When a resource in a data center reaches its end-of-life, instead of investing in upgrading, it is possibly the time to decommission such a resource and migrate workloads to other resources in the data center. Data migration between different cloud servers is risky due to the possibility of data loss. The current studies in the literature do not optimize the data before migration, which could avoid data loss. MapReduce is a software framework for distributed processing of large data sets with reduced overhead of migrating data. For this study, we design a MapReduce based algorithm and introduce a few metrics to test and evaluate our proposed framework. We deploy an architecture for creating an Apache Hadoop environment for our experiments. We show that our algorithm for data migration works efficiently for text, image, audio and video files with minimum data loss and scale well for large files as well.
{"title":"A MapReduce based Algorithm for Data Migration in a Private Cloud Environment","authors":"A. Pandey, R. Thulasiram, A. Thavaneswaran","doi":"10.5121/CSIT.2019.90916","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90916","url":null,"abstract":"When a resource in a data center reaches its end-of-life, instead of investing in upgrading, it is possibly the time to decommission such a resource and migrate workloads to other resources in the data center. Data migration between different cloud servers is risky due to the possibility of data loss. The current studies in the literature do not optimize the data before migration, which could avoid data loss. MapReduce is a software framework for distributed processing of large data sets with reduced overhead of migrating data. For this study, we design a MapReduce based algorithm and introduce a few metrics to test and evaluate our proposed framework. We deploy an architecture for creating an Apache Hadoop environment for our experiments. We show that our algorithm for data migration works efficiently for text, image, audio and video files with minimum data loss and scale well for large files as well.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126041033","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}
Colonoscopy examinations are widely used for detecting colon cancer and many other colon abnormalities. Unfortunately, the resulting colon videos often have artifacts caused by camera motion and specular highlights caused by light reflections from the wet colon surface. To address these problems, we have developed a method for motion compensated colonoscopy image restoration. Our approach utilizes RANSAC-based image registration to align sequences of N consecutive images in the colonoscopy video and restores each frame of the video using information from these aligned images. We compare image alignment quality when N adjacent images are registered to each other versus registering images with larger step sizes between them. Three types of image pre processing were evaluated in our work. We found that the removal of non-informative images prior to image registration produced better alignment results and reduced processing time. We also evaluated the effects of image smoothing and resizing as a pre processing step for image registration.
{"title":"Motion Compensated Restoration of Colonoscopy Images","authors":"Nidhal Azawi, J. Gauch","doi":"10.5121/CSIT.2019.90920","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90920","url":null,"abstract":"Colonoscopy examinations are widely used for detecting colon cancer and many other colon abnormalities. Unfortunately, the resulting colon videos often have artifacts caused by camera motion and specular highlights caused by light reflections from the wet colon surface. To address these problems, we have developed a method for motion compensated colonoscopy image restoration. Our approach utilizes RANSAC-based image registration to align sequences of N consecutive images in the colonoscopy video and restores each frame of the video using information from these aligned images. We compare image alignment quality when N adjacent images are registered to each other versus registering images with larger step sizes between them. Three types of image pre processing were evaluated in our work. We found that the removal of non-informative images prior to image registration produced better alignment results and reduced processing time. We also evaluated the effects of image smoothing and resizing as a pre processing step for image registration.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128601556","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 SD-WAN being increasingly adopted by corporations, and Kubernetes becoming the defacto container orchestration tool, the opportunities for deploying edge-computing applications running over SD-WAN are vast. Unfortunately, service orchestration in SD-WAN has not been provided with enough attention, resulting in the lack of research focused on service discovery in these scenarios. In this document, an in-house service discovery solution that works alongside Kubernetes’ master node for allowing an improved traffic handling and better user experience is developed. The service discovery solution was conceived following a design science research approach. Our research includes the implementation of a proof-of-concept SD-WAN topology alongside a Kubernetes cluster that allows us to deploy custom services and delimit the necessary characteristics of our in-house solution. Also, the implementation's performance is tested based on the required times for updating the discovery solution according to service updates. Finally, some conclusions and modifications are pointed out based on the results, while also discussing possible enhancements.
