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.

Integrating dynamic effects has shown its significance in enhancing the accuracy and robustness of Visual-Inertial Odometry (VIO) systems in dynamic scenarios. Existing methods either prune dynamic features or rely heavily on prior semantic knowledge or kinetic models, proved unfriendly to scenes with a multitude of dynamic elements. This work proposes a novel dynamic feature fusion method for monocular VIO, named DFF-VIO, which requires no prior models or scene preference. By combining IMU-predicted poses with visual clues, it initially identifies dynamic features during the tracking stage by constraints of consistency and degree of motion. Then, we innovatively design a Dynamic Transformation Operation (DTO) to separate the effect of dynamic features on multiple frames into pairwise effects and construct a Dynamic Feature Cell (DFC) to preserve the eligible information. Subsequently, we reformulate the VIO nonlinear optimization problem and construct dynamic feature residuals with the transformed DFC as a unit. Based on the proposed inter-frame model of moving features, a so-called motion compensation is developed to resolve the reprojection issue of dynamic features, allowing their effects to be incorporated into the VIO’s tight coupling optimization, thereby realizing robust positioning in dynamic scenarios. We conduct accuracy evaluations on ADVIO and VIODE, degradation tests on EuRoC dataset, as well as ablation studies to highlight the joint optimization of dynamic residuals. Results reveal that DFF-VIO outperforms state-of-the-art methods in pose accuracy and robustness across various dynamic environments.

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.

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.

Mobile crowdsensing is a new paradigm by which a platform can recruit mobile workers to perform some sensing tasks by using their smart mobile devices. In this paper, we focus on a privacy-preserving unknown worker recruitment issue. The platform needs to recruit some workers without knowing the qualities of them completing tasks. Meanwhile, these quality information also needs to be protected from disclosure. To tackle these challenges, we model the unknown worker recruitment as a Differentially Private Multi-Armed Bandit (DP-MAB) game by seeing each worker as an arm of DP-MAB and the task completion quality contributed by each worker as the reward of pulling arm. Then, recruiting workers is equivalent to designing a bandit policy of pulling DP-MAB arms. Under this model, we propose a Differentially Private ϵ-First-based arm-pulling (DPF) algorithm and a Differentially Private UCB-based arm-pulling (DPU) algorithm, which can achieve the nearly optimal expected accumulative rewards under a given budget. We also analyze the regrets of the DPF and DPU algorithms and prove that both of them are δ-differentially private on the task completion qualities (δ > 0δ). Finally, we conduct extensive simulations to verify the significant performances of DPF and DPU based on both the real-trace and synthetic datasets.

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.

Federated Learning (FL) is a newly-emerging distributed ML model, where a server can coordinate multiple workers to cooperatively train a learning model by using their private datasets, while ensuring these datasets not to be revealed to others. In this paper, we focus on the incentive mechanism design for FL systems. Taking the incentives into consideration, we first design two utility functions for the server and workers, respectively. Then, we model the corresponding utility optimization problem as a two-stage Stackelberg game by seeing the server as a leader and the workers as some followers. Next, we derive an optimal Equilibrium solution for the both stages of the whole game. Based on this solution, we design an incentive mechanism that can ensure the server to achieve the optimal utility, while stimulating workers to do their best to train the ML model. Finally, we conduct extensive simulations to demonstrate the significant performance of the proposed mechanism.

In this paper, we study a new type of spatial crowdsourcing, namely competitive detour tasking, where workers can make detours from their original travel paths to perform multiple tasks, and each worker is allowed to compete for preferred tasks by strategically claiming his/her detour costs. The objective is to make suitable task assignment by maximizing the social welfare of crowdsourcing systems and protecting workers' private sensitive information. We first model the task assignment problem as a reverse auction process. We formalize the winning bid selection of reverse auction as an n-to-one weighted bipartite graph matching problem with multiple 0-1 knapsack constraints. Since this problem is NP-hard, we design an approximation algorithm to select winning bids and determine corresponding payments. Based on this, a Secure Reverse Auction (SRA) protocol is proposed for this novel spatial crowdsourcing. We analyze the approximation performance of the proposed protocol and prove that it has some desired properties, including truthfulness, individual rationality, computational efficiency, and security. To the best of our knowledge, this is the first theoretically provable secure auction protocol for spatial crowdsourcing systems. In addition, we also conduct extensive simulations on a real trace to verify the performance of the proposed protocol.

