Traditional Mobile Edge Computing (MEC) is over whelmed by time-sensitive applications in the Internet of Vehicles (IoV), leading to significant task completion delays because existing methods fail to leverage vehicle-to-infrastructure collaboration in dynamic network topologies. This paper proposes a vehicle-assisted adaptive task offloading strategy to minimize completion time through a dual-mode framework that adapts based on a vehicle's position relative to an edge server. When a vehicle is outside a server's range, the Best Service Vehicle Selection Algorithm (BSVSA) offloads tasks to the most suitable nearby vehicle while ensuring communication stability. When within server coverage, our novel Hybrid Differential Teaching Optimization Algorithm (HDTOA) determines the optimal offloading ratio and schedules tasks across edge servers to balance the computational load. Simulation results validate that our integrated approach (HDTOA+BSVSA) outperforms benchmarks like Differential Evolution (DE) and Particle Swarm Optimization (PSO), demonstrating faster convergence and lower average task execution times under heavy load. Under scenarios with a large task data size, the HDTOA reduces the average task execution time by 69.99% compared to the PSO algorithm. The strategy also provides a more balanced workload across servers, thus enhancing overall system efficiency

In the framework of unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC), the UAV’s trajectory design is crucial for the effectiveness of task offloading. However, existing studies suffer from some limitations such as reliance on static decision metrics, prediction-based strategies with poor generalization, or high computational complexity, making them unsuitable for large-scale emergency scenarios with surging task demands. To address this issue, this study introduces the Location Priority Level (LPL) and proposes LPL-based Bi-objective Task Offloading Strategy (LBTOS), which tries to simultaneously minimize task completion time (makespan) and total energy consumption. By jointly considering task load, regional deadlines, and UAV energy constraints, LBTOS introduces the LPL to dynamically map task urgency to UAV trajectories. Leveraging LPL and an adaptive triggering mechanism, the proposed UAV Trajectory Design algorithm (UAVTD) achieves high responsiveness under computationally constrained emergency scenarios. Additionally, a task offloading algorithm named HWPSO-SA is designed, which integrates adaptive and time-varying inertia weights, and combines the Particle Swarm Optimization with Simulated Annealing, thereby enhancing the algorithm’s capability to obtain the global optimal solution. Lastly, simulation experiments are conducted to validate the significant effectiveness of LBTOS in the dual optimization objectives of minimizing task completion time and the total computing and transmission energy consumption of the system.

In the edge computing environment, the uneven geographical distribution of tasks may lead to unbalanced load on the edge server. In addition, some larger tasks are difficult to completely offload to edge servers, which cannot fully utilize edge server resources. To solve the above problems, we propose a task scheduling method to minimize the completion time by combining the horizontal edge collaboration and fine-grained task partial offloading technology. First, combining horizontal edge collaboration and fine-grained task partial offloading technology, considering the location relationship between users and edge servers in multiuser multiedge server scenario, a task partial offloading optimization problem is established to minimize task completion time. Second, due to the nonconvex and variables coupling, we decompose the original problem into resource allocation, user-server association, and offloading strategy subproblems. A task scheduling algorithm based on improved teaching-learning-based optimization (ITLBO) is proposed to obtain the best task scheduling decision which includes task offloading location and offloading ratio. Simulation results show that the proposed method can effectively reduce the task completion time in edge collaboration environment.

Mobile CrowdSensing (MCS), through which a requester can coordinate a crowd of workers to accomplish some data collection tasks, has been recognized as a promising paradigm for large-scale data acquisition in recent years. Many researches focus on the worker recruitment problem in MCS, but most of them either have the assumption that workers’ qualities are known ahead of time or cannot ensure that workers report costs honestly. In this paper, we propose an incentive mechanism based on Combinatorial Multi-Armed Bandit and reverse Auction, called CMABA, to solve the multiple unknown workers recruitment problem in MCS. Our objective is to determine a recruiting strategy to maximize the total sensing quality under a limited budget, while ensuring truthfulness and individual rationality of sensing workers. We theoretically prove that our CMABA mechanism achieves truthfulness and individual rationality, and then analyze the regret of the mechanism. Based on CMABA, we ulteriorly propose an adaptive incentive mechanism, called ACMABA, to recruit workers via the alternative worker recruitment and quality update, which can achieve a higher total sensing quality and lower regret. Additionally, we also demonstrate significant performances of the CMABA and ACMABA mechanisms through extensive simulations on real-world data traces.

