With the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.

The vigorous development of IoT technology has spawned a series of applications that are delay-sensitive or resource-intensive. Mobile edge computing is an emerging paradigm that provides services between end devices and traditional cloud data centers to users. However, with the continuously increasing investment of demands, it is nontrivial to maintain a higher quality-of-service (QoS) under the erratic activities of mobile users. In this paper, we investigate the service provisioning and updating problem under the multiple-users scenario by improving the performance of services with long-term cost constraints. We first decouple the original long-term optimization problem into a per-slot deterministic one by using Lyapunov optimization. Then, we propose two service updating decision strategies by considering the trajectory prediction conditions of users. Based on that, we design an online strategy by utilizing the committed horizon control method looking forward to multiple slots predictions. We prove the performance bound of our online strategy theoretically in terms of the trade-off between delay and cost. Extensive experiments demonstrate the superior performance of the proposed algorithm.

Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the erratic activities of end-users. In this paper, we focus on the service placement problem under the continuous provisioning scenario in mobile edge computing for multiple mobile users. We propose a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflicting resolution mechanism to avoid the invalid state in the decision module. We first formulate the service placement under the migration confliction into a mixed-integer linear programming (MILP) problem. Then, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the introduced migration feasibility factor. Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency.

Edge computing has emerged as a promising paradigm to meet the increasing demands of latency-sensitive and computationally intensive applications. In this context, efficient server deployment and service placement are crucial for optimizing performance and increasing platform profit. This paper investigates the problem of server deployment and service placement in a multi-user scenario, aiming to enhance the profit of Mobile Network Operators while considering constraints related to distance thresholds, resource limitations, and connectivity requirements. We demonstrate that this problem is NP-hard. To address it, we propose a two-stage method to decouple the problem. In stage I, server deployment is formulated as a combinatorial optimization problem within the framework of a Markov Decision Process (MDP). We introduce the Server Deployment with Q-learning (SDQ) algorithm to establish a relatively stable server deployment strategy. In stage II, service placement is formulated as a constrained Integer Nonlinear Programming (INLP) problem. We present the Service Placement with Interior Barrier Method (SPIB) and Tree-based Branch-and-Bound (TDB) algorithms and theoretically prove their feasibility. For scenarios where the number of users changes dynamically, we propose the Distance-and-Utilization Balance Algorithm (DUBA). Extensive experiments validate the exceptional performance of our proposed algorithms in enhancing the profit.

For heterogeneous computing systems, various types of processor cores cause system performance degradation due to uneven load. In addition, the inability of multitasking to match the appropriate processor core is also an urgent problem. This article proposes a swarm intelligence task scheduling strategy based on the genetic algorithm (GA) for high-performance heterogeneous multicore processors. In order to avoid the falling into local optimal solutions, we employ an adaptive mutation and injection strategy in the algorithm design. This swarm intelligence solution detects the computing capacities of different cores by processing specified tasks beforehand, and then an appropriate solution will be explored by introducing an adaptive mutation GA. Our technique aims to execute various types of tasks on heterogeneous processing cores for optimal performance. Experimental results show that this scheduling strategy can reduce the additional overhead and improve parallel computing efficiency and system performance.

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper investigates optimizing the service caching and updating problem within multi-stage and proposes a novel framework with three proposed strategies for the different stages to jointly optimize the delay and cost. At the initial service caching stage, we propose a basic caching strategy based on dynamic programming for the single-area scenario, taking into account the constraint of limited memory resources. To improve the caching strategy, we extend our consideration to the multiple-area scenario and design an improved algorithm based on tabu search. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on reinforcement learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost.