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Based on the project portfolio selection and scheduling problem (PPSS), the development of a systematic and scientific project scheduling plan necessitates comprehensive consideration of individual preferences and multiple realistic constraints, rendering it an NP-hard problem. Simultaneously, accurately and swiftly evaluating the value of projects as a complex entity poses a challenging issue that requires urgent attention. This paper introduces a novel qualitative evaluation-based project value assessment process that significantly reduces the cost and complexity of project value assessment, upon which a preference-based deep reinforcement learning method is presented for computing and solving project subsets and time scheduling plans. This paper first determines the key parameter values of the algorithm through specific examples. Then, using the method of controlling variables, it explores the sensitivity of the algorithm to changes in problem size and dimensionality. Finally, the proposed algorithm is compared with two classical algorithms and two heuristic algorithms across different instances. The experimental results demonstrate that the proposed algorithm exhibits higher effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

For permanent faults (PF) in the power communication network (PCN), such as link interruptions, the time-sensitive networking (TSN) relied on by PCN, typically employs spatial redundancy fault-tolerance methods to keep service stability and reliability, which often limits TSN scheduling performance in fault-free ideal states. So this paper proposes a graph attention residual network-based routing and fault-tolerant scheduling mechanism (GRFS) for data flow in PCN, which specifically includes a communication system architecture for integrated terminals based on a cyclic queuing and forwarding (CQF) model and fault recovery method, which reduces the impact of faults by simplified scheduling configurations of CQF and fault-tolerance of prioritizing the rerouting of faulty time-sensitive (TS) flows; considering that PF leading to changes in network topology is more appropriately solved by doing routing and time slot injection decisions hop-by-hop, and that reasonable network load can reduce the damage caused by PF and reserve resources for the rerouting of faulty TS flows, an optimization model for joint routing and scheduling is constructed with scheduling success rate as the objective, and with traffic latency and network load as constraints; to catch changes in TSN topology and traffic load, a D3QN algorithm based on a multi-head graph attention residual network (MGAR) is designed to solve the problem model, where the MGAR based encoder reconstructs the TSN status into feature embedding vectors, and a dueling network decoder performs decoding tasks on the reconstructed feature embedding vectors. Simulation results show that GRFS outperforms heuristic fault-tolerance algorithms and other benchmark schemes by approximately 10% in routing and scheduling success rate in ideal states and 5% in rerouting and rescheduling success rate in fault states. [ABSTRACT FROM AUTHOR]

To analyze performance in cloud computing, some unpredictable perturbations that may lead to performance degradation are essential factors that should not be neglected. To prevent performance degradation in cloud computing systems, it is reasonable to measure the impact of the perturbations and propose a robust configuration strategy to maintain the performance of the system at an acceptable level. In this article, unlike previous research focusing on profit maximization and waiting time minimization, our study starts with the bottom line of expected performance degradation due to perturbation. The bottom line is quantified as the minimum acceptable profit and the maximum acceptable waiting time, and then the corresponding feasible region is defined. By comparing between the system's actual working performance and the bottom line, the concept of robustness is invoked as a guiding basis for configuring server size and speed in feasible regions, so that the performance of the cloud computing system can be maintained at an acceptable level when perturbed. Subsequently, to improve the robustness of the system as much as possible, discuss the robustness measurement method. A heuristic optimization algorithm is proposed and compared with other heuristic optimization algorithms to verify the performance of the algorithm. Experimental results show that the magnitude error of the solution of our algorithm compared with the most advanced benchmark scheme is on the order of 10−6, indicating the accuracy of our solution. [ABSTRACT FROM AUTHOR]

Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users’ decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives. [ABSTRACT FROM AUTHOR]

Fog computing, evolves from the cloud and migrates the computing to the edge, is a promising solution to meet the increasing demand for ultra-low latency services in wireless networks. Via the forward-looking perspective, we advocate a ${Fog\, Radio\, Access}$FogRadioAccess ${Network\, (F\hbox{-}RAN)}$Network(F-RAN) model, which leverages the existing infrastructure such as small cells with limited computing power, to achieve the ultra-low latency by joint edge computing and near-range communications across multiple Fog groups. We formulate the low latency design as an $\mathcal {NP}$NP-hard optimization problem, which demonstrates the tradeoff between communication and computing in the time domain. Due to each F-RAN node's potential as each user's master F-RAN node with 1) different self computing power; and 2) different cooperative power of assisted F-RAN nodes, we first tackle globally optimized master F-RAN node selection for each user and propose a latency-driven cooperative Fog algorithm with dynamic programming solution for simultaneous selection of the F-RAN nodes to serve proper heterogeneous Fog resource allocation for multi-Fog groups. Considering the limited heterogeneous Fog resources shared among all users, we propose the one-for-all strategy for every user putting him/herself into others' shoes and reaching a "win-win" outcome. The numerical results show that the low latency services can be accomplished by F-RAN via latency-driven Fog cooperation approach. [ABSTRACT FROM AUTHOR]

The explosion of traffic demands in the edge of the Internet, mostly by mobile users, is putting under pressure current networking infrastructures. This is particularly acute when huge amounts of users and active wireless devices gather in reduced geographical spaces, increasing the risk of exceeding planned capacity of deployed infrastructure. This trend motivates research on edge computing, and in particular, on mechanisms to offload or address locally part of the user injected traffic at the access infrastructure, thus reducing the need of Internet requests and retrievals. This paper concentrates on the ability of mobile crowds –and corresponding access networks—to fulfill content requests originated within the mesh, with minimal intervention of the Internet infrastructure. Simple heuristics are revisited, proposed, discussed and evaluated to improve autonomous content discovery and dissemination within high-density, low-mobility crowds, by combining notions already explored for MANET routing: deliberate jittering and autonomous distance-based overlay pruning. Results over synthetic networks and real mobility traces indicate that these mechanisms improve efficiency and quality of content request discoveries, by reducing significantly collisions and increasing stability of discovered paths in dense pedestrian crowds. [ABSTRACT FROM AUTHOR]