High energy consumption has become a major problem of cloud platform. Most of the current task scheduling methods neglect the heterogeneity of cloud platform, which may consume more power consumption of heterogeneous cloud platform. In this paper, we propose a power-aware scheduling strategy (PASS) for multiple DAGs workflow in the heterogeneous cloud with the goal of minimizing the energy consumption. First, we predict the PM energy consumption considering VM status after scheduling tasks, and then we formulate the power-aware DAGs task scheduling as a NP-hard problem, which tries to minimize the energy consumption of heterogeneous cloud platform. Second, we propose a multiple DAGs workflow scheduling algorithm to solve the formulated NP-hard problem. We consider the combination of the coarse-grained sorting for DAGs and the fine-grained sorting for sub-tasks to obtain the optimal sorting of DAG workflows. We then assign tasks to the appropriate computing nodes considering the heterogeneity of the cloud platform to minimize its energy consumption. Third, experiments are conducted to evaluate PASS, and the experimental results verify its efficiency.

Channel selection is a key topic in brain-computer interface (BCI). Task-irrelevant and redundant channels used in BCI may lead to low classification accuracy, high computational complexity, and inconvenience for application. By selecting optimal channels, the performance of BCI could enhance significantly. In this paper, a new binary harmony search (BHS) is proposed to select the optimal channel sets and optimize the system accuracy. The BHS is implemented on the training data sets to select the optimal channels and the test data sets are used to evaluate the classification performance on the selected channels. The sparse representation-based classification, linear discriminant analysis, and support vector machine are performed on the common spatial pattern (CSP) features for motor imagery (MI) classification. Two public EEG datasets are employed to validate the proposed BHS method. The paired t-test is conducted on the test classification performance between the BHS and traditional CSP with all channels. The results reveal that the proposed BHS method significantly improved classification accuracy as compared to the conventional CSP method (p < 0.05). This study proposed the BHS method to select the optimal channels in MI -based BCI. On the one hand, the results confirm the validity of the BHS algorithm as a channel selection method for motor imagery data. On the other hand, the BHS method with costing shorter computation time relatively yields a better average test accuracy than the steady-state genetic algorithms. The proposed method could significantly improve the practicability and convenience of the BCI system.

Recently, many video-on-demand (VoD) providers have begun storing multiple versions of the same video to offer multiple-quality video services with different bitrates to users, called multi-version VoD. To improve users' quality of experience (QoE), it is a good idea to cache videos at edge servers in multi-version VoD systems. However, determining which versions of which videos should be cached or replaced in an edge server is still a major challenge for a multi-version VoD system because of its limited cache storage. In this paper, we propose a popularity-based and version-aware caching scheme (PVCS) at edge servers for multi-version VoD systems. First, based on video popularity, we formulate cache placement as a knapsack problem under constraints such as the cache storage and transcoding computation of the edge server, which aims to maximize the cache hit ratio. Second, we use the transcoding relations among versions to calculate a version-aware caching profit when caching a certain version or multiple versions of a video. The version-aware caching profit is the basis for the subsequent cache replacement algorithm. Third, we propose two algorithms, the video cache placement (VCP) algorithm and the video cache replacement (VCrP) algorithm, to solve the cache placement and replacement problems respectively. VCP utilizes the Lagrangian relaxation algorithm to decide which video files should be cached initially, and VCrP decides which video files cached at the edge server will be replaced dynamically based on the version-aware profit. In this way, the PVCS can improve the cache hit ratio and decrease the average start-up delay. Our simulation results have shown that the PVCS outperforms the other schemes in terms of the cache hit ratio and the average start-up delay.

Virtual machine placement (VMP) technology is widely used in cloud computing systems. The existing VMP methods mainly aimed at improving the cloud resource utilization, such as load balancing among physical machines (PMs), but they may result in virtual machine (VM) performance degradation because of the great resource contention among VMs running on top of the same PM. In contract to existing VMP algorithms, this paper proposes a virtual machine (VM) Performance maximization and physical machine (PM) Load balancing Virtual Machine Placement method (PLVMP) in cloud, which tries to maximize VM performance and balance PM workload from both users' and cloud providers' perspectives. First, we study the relationship between PM workload and VM performance to train a new and improved VM performance model, which can predict VM performance more accurately and offer help to the following VMP. Second, we take VM performance maximization and PM workload balancing into account to formulate the VMP as an optimization problem, which tries to maximize VM performance for users and make load balancing among PMs for cloud providers. Third, we propose a greedy-based algorithm to solve the VMP problem efficiently. We then evaluate PLVMP with other VMP methods on CloudSim platform and a real OpenStack platform. The results show PLVMP can maximize the VM performance significantly and make a good load balancing among PMs.

