Pub Date : 2015-03-30DOI: 10.1109/MobileCloud.2015.20
C. Shih, Yu-Hsin Wang, N. Chang
Federating the portability and mobility of mobile devices with the computation capacity on desktop computers have been a widely discussed computation model for the next decade. However, the mobility of the mobile devices also introduces challenges on the federation. This work developed the elastic computation framework to tackle the aforementioned challenge. The elastic computation framework federates the computation resources on wearable devices, mobile devices, nearby computers, and remote computers into a pool of computation resources. Each resource in the pool is characterized by its network delay, expected response time, and computation capability. Each mobile device is also characterized by its mobility and computation workload requirements. The elastic computation framework assigns computation resources in the pool to meet the workload requirements on mobile devices. The framework consists of resource allocation component, task scheduling algorithm, and task dispatch middleware. In the experiment, we compare the developed scheduling algorithm with other known algorithms by simulation. The results show that the developed scheduling algorithm does not complete the most tasks though, the cost/performance of system resources is the best among all the algorithms. To be specific, the cost-performance of the developed algorithm can be at least two times better than that of compared algorithms.
{"title":"Multi-tier Elastic Computation Framework for Mobile Cloud Computing","authors":"C. Shih, Yu-Hsin Wang, N. Chang","doi":"10.1109/MobileCloud.2015.20","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.20","url":null,"abstract":"Federating the portability and mobility of mobile devices with the computation capacity on desktop computers have been a widely discussed computation model for the next decade. However, the mobility of the mobile devices also introduces challenges on the federation. This work developed the elastic computation framework to tackle the aforementioned challenge. The elastic computation framework federates the computation resources on wearable devices, mobile devices, nearby computers, and remote computers into a pool of computation resources. Each resource in the pool is characterized by its network delay, expected response time, and computation capability. Each mobile device is also characterized by its mobility and computation workload requirements. The elastic computation framework assigns computation resources in the pool to meet the workload requirements on mobile devices. The framework consists of resource allocation component, task scheduling algorithm, and task dispatch middleware. In the experiment, we compare the developed scheduling algorithm with other known algorithms by simulation. The results show that the developed scheduling algorithm does not complete the most tasks though, the cost/performance of system resources is the best among all the algorithms. To be specific, the cost-performance of the developed algorithm can be at least two times better than that of compared algorithms.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125916717","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.26
Andreas Roos, Steffen Druesedow, M. Hosseini, G. Coşkun, Sebastian Zickau
In the face of enormously increasing amount of personal digital data distributed over various devices, end users are challenged to efficiently store and administrate them. Mostly, users are making use of public storage services in the cloud and local storage devices. Whereas, people with IT expertise make use of sophisticated and expensive network attached storage solutions or self-managed server solutions. Moreover, besides the pure data storage process itself, privacy aware data handling will become important in the future which enables access control to the data in order to avoid malicious access from other users, applications and / or services. For taking advantages from the benefits of the aforementioned different approaches, we advocate an integrated solution. Due to privacy concerns, the most important aspect to take into consideration in such a combined solution is trustworthiness. This paper introduces a trust level based data storage and trust level based data access control solution which changes the control process of data storage and data access. The introduced solution enables user-friendly data handling based on assigned trust levels to storage solutions in a distributed data storage environment and the classified sensitivity level of the data to be stored.
