Gamal Attiya

Mobile Agent Based New Framework for Improving Big Data Analysis

The rising number of applications serving millions of users and dealing with terabytes of data need to a faster processing paradigms. Recently, there is growing enthusiasm for the notion of big data analysis. Big data analysis becomes a very important aspect for growth productivity, reliability and quality of services (QoS). Processing of big data using a powerful machine is not efficient solution. So, companies focused on using Hadoop software for big data analysis. This is because Hadoop designed to support parallel and distributed data processing. However, Hadoop has several drawbacks effect on its performance and reliability against big data analysis. In this paper, a new framework is proposed to improve big data analysis and overcome the drawbacks of Hadoop. The proposed framework is called MapReduce Agent Mobility (MRAM). MRAM is developed by using mobile agent and MapReduce paradigm under Java Agent Development Framework (JADE).

Enhanced TCP-friendly rate control for supporting video traffic over internet

Video traffic nowadays forms the majority of traffic over the Internet, and is predicted to be the most prevailing traffic type in the coming few years. TCP-Friendly Rate Control (TFRC) is one of the most promising end-to-end congestion control protocols that is intended for unicast playback of Internet streaming applications. This paper presents a new TCP-Friendly congestion control protocol, called Enhanced TCP-Friendly Rate Control (ETFRC), for supporting real time video traffic over the Internet. The proposed protocol is developed by adjusting the sending rate, at the sender side, dynamically based on the current state of the network, and the current state of the receiver. In other words, ETFRC embodies a new algorithm to tune (increase or decrease) the sending rate, at the sender side, according to the difference between the calculated rate by the sender and the reported rate from the receiver side. The performance of the proposed ETFRC protocol is evaluated using the network simulator ns-2 considering different scenarios. In these scenarios, simulated video traffic from the Evalvid framework is sent over the designed topology and different performance parameters are measured and compared with that obtained by applying the original TFRC protocol. The simulation results show that ETFRC performance surpassed TFRC in terms of throughput, jitter, and packet loss.

Modified Fast Recovery Algorithm for Performance Enhancement of TCP-NewReno

One of the keys to the success of the Internet is relying on using efficient congestion control mechanisms. Congestion control is required not only to prevent congestion collapse in the network, but also to improve network utilization. Without congestion control, a sending node may continue transmitting packets that may be dropped later due to congestion collapse. This paper presents a modified fast recovery algorithm to enhance the performance of the most widespread congestion control protocol; TCP-NewReno. The proposed mechanism is evaluated by using the network simulator NS-2 and compared with both the TCP-NewReno and the TCP-Reno. The simulation results show that the proposed mechanism improves the performance of the TCP-NewReno against throughput and packet delay.

Comparative Study on CBIR based on Color Feature

Content Based Image Retrieval (CBIR) system helps users to retrieve relevant images based on their contents. It finds images in large databases by using a unique image feature such as texture, color, intensity or shape of the object inside an image. This paper presents a comparative study between the feature extraction techniques that based on color feature. These techniques include Color Histogram, HSV Color Histogram and Color Histogram Equalization. In this study, the retrieval process is first done by measuring the similarities between the query image and the images within the WANG database using two approaches: Euclidean distance and correlation coefficients. Then, the comparison is carried out by measuring the accuracy, error rate and elapsed time of each technique.

New Framework For Improving Big Data Analysis Using Mobile Agent

the rising number of applications serving millions of users and dealing with terabytes of data need to a faster processing paradigms. Recently, there is growing enthusiasm for the notion of big data analysis. Big data analysis becomes a very important aspect for growth productivity, reliability and quality of services (QoS). Processing of big data using a powerful machine is not efficient solution. So, companies focused on using Hadoop software for big data analysis. This is because Hadoop designed to support parallel and distributed data processing. Hadoop provides a distributed file processing system that stores and processes a large scale of data. It enables a fault tolerant by replicating data on three or more machines to avoid data loss. Hadoop is based on client server model and used single master machine called NameNode. However, Hadoop has several drawbacks affecting on its performance and reliability against big data analysis. In this paper, a new framework is proposed to improve big data analysis and overcome specified drawbacks of Hadoop. These drawbacks are replication tasks, Centralized node and nodes failure. The proposed framework is called MapReduce Agent Mobility (MRAM). MRAM is developed by using mobile agent and MapReduce paradigm under Java Agent Development Framework (JADE).

Intelligent decision support system for breast cancer diagnosis by gene expression profiles

Breast cancer transpires as one of the main source of deathly diseases among ladies around the world. Nevertheless, there is confirmation that early recognition and treatment can raise the survival rate of breast cancer patients. This paper presents an Intelligent Decision Support System (IDSS) for breast cancer diagnosis by using gene expression profiles. The proposed system first extracts significant features from the input patterns by utilizing Information Gain (IG) and then employs Deep Genetic Algorithm (DGA) for feature reduction as well as for breast cancer diagnosis. The proposed system is evaluated by considering a benchmark microarray dataset and compared with the most recent systems. The outcomes demonstrate that the proposed IDSS outperforms other systems in terms of diagnosis time and accuracy. The proposed system produces 99.94% classification accuracy. In addition, the proposed system reduces the required memory space.

