ABSTRACT In the Spark distributed framework, data communication problems (network transfer overhead, network IO bottlenecks) caused by data transfer across nodes/racks are a common cause of performance degradation due to the inconsistency between the task execution location and the data location. Additionally, in heterogeneous environments, Spark's task scheduling strategy cannot fully utilize the advantages of high‐performance nodes. To address the above issues, firstly, this paper proposes a cost‐aware task selection strategy. The strategy models the cost of tasks by considering the impact of data locality and heterogeneous factors on the efficiency of job execution. For scenarios where data locality needs to be reduced for scheduling tasks, the task scheduling problem is transformed into a minimum weighted bipartite graph matching problem, and a greedy matching algorithm is used to solve for the minimum processing cost option. For scenarios that maintain the current data localization level for scheduling tasks, select the task execution with the largest change in task processing cost due to data localization changes. Secondly, the problem is that Spark's delay scheduling algorithm causes resources in the cluster to be in an unnecessary waiting state and reduces cluster resource utilization. In this paper, we propose an adaptive adjustment strategy for delay waiting time based on benefit assessment. This policy improves the resource utilization of the cluster by evaluating the benefit of delay waiting of the scheduler and dynamically adjusting the delay time based on the result of the evaluation. Finally, we implement the proposed strategy in Spark 3.0.0 and evaluate its performance using some representative benchmarks. The experimental results show that, compared with other task scheduling algorithms, the strategy proposed in this paper can effectively improve the execution efficiency of jobs, reduce the execution time of jobs by 15.8%–31.9%, and at the same time reduce the network traffic and improve the CPU utilization.

The rapid development of social networks, coupled with the proliferation of Internet of Things image-capture devices, has made sharing images easier but also raised concerns about privacy leakage. For this reason, image content privacy protection has attracted great attention and focused research. However, existing schemes suffer from participants inability to self-manage privacy, poor visual usability of protected images, and risks of key theft during transmission. To address these challenges, this article proposes a self-managed privacy control and sharing scheme (SMPCS) based on thumbnail-preserving and elliptic curve Diffie-Hellman (ECDH) for social networks. In SMPCS, we first design a key generation and sharing scheme supporting multikey distribution based on ECDH and public key authentication. This scheme generates a secure data block for each participant, containing their facial codes and the owner’s public key, to ensure the security of key transmission. Next, the sensitive areas in social image are encrypted using the designed adaptive thumbnail-preserving encryption based on sensitive area (ATPE-SA), ensuring that the encrypted social image maintains visual usability. Moreover, utilizing the designed adaptive blocking module, participants can balance the privacy and usability of encrypted images by adjusting privacy parameters to meet diverse wishes. Finally, participants can choose whether to reconstruct social images according to their privacy wishes, achieving autonomous privacy control. Extensive experimental analyses and comparisons with state-of-the-art schemes demonstrate the superiority of the proposed SMPCS in terms of autonomous privacy control, usability and security.

Abstract The Siamese network-based tracker calculates object templates and search images independently, and the template features are not updated online when performing object tracking. Adapting to interference scenarios with performance-guaranteed tracking accuracy when background clutter, illumination variation or partial occlusion occurs in the search area is a challenging task. To effectively address the issue with the abovementioned interference and to improve location accuracy, this paper devises a Siamese residual attentional aggregation network framework for self-adaptive feature implicit updating. First, SiamRAAN introduces Self-RAAN into the backbone network by applying residual self-attention to extract effective objective features. Then, we introduce Cross-RAAN to update the template features online by focusing on the high-relevance parts in the feature extraction process of both the object template and search image. Finally, a multilevel feature fusion module is introduced to fuse the RAAN-enhanced feature information and improve the network’s ability to perceive key features. Extensive experiments conducted on benchmark datasets (GOT-10K, LaSOT, OTB-50, OTB-100 and UAV123) demonstrated that our SiamRAAN delivers excellent performance and runs at 51 FPS in various challenging object tracking tasks. Code is available at https://github.com/MallowYi/SiamRAAN .

Abstract The ability of modern detectors to detect small targets is still an unresolved topic compared to their capability of detecting medium and large targets in the field of object detection. Accurately detecting and identifying small objects in the real‐world scenario suffer from sub‐optimal performance due to various factors such as small target size, complex background, variability in illumination, occlusions, and target distortion. Here, a small object detection method for complex traffic scenarios named deformable local and global attention (DLGADet) is proposed, which seamlessly merges the ability of hierarchical attention mechanisms (HAMs) with the versatility of deformable multi‐scale feature fusion, effectively improving recognition and detection performance. First, DLGADet introduces the combination of multi‐scale separable detection and multi‐scale feature fusion mechanism to obtain richer contextual information for feature fusion while solving the misalignment problem of classification and localisation tasks. Second, a deformation feature extraction module (DFEM) is designed to address the deformation of objects. Finally, a HAM combining global and local attention mechanisms is designed to obtain discriminative features from complex backgrounds. Extensive experiments on three datasets demonstrate the effectiveness of the proposed methods. Code is available at https://github.com/ACAMPUS/DLGADet

Smart device users spend most of the fragmentation time in the entertainment applications such as videos and films. The migration and reconstruction of video copies can improve the storage efficiency in distributed mobile edge computing, and the prediction of video hits is the premise for migrating video copies. This paper proposes a new prediction approach for video hits based on the combination of correlation analysis and wavelet neural network (WNN). This is achieved by establishing a video index quantification system and analyzing the correlation between the video to be predicted and already online videos. Then, the similar videos are selected as the influencing factors of video hits. Compared with the autoregressive integrated moving average (ARIMA) and gray prediction, the proposed approach has a higher prediction accuracy and a broader application scope.

Summary With the increasing scale of tasks in cloud computing, the problem of high energy consumption becomes increasingly serious. To deal with the problem, we propose a cloud computing energy consumption model, which takes into account the execution and transmission cost of the processor. Then, based on this model, we put forward a task scheduling optimization algorithm named modified particle swarm optimization (M‐PSO) to handle the local optimum and slow convergence problem. Different from the PSO, M‐PSO can dynamically adjust the inertia weight coefficient to improve the speed of convergence according to the number of iterations. Finally, the performance of the proposed algorithm is evaluated through the CloudSim toolkit, and the experimental results show that the M‐PSO can efficiently reduce total cost compared with other algorithms.

Summary Cloud computing has gained more and more attention from industrial and academic circle since it offers pay‐as‐you‐go model, and business applications based on the cloud are also increasing. These applications meet the requirement of users while at the same time triggering the problem of high energy consumption in data centers. To deal with the problem, we propose a new algorithm named EEOM (Energy Efficiency Optimization of VM Migrations). Under considering CPU and memory factors, the key three steps for EEOM algorithm, including trigger time, VM selection, and host location, are optimized. EEOM algorithm takes use of the virtualization technology and migrates some VMs on the lightly loaded host and heavily loaded host to other hosts. The idle hosts are switched to low‐power mode or shut down so as to save energy consumption. The experimental results show that, as compared with Double Threshold (DT) algorithm, the EEOM algorithm saves 7% energy consumption and reduces 13% SLA violations.