{"title":"Trust Level Based Data Storage and Data Access Control in a Distributed Storage Environment","authors":"Andreas Roos, Steffen Druesedow, M. Hosseini, G. Coşkun, Sebastian Zickau","doi":"10.1109/MobileCloud.2015.26","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.26","url":null,"abstract":"In the face of enormously increasing amount of personal digital data distributed over various devices, end users are challenged to efficiently store and administrate them. Mostly, users are making use of public storage services in the cloud and local storage devices. Whereas, people with IT expertise make use of sophisticated and expensive network attached storage solutions or self-managed server solutions. Moreover, besides the pure data storage process itself, privacy aware data handling will become important in the future which enables access control to the data in order to avoid malicious access from other users, applications and / or services. For taking advantages from the benefits of the aforementioned different approaches, we advocate an integrated solution. Due to privacy concerns, the most important aspect to take into consideration in such a combined solution is trustworthiness. This paper introduces a trust level based data storage and trust level based data access control solution which changes the control process of data storage and data access. The introduced solution enables user-friendly data handling based on assigned trust levels to storage solutions in a distributed data storage environment and the classified sensitivity level of the data to be stored.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123680531","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.33
Shuyu Li, J. Gao
Recently, mobile cloud computing has been named as the top one emerging technology in 2014 by IEEE Computer Society. This brings a strong demand on new emergent mobile data service solutions and technologies in the wireless world and implies that more innovative mobile data service solutions are needed to support on-demand elastic and large-scale mobile data service requests. This paper focuses on mobile data service topic. It first analyzes the existing research results on mobile data services. Then, it discusses cloud-based mobile data service solutions. Finally, the paper examines the issues and challenges on mobile data service in mobile cloud computing.
{"title":"Moving from Mobile Databases to Mobile Cloud Data Services","authors":"Shuyu Li, J. Gao","doi":"10.1109/MobileCloud.2015.33","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.33","url":null,"abstract":"Recently, mobile cloud computing has been named as the top one emerging technology in 2014 by IEEE Computer Society. This brings a strong demand on new emergent mobile data service solutions and technologies in the wireless world and implies that more innovative mobile data service solutions are needed to support on-demand elastic and large-scale mobile data service requests. This paper focuses on mobile data service topic. It first analyzes the existing research results on mobile data services. Then, it discusses cloud-based mobile data service solutions. Finally, the paper examines the issues and challenges on mobile data service in mobile cloud computing.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124502665","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.36
M. Bahrami, M. Singhal
Cloud computing paradigm provides virtual IT infrastructures with a set of resources that are shared with multi-tenant users. Data Privacy is one of the major challenges when users outsource their data to a cloud computing system. Privacy can be violated by the cloud vendor, vendor's authorized users, other cloud users, unauthorized users, or external malicious entities. Encryption is one of the solutions to protect and maintain privacy of cloud-stored data. However, encryption methods are complex and expensive for mobile devices. In this paper, we propose a new light-weight method for mobile clients to store data on one or multiple clouds by using pseudo-random permutation based on chaos systems. The proposed method can be used in the client mobile devices to store data in the cloud(s) without using cloud computing resources for encryption to maintain user's privacy. We consider JPEG image format as a case study to present and evaluate the proposed method. Our experimental results show that the proposed method achieve superior performance compared to over encryption methods, such as AES and encryption on JPEG encoders while protecting the mobile user data privacy. We review major security attack scenarios against the proposed method that shows the level of security.
{"title":"A Light-Weight Permutation Based Method for Data Privacy in Mobile Cloud Computing","authors":"M. Bahrami, M. Singhal","doi":"10.1109/MobileCloud.2015.36","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.36","url":null,"abstract":"Cloud computing paradigm provides virtual IT infrastructures with a set of resources that are shared with multi-tenant users. Data Privacy is one of the major challenges when users outsource their data to a cloud computing system. Privacy can be violated by the cloud vendor, vendor's authorized users, other cloud users, unauthorized users, or external malicious entities. Encryption is one of the solutions to protect and maintain privacy of cloud-stored data. However, encryption methods are complex and expensive for mobile devices. In this paper, we propose a new light-weight method for mobile clients to store data on one or multiple clouds by using pseudo-random permutation based on chaos systems. The proposed method can be used in the client mobile devices to store data in the cloud(s) without using cloud computing resources for encryption to maintain user's privacy. We consider JPEG image format as a case study to present and evaluate the proposed method. Our experimental results show that the proposed method achieve superior performance compared to over encryption methods, such as AES and encryption on JPEG encoders while protecting the mobile user data privacy. We review major security attack scenarios against the proposed method that shows the level of security.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121771186","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.19
Heungsik Eom, R. Figueiredo, Huaqian Cai, Ying Zhang, Gang Huang
This paper proposes and evaluates MALMOS, a novel framework for mobile offloading scheduling based on online machine learning techniques. In contrast to previous works, which rely on application-dependent parameters or predefined static scheduling policies, MALMOS provides an online training mechanism for the machine learning-based runtime scheduler such that it supports a flexible policy that dynamically adapts scheduling decisions based on the observation of previous offloading decisions and their correctness. To demonstrate its practical applicability, we integrated MALMOS with an existing Java-based, offloading-capable code recapturing framework, Partner. Using this integration, we performed quantitative experiments to evaluate the performance and cost for three machine learning algorithms: instance-based learning, perception, and naive Bays, with respect to classifier training time, classification time, and scheduling accuracy. Particularly, we examined the adaptability of MALMOS to various network conditions and computing capabilities of remote resources by comparing the scheduling accuracy with two static scheduling cases: threshold-based and linear equation-based scheduling policies. Our evaluation uses an Android-based prototype for experiments, and considers benchmarks with different computation/communication characteristics, and different computing capabilities of remote resources. The evaluation shows that MALMOS achieves 10.9%~40.5% higher scheduling accuracy than two static scheduling policies.
