The continuous growing developments in the traffic of mobile data limits the data throughput and capacity of cellular networks. “Heterogeneous Networks (HetNets)” are efficient solution to realize such demands. However, in HetNets, the congestion on the overloaded cellular network can be increased when the traffic of data is pushed from a cellular network to the Wi-Fi. In practice, offloading the cellular data traffic to a Wireless Local Area Network (WLAN) depending on the signal quality is a broadly deployed method to solve such problem. The use of Device to Device (D2D) communication further enhances the traffic offloading in WLAN systems and helps to obtain better throughput, end-to-end delay and network load. However, the critical offloading potential and its impacts on the whole performance is not totally understood. In this paper, the offloading of Long Term Evolution (LTE) traffic is presented using a WLAN for voice and video applications. A comparison is performed among two WLAN mecha- nisms; Distributed coordination function (DCF) and Point Coordination Function (PCF). As well, the effect of add- ing a D2D technology to the PCF is discussed. The WLAN effectively offloaded nodes at their Signal to Interference and Noise Ratio (SINR) becomes more than a specific threshold. Results presented that the PCF mechanism outper- forms the DCF one in terms of packet loss ratio, throughput and the maximum load of the entire network. In addi- tion, the use of a D2D serviced in the PCF helps in further reduction in the network load.
With the substantial growth of mobile applications and the emergence of cloud computing concepts, therefore mobile Cloud Computing (MCC) has been introduced as a potential mobile service technology. Mobile has limited resources, battery life, network bandwidth, storage, and processor, avoid mobile limitations by sending heavy computation to the cloud to get better performance in a short time, the operation of sending data, and get the result of computation call offloading. In this paper, a survey about offloading types is discussed that takes care of many issues such as offloading algorithms, platforms, metrics (that are used with this algorithm and its equations), mobile cloud architecture, and the advantages of using the mobile cloud. The trade-off between local execution of tasks on end-devices and remote execution on the cloud server for minimizing delay time and energy saving. In the form of a multi-objective optimization problem with a focus on reducing overall system power consumption and task execution latency, meta-heuristic algorithms are required to solve this problem which is considered as NP-hardness when the number of tasks is high. To get minimum cost (time and energy) apply partial offloading on specific jobs containing a number of tasks represented in sequences of zeros and ones for example (100111010), when each bit represents a task. The zeros mean the task will be executed in the cloud and the ones mean the task will be executed locally. The decision of processing tasks locally or remotely is important to balance resource utilization. The calculation of task completion time and energy consumption for each task determines which task from the whole job will be executed remotely (been offloaded) and which task will be executed locally. Calculate the total cost (time and energy) for the whole job and determine the minimum total cost. An optimization method based on metaheuristic methods is required to find the best solution. The genetic algorithm is suggested as a metaheuristic Algorithm for future work.
Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mobile vehicles. Several studies have tackled the task offloading problem in the VFC field. However, recent studies have not carefully addressed the transmission path to the destination node and did not consider the energy consumption of vehicles. This paper aims to optimize the task offloading process in the VFC system in terms of latency and energy objectives under deadline constraint by adopting a Multi-Objective Evolutionary Algorithm (MOEA). Road Side Units (RSUs) x-Vehicles Mutli- Objective Computation offloading method (RxV-MOC) is proposed, where an elite of vehicles are utilized as fog nodes for tasks execution and all vehicles in the system are utilized for tasks transmission. The well-known Dijkstra's algorithm is adopted to find the minimum path between each two nodes. The simulation results show that the RxV-MOC has reduced significantly the energy consumption and latency for the VFC system in comparison with First-Fit algorithm, Best-Fit algorithm, and the MOC method.