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.

Multi-version video-on-demand (VoD) providers either store multiple versions of the same video or transcode video to multiple versions in real time to offer multiple-bitrate streaming services to heterogeneous clients. However, this could incur tremendous storage cost or transcoding computation cost. There have been some works regarding trading off between transcoding and storing whole videos, but they did not take into account video segmentation and internal popularity. As a result, they were not cost-efficient. This paper introduces video segmentation and proposes a segment-based storage and transcoding trade-off strategy for multi-version VoD systems in the cloud. First, we split each video into multiple segments depending on the video internal popularity. Second, we describe the transcoding relationships among versions using a transcoding weighted graph, which can be used to calculate the version-aware transcoding cost from one version to another. Third, we take the video segmentation, version-aware transcoding weighted graph, and video internal popularity into account to propose a storage and transcoding trade-off strategy, which stores multiple versions of popular segments and transcodes unpopular segments. We then formulate it as an optimization problem and present a heuristic divide-and-conquer algorithm to get an approximate optimal solution. Finally, we conduct extensive simulations to evaluate the solution; the results show that it can significantly lower the storage and transcoding cost of multi-version VoD systems.

Cloud service providers can offer users virtual machines (VMs) on-demand as a service over the Internet. VMs running on top of a physical machine (PM) share physical resources (CPU, memory, and/or bandwidth), and there may be a great resource contention among them, which results in VMs performance degradation. To prevent this, cloud providers need to study how to place VMs on PMs efficiently. However, the existing virtual machine placement (VMP) methods mainly tried to optimize the cloud resources instead of the VM performance. In this paper, we propose a VMP method based on the VM performance models in cloud. Firstly, with a real OpenStack cloud platform, we study the virtualization resource scheduling principle, analyze the interaction among VMs with shared hardware, consider the relationship between VMs and the host PM, and then we introduce the VM performance models to present the VM performance degradation problem. Secondly, to choose an appropriate PM for placing VM, we take into consideration the application-aware resource consumption characteristic, the VM resource requirement and the VM performance models, so as to minimize the PM performance degradation and guarantee the VM performance. Finally, we take the streaming media services for examples, and conduct some experiments to evaluate our method. The results show it works better than others and guarantees the VM performance significantly.

Nowdays, many Video-on-Demand (VoD) providers offer multiple-quality video streaming services to heterogeneous clients, called as multi-version VoD. Some researches focus on video transcoding in real-time or video layered encoding/decoding, but they are not widely used in VoD industry. Storing multiple versions of the same video is an easy solution, but it consumes lots of storage space. Although there are also a few works about trading-off between transcoding and storage, they did not utilize the transcoding relationships among different versions and took the video popularity into account, which bring that they may have little cost-efficiency for multi-version VoD systems. To minimize the cost, in this paper, we propose a version-aware transcoding computation and storage trade-off strategy for multi-version VoD systems in the cloud. Firstly, it utilizes the transcoding weight graph to describe the transcoding relationships among different versions of a video. According to the graph, the transcoding computation cost from one version to another version can be calculated. Secondly, it takes the video popularity of different versions, the prices of storage and computation resources in the cloud into account to decide which versions of which videos should be stored or transcoded. We then formulate it as an optimization problem and present a heuristic approximate optimal solution. Finally, we conduct extensive simulations to evaluate our strategy and solution, and the results show that they can significantly lower the cost of multi-version VoD systems.