{"title":"MALMOS: Machine Learning-Based Mobile Offloading Scheduler with Online Training","authors":"Heungsik Eom, R. Figueiredo, Huaqian Cai, Ying Zhang, Gang Huang","doi":"10.1109/MobileCloud.2015.19","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.19","url":null,"abstract":"This paper proposes and evaluates MALMOS, a novel framework for mobile offloading scheduling based on online machine learning techniques. In contrast to previous works, which rely on application-dependent parameters or predefined static scheduling policies, MALMOS provides an online training mechanism for the machine learning-based runtime scheduler such that it supports a flexible policy that dynamically adapts scheduling decisions based on the observation of previous offloading decisions and their correctness. To demonstrate its practical applicability, we integrated MALMOS with an existing Java-based, offloading-capable code recapturing framework, Partner. Using this integration, we performed quantitative experiments to evaluate the performance and cost for three machine learning algorithms: instance-based learning, perception, and naive Bays, with respect to classifier training time, classification time, and scheduling accuracy. Particularly, we examined the adaptability of MALMOS to various network conditions and computing capabilities of remote resources by comparing the scheduling accuracy with two static scheduling cases: threshold-based and linear equation-based scheduling policies. Our evaluation uses an Android-based prototype for experiments, and considers benchmarks with different computation/communication characteristics, and different computing capabilities of remote resources. The evaluation shows that MALMOS achieves 10.9%~40.5% higher scheduling accuracy than two static scheduling policies.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131940172","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.40
M. Bahrami
The tutorial will begin with an explanation of the concepts behind cloud computing systems, cloud software architecture, the need for mobile cloud computing as an aspect of the app industry to deal with new mobile app design, network apps, app designing tools, and the motivation for migrating apps to cloud computing systems. The tutorial will review facts, goals and common architectures of mobile cloud computing systems, as well as introduce general mobile cloud services for app developers and marketers. This tutorial will highlight some of the major challenges and costs, and the role of mobile cloud computing architecture in the field of app design, as well as how the app-design industry has an opportunity to migrate to cloud computing systems with low investment. The tutorial will review privacy and security issues. It will describe major mobile cloud vendor services to illustrate how mobile cloud vendors can improve mobile app businesses. We will consider major cloud vendors, such as Microsoft Windows Azure, Amazon AWS and Google Cloud Platform. Finally, the tutorial will survey some of the cuttingedge practices in the field, and present some opportunities for future development.
