed to predict impending faults and to determine remaining useful life of machinery. An efficient prognostic system can speed up fault diagnosis by providing an indication of what parts of the machinery are most likely to fail and will need maintenance in the near future. PHM systems for manufacturing industry have not been widely implemented despite the extensive research on PHM in academia, which is mostly due to high costs in both development and implementation of PHM solutions in industrial applications. In this paper, we are defined the predictive maintenance, prognostic and health management (PHM) architecture and present the state of the art of prognostic approaches and display the related works in this domain. After that we are proposed a new approach that is adapting cloud computing paradigm with PHM systems that is Prognostic as a Service to provide high readiness, easy-to-configure, low cost and ondemand PHM services. We have presented our obtained results and its simulation.
{"title":"Neuro-fuzzy model for Prognostic as a Service in private cloud computing","authors":"Z. Bouzidi, L. Terrissa, Ahmed Lahmadi, N. Zerhouni, R. Gouriveau","doi":"10.1109/CLOUDTECH.2016.7847721","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847721","url":null,"abstract":"Prognostic and health management (PHM) systems are designed to predict impending faults and to determine remaining useful life of machinery. An efficient prognostic system can speed up fault diagnosis by providing an indication of what parts of the machinery are most likely to fail and will need maintenance in the near future. PHM systems for manufacturing industry have not been widely implemented despite the extensive research on PHM in academia, which is mostly due to high costs in both development and implementation of PHM solutions in industrial applications. In this paper, we are defined the predictive maintenance, prognostic and health management (PHM) architecture and present the state of the art of prognostic approaches and display the related works in this domain. After that we are proposed a new approach that is adapting cloud computing paradigm with PHM systems that is Prognostic as a Service to provide high readiness, easy-to-configure, low cost and ondemand PHM services. We have presented our obtained results and its simulation.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124611995","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 : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847711
N. Alouane, J. Abouchabaka, N. Rafalia
Amazon Ec2 service offers two diverse instance purchasing options. Users can either run instances by using on-demand plan and pay only for the incurred instance-hours, or by renting instances for a long period, while taking advantage of significant reductions (up to 60%). One of the major problems facing these users is cost management. How to dynamically combine between these two options, to serve sporadic workload, without knowledge of future demands? Many strategies in the literature, require either using exact historic workload as a reference or relying on long-term predictions of future workload. Unlike existing works we propose two practical online deterministic algorithms for the multi-slope case, that incur no more than 1+1/1−α and 2/1−α respectively, compared to the cost obtained from an optimal offline algorithm, where α is the maximum saving ratio of a reserved instance offer over on-demand plan.
{"title":"Online multi-instance acquisition for cost optimization in IaaS Clouds","authors":"N. Alouane, J. Abouchabaka, N. Rafalia","doi":"10.1109/CLOUDTECH.2016.7847711","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847711","url":null,"abstract":"Amazon Ec2 service offers two diverse instance purchasing options. Users can either run instances by using on-demand plan and pay only for the incurred instance-hours, or by renting instances for a long period, while taking advantage of significant reductions (up to 60%). One of the major problems facing these users is cost management. How to dynamically combine between these two options, to serve sporadic workload, without knowledge of future demands? Many strategies in the literature, require either using exact historic workload as a reference or relying on long-term predictions of future workload. Unlike existing works we propose two practical online deterministic algorithms for the multi-slope case, that incur no more than 1+1/1−α and 2/1−α respectively, compared to the cost obtained from an optimal offline algorithm, where α is the maximum saving ratio of a reserved instance offer over on-demand plan.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"71 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122510870","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 : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847696
M. Amar, M. Lemoudden, B. El Ouahidi
The usage of cloud-computing architectures and characteristics has been enhanced in recent years. This approach brings the availability of storage and user services as needed. But it also brings many drawbacks that put the privacy and the security of the system at stake. Log file generation has Big Data characteristics that should be considered for upgrade from a manual method to an automatic one based on Big Data solutions. Therefore, this paper proposes a log file centralization and a diagnostic approach based on the misuse and the anomaly detection techniques, which will improve the detection of attacks. The FP-growth algorithm presented will ensure the prevention of consecutive violations.