{"title":"Enabling Edge Computing Using Container Orchestration and Software Defined Wide Area Networks","authors":"Felipe Rodriguez Yaguache, K. Ahola","doi":"10.5121/CSIT.2019.90930","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90930","url":null,"abstract":"With SD-WAN being increasingly adopted by corporations, and Kubernetes becoming the defacto container orchestration tool, the opportunities for deploying edge-computing applications running over SD-WAN are vast. Unfortunately, service orchestration in SD-WAN has not been provided with enough attention, resulting in the lack of research focused on service discovery in these scenarios. In this document, an in-house service discovery solution that works alongside Kubernetes’ master node for allowing an improved traffic handling and better user experience is developed. The service discovery solution was conceived following a design science research approach. Our research includes the implementation of a proof-of-concept SD-WAN topology alongside a Kubernetes cluster that allows us to deploy custom services and delimit the necessary characteristics of our in-house solution. Also, the implementation's performance is tested based on the required times for updating the discovery solution according to service updates. Finally, some conclusions and modifications are pointed out based on the results, while also discussing possible enhancements.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122691343","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}
The traditional models of electronic data interchange (EDI) and out-of-application methods for messaging and collaborations are not suitable to achieve the full benefits of VEASC because of multiple limitations. The limitations are: multiple human interventions, lack of real time visibility into the supply chain flows, inability to accurately synchronise the demand and supplyside information, and inability to build dynamic capabilities required for facing supply chain dynamics. The existing studies about deploying supply chain applications on cloud computing are focussed on overcoming these limitations through service-oriented architectures and their components. However, their focus needs to be expanded to virtual enterprise architecture modelling to overcome the limitations of EDI and out-of-application methods effectively. The virtual enterprise architecture supply chain (VEASC) model has been studied in this research employing Optimised Networking (OPNET) modelling and simulations of a commercial application called INTEND. The simulation results reflect a potential to overcome the limitations of traditional EDI and out-of-application methods. However, the true potential of the proposed system and the changes needed to automatically recover from failures can be determined after testing actual transactions in a real world VEASC implementation.
{"title":"ual Enterprise Architecture Supply Chain (VEASC) Model on Cloud Computing: A simulation-based study through OPNET modelling","authors":"Tlamelo Phetlhu, S. Lubbe","doi":"10.5121/CSIT.2019.90914","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90914","url":null,"abstract":"The traditional models of electronic data interchange (EDI) and out-of-application methods for messaging and collaborations are not suitable to achieve the full benefits of VEASC because of multiple limitations. The limitations are: multiple human interventions, lack of real time visibility into the supply chain flows, inability to accurately synchronise the demand and supplyside information, and inability to build dynamic capabilities required for facing supply chain dynamics. The existing studies about deploying supply chain applications on cloud computing are focussed on overcoming these limitations through service-oriented architectures and their components. However, their focus needs to be expanded to virtual enterprise architecture modelling to overcome the limitations of EDI and out-of-application methods effectively. The virtual enterprise architecture supply chain (VEASC) model has been studied in this research employing Optimised Networking (OPNET) modelling and simulations of a commercial application called INTEND. The simulation results reflect a potential to overcome the limitations of traditional EDI and out-of-application methods. However, the true potential of the proposed system and the changes needed to automatically recover from failures can be determined after testing actual transactions in a real world VEASC implementation.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123119846","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}
Cloud-based Business is a Business running and relying on Cloud computing IT paradigm. Cloud computing is an emerging technology paradigm that transfers current technological and computing concepts into utility-like solutions similar to electricity and communication systems. It provides the full scalability, reliability, computing resources configurability and outsourcing, resource sharing, external data warehousing, and high performance and relatively low cost feasible solutions and services as compared to dedicated infrastructures. Cloud-based Businesses store, access, use, and manage their data and software applications over the internet on a set of servers in the cloud without the need to have them stored/installed locally on their local devices. The cloud technology is used daily by many businesses/people around the world from using web based email services to executing heavy complex business transactions. Like any other emerging technology, Cloud computing comes with a baggage of some pros and cons. It is very useful in business development as it brings amazing results in a timely manner; however, it comes with increasing security and privacy concerns and issues. In this paper we will investigate, analyse, classify, and discuss the new security concerns and issues introduced by cloud computing. In addition, we present some security requirements that address and may alleviate these concerns and issues.