Fuzzy based Tuning Congestion Window for Improving End-to-End Congestion Control Protocols

Transmission Control Protocol (TCP) is the transport-layer protocol widely used in the internet today. TCP performance is strongly influenced by its congestion control algorithms which limit the amount of transmitted traffic based on the estimated network capacity to avoid sending packets that may be dropped later. In other words Congestion Control is Algorithms that prevent the sender from overloading the network. This paper presents a modified fuzzy controller implementation to estimate the network capacity which reflected by congestion window size. Fuzzy controller use Round Trip Time “RTT” as network traffic indication as well as current window size and slow start threshold “ssthresh” as currently occupied bandwidth indicator. NS2 used as a simulation tool to compare proposed fuzzy approach with most widespread congestion control protocols including; TCP-Tahoe, Reno, New Reno, and Sack. Simulation results show that the proposed mechanism improves the performance against throughput, packet drop, packet delay, and connection fairness. General Terms: Computer Networks, Network Protocols.

Using the TSP Solution Strategy for Cloudlet Scheduling in Cloud Computing

Cloudlet scheduling in cloud computing is one of the most issues that face the cloud computing environment. This paper presents a new efficient approach, called Traveling Salesman Approach for Cloudlet Scheduling (TSACS), to solve the cloudlet-scheduling problem. The main idea is to convert the cloudlet-scheduling problem into an instance of the Traveling Salesman Problem (TSP) and then apply one of the TSP solution strategies to solve the problem. The proposed approach consists of three phases: clustering phase, converting phase, and assignment phase. In the clustering phase, the proposed approach converts the large size cloudlet-scheduling problem into a small size cluster-scheduling problem to minimize computation time complexity of the proposed approach. In the converting phase, the approach forms the cluster-scheduling problem as an instance of the TSP. In the assignment phase, the approach schedules the clusters into the available virtual machines by using the nearest neighbor algorithm. The proposed approach is evaluated by using the CloudSim and the results are compared with that obtained by the most recent algorithms. The results show that the proposed approach enhances the overall system performance in terms of schedule length, balancing degree, and time complexity. In addition, the proposed TSACS overcomes the oscillation problem of the existing cloudlet-scheduling algorithms.

Towards Online Smart Disguise: Real-Time Diversification Evading Co-Residency Based Cloud Attacks

Security is a major challenge in Cloud Computing. In this paper, we propose an Online Smart Disguise Framework (OSDF). OSDF employs dynamic, proactive, real-time moving-target defense against cloud attacks. OSDF relies on two main pillars. The first, is a behavior obscuring module that frequently live-migrates virtual machines (VMs) between heterogeneously configured compute nodes to avoid co-residency and virtualization based attacks. The second module limits attack dispersion between same-host VMs by migrating maliciously behaving VMs to remote isolated compute node acting as a quarantine zone. The second module is guided by a smart intrusion detection system that monitors the VM system calls searching for suspicious activities. To evaluate OSDF efficiency and effectiveness on limiting attack dispersion, we devised the vulnerable, exposed, attacked, recovered model based on the susceptible, exposed, infected, recovered (SEIR) epidemic model. The SEIR model is an epidemiological model commonly used to investigate disease dispersion on cooperative communities. The implementation of OSDF is tested on OpenStack private cloud. Simulation results show the effectiveness of OSDF MTD approach in decreasing the number of attacked VMs even for fast-spreading worms. Furthermore, NAS Parallel Benchmark is used to evaluate OSDF efficiency for cloud-hosted VMs running both stateful and stateless applications.

Improving Host-to-Host Congestion Control Protocols by Dynamic Bandwidth Estimation of the Network

is one of the major problems that affects on throughput, delay, losses and other performance metrics of the network. During the last decade, several congestion control protocols have been proposed to overcome this problem. The most widely protocols are TCP Tahoe, Reno, New Reno, Vegas and SACK. In this paper, a new approach is developed to enhance most of the existing host-to-host congestion control protocols. The main idea is to adjust the congestion window size (cwnd) dynamically according to the available bandwidth of the network. In the proposed strategy, instead of increasing the cwnd size linearly by the AIMD, the cwnd is increased according to the available bandwidth of the network. Also, instead of decreasing the cwnd to half of its size as congestion happens, the cwnd is decreased to latest value that was used effectively without losses. The proposed approach is implemented in the TCP Tahoe, Reno, Newreno, Vegas and SACK and the performance is evaluated by using the network simulator NS-2 considering a realistic network topology generated by the GT-ITM.