As the key technology of edge computing, computing offloading has attracted the attention of many scholars in recent years. Many people use heuristic algorithm as the basic algorithm to study the algorithm of computing offloading, but a single heuristic algorithm has some defects, such as some will fall into local optimal, and some will converge prematurely. In order to make up for the defects of single heuristic algorithm applied to the calculation offloading and improve the efficiency of the algorithm, this paper combines genetic algorithm with ant colony algorithm, and designs the calculation offloading strategy of gene-ant colony fusion algorithm. Firstly, a group of solutions are obtained through the selection, crossover, mutation and other operations of genetic algorithm, and the solution is improved as the initial solution of ant colony algorithm. The fusion algorithm makes full use of the feedback value of genetic algorithm and the high efficiency of ant colony algorithm to overcome the shortcomings of the two algorithms. The feasibility of the algorithm is verified by several groups of experiments. The simulation results show that compared with GA, ACA and PSO, the number of iterations is reduced by 17.96%, 24.43% and 36.25% respectively. When the base station remains unchanged, the G-ACA has the lowest objective function value. Compared with GA, ACA and PSO algorithm, the objective function value is reduced by 36.68%, 16.15% and 11.35% respectively. That is, the fused algorithm is better than the non fused GA,ACA and PSO in time delay, energy consumption and objective function value. [ABSTRACT FROM AUTHOR]

As container technology and microservices mature, applications increasingly shift to microservices and cloud deployment. Growing microservices scale complicates resource scheduling. Traditional methods, based on fixed thresholds, are simple but lead to resource waste and poor adaptability to traffic spikes. To address this problem, we design a new resource scheduling strategy Saver based on the container cloud platform, which combines a microservice request prediction model with a microservice performance evaluation model that predicts SLO (Service Level Objective) violations and a heuristic algorithm to solve the optimal resource scheduling for the cluster. We deploy the microservices open-source project sock-shop in a Kubernetes cluster to evaluate Saver. Experimental results show that Saver saves 7.9% of CPU resources, 13% of the instances, and reduces the SLO violation rate by 31.2% compared to K8s autoscaler. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%. [ABSTRACT FROM AUTHOR]

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A 360° video offers a more immersive experience, gaining increasing popularity among users. However, enhancing the transmission efficiency of 360° videos under limited bandwidth conditions remains a significant challenge. This paper segments the video into three areas: the attention area, the edge area, and the viewpoint-switching transition area. Based on the segmentation of these three distinct video areas, a novel non-uniform coding transmission method for 360° videos is presented, along with mathematical modeling to define the optimization problem. A heuristic algorithm is subsequently introduced, which adaptively determines the optimal number of tiles and allocates the bitrate for each tile in real time to enhance the user's quality of experience (QoE). Finally, a simulation platform has been developed to validate the efficacy of the proposed algorithm by conducting comparative analyses with existing algorithms. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) is highly flexible and versatile, ranging from monitoring and surveying to rescue and military applications, but finding the best path requires a large amount of computing resources. Through intelligent path planning algorithms, UAV can find the best path according to task requirements and environmental conditions, avoid obstacles, and bypass dangerous areas, thereby effectively reducing the risks and errors of task execution. However, ordinary heuristic algorithms often do not achieve satisfactory results. To address this problem, A multi-mechanism balanced advanced sparrow search algorithm (BALSSA) is proposed. To achieve a balance between exploration and exploitation in the algorithm, we introduce two innovative techniques: an adaptive weight jumping mechanism and a suicide mutation perturbation. Then a balanced advanced learning is proposed. this approach enhances the evolutionary learning capabilities of SSA and aids the algorithm in timely escaping from local optima, and then propose a spiral factor improved progressive learning to improve the mutual learning performance between individuals, tested on 23 benchmark test functions, CEC2017 and CEC2022 The set is compared with algorithms in recent years and proposed algorithm variants, and the results show that BALSSA has better optimization and robustness. Finally, the proposed BALSSA is applied to UAV path planning. Compared with the variant SSA in recent years, BALSSA shows more stable and accurate optimization performance to verify the practicability of the improved algorithm. [ABSTRACT FROM AUTHOR]

The continual increase in the amount of generated data by social media, IoT devices, and monitoring systems have motivated the use of Distributed Data Stream Processing (DSP) systems to harness data in a real-time manner. The scheduling of processing tasks in DSP systems across the machines in a cluster or cloud environment is an NP-Hard problem. Different scheduling schemes have been proposed to address the scheduling problem, but most fail to take into account the runtime adaptation and workload changes after initial scheduling. In this paper, we propose a new scheduler (GT-Scheduler) that leverages a heuristic and rule-based algorithm to schedule tasks at near-optimal performance alongside using a meta-heuristic algorithm to make runtime adaptation. Firstly, K-way graph partitioning divides the tasks in an application graph according to the communication patterns. It places tasks with the highest amount of communication near each other to limit an increase in the topology response time. Secondly, instead of assigning tasks to the worker nodes, a partition of tasks is assigned to the nodes by adopting a greedy strategy. If the capacity of nodes is insufficient to host a specific partition of tasks, this partition is iteratively divided by the k-partitioning algorithm to assign to a proper node. The idea of runtime adaptation lies in detecting overutilized worker nodes and reassigning their tasks by exploiting a Tabu-Search and a new scoring strategy to find the best solution in a way that no worker node is overutilized. GT-Scheduler is implemented on the standard Apache Storm and using the standard benchmarks, it is shown that GT-Scheduler outperforms the R-Storm and the Online-Scheduler by at least 35% in reducing the topology response time. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of 586.7 - 1089 ms over the benchmark algorithm, saving 18.3 - 39 % in energy consumption. The total latency of IMOA is reduced by 59.8 - 68.5 % , and the maximum latency is reduced by 48.8 - 69.2 % . The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption. [ABSTRACT FROM AUTHOR]

