Future wireless networks provide research challenges with many fold increase of smart devices and... more Future wireless networks provide research challenges with many fold increase of smart devices and the exponential growth in mobile data traffic. The advent of highly computational and real-time applications cause huge expansion in traffic volume. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of caches at the edge of the networks. Storing most popular files at the edge of mobile edge networks (MENs) in user terminals (UTs) and small base stations (SBSs) caches is a promising approach to the challenges that face data-rich wireless networks. Caching at the mobile UT allows to obtain requested contents directly from its nearby UTs caches through the device-to- device (D2D) communication.In this survey article, solutions for mobile edge computing and caching challenges in terms of energy and latency are presented. Caching in MENs and comparisons between different caching techniques in MENs are presented....
2021 International Wireless Communications and Mobile Computing (IWCMC), 2021
With the growth of mobile data traffic in wireless networks, caches are used to bring data closer... more With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimize the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges on which data to cache and where to cache these data. The process of deciding the cache contents involves multiple objectives regarding the content popularity, contact duration between UT and SBSs, communication ranges between UT and SBSs caches, and contact probability between UT and SBSs. In this paper, we propose a new strategy on cache placement decisions for mobile edge networks based on binary classification technique. The aim is to formulate the cache placement as a classification problem that is solved using machine learning techniques in order to define an optimal decision boundary on cache or not cache decisions. Simulation results show that the performance of cache placement algorithms using classifier based learning techniques can achieve higher hit rate than other algorithms.
2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018
5G networks provide an interesting research challenge with the widespread use of Internet and the... more 5G networks provide an interesting research challenge with the widespread use of Internet and the scale of mobile data traffic that grows explosively. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of small, low power small base stations (SBS) with small cache space (termed femto caches, or more popularly, helpers). Helpers have low rate backhaul but high storage capacity that can be used to cache most popular files. When users request files that do not exist in helpers, then the files will be transmitted from MBS to helpers. The availability of the data in local cache can significantly improve performance since it overcomes the constraints in wireless environment. This leads to improving network throughput and reducing end-toend and backhaul delay. Caching the optimal contents into femto caches proactively depending on the knowledge about files popularity distribution is not enoughthe since Internet users have different context and different preferences. In this paper, we propose an algorithm for proactive caching based on fuzzy softset (FSS) approach for decision making. The algorithm decides which files to cache and where to cache them depending on file popularity distribution, file to user preferences, file clustering, and helpers to connected users clustering. The simulation results show significant overall cache hit rate increase with the increasing number of user requests. The algorithm can effectively improve system performance by reducing the delay for downloading the files and proactively cache those files which will improve user satisfaction.
2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2020
Future mobile services and applications are bounded by user location, data, and network. These se... more Future mobile services and applications are bounded by user location, data, and network. These services will suffer from poor support from wireless networks due to the huge amount of mobile traffic and user mobility. The demand for contents by these services results in constraints put on latency and quality of service (QoS). Considering these problems, researchers investigated caching contents locally and proactively at the edge of the mobile edge networks (MENs). In this work, we proposed new formulation of mobility-aware latency-efficient cache placement problem for mobile edge networks (MENs) taking into account different storage capacities, users mobility, content popularity, contact probability, and latency to download the contents to user terminals (UTs). Our formulated multi-objective optimization problem aims to maximize the cache hit rate. We apply weighted-sum decision theory approach to model the decision of placing contents at the edge of the network. Simulation results ...
A protocol accelerator includes a first processor connected to a host machine and programmed to p... more A protocol accelerator includes a first processor connected to a host machine and programmed to provide a first protocol layer for data to be sent to a destination device. A second processor is connected to the first processor and is programmed to provide a second protocol layer for the data. A third processor is connected to the second processor and is programmed to provide a third protocol layer for the data. The third processor is connected to a network by which the data is sent to the destination device. The system can be configured for any number of protocol layers, by providing a dedicated processor in a pipelined configuration for each respective layer.
