Concurrency and Computation-Practice & Experience最新文献

A covert network channel is a communication channel in which the message is secretly transmitted to the recipient. Sometimes, covert network channels are vulnerable to multiple attacks. Therefore, the message must be properly secure. In most cases, the covert channel is used to ensure data protection and allow users to freely access the Internet. In this paper, several recent studies are reviewed on covert network channels and examine the existing works from 2015 to 2024. This review article also discusses the undetectability and reliability of different types of covert network channels. Furthermore, a detailed description of the covert network channel's ability to hide in containers is provided. Existing research on covert network channels explains a few techniques for detecting attacks in secret data communication. However, several machine learning and deep learning techniques have been discussed in this article. Additionally, this article describes the accuracy of detection through an overview of current technologies. In addition, various countermeasures to prevent attacks in covert channels are also discussed in detail. However, in this case, the bandwidth limitations, data set limitations, and covert channel capacity are clearly defined, which will help future researchers build covert network channels and detect attacks. Finally, this work considers the challenges faced by covert network channels and the future scope of application.

Urban social media mining is the process of discovering urban patterns from spatio-temporal social media datasets. Urban social clusters are the clusters formed by the social media posts of users living in cities at a certain time and place. Discovering and identifying urban social clusters is of great importance for urban and regional planning, target audience identification, a better understanding of city dynamics and so forth. Discovering and ranking urban social clusters out of streaming social media datasets require efficient filtering approaches and mining algorithms. In the literature, there are several studies performed that address the discovery of the importance of urban clusters. Most of these studies take into account the spatial expansions over time and the changes in the numbers of elements within clusters when identifying the significance of urban clusters. However, in contrast to these studies, we have also considered cluster temporal formation stability, spatial density variation, and the impact of meta-information on urban social clusters. In this study, Temporal, Spatial, and Meta Important Urban Social Clusters Miner (TSMIUSC-Miner) algorithm is proposed. In the proposed algorithm, urban social clusters are discovered, and their importance relative to each other are compared and ranked. The temporal, spatial and meta importance scores of the clusters are calculated and then, the clusters that satisfy predefined score thresholds are discovered. The performance of the proposed TSMIUSC-Miner algorithm compared with that of a naive approach using real-life streaming Twitter/X dataset. The results showed that the proposed TSMIUSC-Miner algorithm outperforms the naive approach in terms of execution time.

Edge computing extends computing resources from the data center to the edge of the network to better handle latency-sensitive tasks. However, with the rise of the Internet of Things, edge devices with limited processing capabilities face difficulties in executing requests with fluctuating request peaks. In order to meet the deadline constraints of latency-sensitive tasks, a feasible solution is to offload some latency-sensitive tasks to other nearby edge devices. This article studies the problem of request migration in edge computing systems and minimizes the request deadline violation rate based on actual online arrival patterns, performance interference phenomena, and deadline constraints. Since a request contains multiple services and request migration will lead to changes in server resource competition pressure, we split the problem into three sub-problems, dividing the request deadline to determine the maximum response time of the service, determining the performance of the service under different resource pressures and the request migration strategies. To this end, we propose two deadline splitting methods, a performance interference model under multi-resource pressure, and two heuristic request migration strategies. Since this article considers online edge scenarios, the number and type of requests are black boxes. We conduct simulation experiments and find that our method has only one-third the number of request violations of other methods.

Complex websites comprise a variety of diverse web entities, which require constant restructuring resonating with the latest trends, shifting consumer expectations and market driven changes. Therefore, designing suitable models to optimally restructure such websites is of paramount importance and must take into consideration several factors about the web entities such as display size, download time, type, location in the page, sales likelihood, discounts, and the ongoing trend. A recent study has taken all these attributes into consideration and designed a model based on the Access Score, Interface Score, and Purchase Score. However, this model suffers from certain drawbacks such as it did not address the underlying cohesiveness between these attributes. Further, it provided a single optimal solution to the adaptive website structure optimization (AWSO) problem and relied on the a priori knowledge of weights. The basis of the new proposed model is that there can be more than one optimal solution to the AWSO problem in the real world. The novel tri-objective optimization model uses NSGA-II algorithm to simultaneously optimize the attributes and finds advantageous trade-off solutions without requiring a priori knowledge of weights. The proposed MO-AWSO_NSGA-II model is shown to outperform the existing model proving it better suited for the AWSO problem.