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay. [ABSTRACT FROM AUTHOR]

With the development of network infrastructure, a large volume of data will be exchanged with increased bandwidth. Many applications are connecting people to the rest of the world through the public network. Thus, privacy and security have become a concern. Under this circumstance, it becomes a trend that the enterprises tend to host their data and services on private clouds dedicated to their own use, rather than the public cloud services. However, in contrary to the well-investigated total cost of ownership (TCO) for public clouds, the analytic research on the cost of purchase and operation for private clouds is still a blank. In this work, we first review the state-of-the-art TCO literature to summarize the models, tools, and cost optimization techniques for public clouds. Based on our survey, we envision the TCO modeling and optimization for private clouds by comparing the differences of features between public and private clouds. Finally, we propose a heuristic algorithm, conflict-aware first-fit to optimize the total cost of ownership of private cloud by minimizing the number of racks when deploying servers. [ABSTRACT FROM AUTHOR]

The engine room is the core area of a ship, critical to its operation, safety, and efficiency. Currently, many researchers merely address the ship engine room layout design (SERLD) problem using optimization algorithms and independent layout strategies. However, the engine room environment is complex, involving two significantly different challenges: equipment layout and pipe layout. Traditional methods fail to achieve optimal collaborative layout objectives. To address this research gap, this paper proposes a collaborative layout method that combines improved reinforcement learning and heuristic algorithms. For equipment layout, the engine room space is first discretized into a grid, and a Markov decision process (MDP) framework suitable for equipment layout is proposed, including state space, action space, and reward mechanisms suitable for equipment layout. An improved adaptive guided multi-agent Q-learning (AGMAQL) algorithm is employed to train the layout model in a centralized manner, with enhancements made to the agent's exploration state, exploration action, and learning strategy. For pipe layout, this paper proposes an improved adaptive trajectory artificial fish swarm algorithm (ATAFSA). This algorithm incorporates a hybrid encoding method, adaptive strategy, scouting strategy, and parallel optimization strategy, resulting in enhanced stability, accuracy, and problem adaptability. Subsequently, by comprehensively considering layout objectives and engine room attributes, a collaborative layout method incorporating hierarchical and adaptive weight strategies is proposed. This method optimizes in phases according to the layout objectives and priorities of different stages, achieving multi-level optimal layouts and providing designers with various reference schemes with different focuses. Finally, based on a typical real-world engine room engineering case, various leading algorithms and strategies are tested and compared. The results show that the proposed AGMAQL-ATAFSA (AGMAQL-ATA) exhibits robustness, efficiency, and engineering practicality. Compared to previous research methods and algorithms, the final layout quality improved overall: equipment layout effectiveness increased by over 4.0%, pipe optimization efficiency improved by over 40.4%, and collaborative layout effectiveness enhanced by over 2.2%. [ABSTRACT FROM AUTHOR]

Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing with dynamic task scheduling for remote-sensing satellites. In high-load and complex remote sensing task scenarios, there is low scheduling efficiency and a waste of resources. The paper proposes a scheduling method for remote-sensing satellite applications based on dynamic task prioritization. This paper combines the and Bound methodologies with an onboard task queue scheduling band in an active task prioritization context. A purpose-built emotional task priority-based scheduling blueprint is implemented to mitigate the flux and unpredictability characteristics inherent in the traditional satellite scheduling paradigm, improve scheduling efficiency, and fine-tune satellite resource allocation. Therefore, the Branch and Bound method in remote-sensing satellite task scheduling will significantly save space and improve efficiency. The experimental results show that comparing the technique to the three heuristic algorithms (GA, PSO, DE), the BnB method usually performs better in terms of the maximum value of the objective function, always finds a better solution, and reduces about 80% in terms of running time. [ABSTRACT FROM AUTHOR]

In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating securityaware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains. [ABSTRACT FROM AUTHOR]

To meet the needs of many IoT applications with low-latency requirement, fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. In this paper, we study the fog computing service deployment problem, where the operator allocates and deploys the required computing and network resources on the edge of the network to accommodate the requests of various applications operated by the application service providers (ASPs). The operator negotiates with the ASPs to determine serving QoS of applications and how much to pay. A queuing-based latency performance model with bulk arrival is proposed for the problem to estimate the resources needed for the fog network to achieve the QoS requirements of applications. We then model and analyze the interactions between the operator and multiple ASPs as sequential one-to-many bargaining using Nash bargaining. Next, to find the optimal bargaining sequence, we propose an improved optimal algorithm, along with fast heuristic algorithms, to find the optimal sequence with low complexity. Through extensive simulations, we show that the fog service can benefit all parties, and the proposed optimal and heuristic algorithms can improve the OP's payoff by averages of 21.24 and 14.16 percent respectively. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been widely adopted in various areas. However, large-scale sensor networks face four critical problems. Information transmissions through the network would cause the energy hole problem; deploying sensors in a distributed manner might cause the isolated problem; simultaneous transmissions lead to the communication collision problem; and the lack of intelligent computing incurs the limited computation problem. Due to drones' high mobility and flexibility, drone-based aerial computing emerges as a new paradigm to address these problems. In this article, we propose an aerial-computing-assisted architecture for large-scale sensor networks; the core layer contains the following modules: sensor localization, trajectory planning of drones, communication control between drones and sensors, and enabling intelligent computation on drones. The architecture significantly advances large-scale sensor networks with higher efficiency, smaller delay, larger lifetime, and more powerful computing capability. [ABSTRACT FROM AUTHOR]