{"title":"Cloud Computing for Emerging Mobile Cloud Apps","authors":"M. Bahrami","doi":"10.1109/MobileCloud.2015.40","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.40","url":null,"abstract":"The tutorial will begin with an explanation of the concepts behind cloud computing systems, cloud software architecture, the need for mobile cloud computing as an aspect of the app industry to deal with new mobile app design, network apps, app designing tools, and the motivation for migrating apps to cloud computing systems. The tutorial will review facts, goals and common architectures of mobile cloud computing systems, as well as introduce general mobile cloud services for app developers and marketers. This tutorial will highlight some of the major challenges and costs, and the role of mobile cloud computing architecture in the field of app design, as well as how the app-design industry has an opportunity to migrate to cloud computing systems with low investment. The tutorial will review privacy and security issues. It will describe major mobile cloud vendor services to illustrate how mobile cloud vendors can improve mobile app businesses. We will consider major cloud vendors, such as Microsoft Windows Azure, Amazon AWS and Google Cloud Platform. Finally, the tutorial will survey some of the cuttingedge practices in the field, and present some opportunities for future development.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126424332","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.8
Hiroshi Miyake, N. Kami
Mobility-aware cloud services such as fleet management systems need to understand the positions of mobile devices accurately in a real-time manner. Generally speaking, positioning accuracy and data traffic load are in a trade-off relation. Highly accurate real-time positioning requires frequent location data upload and hence results in heavy data traffic load. Although not all data are equally important, data of low importance often consumes a lot of network resources. This paper presents a data upload control method that the dynamically assesses quality of information (QoI) of measured data at mobile devices. The proposed method balances high accuracy with low traffic loads to achieve efficient vehicle position management. We evaluated the performance of the proposed method using both artificial and actual GPS data and confirmed that it successfully controlled the accuracy and network traffic load according to application requirements.
{"title":"QoI-Based Data Upload Control for Mobility-Aware Cloud Services","authors":"Hiroshi Miyake, N. Kami","doi":"10.1109/MobileCloud.2015.8","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.8","url":null,"abstract":"Mobility-aware cloud services such as fleet management systems need to understand the positions of mobile devices accurately in a real-time manner. Generally speaking, positioning accuracy and data traffic load are in a trade-off relation. Highly accurate real-time positioning requires frequent location data upload and hence results in heavy data traffic load. Although not all data are equally important, data of low importance often consumes a lot of network resources. This paper presents a data upload control method that the dynamically assesses quality of information (QoI) of measured data at mobile devices. The proposed method balances high accuracy with low traffic loads to achieve efficient vehicle position management. We evaluated the performance of the proposed method using both artificial and actual GPS data and confirmed that it successfully controlled the accuracy and network traffic load according to application requirements.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123011751","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.25
Toru Kobayashi
Smart education, that is the learning environment utilizing mobile devices like tablet computers, has attracted a great deal of attention. In order to expand this environment, we need a mechanism that can establish the learning environment armed mobile devices and Information Communication Technology (ICT), instantly utilizing the original digital learning material without the significant strain on teaching staff. Therefore, this paper proposes a Mobile Software-as-a-Service (MSaaS) type Smart Education Support System that would allow a teaching staff to apply the original digital learning material and the ICT environment, including mobile devices, in classrooms without disrupting work. This proposed system focuses on technical terms embedded in the original digital learning material and MSaaS-type architecture. The multi-aspect information, that is the multi-view point information of the technical term extracted from the original digital learning material, will be gathered from social media. Then, such multi-aspect information will be distributed on a multi-screen environment such as mobile devices or an electronic black board by a multimodal user interface including motion sensors or voice recognition. In this way, if we can extract only the technical term from the original digital learning material, we can transform the original digital learning material into one for the smart education environment automatically. We explained this transforming model for utilizing the original digital learning material. In terms of MSaaS-type architecture, it consists of three layers that enable three kinds of smashup according to web service technologies in order to establish the smart education environment instantly. We confirmed the effectiveness of the proposed system through the examination based on the evaluation environment.
{"title":"MSaaS-Type Smart Education Support System Using Social Media","authors":"Toru Kobayashi","doi":"10.1109/MobileCloud.2015.25","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.25","url":null,"abstract":"Smart education, that is the learning environment utilizing mobile devices like tablet computers, has attracted a great deal of attention. In order to expand this environment, we need a mechanism that can establish the learning environment armed mobile devices and Information Communication Technology (ICT), instantly utilizing the original digital learning material without the significant strain on teaching staff. Therefore, this paper proposes a Mobile Software-as-a-Service (MSaaS) type Smart Education Support System that would allow a teaching staff to apply the original digital learning material and the ICT environment, including mobile devices, in classrooms without disrupting work. This proposed system focuses on technical terms embedded in the original digital learning material and MSaaS-type architecture. The multi-aspect information, that is the multi-view point information of the technical term extracted from the original digital learning material, will be gathered from social media. Then, such multi-aspect information will be distributed on a multi-screen environment such as mobile devices or an electronic black board by a multimodal user interface including motion sensors or voice recognition. In this way, if we can extract only the technical term from the original digital learning material, we can transform the original digital learning material into one for the smart education environment automatically. We explained this transforming model for utilizing the original digital learning material. In terms of MSaaS-type architecture, it consists of three layers that enable three kinds of smashup according to web service technologies in order to establish the smart education environment instantly. We confirmed the effectiveness of the proposed system through the examination based on the evaluation environment.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125165025","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.34
G. Deka
NoSQL databases are the new breed of databases developed to overcome the drawbacks of RDBMS. The goal of NoSQL is to provide scalability, availability and meet other requirements of cloud computing. The common motivation of NoSQL design is to meet scalability and fail over. In most of the NoSQL database systems, data is partitioned and replicated across multiple nodes. Inherently, most of them use either Google's MapReduce or Hadoop Distributed File System or Hadoop MapReduce for data collection. Cassandra, HBase and MongoDB are mostly used and they can be termed as the representative of NoSQL world. This tutorial discusses the features of NoSQL databases in the light of CAP theorem.
{"title":"Tutorial on NoSQL Databases","authors":"G. Deka","doi":"10.1109/MobileCloud.2015.34","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.34","url":null,"abstract":"NoSQL databases are the new breed of databases developed to overcome the drawbacks of RDBMS. The goal of NoSQL is to provide scalability, availability and meet other requirements of cloud computing. The common motivation of NoSQL design is to meet scalability and fail over. In most of the NoSQL database systems, data is partitioned and replicated across multiple nodes. Inherently, most of them use either Google's MapReduce or Hadoop Distributed File System or Hadoop MapReduce for data collection. Cassandra, HBase and MongoDB are mostly used and they can be termed as the representative of NoSQL world. This tutorial discusses the features of NoSQL databases in the light of CAP theorem.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114894527","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 : 2015-03-30DOI: 10.1109/MobileCloud.2015.15
Yue Shi, Sampatoor Abhilash, K. Hwang
This paper presents a new cloudlet mesh architecture for security enforcement to establish trusted mobile cloud computing. The cloudlet mesh is WiFi-or mobile-connected to the Internet. This security framework establishes a cyber trust shield to fight against intrusions to distance clouds, prevent spam/virus/worm attacks on mobile cloud resources, and stop unauthorized access of shared datasets in offloading the cloud. We have specified a sequence of authentication, authorization, and encryption protocols for securing communications among mobile devices, cloudlet servers, and distance clouds. Some analytical and experimental results prove the effectiveness of this new security infrastructure to safeguard mobile cloud services.
{"title":"Cloudlet Mesh for Securing Mobile Clouds from Intrusions and Network Attacks","authors":"Yue Shi, Sampatoor Abhilash, K. Hwang","doi":"10.1109/MobileCloud.2015.15","DOIUrl":"https://doi.org/10.1109/MobileCloud.2015.15","url":null,"abstract":"This paper presents a new cloudlet mesh architecture for security enforcement to establish trusted mobile cloud computing. The cloudlet mesh is WiFi-or mobile-connected to the Internet. This security framework establishes a cyber trust shield to fight against intrusions to distance clouds, prevent spam/virus/worm attacks on mobile cloud resources, and stop unauthorized access of shared datasets in offloading the cloud. We have specified a sequence of authentication, authorization, and encryption protocols for securing communications among mobile devices, cloudlet servers, and distance clouds. Some analytical and experimental results prove the effectiveness of this new security infrastructure to safeguard mobile cloud services.","PeriodicalId":373443,"journal":{"name":"2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114679579","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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