Secure Task Offloading and Resource Allocation Strategies in Mobile Applications Using Probit Mish-Gated Recurrent Unit and an Enhanced-Searching-Based Serval Optimization Algorithm

Mobile edge computing (MEC) is an emerging computing and communication model that brings extremely resourceful servers at a network's edge. This reduces the latency associated with cloud computing as applications executing on mobile devices do not always need to communicate with a cloud server because most of the functionalities provided by the cloud server are available on an edge server. Moreover, with the emergence of 5G really high amount of bandwidth is available to a mobile device, hence communication latency between the edge server and a mobile device is low. The combination of 5G and edge computing has a potential to better enable many interesting time-critical and/or computationally expensive computing and communication use cases, for example, connected cars. The stated combination enables development of computationally expensive applications for mobile devices and sensors nodes as these devices can offload their computational tasks to the edge server. Task offloading is a promising feature of edge computing, therefore here priority-class-based methods are presented to allocated CPU cycles to an offloaded task at the edge server. In one of the presented methods, a mobile device or a senor node assign their task to one of the available priority classes, and then offload the task to the edge server. Afterwards, the edge server assigns CPU cycles to such a task depending upon its priority class. Similarly, another method is presented that assigns priority to each device based on the type of tasks the device is offloading. While assigning CPU cycles to different offloaded tasks, the edge server uses their device priority. The simulation results demonstrate that the presented methods are capable of providing service differentiation.

In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.

Mobile Edge Computing has already become a new paradigm to reduce the latency in data transmission for resource-limited mobile devices by offloading computation tasks onto edge servers. However, for mobility-aware computation-intensive services, existing offloading strategies cannot handle the offloading procedure properly because of the lack of collaboration among edge servers. A data stream application is partitionable if it can be presented by a directed acyclic dataflow graph, which makes cross-edge collaboration possible. In this paper, we propose a cross-edge computation offloading (CCO) framework for partitionable applications. The transmission, execution and coordination cost, as well as the penalty for task failure, are considered. An online algorithm based on Lyapunov optimization is proposed to jointly determine edge site-selection and energy harvesting without priori knowledge. By stabilizing the battery energy level of each mobile device around a positive constant, the proposed algorithm can obtain asymptotic optimality. The-oretical analysis about the complexity and the effectiveness of the proposed framework is provided. Experimental results based on a real-life dataset corroborate that CCO can achieve superior performance compared with benchmarks where crossedge collaboration is not allowed.

Device to device (D2D) communication and mobile edge computing (MEC) are two promising technologies in fifth generation (5G) cellular mobile communication. Besides MEC, a new task offloading technique attracts the attention as D2D collaboration. However, there is lack of integrated D2D and MEC framework to address the energy and delay costs in a joint approach. This work, proposes an energy efficient and delay-aware offloading scheme (EEDOS) based on D2D collaboration in MEC. In EEDOS, mobile devices can offload their task to the MEC or an idle mobile device in their proximity. The task execution and offloading to the MEC or an idle nearby device is formulated, and the optimization problem is defined. The whole process of allocating proper offloading destination is designed in the edge server. EEDOS, classifies offloading requests according to the deadline and energy constraint of requesting device. Then, it finds the proper offloading destination by utilising the maximum matching with minimum cost graph algorithm. Through simulation, we show that EEDOS achieves 95 percent of energy efficiency in comparison of no-offloading task execution and outperforms existing studies in term of energy efficiency with an improved delay in task execution. Moreover, EEDOS is capable of performing more successful task offloading and requires less edge server resources.