Mobile edge computation partitioning is an effective technique to improve the applications performance on the mobile devices by selectively offloading some computations from the devices to the nearby edge cloud. Most previous works focus on the computation partitioning for a single user. Recent works begin to study the computation partitioning in a multiple user environment in which a number of users compete for the constrained computation resources on the edge cloud. However, these works neglect the fact that the users normally also share the network resources to access the edge cloud, and thus the allocation of bandwidth to the users significantly affects the overall performance of the users. In this paper, we study network aware mobile edge computation partitioning in multi-user environments, i.e., to decide for each user which parts of the application should be offloaded onto the edge cloud, and which others should be executed locally, and meanwhile to allocate the access bandwidth among the users, such that the average application performance of the users is maximized. This problem is new in that we consider the competition among users for the network bandwidth as well as the computation resources in a multi-user environment. With a set of novel models and formulations, we transform the problem into the classic Multi-class Multi-dimensional Knapsack Problem, and develop an effective algorithm, namely Performance Function Matrix based Heuristic (PFM-H), to solve it. We further consider the user mobility and design effective online algorithms that could be easily deployed in practical systems. Comprehensive trace driven simulations show that our proposed algorithm outperforms the benchmark algorithms significantly in the average application performance. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is one important application scenarios for the development of the Internet of things. The software-defined network (SDN) and fog computing could effectively improve the IoV network dynamics, which enables the application to achieve better performance by offloading some tasks to fog node or cloud center. Current computation offloading approaches for IoV and fog computing mostly focus on resource utilization. However, the energy-aware offloading has not been adequately addressed, especially for IoV systems with many battery-powered roadside units (RSU) and electric vehicles (EV). In this paper, we study the offloading problem in SDN and fog computing-based IoV systems. An energy-aware dynamic offloading scheme is proposed to prolong the running time of the IoV system by leveraging available battery power to execute more applications. The remaining battery power is defined as a dynamic weight factor in the execution cost model to adjust the optimization objective. Meanwhile, the dependence between applications is also taken into consideration in the cost model. A heuristic optimization algorithm is designed to solve the optimization problem. We conducted comprehensive experiments and results have shown that the offloading scheme could execute more applications with the available battery power under the constraints of application dependence. [ABSTRACT FROM AUTHOR]

The maximum bisection problem (max-bisection) belongs to a family of well-known graph partitioning problems with wide applications. In this study, we develop a simple and efficient local search algorithm called DSLS for max-bisection. The main idea in DSLS is the dynamic step strategy, which automatically adjusts the intensification and diversification to complete the search by changing the length of the step. Moreover, we design an efficient initial constructive method based on the degree information of the vertices, which provides high-quality initial solutions to the DSLS algorithm. In the data preprocessing, a reduction rule is proposed to cut down the size of benchmark graphs and improve the performance of the DSLS algorithm on benchmark graphs. We adopt 71 well-known benchmark graphs to evaluate our algorithm, and the experiments show that the DSLS algorithm is highly competitive with the state-of-the-art heuristic algorithms and discovers improved best-known results (new lower bounds) for 12 benchmark graphs. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a network architecture where the control and data plane are separated. As the network size grows, relying on just one controller can lead to various problems. Thus, in highly scalable networks, multiple controllers are needed. This critical issue of determining the number and placement of controllers is known as the controller placement problem (CPP). In this paper, game theory is used to solve CPP by identifying the optimal number of controllers. Two algorithms, golden eagle optimization (GEO) and grey wolf optimization (GWO), are utilized to find the most efficient mapping between switches and controllers. Since CPP is a discrete problem, GEO and GWO have first been discretized and then hybridized to form a new algorithm called GEWO. This algorithm is used to discover the most efficient mapping between switches and controllers. Additionally, simulated annealing is employed for better local search. The effectiveness of this approach is evaluated using different numbers of controllers on four well-known software-defined networks from the Internet Topology Zoo. The results are compared against various existing algorithms in the field, and it is observed that GEWO outperforms the competition. The findings demonstrate that GEWO reduces load imbalance by 24.07%, decreases end-to-end delay by 20.95%, and lowers average energy consumption by 11.65%. [ABSTRACT FROM AUTHOR]

Currently, huge amounts of data are produced by edge device. Considering the heavy burden of network bandwidth and the service delay requirements of delay-sensitive applications, processing the data at network edge is a great choice. However, edge devices such as smart wearables, connected and autonomous vehicles usually have several limitations on computational capacity and energy which will influence the quality of service. As an effective and efficient strategy, offloading is widely used to address this issue. But when facing device heterogeneity problem and task complexity increase, service quality degradation and resource utility decrease often occur due to unreasonable task distribution. Since conventional simplex offloading strategies show limited performance in complex environment, we are motivated to design a dynamic regional resource scheduling framework which is able to work effectively taking different indexes into consideration. Thus, in this article we first propose a double offloading framework to simulate the offloading process in real edge scenario which consists of different edge servers and devices. Then we formulate the offloading as a Markov Decision Process (MDP) and utilize a deep reinforcement learning (DRL) algorithm named asynchronous advantage actor-critic (A3C) as the offloading decision making strategy to balance the workload of edge servers and finally reduce the overhead in terms of energy and time. Comparison experiments for local computing and wide-used DRL algorithm DQN are conducted in a comprehensive benchmark and the results show that our work performs much better on self-adjusting and overhead reduction. [ABSTRACT FROM AUTHOR]

