The smart phones are used in many aspects of our life, shopping on the Internet, and creating and distributing many types of files. But these devices have many limitations including: short battery life time and limited storage and processing. Mobile Cloud Computing technology can help to overcome these limitations. Offloading technique reduces the power consumption and saves the mobile storage by executing the huge tasks at the cloud. The mobile devices are connecting to cloud service providers using 3G or LTE technologies, which introduces some challenges including, limited bandwidth, cost, and latency. In this paper, we propose efficient and secure mobile cloud computing model based on the Cloudlet concept were the mobile devices users can connect directly to cloud resources using cheaper technologies such as Wi-Fi. Once needed, and only if the service is not available in the cloudlet, the user will be connected to the enterprise cloud. The proposed cloudlet-based model can be used in many applications where security and efficiency is required. It can be used for health applications to save and analyze patients medical records. The simulation results of our model show that it is more efficient and reliable than the other mobile could computing models that don't use the cloudlet

In this paper, we discuss and evaluate our two metro wavelength division multiplexing (WDM) ring network architectures for variable-length packet traffic. The paper begins with a brief review of the relevant architectures and protocols in the literature. Subsequently, the network architectures along with their medium access control (MAC) protocols are described. Performance for the two network architectures is studied using both simulation and Markovian modeling approaches with the help of their results for queuing delay, node throughput and proportion of packet dropped. Performance for the network is evaluated under symmetric and asymmetric traffic scenarios with Poisson and self-similar traffic.

This paper considers the steady-state solution of Continuous Time Markov Chains (CTMCs). CTMCs are a widely used formalism for the performance analysis of computer and communication systems. A large variety of useful performance measures can be derived from a CTMC via the computation of its steady-state probabilities. However, CTMC models for realistic systems are very large. We address this largeness problem in this paper by considering parallelisation of implicit methods. In particular, we consider a modified form of Multi- Terminal Binary Decision Diagrams (MTBDDs) to compactly store CTMCs, and, using Jacobi iterative method, we present a parallel method for the CTMC steady-state solution. Employing a 24-node processor bank, we analyse our parallel implicit method using the experimental results for three widely used CTMC benchmark models, with well over a billion states and sixteen billion transitions.