In mobile cloud computing, traditional frameworks often treat mobile devices as passive data collectors, relying heavily on centralized cloud centers for model processing. This centralized approach limits data-processing accuracy and efficiency due to bandwidth constraints, communication latency, and limited computational utilization of mobile devices. Furthermore, as mobile applications increasingly require real-time, personalized services, these limitations restrict model adaptability and responsiveness, emphasizing the need for a distributed approach that can dynamically adjust to varying service demands and network conditions. This paper proposes FLOM (Framework-Level Self-evolution Optimization Model), a novel framework designed to support autonomous, adaptive model optimization across multi-terminal systems. Unlike traditional methods, FLOM leverages the computational capacity of mobile devices to enable on-site model updates and intelligent data allocation through a collaborative network of cloud and mobile nodes. FLOM incorporates deep reinforcement learning for dynamic model evolution, allowing real-time adaptation across terminals. By utilizing model information from each device, FLOM achieves balanced self-evolution, optimizing processing accuracy and system resilience. The framework introduces three major innovations: (1) Self-adaptive Model Evolution by utilizing mobile terminal resources to locally update models, FLOM reduces dependence on centralized data centers, ensuring faster, context-aware adjustments that enhance real-time service delivery; (2) Intelligent Resource Allocation supporting real-time, task-targeted distribution to enhance fault tolerance and system efficiency, FLOM effectively allocates resources to handle diverse service needs across terminals; and (3) Collaborative Optimization by integrating model characteristics from multiple terminals, FLOM enables unified, system-wide optimization that improves model robustness and adaptability. Experimental results show that FLOM significantly improves data accuracy, reduces processing time, and enhances fault tolerance compared to traditional frameworks, making it a robust and versatile solution for real-time, adaptive mobile cloud applications.

Dynamic edge networks revolutionize mobile edge computing by enabling real-time applications in intelligent transportation, augmented reality, and industrial Internet of Things (IoT). Efficient workload offloading in dynamic edge networks is crucial for addressing the increasing demands of time-varying workloads while contending with limited computational and communication resources. Existing deep reinforcement learning (DRL)-based offloading decision-making schemes are inadequate for managing scenarios involving multiple workloads and edge servers, particularly when faced with time-varying workload arrivals and fluctuating channel states. To this end, we propose a flexible module weighted fusion DRL framework (DRL-MWF) for scalable and robust multi-workload offloading in edge environments. Unlike traditional monolithic networks, DRL-MWF employs a weighted fusion modular architecture that adapts flexibly to diverse workload distributions. Specifically, DRL-MWF introduces a state representation and normalization strategy to model state and workload characteristics, enabling precise and adaptive decision-making. Furthermore, we design two key mechanisms: a weighted policy correction method to stabilize learning and a prioritized experience replay with weighted importance sampling to accelerate convergence by emphasizing critical transitions. Extensive evaluations on real-world datasets demonstrate that DRL-MWF consistently outperforms state-of-the-art baselines. These results reveal DRL-MWF's potential to transform workload offloading in next-generation edge computing systems, ensuring high performance in dynamic scenarios.

The cost control challenge in construction and installation projects has always been a critical concern for construction entities. The complexity of task collaboration among various equipment and nodes during the installation process leads to extended construction duration, resulting in increased construction costs. To address this issue, this paper proposes a heuristic two-stage optimal deployment approach called MERD. The MERD approach incorporates intelligent computing principles from computer science into the resource scheduling of the construction process, modelling the installation scheduling problem into a combinatorial optimisation problem. Designing the probability method based on Bayes theorem, the MERD approach carries out an installation provisioning mechanism to optimise personnel and device allocation in the selected area. As a result, the MERD approach minimises construction hours and reduces labour costs in the construction process. Experimental results demonstrate the effectiveness and efficiency of the MERD approach in reducing work time and cost in engineering projects.

Predicting impending failure of hard disk drives (HDDs) is crucial to avoid losing essential data and service downtime. However, most HDD failure prediction is being challenged by using labelled data itself to evaluate failure rate, while the fact that HDDs deteriorate gradually cannot be described and exploited suitably. Most works on the Self-Monitoring and Reporting Technology (SMART) system attributes utilize simple and traditional methods from machine learning and statistics to achieve HDD failure prediction. So, we propose a novel two-level prediction model Dab, hard Drive failure prediction based on deep Auto-coder and Big data learning, to exploit SMART data for better online HDD failure prediction, constructing detection sub-models of anomaly and health degree. With better accuracy, better performance, better prediction earnings, and proactive fault tolerance, Dab has reduced false alarm rate (FAR) and maintenance cost, and improved failure detection rate (FDR), reliability and robustness of large-scale storage systems.