We consider the latency minimization problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks. To account for the temporally dynamic nature of the wireless links and the availability of the computing resources, we model the server selection as a multi-armed bandit (MAB) problem. In the considered MAB framework, rewards are characterized in terms of the end-to-end latency. We propose a novel online learning algorithm based on the principle of optimism in the face of uncertainty, which outperforms the state-of-the-art algorithms by up to ~35% reduction in latency. Our results highlight the significance of heavily discounting the past rewards in dynamic environments. [ABSTRACT FROM AUTHOR]

In recent years, large computational problems have beensolved by the distributed environment in which applications are executed in parallel. Also, lately, fog computing or edge computing as a new environment is applied to collect data from the devices and preprocessing is done before sending for main processing in cloud computing. Since one of the crucial issues in such systems is task scheduling, this issue is addressed by considering reducing energy consumption. In this study, an energy-aware method is introduced by using the Dynamic Voltage and Frequency Scaling (DVFS) technique to reduce energy consumption. In addition, in order to construct valid task sequences, a hybrid Invasive Weed Optimization and Culture (IWO-CA) evolutionary algorithm is applied. The experimental results revealed that the proposed algorithm improves some current algorithms in terms of energy consumption. • Proposing an energy-aware task scheduling approach to minimize energy consumption in fog computing. • Presenting a hybrid invasive weed optimization (IWO) along with the cultural evolution algorithm (CEA) for proposed task scheduling approach in fog computing. • Guaranteeing optimal solutions for proposed energy-aware task scheduling approach based on maximum level of QoS factors. [ABSTRACT FROM AUTHOR]

However, most heuristic algorithms are solely based on image features, due to the lack of diverse attributes of samples. Rather than rely on the existing datasets, this paper fully processes the original data. Firstly, the original videos were collected from several intelligent traffic surveillance cameras (monitoring nodes). Then, the three categories of objects in all videos were classified by a video image classifier. For each object in the critical class of cars, four attributes were identified, including ID, region of interest (ROI), sampling time and sampling position (position of sampling node). Next, the moving attitude (i.e. moving direction and speed) of each object was estimated through moving target tracking, and added to the preprocessed data. On this basis, a basic hyper-spherical hash search algorithm was created for the initial identification of similar objects. Furthermore, 93% of the objects were accurately identified through the recognition of license plate. Thereafter, a spatiotemporal correlation Bayesian network (STCBN) was established to predict the probability of each object to appear at a certain node in a period of time. Based on the probability, the heuristic factor was designed and used to enhance the basic hyper-spherical hash search algorithm. The enhancement improves the search performance, while reducing the environmental requirements of a single node. Therefore, the enhanced algorithm can be deployed effectively in edge computing systems. Our method was proved effective through experiment on a number of actual monitoring nodes. [ABSTRACT FROM AUTHOR]

Provisioning services for Internet of Things (IoT) devices leads to several challenges: heterogeneity of IoT devices, varying Quality of Services requirements, and increasing availability of both Cloud and Fog resources. The last of these is most significant to cope with Cloud infrastructure providers (CIPs) limitations for latency-sensitive services. Many Fog infrastructure providers (FIPs) have recently emerged, and their number is increasing continually. Selecting a suitable provider for each service involves considering multiple factors such as the provider's available resources, geographic location, quality of service, and cost. Motivated by this, FLEX is proposed in this work as a platform for service placement in a multi-Fog and multi-Cloud environment. For each service, FLEX broadcasts service requirements to the resource managers (RMs) of the available Fog and Cloud service providers and then selects the most suitable provider for that service. FLEX is scalable and flexible as it leaves it up to the RMs to have their own policy for the placement of submitted services. Also, the problem of service placement in multi-provider environments has been formulated as an optimization problem to jointly minimize the total weighted delay and cost of services. Next, a heuristic algorithm, namely minimum cost and delay first (MCD1), is proposed to map services to FIPs and CIPs efficiently.To evaluate the performance of the proposed algorithm, extensive experiments are conducted to analyze the behavior of the algorithm under different scenarios, such as a varying number of services, providers, and the ratio of FIPs. Results show that MCD1 significantly performs better than baseline methods and genetic algorithms. In particular, the proposed algorithm can reduce the objective function value up to 26.8%. [ABSTRACT FROM AUTHOR]

In this paper, we present a consistent methodology for the reliable design of 6G-oriented filters with enhanced endurance to construction imperfections. The systematic formulation does not depend on the filter's operating frequency and employs a robust strategy for obtaining new roots and poles of the filtering function. Essentially, it requires that all the local maxima of the filtering function do not fluctuate beyond the design attenuation levels for a set of predefined roots/poles distortions. To this purpose, two novel algorithms for the derivation of the appropriate filtering functions are developed, in the prior basis, together with a versatile optimization criterion and a heuristic comparison approach that guarantee optimal outcomes. Specifically, the principal idea of the first technique is to accurately extract the roots of the new polynomial from a system of equations on condition that the maximum local peaks of the distorted (due to imperfections) initial polynomial are below a prefixed threshold, such as the unit. Conversely, the second method develops an alternative polynomial, compressed in the amplitude and frequency range, so that a similar prerequisite regarding the maximum local peaks, is satisfied. It is stressed that both methods are fully generalized and may be applied to any polynomial combination, without increasing the overall complexity. The proposed framework is successfully verified in terms of theoretical examples and the numerical simulation of realistic waveguide and mictrostrip line filters, operating at frequencies from 2 GHz to 65 GHz, which unveil its superiority over existing schemes and implementations. [ABSTRACT FROM AUTHOR]

The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments. [ABSTRACT FROM AUTHOR]