{"title":"Log file's centralization to improve cloud security","authors":"M. Amar, M. Lemoudden, B. El Ouahidi","doi":"10.1109/CLOUDTECH.2016.7847696","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847696","url":null,"abstract":"The usage of cloud-computing architectures and characteristics has been enhanced in recent years. This approach brings the availability of storage and user services as needed. But it also brings many drawbacks that put the privacy and the security of the system at stake. Log file generation has Big Data characteristics that should be considered for upgrade from a manual method to an automatic one based on Big Data solutions. Therefore, this paper proposes a log file centralization and a diagnostic approach based on the misuse and the anomaly detection techniques, which will improve the detection of attacks. The FP-growth algorithm presented will ensure the prevention of consecutive violations.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133125866","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 : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847717
E. Caron, Marcos Dias de Assunção
The use of large distributed computing infrastructure is a means to address the ever increasing resource demands of scientific and commercial applications. The scale of current large-scale computing infrastructures and their heterogeneity make scheduling applications an increasingly complex task. Cloud computing minimises the heterogeneity by using virtualisation mechanisms, but poses new challenges to middleware developers, such as the management of virtualisation, elasticity and economic models. In this context, this work proposes algorithms for efficient scheduling and execution of malleable computing tasks with high granularity while taking into account multiple optimisation criteria such as resource cost and computation time. We focus on hybrid platforms that comprise both clusters and cloud providers. We define and formalise the main aspects of the problem, introduce the difference between local and global scheduling algorithms and evaluate their efficiency using discrete-event simulation.
{"title":"Multi-criteria malleable task management for hybrid-cloud platforms","authors":"E. Caron, Marcos Dias de Assunção","doi":"10.1109/CLOUDTECH.2016.7847717","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847717","url":null,"abstract":"The use of large distributed computing infrastructure is a means to address the ever increasing resource demands of scientific and commercial applications. The scale of current large-scale computing infrastructures and their heterogeneity make scheduling applications an increasingly complex task. Cloud computing minimises the heterogeneity by using virtualisation mechanisms, but poses new challenges to middleware developers, such as the management of virtualisation, elasticity and economic models. In this context, this work proposes algorithms for efficient scheduling and execution of malleable computing tasks with high granularity while taking into account multiple optimisation criteria such as resource cost and computation time. We focus on hybrid platforms that comprise both clusters and cloud providers. We define and formalise the main aspects of the problem, introduce the difference between local and global scheduling algorithms and evaluate their efficiency using discrete-event simulation.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122130077","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 : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847724
M. Aftatah, A. Lahrech, A. Abounada
This paper deals with the hybridization of Global Positioning System (GPS) with an Inertial Navigation System (INS) and an Odometer, for land vehicle navigation, and more precisely with the calibration of inertial sensor errors. We focus here on the complementary characteristics of GPS and odometer to provide periodic corrections to Inertial Navigation System alternatively in different environmental conditions. In open sky, where GPS signals are available, GPS is integrated with INS. Meanwhile, in degraded GPS environments, where GPS is unreliable or unavailable, odometer replaces GPS to integrate with INS. This estimation problem is solved by a Kalman filter (KF), since it deals with non-linear problems and statistics.
{"title":"Fusion of GPS/INS/Odometer measurements for land vehicle navigation with GPS outage","authors":"M. Aftatah, A. Lahrech, A. Abounada","doi":"10.1109/CLOUDTECH.2016.7847724","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847724","url":null,"abstract":"This paper deals with the hybridization of Global Positioning System (GPS) with an Inertial Navigation System (INS) and an Odometer, for land vehicle navigation, and more precisely with the calibration of inertial sensor errors. We focus here on the complementary characteristics of GPS and odometer to provide periodic corrections to Inertial Navigation System alternatively in different environmental conditions. In open sky, where GPS signals are available, GPS is integrated with INS. Meanwhile, in degraded GPS environments, where GPS is unreliable or unavailable, odometer replaces GPS to integrate with INS. This estimation problem is solved by a Kalman filter (KF), since it deals with non-linear problems and statistics.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127198481","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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