A mobile community can be composed of multiple mobile devices through D2D (Device-to-Device) network. In many cases, these mobile devices cannot conveniently connect to the Internet, for various reasons. To overcome this obstacle, one solution is to let the mobile devices cooperate with each other through a D2D-enabled network, forming a mobile community that, as a whole, may be able to autonomously execute the tasks requested by its members. To maximize the overall benefits of mobile communities, this paper proposes a novel task allocation approach, EDTG (Energy-aware and Deadline-constrained Task allocation using Game theory). In mobile communities, energy consumption is responsible for the largest part of the cost. Energy management can lead to performance degradation and even be perceived as a bottleneck, while load balancing between devices can improve service performance and resource utilization to the largest extent. EDTG has considered both the inevitable performance constraints at each device and a method based on the connectivity of graph theory, in order to narrow down the search scope of optimal target mobile devices where requested tasks can be executed. The “Bargaining Game” method is designed and exploited to obtain the final task allocation solution. Final experimental results demonstrate that compared to existing approaches, EDTG ensures high-performance task execution and reaches the goal of maximizing the overall benefits to some extent, by achieving better energy savings and exploiting load balancing between devices.

Large-scale tasks processing based on cloud computing has become crucial to big data analysis and disposal in recent years. Most previous work, generally, utilize the conventional methods and architectures for general scale tasks to achieve tons of tasks disposing, which is limited by the issues of computing capability, data transmission, etc. Based on this argument, a fat-tree structure-based approach called LTDR (Large-scale Tasks processing using Deep network model and Reinforcement learning) has been proposed in this work. Aiming at exploring the optimal task allocation scheme, a virtual network mapping algorithm based on deep convolutional neural network and [Formula: see text]-learning is presented herein. After feature extraction, we design and implement a policy network to make node mapping decisions. The link mapping scheme can be attained by the designed distributed value-function based reinforcement learning model. Eventually, tasks are allocated onto proper physical nodes and processed efficiently. Experimental results show that LTDR can significantly improve the utilization of physical resources and long-term revenue while satisfying task requirements in big data.

The data are rapidly expanding nowadays, which makes it very difficult to analyze valuable information from big data. Most of the existing data mining algorithms deal with big data problems at large time and space costs. This paper focuses on the sampling problem of big data and puts forward an efficient heuristic Cluster Sampling Arithmetic, called CSA. Many of the former researchers adopted random method to extract early sample set from the original data and then made a variety of different processing of the sample in order to obtain the corresponding minimum sample set, which is regarded as a representation of the original big data set. However, the final processing results of big data will be severely affected by the random sampling process at the beginning, resulting in lower comprehensiveness and quality of the final data results and longer processing time. Based on this view, CSA introduces the idea of clustering to obtain minimum sample set of big data, which is in contrast to the random sampling method in the current literature. CSA makes cluster analysis of the original data set and selects the center of each class as centralized members of the minimum sample set. It aims at ensuring that the sample distribution accords with the characteristics of the original data, guarantees the data integrity and reduces the processing time. The max–min distance means that the pattern recognition has been integrated into the clustering process in order to get the clustering center and prevent algorithm from local optimum. The final experimental results show that, compared with the existing work, CSA algorithm can efficiently reflect the characteristics of the original data and reduce the time of data processing. The obtained minimum sample set has also achieved good effects in the classification algorithm.

Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation.

Aiming at the current problems that most physical hosts in the cloud data center are so overloaded that it makes the whole cloud data center'load imbalanced and that existing load balancing approaches have relatively high complexity, this paper has focused on the selection problem of physical hosts for deploying requested tasks and proposed a novel heuristic approach called Load Balancing based on Bayes and Clustering (LB-BC). Most previous works, generally, utilize a series of algorithms through optimizing the candidate target hosts within an algorithm cycle and then picking out the optimal target hosts to achieve the immediate load balancing effect. However, the immediate effect doesn't guarantee high execution efficiency for the next task although it has abilities in achieving high resource utilization. Based on this argument, LB-BC introduces the concept of achieving the overall load balancing in a long-term process in contrast to the immediate load balancing approaches in the current literature. LB-BC makes a limited constraint about all physical hosts aiming to achieve a task deployment approach with global search capability in terms of the performance function of computing resource. The Bayes theorem is combined with the clustering process to obtain the optimal clustering set of physical hosts finally. Simulation results show that compared with the existing works, the proposed approach has reduced the failure number of task deployment events obviously, improved the throughput, and optimized the external services performance of cloud data centers.