Mobile Cloud Computing or Fog computing refers to offloading computationally intensive algorithms from a mobile device to the cloud or an intermediate cloud in order to save resources e.g., time and energy in the mobile device. This paper proposes new solutions for situations when the cloud or fog is not available. First, the sensor network is modelled using a network of queues, then a linear programming technique is used to make scheduling decisions. Various centralized and distributed algorithms are then proposed, which improves overall system performance. Extensive simulations show slightly higher energy usage in comparison to the baseline non-offloading case, however, the job completion rate is significantly improved, the efficiency score metric shows the extra energy usage is justified. The algorithms have been simulated in various environments including high and low bandwidth, partial connectivity, and different rate of information exchanges to study the pros and cons of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of edge computing, heterogeneous smart devices, serving as operators, require to be deployed closely to data sources, and cooperate to accomplish big data applications considering factors including energy consumption and fast response. Specifically, an operators composition can be encapsulated and represented as feasible solution, and an operator in a certain solution prefers to be encoded with abstract matrix defined by evaluation indicators including spatial and temporal constraint, priority between consecutive operators, the load-balancing factor, and energy consumption. Discovering an optimal composition strategy in the constructed edge cluster can boil down to a multi-objective and multi-constrained optimization problem, which can be solved through adopting heuristic algorithms. Experimental evaluation demonstrates that the Grey Wolf Optimization outperforms Particle Swarm Optimization in discovering an approximately optimal operators composition in the corresponding edge cluster, and can minimize the energy consumption of the network. [ABSTRACT FROM AUTHOR]

• Service-oriented reliable network deployment is studied over MEC enabled CRAN. • A presented MILP is to minimizing total latency under the constraints. • A heuristic algorithm is proposed in the completely protection against failures. • Both data transport and NFV processing in multiple protections are guaranteed. New emerging ultra-reliable and low latency communications (URLLC) services have imposed challenged stringent low latency and reliability requirements on fifth generation and beyond (5G/B5G) cloud radio access network (CRAN). However, limited research has been discussed service-oriented reliable network deployment in the mobile edge computing (MEC)-enabled time-division multiplexing-passive optical network (TDM-PON) based CRAN. Moreover, different from most previous protection studies, both optical transport and functional processing resources in multiple domains should be considered in the network deployment of a converged network. To mitigate these limitation, we present a mixed integer linear programming (MILP) for URLLC service-oriented reliable network deployment under the physical and management constraints in MEC-enabled TDM-PON based CRAN. Given the model's complexity, a heuristic algorithm called "Time-efficient completely isolated protection scheme (TECIPS)" is proposed, which can completely guarantee protection through establishing both primary network topology and strong isolated backup network topology against multiple network failures, while reducing redundant transmission latency and backup recovery latency for URLLC services. Simulation results indicate that our proposed algorithm significantly reduces total latency compared to the benchmark algorithm and receives the near-optimal solution of MILP in both sparse and dense networks with an acceptable time complexity. In addition, the impact of key parameters on the consumption of total transmission latency and backup recovery latency is also evaluated. [ABSTRACT FROM AUTHOR]

In order to improve the reliability of power supply, the sophisticated design of the structure of electric cable network has become an important issue for modern urban distribution networks. In this paper, an electric cable path planning model based on heuristic optimization algorithm considering mixed-land scenario is proposed. Firstly, based on different land samples, the kernel density estimation (KDE) and the analytic hierarchy process (AHP) are used to estimate the construction cost of each unit grid, in order to construct the objective function of comprehensive investment for electric cable loop network. Then, the ant colony optimization (ACO) was improved in pheromone concentration, factor increment and search direction to accelerate the solving speed, and the cable path planning result with minimum construction cost is obtained. Finally, the feeder’s tie line of the cable loop network is planned by the genetic algorithm (GA) to achieve the minimum operating cost. In the case analysis, compared with the traditional method, not only the subjective factors in the process of investment estimation can be avoided, but also the speed of model solving and the quality of the optimal solution are improved. [ABSTRACT FROM AUTHOR]

Opportunistic Mobile Edge Cloud in which opportunistically connected mobile devices run in a cooperative way to augment the capability of a single device has become a timely and essential topic due to its widespread prospect under resource-constrained scenarios (e.g., disaster rescue). Because of the mobility of devices and the uncertainty of environments, it is inevitable that failures occur among the mobile nodes. Being different from existing studies that mainly focus on either data offloading or computing offloading among mobile devices in an ideal environment, we concentrate on how to guarantee the reliability of the task execution with the consideration of both data offloading and computing offloading under opportunistically connected mobile edge cloud. To this end, an optimization of mobile task offloading when considering reliability is formulated. Then, we propose a probabilistic model for task offloading and a reliability model for task execution, which estimates the probability of successful execution for a specific opportunistic path and describes the dynamic reliability of the task execution. Based on these models, a heuristic algorithm UNION (Fault-Tolerant Cooperative Computing) is proposed to solve this NP-hard problem. Theoretical analysis shows that the complexity of UNION is O(|I|2 + |N |) with guaranteeing the reliability of 0.99. Also, extensive experiments on real-world traces validate the superiority of the proposed algorithm UNION over existing typical strategies. [ABSTRACT FROM AUTHOR]