IEEE International Conference on High Performance Computing and Communications, 2018
5G networks provide an interesting research challenge with the widespread use of Internet and the... more 5G networks provide an interesting research challenge with the widespread use of Internet and the scale of mobile data traffic that grows explosively. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of small, low power small base stations (SBS) with small cache space (termed femto caches, or more popularly, helpers). Helpers have low rate backhaul but high storage capacity that can be used to cache most popular files. When users request files that do not exist in helpers, then the files will be transmitted from MBS to helpers. The availability of the data in local cache can significantly improve performance since it overcomes the constraints in wireless environment. This leads to improving network throughput and reducing end-to-end and backhaul delay. Caching the optimal contents into femto caches proactively depending on the knowledge about files popularity distribution is not enoughthe since Internet users have different context and different preferences. In this paper, we propose an algorithm for proactive caching based on fuzzy soft-set (FSS) approach for decision making. The algorithm decides which files to cache and where to cache them depending on file popularity distribution, file to user preferences, file clustering, and helpers to connected users clustering. The simulation results show significant overall cache hit rate increase with the increasing number of user requests. The algorithm can effectively improve system performance by reducing the delay for downloading the files and proactively cache those files which will improve user satisfaction.
Support Vector Machine (SVM) learning algorithm is considered as the most popular classification ... more Support Vector Machine (SVM) learning algorithm is considered as the most popular classification algorithm. It is a supervised learning technique that is mainly based on the conception of decision planes. These decision planes define decision boundaries which are used to separate a set of objects. It is important to extract the main features of the training datasets. These features can be used to define the separation boundaries. The separation boundaries can also be improved by tuning the parameters of the separation hyperplane. In literature, there are different techniques for feature selection and SVM parameters optimization that can be used to improve classification accuracy. There are a wide variety of applications that use SVM classification algorithm, such as text classification, disease diagnosis, gene analysis, and many others. The aim of this paper is to investigate the techniques that can be used to improve the classification accuracy of SVM based on kernel parameters optimization. The datasets are collected from different applications; having different number of classes and different number of features. The analysis and comparison among different kernel parameters were implemented on different datasets to study the effect of the number of features, the number of classes, and kernel parameters on the performance of the classification process.
Future wireless networks provide research challenges with many fold increase of smart devices and... more Future wireless networks provide research challenges with many fold increase of smart devices and the exponential growth in mobile data traffic. The advent of highly computational and real-time applications cause huge expansion in traffic volume. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of caches at the edge of the networks. Storing most popular files at the edge of mobile edge networks (MENs) in user terminals (UTs) and small base stations (SBSs) caches is a promising approach to the challenges that face data-rich wireless networks. Caching at the mobile UT allows to obtain requested contents directly from its nearby UTs caches through the device-to- device (D2D) communication.In this survey article, solutions for mobile edge computing and caching challenges in terms of energy and latency are presented. Caching in MENs and comparisons between different caching techniques in MENs are presented....
2021 International Wireless Communications and Mobile Computing (IWCMC), 2021
With the growth of mobile data traffic in wireless networks, caches are used to bring data closer... more With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimize the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges on which data to cache and where to cache these data. The process of deciding the cache contents involves multiple objectives regarding the content popularity, contact duration between UT and SBSs, communication ranges between UT and SBSs caches, and contact probability between UT and SBSs. In this paper, we propose a new strategy on cache placement decisions for mobile edge networks based on binary classification technique. The aim is to formulate the cache placement as a classification problem that is solved using machine learning techniques in order to define an optimal decision boundary on cache or not cache decisions. Simulation results show that the performance of cache placement algorithms using classifier based learning techniques can achieve higher hit rate than other algorithms.
2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018
5G networks provide an interesting research challenge with the widespread use of Internet and the... more 5G networks provide an interesting research challenge with the widespread use of Internet and the scale of mobile data traffic that grows explosively. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of small, low power small base stations (SBS) with small cache space (termed femto caches, or more popularly, helpers). Helpers have low rate backhaul but high storage capacity that can be used to cache most popular files. When users request files that do not exist in helpers, then the files will be transmitted from MBS to helpers. The availability of the data in local cache can significantly improve performance since it overcomes the constraints in wireless environment. This leads to improving network throughput and reducing end-toend and backhaul delay. Caching the optimal contents into femto caches proactively depending on the knowledge about files popularity distribution is not enoughthe since Internet users have different context and different preferences. In this paper, we propose an algorithm for proactive caching based on fuzzy softset (FSS) approach for decision making. The algorithm decides which files to cache and where to cache them depending on file popularity distribution, file to user preferences, file clustering, and helpers to connected users clustering. The simulation results show significant overall cache hit rate increase with the increasing number of user requests. The algorithm can effectively improve system performance by reducing the delay for downloading the files and proactively cache those files which will improve user satisfaction.