Joint task offloading and data caching in mobile edge computing networks

We consider the efficient resource allocation task in the blockchain-based mobile edge computing (MEC) system that requires decentralized transaction management to validate transactions between edge servers (ESs) and mobile devices (MDs). In such task allocation process (where MDs' resources are limited and privacy-sensitive), it is a significant challenge to guarantee individual rationality with satisfactory system stability while enabling flexible task offloading under various locality constraints (e.g., communication distance, bandwidth and delay). In this paper, we formulate the target problem as a blockchain-assisted task-resource matching model, and then propose a near optimal locality-aware resource allocation mechanism over smart contract to enable automatic and efficient transactions in MEC system. More specifically, for the service agents selection, we design the preference-based selection strategy to get highest estimated profit. For the flexible task offloading, we develop the minimum delay task graph partitioning algorithm to determine the optimal task offloading solution for MD under different resource bundles. For the task-resource matching, we propose a task-resource matching game (based on potential game) with the second lowest cost strategy to determine the matching of task-resource and decide the price of resource bundle. For the transaction verification and block allocation, we propose a social welfare-driven consensus mechanism to enable verified transaction and fair block allocation in a reward-free way. Strict theoretical analysis and extensive simulations demonstrate that our mechanism guarantees individual rationality, Nash Equilibrium, and stable near optimal solution.

A Novel Lyapunov based Dynamic Resource Allocation for UAVs-assisted Edge Computing

Task offloading and scheduling is an important issue in mobile edge computing (MEC). A good offloading decision can fully utilize the computing capabilities of edge servers to deliver high-quality computing services. This paper takes into account the concerns about task processing latency and mobile device's energy consumption, and formulates the task offloading problem as a deadline-constrained energy-minimization integer program. We propose a partial offloading and scheduling method based on the whale optimization algorithm to solve the formulated optimization problem. This method employs a probability model-based mapping operator to convert an individual whale into a valid offloading solution represented by a task sequence. This mapping scheme is advantageous over sorting-based rules in producing high-quality task sequence. We develop an efficient heuristic strategy to decide each task should be processed locally on the mobile device or offloaded to the edge server for execution. With all tasks in the sequence having been scheduled, the mobile device's energy consumption for task execution and data transmission can be calculated. Accordingly, we can identify the best individual whale in the population leading to the solution with lowest energy consumption. We iteratively apply the population updating rule to explore better task sequence and offloading solution. We perform extensive experiments to verify that the proposed algorithm achieves more energy-efficient offloading solutions as compared to baseline algorithms while satisfying the deadline constraint.

QoS prediction for service recommendations in mobile edge computing

Existing studies of mobile edge computing resource allocation strategy problem merely optimize delay and energy cost, seldom considering the benefit of edge servers. So, a two-way update strategy based on game theory (TUSGT) was proposed. TUSGT converts the task competition relationship among edge servers into a noncollaborative game issue and adopts a potential game-based joint optimization strategy, allowing edge servers to determine task selection preference by maximizing their own benefit as the objective. At mobile device side, the EWA algorithm of online learning was used to update parameters, exerting impact on edge server’s task selection preference from a global perspective and improving overall deadline hit rate. The simulation test results show that, compared to BGTA, MILP, greedy strategy, random strategy, and ideal strategy, TUSGT promotes deadline hit rate by up to 30% and increases edge server’s average benefit by up to 65%.

In the Internet of Things (IoT) environment, offloading computational tasks to edge servers is a key technology for alleviating the resource bottlenecks of mobile devices (MDs). However, in high-concurrency task offloading scenarios, edge servers face the issue of low resource allocation efficiency. Existing resource allocation schemes typically aim to improve resource utilization by indiscriminately redistributing idle resources, but these approaches fail to address the diversity of task demands. Due to the non-monotonic relationship between the computational load and latency constraints of different tasks, tasks exhibit heterogeneous resource demands under heterogeneous workloads. Therefore, fixed resource allocation strategies are difficult to adapt to the changes in task characteristics, leading to some tasks failing to complete on time due to insufficient resources, which in turn violates latency constraints and degrades overall system performance. To address this issue, this paper proposes a Task-Requirement-Based Resource Adaptive Allocation Algorithm (TRRAA). First, we construct a mathematical model to evaluate task requirements and quantify the computational resource demands of tasks at different time steps. Then, the algorithm dynamically allocates idle resources based on the real-time demands of tasks, prioritizing resources for tasks that are expected to miss their time constraints, ensuring timely completion. Finally, experimental results show that, compared to existing methods, the TRRAA algorithm improves task completion rates by 10 % when handling latency-constrained tasks.