Availability and accessibility of data objects in a reasonable time is a main issue in distributed systems like cloud computing services. As a result, the reduction of data-related operation times in distributed systems such as data read/write has become a major challenge in the development of these systems. In this regard, replicating the data objects on different servers is one commonly used technique. In general, replica placement plays an essential role in the efficiency of distributed systems and can be implemented statically or dynamically. Estimation of the minimum number of data replicas and the optimal placement of the replicas is an NP-complete optimization problem. Hence, different heuristic algorithms have been proposed for optimal replica placement in distributed systems. Reducing data processing costs as well as the number of replicas, and increasing the reliability of the replica placement algorithms are the main goals of this research. This paper presents a discrete and swarm-evolutionary method using a combination of shuffle-frog leaping and genetic algorithms to data-replica placement problems in distributed systems. The experiments on the standard dataset show that the proposed method reduces data access time by up to 30% with about 14 replicas; whereas the generated replicas by the GA and ACO are, respectively, 24 and 30. The average reduction in data access time by GA and ACO 21% and 18% which shows less efficiency than the SFLA–GA algorithm. Regarding the results, the SFLA–GA converges on the optimal solution before the 10th iteration, which shows the higher performance of the proposed method. Furthermore, the standard deviation among the results obtained by the proposed method on several runs is about 0.029, which is lower than other algorithms. Additionally, the proposed method has a higher success rate than other algorithms in the replica placement problem. [ABSTRACT FROM AUTHOR]

Fog computing paradigm attempts to provide diverse processing at the edge of IoT networks. Energy usage being one of the important elements that may have a direct influence on the performance of fog environment. Effective scheduling systems, in which activities are mapped on the greatest feasible resources to meet various competing priorities, can reduce energy use. Consequently, a hybrid discrete optimization method called HDSOS-GOA, which uses the Dynamic voltage and frequency scaling (DVFS) approach, is proposed to handle scientific workflow scheduling challenges in the fog computing environment. HDSOS-GOA combines the search qualities of Symbiotic Organisms Search (SOS) and the Grasshopper Optimization Algorithm (GOA) algorithms and the selection of these algorithms for performing workflow scheduling is based on the probability calculated by the learning automata. The HEFT method is used to determine the task sequence. Our solution focuses on reducing the energy consumption of the scheduling process by reducing the number of Virtual Machines required for workflow execution in addition to optimizing the makespan. Comprehensive experiments are carried out on four different scientific workflows with different sizes with and without deadline constraints to evaluate the performance of the suggested scheduling strategy. The results of the experiments show that scheduling with the suggested approach outperforms other well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Accurate parameter identification of power distribution network (PDN) has attracted remarkable attention recently. However, power device parameters usually show an instability attributed to both the operating status and manual entry. Therefore, it is urgent to develop reliable algorithms for identifying PDN parameters with both high accuracy and high efficiency. Most of the existing algorithms are gradient-free and based on the heuristic schemes, resulting in an unstable numerical calculation. Herein, based on our previous work about the adaptive gradient-based optimization (AGBO) method, we propose an extensive version, namely, AGBO-Pro model. In this method, both the numerical and categorical features of experimental observations are utilized and incorporated with each via a weighted average. By comparing the proposed method with several heuristic algorithms, it is found that the errors in RMSE, MAE, and MAPE criteria via AGBO-Pro are all about 2 times lower with a much faster and more stable convergence of the loss function. By further taking a linear transformation of the loss function, the AGBO-Pro model achieves a more robust performance with a much lower variance in repeat numerical calculations. This work shows great potential in possible extension of gradient-based optimization methods for parameter identification in PDN. [ABSTRACT FROM AUTHOR]

Cloudlet networks are an emerging distributed data processing paradigm, which contain multiple cloudlets deployed beside base stations to serve local user devices (UDs). Each cloudlet is a small data center with limited memory, in which multiple virtual machines (VMs) can be instantiated. Each VM runs a UD's application components and provides dedicated services for that UD. The number of VMs that serve UDs with low latency is limited by a lack of sufficient memory of cloudlets. Memory deduplication technology is expected to solve this problem by sharing memory pages between VMs. However, maximizing page sharing means that more VMs that can share the same memory pages should be instantiated on the same cloudlet, which prevents the communication distance between UDs and their VMs from minimizing, as each VM cannot be instantiated in the cloudlet with the shortest communication distance from its UD. In this paper, we study the problem of VM instantiation with the joint optimization of memory sharing and communication distance in cloudlet networks. First, we formulate this problem as a bi-objective optimization model. Then, we propose an iterative heuristic algorithm based on the ε-constraint method, which decomposes original problems into several single-objective optimization subproblems and iteratively obtains the subproblems' optimal solutions. Finally, the proposed algorithm is evaluated through a large number of experiments on the Google cluster workload tracking dataset and the Shanghai Telecom base station dataset. Experimental results show that the proposed algorithm outperforms other benchmark algorithms. Overall, the memory sharing between VMs increased by 3.6%, the average communication distance between VMs and UDs was reduced by 22.7%, and the running time decreased by approximately 29.7% compared to the weighted sum method. [ABSTRACT FROM AUTHOR]

Summary: Serverless computing has emerged as a powerful deployment model based on the Function‐as‐a‐Service (FaaS) paradigm, where applications are orchestrated through a set of independent functions. The function orchestration within an application can be represented through a serverless workflow, which defines the overall execution plan of the application. To ensure the quality of service for serverless computing platforms, it is essential to develop performance and cost models that can predict the service quality that can be obtained from deploying and executing applications in the cloud platform. While several analytical models have been developed for various cloud deployment frameworks in recent years, there has been a lack of performance and cost analysis models for serverless computing platforms. The existing performance and cost monitoring tools available in serverless frameworks face several challenges, such as complexity, lack of transparency, and incomplete monitoring data. In this paper, we fill the gap by proposing an efficient workflow‐based analytical model that can estimate the end‐to‐end response time and cost of the serverless execution plan. The proposed model can handle complex structures like loop, cycles, self‐loop, and parallel substructures that exist in serverless workflows. Additionally, we propose a heuristic optimization algorithm to identify the optimal resource configuration to achieve the optimal response time under a given budget constraint. We evaluated the effectiveness of the proposed model by considering seven serverless applications in both AWS Lambda and Microsoft Azure platforms. We compared the accuracy of the proposed model with the real values of response time and cost obtained in AWS Lambda and Microsoft Azure serverless platforms. The proposed performance and cost model in the AWS Lambda platform has been observed to have an average accuracy of 99.2% and 98.7% respectively. In the Microsoft Azure platform, the average accuracy of the performance and cost model has been observed to be 98.6% and 98.2% respectively. [ABSTRACT FROM AUTHOR]