The Internet of Things (IoT) facilitates the connectivity of billions of physical devices for exchanging information and enabling a wide range of applications. These applications can be presented in the form of dependent tasks, as outlined in a workflow. These workflows face limitations due to constraints in IoT sensors. To address these limitations, cloud computing has emerged to offer a large capacity of computing and storing with a great capability to adjust resources according to the need. However, cloud computing might not adequately meet the low-latency of IoT workflow requirements when scheduling a workflow composed of IoT tasks due to its centralized nature. Moreover, cloud computing is not ideal for delay-sensitive workflows and may increase communication costs. In response to these challenges, the use of fog computing as an extension to cloud computing scheme is recommended. Fog computing aims to process workflow tasks close to IoT devices. While fog computing offers various advantages, integrating these systems into workflow scheduling remains one of the most formidable challenges in distributed environments. Indeed, significant issues arise due to the timely execution and the resource limitations. In this survey paper, we present a Systematic Literature Review (SLR) on the current state of the art in this domain. We propose a taxonomy to compare and evaluate the existing studies on workflow scheduling approaches in cloud–fog computing environments. This taxonomy encompasses various criteria, including scheduling techniques, performance metrics, workflow dependencies, scheduling policies, and evaluation tools. We highlight certain recommendations for open issues which require more investigations. Our aim is to provide valuable insights for researchers and developers interested in understanding the contributions and challenges of current workflow scheduling approaches in cloud–fog computing environments.

The crowdsourcing platform serves as an intermediary managing the interaction between a requester who posts a decomposable task and a pool of workers who bid to solve it. Each worker intending to take up the task (partially or fully) decomposes it into multiple independent subtasks and submits it to the platform. Selection of a diverse set of workers (based on the bids received) to solve the decomposable task is challenging as it requires balancing factors like cost and quality while encouraging collaboration. We propose a Worker Set Computation (WSC) methodology to address these challenges by selecting a custom set of potential workers who can collaboratively complete the task with the optimal cost, in an efficient way. The aging technique is employed to dynamically update the weight of each worker, giving more weightage to the feedback received in the recent past. This, in turn, not only favors those workers who were rated well in the immediate past but also ensures that one odd feedback does not influence the overall rating heavily. We compare the performance of the proposed method against the state-of-the-art methods, considering the computational (and budget) requirements, as well as the aging-based worker rating.

In recent years, with the significant increase in the volume of three-dimensional medical image data, three-dimensional medical models have emerged. However, existing methods often require a large number of model parameters to deal with complex medical datasets, leading to high model complexity and significant consumption of computational resources. In order to address these issues, this paper proposes a 3D Lightweight Volume Convolutional Neural Network (3D LVCN), aiming to achieve efficient and accurate volume segmentation. This network architecture combines the design principles of convolutional neural network modules and hierarchical transformers, using large convolutional kernels as the basic framework for feature extraction, while introducing 1 × 1 × 1 convolutional kernels for deep convolution. This improvement not only enhances the computational efficiency of the model but also improves its generalization ability. The pro-posed model is tested on three challenging public datasets, namely spleen, liver, and lung, from the medical segmentation decathlon. Experimental results show that the proposed model performance has in-creased from 0.8315 to 0.8673, with a reduction in parameters of approximately 5%. This indicates that compared to currently advanced model structures, our proposed model architecture exhibits significant advantages in segmentation performance.

In recent times, there has been a notable surge in the utilization of Internet of Things (IoT) network devices due to their vast applications. However, this rapid growth has undoubtedly led to raised energy consumption, which, in turn, has raised significant concerns about the environment. Consequently, there is a growing demand for green computing techniques that can mitigate IoT device's energy usage and carbon footprint. Clustering IoT networks is a useful strategy for extending their lifespan. However, clustering presents a complex optimization problem that falls under the category of NP-hard; hence making it a challenging issue. Nevertheless, using meta-heuristics algorithms has greatly improved our ability to tackle such challenges. Therefore, this study introduces a clustering scheme called EQ-AHA, which combines Equilibrium optimization and artificial hummingbird optimization techniques to enhance the efficiency of IoT-based Software-Defined Wireless Sensor Networks (IoT-SDWSN). The primary goal of EQ-AHA is to select the Cluster Heads (CHs) and determine the optimal path between CHs and the Base Station (BS). EQ-AHA employs a fitness function that considers three important factors: the distance between CHs, the distance between nodes and the CHs, and the energy levels of the nodes. Overall, this strategy improves the network's performance by 31.6% compared to other State-of-the-Art (SoA) algorithms.