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With the rapid development of Internet‐of‐Things (IoT) and mobile devices, the IoT applications become more computation‐intensive and latency‐sensitive, which bring severe challenges to the resource‐limited devices. Mobile Edge Computing has served as a key promising method to enhance the network's computing capability by enabling resource‐constrained devices to offload tasks to the edge servers. A major challenge, which has been overlooked by most existing works on task offloading, is the dependencies among tasks and subtasks. In this paper, the subtask offloading with logical dependency for IoT applications is focused on. Specifically, subtask dependent graphs are employed to explore the dependency of subtasks and consider the priority of task scheduling. Further, an offloading scheme is put forward for minimizing both task latency and energy consumption of the device with dependency guarantees for all IoT tasks in multi‐server edge networks. Finaly, the simulation results demonstrate that the overall reduction rate is around 14% and relatively stable can effectively reduce task latency in multi‐server edge networks. [ABSTRACT FROM AUTHOR]

Due to the rise of mobile users and their demand for various applications, the traditional cloud computing remote servers consume huge energy and execution time because it is far away from the mobile devices. The Mobile edge computing is a technology which offloads the workloads of mobile user device to the edge of the network using Mobile Edge Computing thereby reducing the burden of cloud and reducing the latency and energy consumption. However, offloading of the complex tasks face challenges like increased delay and energy consumption. To solve these challenges, proposed approach designs a Delay-Energy aware Task Offloading Algorithm (DETOA) where the tasks gets partitioned based on the delay, energy and communication constraints. The proposed task partitioning based approach executes the tasks locally, if the MUD can process the task else the tasks are offloaded to MEC and processed remotely. In addition, a VM migration scheme is introduced to prevent service degradation through task and VM replication approach. The proposed approach exhibited better performance in terms of energy, delay and QoS. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has recently emerged as an effective paradigm to enhance the computing capability of capability-limited mobile devices (MDs). In this article, we consider an MEC system consisting of a number of MEC servers and one MD which generates a series of tasks characterized by their dependency relationships. We study computation scheduling and offloading problem of the tasks. To improve the task processing performance, we first propose a parallel transmission and execution (PTE) scheme, based on which we design a computation scheduling and offloading algorithm. Considering the fairness among tasks in terms of task transmission and execution time, we formulate the computation scheduling and offloading problem as a constrained worst-case latency optimization problem which minimizes the maximum completion time of all the tasks. As the original optimization problem cannot be solved conveniently, we first categorize the tasks into high priority tasks (HPTs), medium priority tasks (MPTs) and low priority tasks (LPTs) based on their task execution status and causal relationship. Then, a dynamic priority-based computation scheduling and offloading algorithm is proposed, which designs computation scheduling and offloading strategy for dynamically-changed HPTs and MPTs, respectively. In particular, for HPTs, a multiple knapsack-based heuristic algorithm is proposed, and a task weight and data size-based computation scheduling and offloading algorithm is further proposed for MPTs. Numerical results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Due to the limitations associated with the processing capability of mobile devices in cloud environments, various tasks are offloaded to the cloud server. This has led to an increase in the efficiency of mobile applications in the two decades since the advent of the cloud paradigm. However, task offloading may not be a suitable option for delay-sensitive mobile applications because the cloud server is usually located remotely from mobile users. To overcome this problem, fog computing, also known as "Cloud at the Edge", has been introduced as a complementary solution. On the other hand, although fog computing brings computing and radio resources closer to mobile devices, fog nodes cannot adequately meet users' needs due to limited computing resources. To minimize delays in responding to mobile users' requests, it is necessary to establish a trade-off between local execution of requests on end-devices and the fog environment. In this paper, we present task offloading in the form of a multi-objective optimization problem with a focus on reducing both total power consumption of the system and the delay in executing tasks. Then, considering the NP-hardness of the problem, we solve it using two meta-heuristic methods, namely the non-dominated sorting genetic algorithm (NSGA-II) and the Bees algorithm. The simulation results supported the robustness of both meta-heuristic algorithms in terms of energy consumption and delay reduction. The proposed methods achieve a better tradeoff concerning both offloading probability and the power required for data transmission. [ABSTRACT FROM AUTHOR]

Mobile cloud computing augments smart-phones with computation capabilities by offloading computations to the cloud. Recent works only consider the energy savings of mobile devices while neglecting the cost incurred to the tasks which are offloaded. We might offload several tasks to minimize the total energy consumption of mobile devices; however, this could incur a huge monetary cost. Furthermore, these issues become more complex in considering the multi-tenant cloud, which is not addressed in literature adequately. Thus, to balance the trade-off between monetary cost and energy consumption of the mobile devices, we need to decide whether to offload the task to the cloud or run it locally. In this article, first, we have formulated a 'MinEMC' optimization problem to minimize both the energy as well as the monetary cost of the mobile devices. The 'MinEMC' problem is proven to be NP-hard. We formulate a special case with an equal amount of resource requirement by each task for which a polynomial-time solution is presented. Further various policies are proposed, the cloud can employ to solve the general case. Then we proposed an efficient heuristic named 'Off-Mat' based on distributed stable matching, the solution for which determines whether the tasks are to be offloaded or not under multi-constraints. We also analyze the complexity of this proposed heuristic algorithm. Finally, performance evaluation through simulation results demonstrates that the Off-Mat algorithm attains high-performance in computational tasks offloading and scale well as the number of tenants increases. [ABSTRACT FROM AUTHOR]

With the rapid development of the internet of things, there are more and more end devices, such as wearable devices, USVs and intelligent automobiles, connected to the internet. These devices tend to require large amounts of computing resources with stringent latency requirements, which inevitably increases the burden on edge server nodes. Therefore, in order to alleviate the problem that the computing capacity of edge server nodes is limited and cannot meet the computing service requirements of a large number of end devices in the internet of things scenario, we combined the characteristics of rich computing resources of cloud servers and low transmission delay of edge servers to build a hybrid computing task-offloading architecture of cloud-edge-end collaboration. Then, we study offloading based on this architecture for complex dependent tasks generated on end devices. We introduce a two-dimensional offloading decision factor to model latency and energy consumption, and formalize the model as a multi-objective optimization problem with the optimization objective of minimizing the average latency and average energy consumption of the task's computation offloading. Based on this, we propose a multi-objective offloading (SPMOO) algorithm based on an improved strength Pareto evolutionary algorithm (SPEA2) for solving the problem. A large number of experimental results show that the algorithm proposed in this paper has good performance. [ABSTRACT FROM AUTHOR]

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices' trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach. [ABSTRACT FROM AUTHOR]