Task scheduling (TS) and multi-agent-path-finding (MAPF) are two cruxes of pickup-and-delivery in automated warehouses. In this paper, the two cruxes are optimized simultaneously. Firstly, the system model, task model, and path model are established, respectively. Then, a task scheduling algorithm based on enhanced HEFT, a heuristic MAPF algorithm and a TS- MAPF algorithm are proposed to solve this combinatorial optimization problem. In EHEFT, a novel rank priority rule is used to determine task sequencing and task allocation. In MAPF algorithm, a CBS algorithm with priority rules is designed for path search. Subsequently, the TS-MAPF algorithm which combines EHEFT and MAPF is proposed. Finally, the proposed algorithms are tested separately against relevant typical algorithms at different scales. The experimental results indicate that the proposed algorithms exhibited good performance. [ABSTRACT FROM AUTHOR]

This paper proposes an energy-efficient service scheduling mechanism in federated edge cloud (FEC) called ESFEC, which consists of a placement algorithm and three types of reconfiguration algorithms. Unlike traditional approaches, ESFEC places delay-sensitive services on the edge servers in nearby edge domains instead of clouds. In addition, ESFEC schedules services with actual traffic requirements rather than maximum traffic requirements to ensure QoS. This increases the number of services co-located in a single server and thereby reduces the total energy consumed by the services. ESFEC reduces the service migration overhead using a reinforcement learning (RL)-based reconfiguration algorithm, ESFEC-RL, that can dynamically adapt to a changing environment. Additionally, ESFEC includes two different heuristic algorithms, ESFEC-EF (energy first) and ESFEC-MF (migration first), which are more suitable for real-scale scenarios. The simulation results show that ESFEC improves energy efficiency by up to 28% and lowers the service violation rate by up to 66% compared to a traditional approach used in the edge cloud environment. [ABSTRACT FROM AUTHOR]

Space-based systems providing remote sensing, communication, and navigation services are essential to the economy and national defense. Users' demand for satellites has increased sharply in recent years, but resources such as storage, energy, and computation are limited. Therefore, an efficient resource scheduling strategy is urgently needed to satisfy users' demands maximally and get high task execution benefits. A hierarchical scheduling method is proposed in this work, which combines improved ant colony optimization and an improved deep Q network. The proposed method considers the quality of current task execution and resource load balance. The entire resource scheduling process contains two steps, task allocation and resource scheduling in the timeline. The former mainly implements load balance by improved ant colony optimization, while the latter mainly accomplishes the high task completion rate by an improved deep Q network. Compared with several other heuristic algorithms, the proposed approach is proven to have advantages in terms of CPU runtime, task completion rate, and resource variance between satellites. In the simulation scenarios, the proposed method can achieve up to 97.3% task completion rate, with almost 50% of the CPU runtime required by HAW and HADRT. Furthermore, this method has successfully implemented load balance. [ABSTRACT FROM AUTHOR]

This paper deals with the reduction in electrical losses and the minimization of voltage deviation in electrical power grids. To this end, a novel heuristic-based approach is proposed for controlling reactive power injections along an electrical grid with distributed generation using distributed photovoltaic (PV) generation. The proposed approach consists of applying a heuristic, namely a genetic or firefly algorithm, to solve the loss and voltage deviation optimization problem by controlling PV reactive power injections. The proposed method was tested on a radial 100-bus network with an intense penetration of PV generation. The obtained results indicate that the proposed method consistently yields superior grid performance when compared to algorithms previously proposed in the literature. In fact, it led to a reduction in the active power supplied by the reference bus to the grid, thereby alleviating the burden imposed on the most upstream parts of the distribution network. As a consequence, the proposed method contributes to a power-efficient network, enhancing its loss performance and voltage stability. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) has the potential to fundamentally transform conventional network architecture through the decoupling of software from dedicated hardware. The convergence of virtualization and cloud computing technologies has revolutionized the networking landscape, offering a wide range of advantages, including improved flexibility, manageability, and scalability. The importance of network capability in enabling ultra-low latency applications has been greatly amplified in the current era due to the increased demand for emerging services such as autonomous driving, teleoperated driving, virtual reality, and remote surgery. This paper presents a novel and efficient methodology for service function chaining (SFC) in an NFV-enabled network that aims to minimize latency and optimize the utilization of physical network resources, with a specific focus on ultra-low latency applications. In our proposed methodology, we offer flow prioritization and an adjustable priority coefficient factor (µ) to reserve a portion of physical network resources exclusively for ultra-low latency applications in order to optimize the deployment paths of these applications further. We formulate the SFC deployment problem as an integer linear programming (ILP) optimization model. Furthermore, we propose a set of heuristic algorithms that yield near-optimal solutions with minimal optimality gaps and execution times, making them practical for large-scale network topologies. Performance evaluations demonstrate the effectiveness of our proposed methodology in enabling ultra-low latency applications in an NFV-enabled network. Compared to existing algorithms, our proposed methodology achieves notable enhancements in terms of the end-to-end delay (up to 22 percent), bandwidth utilization (up to 28 percent), and SFC acceptance rate (up to 13 percent). [ABSTRACT FROM AUTHOR]