2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2020
Future mobile services and applications are bounded by user location, data, and network. These se... more Future mobile services and applications are bounded by user location, data, and network. These services will suffer from poor support from wireless networks due to the huge amount of mobile traffic and user mobility. The demand for contents by these services results in constraints put on latency and quality of service (QoS). Considering these problems, researchers investigated caching contents locally and proactively at the edge of the mobile edge networks (MENs). In this work, we proposed new formulation of mobility-aware latency-efficient cache placement problem for mobile edge networks (MENs) taking into account different storage capacities, users mobility, content popularity, contact probability, and latency to download the contents to user terminals (UTs). Our formulated multi-objective optimization problem aims to maximize the cache hit rate. We apply weighted-sum decision theory approach to model the decision of placing contents at the edge of the network. Simulation results ...
A protocol accelerator includes a first processor connected to a host machine and programmed to p... more A protocol accelerator includes a first processor connected to a host machine and programmed to provide a first protocol layer for data to be sent to a destination device. A second processor is connected to the first processor and is programmed to provide a second protocol layer for the data. A third processor is connected to the second processor and is programmed to provide a third protocol layer for the data. The third processor is connected to a network by which the data is sent to the destination device. The system can be configured for any number of protocol layers, by providing a dedicated processor in a pipelined configuration for each respective layer.
IEEE International Conference on High Performance Computing and Communications, 2018
5G networks provide an interesting research challenge with the widespread use of Internet and the... more 5G networks provide an interesting research challenge with the widespread use of Internet and the scale of mobile data traffic that grows explosively. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of small, low power small base stations (SBS) with small cache space (termed femto caches, or more popularly, helpers). Helpers have low rate backhaul but high storage capacity that can be used to cache most popular files. When users request files that do not exist in helpers, then the files will be transmitted from MBS to helpers. The availability of the data in local cache can significantly improve performance since it overcomes the constraints in wireless environment. This leads to improving network throughput and reducing end-to-end and backhaul delay. Caching the optimal contents into femto caches proactively depending on the knowledge about files popularity distribution is not enoughthe since Internet users have different context and different preferences. In this paper, we propose an algorithm for proactive caching based on fuzzy soft-set (FSS) approach for decision making. The algorithm decides which files to cache and where to cache them depending on file popularity distribution, file to user preferences, file clustering, and helpers to connected users clustering. The simulation results show significant overall cache hit rate increase with the increasing number of user requests. The algorithm can effectively improve system performance by reducing the delay for downloading the files and proactively cache those files which will improve user satisfaction.
Support Vector Machine (SVM) learning algorithm is considered as the most popular classification ... more Support Vector Machine (SVM) learning algorithm is considered as the most popular classification algorithm. It is a supervised learning technique that is mainly based on the conception of decision planes. These decision planes define decision boundaries which are used to separate a set of objects. It is important to extract the main features of the training datasets. These features can be used to define the separation boundaries. The separation boundaries can also be improved by tuning the parameters of the separation hyperplane. In literature, there are different techniques for feature selection and SVM parameters optimization that can be used to improve classification accuracy. There are a wide variety of applications that use SVM classification algorithm, such as text classification, disease diagnosis, gene analysis, and many others. The aim of this paper is to investigate the techniques that can be used to improve the classification accuracy of SVM based on kernel parameters optimization. The datasets are collected from different applications; having different number of classes and different number of features. The analysis and comparison among different kernel parameters were implemented on different datasets to study the effect of the number of features, the number of classes, and kernel parameters on the performance of the classification process.
Uploads
Papers by Lubna Badri Mohammed
The aim of this paper is to investigate the techniques that can be used to improve the classification accuracy of SVM based on kernel parameters optimization. The datasets are collected from different applications; having different number of classes and different number of features. The analysis and comparison among different kernel parameters were implemented on different datasets to study the effect of the number of features, the number of classes, and kernel parameters on the performance of the classification process.
The aim of this paper is to investigate the techniques that can be used to improve the classification accuracy of SVM based on kernel parameters optimization. The datasets are collected from different applications; having different number of classes and different number of features. The analysis and comparison among different kernel parameters were implemented on different datasets to study the effect of the number of features, the number of classes, and kernel parameters on the performance of the classification process.