For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self‐disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single‐edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.

The ongoing increasing number of mobile devices (MDs) with innovative applications yields unprecedented demands for user experience and network capacity expansion. The combination of ultra-dense network (UDN) and mobile edge computing (MEC) has been envisioned as a promising futuristic technology. It can remarkably improve the capacity of system and extend cloud-computing capabilities to proximate edge servers, by which the growing computation requirements of MDs will be met. However, what is not addressed well is how to optimize the configuration of computing resources with varying computation demands of MDs being satisfied, aiming at maximizing the operating earnings of operators while decreasing the cost of MDs. Considering the diverse computation demands in different regions and variational computing resources of edge servers, it is hard to solve this problem by traditional methods. To address this issue, an effective solution is generating an optimal computing resource configuration strategy and task offloading profile in time-varying UDN scenarios. Toward this end, a deep Q-network based scheme is proposed to achieve maximum long-term weighted network utility in such ever-changing environments. Simulation results validate the significant performance improvement of our scheme in weighted network utility and task offloading compared to conventional game-theoretical solution.

Mobile edge computing (MEC) provides a new distributed computing paradigm that overcomes the inability of cloud computing to offer low end-to-end latency. In a MEC environment, app vendors can deliver lower-latency services to mobile app users by placing applications on edge servers in close proximity to app users. From an app vendor's perspective, an optimal edge application placement strategy under a budget ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ) constraint aims to place application instances on <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> edge servers within a specific area to maximize user coverage. However, edge servers may be subject to failure due to multiple reasons, e.g., hardware faults, software exceptions, cyber-attacks, etc. App users served by failed edge servers need to access applications from remote cloud servers if they cannot access any other edge servers. This impacts app users’ quality of experience significantly. Thus, app vendors need to consider the reliability of the edge server network when choosing edge servers for placing their application instances. We make the first attempt in this paper to tackle this problem of joint optimization of coverage and reliability for edge application placement (EAP-CR). We formulate this problem as a constrained optimization problem and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hardness theoretically. Then, we propose an optimal approach to find the optimal solutions with the integer programming technique, an approximation approach is also proposed to find approximate solutions for large-scale EAP-CR problems. We evaluate EAP- <inline-formula><tex-math notation="LaTeX">$OPT$</tex-math></inline-formula> and EAP- <inline-formula><tex-math notation="LaTeX">$APX$</tex-math></inline-formula> against three relevant approaches through experiments conducted on a widely-used real-world data set and a synthetic data set. The results demonstrate that our proposed approaches can solve the EAP-CR problem effectively and efficiently.

Mobile edge computing (MEC) facilitates computation offloading to edge server, as well as task processing via device-to-device (D2D) collaboration. Existing works mainly focus on centralized network-assisted offloading solutions, which are unscalable to scenarios involving collaboration among massive users. In this paper, we propose a joint framework of decentralized D2D collaboration and efficient task offloading for a large-population MEC system. Specifically, we utilize the power of two choices for D2D collaboration, which enables users to beneficially assist each other in a decentralized manner. Due to short-range D2D communication and user movements, we formulate a mean field model on a finite-degree and dynamic graph to analyze the state evolution of D2D collaboration. We derive the existence, uniqueness and convergence of the state stationary point so as to provide a tractable collaboration performance. Complementing this D2D collaboration, we further build a Stackelberg game to model users’ task offloading, where edge server is the leader to determine a service price, while users are followers to make offloading decisions. By embedding the Stackelberg game into Lyapunov optimization, we develop an online offloading and pricing scheme, which could optimize server’s service utility and users’ system cost simultaneously. Extensive evaluations show that our D2D collaboration can mitigate users’ workloads by 73.8% and task offloading can achieve high energy efficiency.