Mobile Cloud Computing (MCC) augments capabilities of mobile devices by offloading applications to the cloud. task allocation is one of the most challenging issues in MCC considering neighboring mobile devices as the service providers. Given an application offloading request, the objective of the task allocation is to select service providers minimizing the completion time of the application offloading as well as consumed energy of all participating mobile devices while satisfying some QoS constraints. This paper models the task allocation problem in MCC as a constrained multi-objective optimization and proposes a two stages approach to solve the problem. In the first stage, a Multi-objective Resource Allocation based on Branch and Bound algorithm (MRABB) is designed to obtain Pareto solution set. In the subsequent stage, using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and given user’s preferences, the best compromise solution is determined. Furthermore, a context-aware software for offloading in Mobile Cloud (OMC) is designed and implemented to collect contextual used in task allocation, and manage the offloading process. The results show the ability of the proposed multi-objective task allocation method to manage the trade-off between time and energy comparing to traditional algorithms.

The proliferation of new applications has led to new challenges in energy consumption, task processing, and data storage. Multi-access Edge Computing (MEC) is a new computational paradigm that can transfer workloads from users' devices to powerful servers in the same location with the least possible time and energy overhead to improve the QoS and performance measures. Since joint task offloading and resource allocation in MEC is one of the main concerns of performance-aware applications, this paper explores a fine-grained view to this problem under the dynamic and time-varying conditions of the entire system. The main goal of this paper is to reduce the normalized cost of the system, which is the weighted sum of the completion time and the consumed energy, by formulating the problem and proposing a new algorithm based on the reinforcement learning approach. The results of the simulations performed under different scenarios corresponding to the real-world systems show the improvement in the completion time and energy consumption of the tasks compared to other existing methods and leads to an average reduction of 22 and 24 percent in the scenarios related to the evaluation of the normalized cost of the system.

Fog computing has emerged as a promising technique to provide agile and pervasive computing services to the Internet of Things devices (IDs) and to support complicated IoT applications. Fog computing brings computation resources to the edge of the network, near to the IDs, and provides low-latency services to users. By offloading computational tasks to fog nodes having greater computing capacities, can address the contradiction between the limited battery capacity of IDs and high computational intensity demand of tasks. Hence, the quality of service (QoS) demands of users can be fulfilled. Although task offloading to fog nodes leads to saving in energy consumption in the battery of IDs, it causes to increase in task completion time due to occurred delay in transmitting the task to the edge of the network. In this paper, to balancing the trade-off between energy consumption and task completion time, a task offloading scheme is proposed. The main objective of the proposed scheme is to minimize offloading overhead in terms of the weighted sum of energy consumption and task completion time by optimizing offloading decision, the destination of offloading, and computation resource allocation. We employ fuzzy logic to determine the weighting coefficient effectively. task offloading to fog nodes is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard. A sub-optimal algorithm based on genetic algorithm (GA) is proposed to solve the formulated problem. Extensive simulations prove the convergence of the proposed algorithm and its superior performance in comparison with some baseline schemes.

Fog computing is a new paradigm which enables offloading IOT data (tasks) to the network devices. The aim of this approach is reducing the response time for delay-sensitive applications and improving the quality of service for users. This paper presents a task offloading scheme with taking advantages of the Software-Defind Networks. In this research a mixed integer linear programming (MILP) optimization model is presented with the aim of minimizing delay and mobility cost of things, which considers local computing, fog nodes participation, applications distribution and resource limitations. Whereas the presented mathematical model is Np-hard, a meta-heuristic algorithm based on the ant colony optimization is proposed by considering constraints of the mathematical model. The results obtained from evaluation of the proposed method is compared with the optimal value obtained from the mathematical model, random method and a heuristic algorithm presented in related works. The results of evaluation show that the delay and the total offloading cost in the proposed method are 22% and 28.75% higher than the optimal values, respectively. Also, the proposed method is capable to reduce the delay by 20% and reduce the migration cost of computing results by 40% compared to the heuristic method in state-of-the-art.

The rapid growth of IoT technology has led to the emergence of various latency-sensitive IoT applications. These applications require significant computational resources for real-time processing, resulting in high energy consumption in IoT devices. To address this issue, task offloading using fog computing has emerged as a novel solution. Fog-based task offloading reduces latency and enhances the flexibility of IoT devices. This study proposes a mathematical model aimed at minimizing end-to-end delay and energy consumption for task offloading in IoT-fog networks based on software-defined networking (SDN) infrastructure. The simulation results of the proposed model are compared with two metaheuristic algorithms (Genetic Algorithm and Firefly Algorithm) and a baseline paper, focusing on delay and energy consumption metrics. After implementing the scenario and conducting analysis, the simulation results indicate that the proposed model, using metaheuristic algorithms, achieved approximate average reductions of 18% in delay and 19% in energy consumption.

Efficient distribution of service requests between fog and cloud nodes considering user mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile user leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

As a growing of IoT devices, new computing paradigms such as fog computing are emerging. Fog computing is more suitable for real-time processing due to the proximity of resources to IoT layer devices. Service providers must dynamically update the hardware and software parameters of the network infrastructure. Software defined network (SDN) proposed as a new network paradigm, whose separate control layer from data layer and provides flexible network management. This paper presents a software-defined fog platform to host real-time applications in IoT. Then, we propose a novel resource allocation method. This method involves scheduling multi-node real-time task graphs over the fog to minimize task execution latency. The proposed method is designed to benefit the centralized structure of SDN. The simulation results show that the proposed method can find near to optimal solutions in a very lower execution time than the brute force method.