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The computing power of mobile devices is too limited to execute computation tasks fast. Mobile edge computing (MEC) allows mobile devices to offload tasks to near servers to reduce the completion time of the tasks (a.k.a makespan). The input data of some critical tasks should be encrypted before the offloading. Aiming at the security critical tasks in the MEC composed of multiple servers, this paper addresses to minimize the makespan by scheduling security-critical tasks. We provide the formulation of the problem which is generally an integer programming problem, and three effective composite heuristics CH1–CH3 are proposed to solve the problem. Task permutations are considered as solutions. We construct a greedy heuristic algorithm to calculate the value of the objective. These three composite heuristics consist of two phases: solution initialization and solution improvement. In the first phase, the solutions of all the proposed algorithms are generated by a task arrangement rule called Biggest data Task First (BTF), and then in the second phase, improved by three searching methods based on different neighborhoods including a insertion neighborhood, a swap neighborhood and a hybrid neighborhood, respectively. Experimental results show that CH1–CH3 outperform the well-known RoundRobin algorithm. Particularly, BTF is demonstrated to initialize highly qualified solutions, making contributions to the high effectiveness. Meanwhile, all the improvement methods are justified to be effective and the method based on the hybrid neighborhood achieves the best effectiveness. [ABSTRACT FROM AUTHOR]

In mobile crowdsensing, it is a challenge to assign tasks to appropriate smartphones. Existing task allocation mechanisms mainly aim at optimizing the global system performance, while ignoring the personal preferences of individual crowdsensing tasks and smartphone users. Nevertheless, in an open crowdsensing system, a task assignment is prone to be unstable if smartphone users or tasks have incentives to deviate from the global assignment, and seek for alternative choices to improve their own utilities. Besides that, during task competition, the rational smartphone users might choose to adjust their payments after the first few failures, which however, brings new challenges in achieving the stability. To address these issues, this paper constructs a distributed many-to-many matching model to capture the interaction between crowdsensing tasks and smartphone users, taking into account the budget constraints of tasks. Then, we design a stable matching algorithm to allocate the tasks to the users, and determine their payments. We prove that the proposed algorithm achieves several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed scheme achieves at least half of the optimal system efficiency when each smartphone provides homogeneous service quality. Finally, simulation results confirm the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the crowdsensing platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users’ preference and reliability. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user’s preference and reliability into consideration. We first present a hybrid preference metric to characterize users’ preference by exploiting their implicit feedback. Then, to profile users’ reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack users’ historical information, we further propose a matrix factorization method to infer the users’ reliability levels on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to the benchmarks in both user profiling and personalized task recommendation. [ABSTRACT FROM AUTHOR]

The resource constraint has become an important factor hindering the further development of mobile devices (MDs). Mobile cloud computing (MCC) is a new approach proposed to extend MDs' capacity and improve their performance by task offloading. In MCC, MDs send task requests to the application service operator (ASO), which provides application services to MDs and needs to determine whether to accept the task request according to the system condition. This paper studies the task admission control problem for ASOs with the consideration of three features (two-dimensional resources, uncertainty, and incomplete information). A task admission control model, which considers radio resource variations, computing, and radio resources, is established based on the semi-Markov decision process with the goal of maximizing the ASO's profits while guaranteeing the quality of service (QoS). To develop the admission policy, a reinforcement learning-based policy algorithm, which develops the admission policy through system simulations without knowing the complete system information, is proposed. Experimental results show that the established model adaptively adjusts the admission policy to accept or reject different levels and classes of task requests based on the ASO load, available radio resources, and event type. The proposed policy algorithm outperforms the existing policy algorithms and maximizes the ASO's profits while guaranteeing the QoS. [ABSTRACT FROM AUTHOR]

Summary: There is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long set‐up time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks. [ABSTRACT FROM AUTHOR]

Summary: It is a visible fact that the growth of mobile devices is enormous. More computations are required to be carried out for various applications in these mobile devices. But the drawback of the mobile devices is less computation power and low available energy. The mobile cloud computing helps in resolving these issues by integrating the mobile devices with cloud technology. Again, the issue is increased in the latency as the task and data to be offloaded to the cloud environment uses WAN. Hence, to decrease the latency, this paper proposes cloudlet‐based dynamic task offloading (CDTO) algorithm where the task can be executed in device environment, cloudlet environment, cloud server environment, and integrated environment. The proposed algorithm, CDTO, is tested in terms of energy consumption and completion time. [ABSTRACT FROM AUTHOR]