{"title":"Security Issues in Cloud-Based Businesses","authors":"M. Ladan","doi":"10.5121/CSIT.2019.90929","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90929","url":null,"abstract":"Cloud-based Business is a Business running and relying on Cloud computing IT paradigm. Cloud computing is an emerging technology paradigm that transfers current technological and computing concepts into utility-like solutions similar to electricity and communication systems. It provides the full scalability, reliability, computing resources configurability and outsourcing, resource sharing, external data warehousing, and high performance and relatively low cost feasible solutions and services as compared to dedicated infrastructures. Cloud-based Businesses store, access, use, and manage their data and software applications over the internet on a set of servers in the cloud without the need to have them stored/installed locally on their local devices. The cloud technology is used daily by many businesses/people around the world from using web based email services to executing heavy complex business transactions. Like any other emerging technology, Cloud computing comes with a baggage of some pros and cons. It is very useful in business development as it brings amazing results in a timely manner; however, it comes with increasing security and privacy concerns and issues. In this paper we will investigate, analyse, classify, and discuss the new security concerns and issues introduced by cloud computing. In addition, we present some security requirements that address and may alleviate these concerns and issues.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123512509","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 the past few years, Generative Adversarial Networks (GANs) have received immense attention by researchers in a variety of application domains. This new field of deep learning has been growing rapidly and has provided a way to learn deep representations without extensive use of annotated training data. Their achievements may be used in a variety of applications, including speech synthesis, image and video generation, semantic image editing, and style transfer. Image synthesis is an important component of expert systems and it attracted much attention since the introduction of GANs. However, GANs are known to be difficult to train especially when they try to generate high resolution images. This paper gives a thorough overview of the state-of-the-art GANs-based approaches in four applicable areas of image generation including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Aging, and 3D Image Synthesis. Experimental results show state-of-the-art performance using GANs compared to traditional approaches in the fields of image processing and machine vision.
{"title":"A Survey of State-of-the-Art GAN-based Approaches to Image Synthesis","authors":"Shirin Nasr Esfahani, S. Latifi","doi":"10.5121/CSIT.2019.90906","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90906","url":null,"abstract":"In the past few years, Generative Adversarial Networks (GANs) have received immense attention by researchers in a variety of application domains. This new field of deep learning has been growing rapidly and has provided a way to learn deep representations without extensive use of annotated training data. Their achievements may be used in a variety of applications, including speech synthesis, image and video generation, semantic image editing, and style transfer. Image synthesis is an important component of expert systems and it attracted much attention since the introduction of GANs. However, GANs are known to be difficult to train especially when they try to generate high resolution images. This paper gives a thorough overview of the state-of-the-art GANs-based approaches in four applicable areas of image generation including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Aging, and 3D Image Synthesis. Experimental results show state-of-the-art performance using GANs compared to traditional approaches in the fields of image processing and machine vision.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126855280","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}
Present state of edge computing is an environment of different computing capabilities connecting via a wide variety of communication paths. This situation creates both great operational capability opportunities and unimaginable security problems. This paper emphasizes that the traditional approaches to security of identifying a security threat and developing the technology and policies to defend against that threat are no longer adequate. The wide variety of security levels, computational capabilities, and communication channels requires a learning, responsive, varied, and individualized approach to information security. We describe a classification of the nature of transactions with respect to security based upon relationships, history, trust status, requested actions and resulting response choices. Problem is that the trust evaluation has to be individualized between each pair of devices participating in edge computing. We propose that each element in the edge computing world utilizes a localized ability to establish an adaptive learning trust model with each entity that communicates with the element. Specifically, the model we propose increments or decrements the value of trust score based upon each interaction.
{"title":"Security Considerations for Edge Computing","authors":"J. Acken, Naresh Sehgal","doi":"10.5121/CSIT.2019.90915","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90915","url":null,"abstract":"Present state of edge computing is an environment of different computing capabilities connecting via a wide variety of communication paths. This situation creates both great operational capability opportunities and unimaginable security problems. This paper emphasizes that the traditional approaches to security of identifying a security threat and developing the technology and policies to defend against that threat are no longer adequate. The wide variety of security levels, computational capabilities, and communication channels requires a learning, responsive, varied, and individualized approach to information security. We describe a classification of the nature of transactions with respect to security based upon relationships, history, trust status, requested actions and resulting response choices. Problem is that the trust evaluation has to be individualized between each pair of devices participating in edge computing. We propose that each element in the edge computing world utilizes a localized ability to establish an adaptive learning trust model with each entity that communicates with the element. Specifically, the model we propose increments or decrements the value of trust score based upon each interaction.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115057773","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}
Data analytics and Business Intelligence (BI) is essential for strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools available. Business Intelligence solutions gather and examine current, actionable data with the determination of providing insights into refining business operations. Data needs to be integrated from disparate sources in order to derive insights. Traditionally organizations employ data warehouses and ETL process to obtain integrated data. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL are often complementary technologies performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
{"title":"Data Virtualization for Analytics and Business Intelligence in Big Data","authors":"M. Muniswamaiah, T. Agerwala, C. Tappert","doi":"10.5121/CSIT.2019.90925","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90925","url":null,"abstract":"Data analytics and Business Intelligence (BI) is essential for strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools available. Business Intelligence solutions gather and examine current, actionable data with the determination of providing insights into refining business operations. Data needs to be integrated from disparate sources in order to derive insights. Traditionally organizations employ data warehouses and ETL process to obtain integrated data. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL are often complementary technologies performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121826895","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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