Mobile CrowdSensing (MCS), through which a requester can coordinate a crowd of workers to accomplish some data collection tasks, has been recognized as a promising paradigm for large-scale data acquisition in recent years. Although many MCS systems have been built for various applications, most of them either assume that workers’ qualities are known in advance or cannot ensure workers to report costs honestly. In this paper, we propose an incentive mechanism based on Combinatorial Multi-Armed Bandit and reverse Auction, called CMABA, to solve the multiple unknown workers recruitment problem in MCS. Our objective is to determine a recruiting strategy to maximize the total sensing quality under a limited budget, while ensuring truthfulness and individual rationality of sensing workers. We theoretically prove that our CMABA mechanism achieves truthfulness and individual rationality, and then analyze the regret of the mechanism. Additionally, we also demonstrate its significant performances through extensive simulations on real-world data traces.

Recently, many video-on-demand (VoD) providers store multiple versions of the same videos to offer multiple-quality video services with different bitrates to users, called as multi-version VoD. To decrease the start-up delay for users, it is a good idea to cache videos at caching server that is in close proximity. However, how to decide which versions of which videos should be cached and replaced in caching server is still one major challenge for multi-version VoD systems because of limited caching storage. In this paper, we propose a version-aware video caching strategy for multi-version VoD systems, which aims to reduce start-up delay and improve cache hit ratio. First, we take into account the transcoding delay among versions and transmit delay from content server to caching server to calculate version-aware caching profit when caching a certain version or multiple versions of a video. It is the basis for the following caching replacement algorithm. Second, we propose version-aware video caching (VaVC) algorithm to decide which versions of which videos will be replaced based on the version-aware caching profit dynamically. In this way, VaVC can reduce start-up delay and improve the cache hit ratio. Our simulation results have shown that VaVC outperforms the others in both the start-up delay and the cache hit ratio.

With the rapid development of mobile Internet and smart devices, VoD (video on demand) providers build media cloud to offer multi-bitrate video streaming services to users at a reduced cost, called as cloud-based multi-version VoD. In cloud-based multi-version VoD, we need to solve the problem of allocating appropriate resources for virtual streaming media server cluster with the aim of optimizing the user experience and reducing the service cost. To address this problem, a resource allocation for virtual streaming media server cluster in cloud-based multi-version VoD is proposed in this paper. We firstly analyze the user historical learning logs to mine the user behavior characteristics, including the average user request arrival rate, the video playing time distribution, and the video popularity distribution, etc. Then, based on the user behavior characteristics and the queueing theory, a resource allocation model for the virtual streaming media server cluster is introduced. It predicts the user arrival rate at first and then allocates appropriate resources dynamically to solve the resources allocation irrationality problem. Simulation results have proved the proposed method can allocate appropriate resources for virtual streaming media server cluster, which can ensure the user experience satisfaction and improve the resources utilization.

MapReduce is a popular programming model in cloud computing to deal with the high computational task, such as video transcoding. It splits the video (task) into multiple segments (subtasks) and transcodes them in parallel in cluster. Due to the complexity of video transcoding and the poor performance of heterogeneous MapReduce cluster, scheduling these subtasks to minimize the total transcoding time is still a challenge. In this paper, we propose a prediction-based and locality-aware task scheduling (PLTS) method for parallelizing video transcoding over heterogeneous MapReduce cluster. First, we analyze video decoding and encoding technologies and predict the segment transcoding complexity, which can provide a foundational base for the following scheduling. Second, we attempt to schedule subtasks on machines that contain the related input data, which are referred to as data locality, so as to reduce large-scale data movement and data transfer during the mapping phase. Third, we formulate the scheduling as a job shop scheduling problem and propose a heuristic PLTS algorithm. It combines the benefits of two traditional heuristic scheduling algorithms, Max-Min and Min-Min, to make load balancing in cluster and short the total transcoding time. The experimental results also show the efficiency of our algorithm.

Cloud service providers offer virtual machines (VMs) as services to users over Internet. As VMs are running on physical machines (PMs), PM power consumption needs to be considered. Meanwhile, VMs running on the same PM share physical resources, and there exists great resource contention, which results in VM performance degradation. Therefore, how to place VMs to reduce PM power consumption and guarantee VM performance is still one major challenge. However, existing VMPs did not study VM performance degradation, so they could not guarantee VM performance. To solve the high power consumption and VMs performance degradation problems, this paper explores the balance between saving PM power and guaranteeing VM performance, and proposes a power-aware and performance-guaranteed VMP (PPVMP). First, we investigate the relationship between power consumption and CPU utilization to build a non-linear power model, which is helpful for the following VMP. Second, we construct VM performance models to present the VM performance degradation trend. Third, based on these models, we formulate VMP as a bi-objective optimization problem, which tries to minimize PM power consumption and guarantee VM performance. We then propose an algorithm based on ant colony optimization to solve it. Finally, the results show the efficiency of our algorithm.