In this paper, we consider parallel and sequential task offloading to multiple mobile edge computing servers. The task consists of a set of inter-dependent sub-tasks, which are scheduled to servers to minimize both offloading latency and failure probability. Two algorithms are proposed to solve the scheduling problem, which are based on genetic algorithm and conflict graph models, respectively. Simulation results show that these algorithms provide performance close to the optimal solution, which is obtained through exhaustive search. Furthermore, although parallel offloading uses orthogonal channels, results demonstrate that the sequential offloading yields a reduced offloading failure probability when compared to the parallel offloading. On the other hand, parallel offloading provides less latency. However, as the dependency among sub-tasks increases, the latency gap between parallel and sequential schemes decreases. [ABSTRACT FROM AUTHOR]

In traditional mobile cloud computing environment, task scheduling is often determined by random algorithm, adjusting working frequency by dynamic voltage regulation technology. Differentiating tasks exist in task integration, which often leads to many unreasonable task migration and heavy CPU load, and resulting in system damage and a large amount of energy consumption. For multi-workflow tasks, this paper proposed CCS algorithm, which included consolidation algorithm and multitask concurrency algorithm. By increasing the concurrency of transmission and execution between tasks, it could increase the probability of task set integration, could increase the rate of task processing, could reduce the response time of tasks, could increase the CPU usage, and control the CPU usage of host and kernel under the upper threshold. It could avoid CPU overload and optimize local algorithm according to multi-task concurrency, which adjusted task execution location, improved migration efficiency and avoided the limitations of random algorithm. Experimental results show that this algorithm can effectively improve system performance, avoid CPU overload problem, optimize energy consumption and workflow completion time. [ABSTRACT FROM AUTHOR]

With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms. [ABSTRACT FROM AUTHOR]

In this study, we consider an edge cloud server in which a lightweight server is placed near a user device for the rapid processing and storage of large amounts of data. For the edge cloud server, we propose a latency classification algorithm based on deadlines and urgency levels (i.e., latency-sensitive and latency-tolerant). Furthermore, we design a task offloading algorithm to reduce the execution time of latency-sensitive tasks without violating deadlines. Unlike prior studies on task offloading or scheduling that have applied no deadlines or task-based deadlines, we focus on a comprehensive deadline-aware task scheduling scheme that performs task offloading by considering the real-time properties of latency-sensitive tasks. Specifically, when a task is offloaded to the edge cloud server due to a lack of resources on the user device, services could be provided without delay by offloading latency-tolerant tasks first, which are presumed to perform relatively important functions. When offloading a task, the type of the task, weight of the task, task size, estimated execution time, and offloading time are considered. By distributing and offloading latency-sensitive tasks as much as possible, the performance degradation of the system can be minimized. Based on experimental performance evaluations, we prove that our latency-based task offloading algorithm achieves a significant execution time reduction compared to previous solutions without incurring deadline violations. Unlike existing research, we applied delays with various network types in the MEC (mobile edge computing) environment for verification, and the experimental result was measured not only by the total response time but also by the cause of the task failure rate. [ABSTRACT FROM AUTHOR]

Task offloading is one of the important research directions in Internet of Vehicles. Mobile edge computing and 5G heterogeneous network frameworks bring new opportunities for task offloading research. Most research works propose resource scheduling schemes and offloading strategies for task offloading within the same period. However, the allocation of resources for offloading tasks at different periods cannot be planned uniformly. The utilization and release of resources in previous periods affect the scheduling of resource allocations in later periods. To address the task offloading problem at different periods, this paper proposes the MPTO-MT algorithm. Specifically, the algorithm first determines whether the task can be offloaded in time at a certain period according to the number of candidate offloading nodes of the task in this period. Secondly, the algorithm selects the offloading node and the task transmission method. In order to select the appropriate offloading node and transmission method, we introduce the service utility and propose three heuristics of selecting the offloading node. Finally, the algorithm updates the resource information of nodes at the next period based on the utilization and release of node resources at the period, and the service time of the nodes. Comparative experiments with other baseline algorithms show that the proposed MPTO-MT algorithm improves the task completion rate by 4.55%, the timely rate of task completion by 3.57%, and shorter average service time. [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]