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The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing faces growing challenges, furthermore, responding to time-sensitive requests in the traditional cloud computing model is one of the major challenges, considering the growth of the Internet of Things. Recently, the powerful fog computing paradigm has been considered for answering these challenges. But a major challenge in fog computing is managing limited FN resources for correctly responding to IoT requests in environments with heterogeneous latency requirements. This study presented a new resource management framework utilizing a software defined network (SDN) architecture and enhanced reinforcement learning methods. This new framework aims to make optimal use of limited FN resources while satisfying the low-latency requirements of IoT applications. Since SDN is the proper choice to support this intelligent distributed structure, an SDN-based fog architecture was proposed. Moreover, FN must allocate its limited and valuable resources intelligently in a heterogeneous IoT environment with different latency requirements. Consequently, this study formulated the problem of resource allocation in fog computing in the form of a Markov decision process (MDP). The study used different reinforcement learning (RL) techniques to solve the MDP problem. Simulation results in different IoT environments with heterogeneous latency requirements corroborate that RL methods achieve the best possible performance regardless of the IoT environment. [ABSTRACT FROM AUTHOR]

Accepted by: Aris Syntetos Management-led productivity improvements are crucial for achieving sustainable development, and the Malmquist productivity index is known to be useful in relevant contexts. This study aims to extend such index by using non-parametric mathematical modelling of production processes. Specifically, and in the spirit of the existing index, we introduce the directional distance function to develop a new one applicable to the joint production of desirable and undesirable outputs. Furthermore, we decompose the new index into two constituent components to provide more intuitive explanations when revealing the root sources of productivity changes over time. Under the cost minimization assumption, the new index is applicable when producers implement resource allocation management, and the input–output quantities and the micro-level input prices are known. The index emphasizes that the allocative efficiency should be regarded as an important aspect of productivity assessment like the technical efficiency. As a practical benchmarking tool, it can offer valuable information and provide appropriate strategies for managerial decision-making. The index's application and usefulness is demonstrated in the commercial bank sector in China. [ABSTRACT FROM AUTHOR]

The emergence of a new generation of applications such as the Internet of Things, Artificial Intelligence, and 5G has brought fog computing as cloud development toward the edge network. It is done by adding an extra layer of resources to meet low latency, high performance, mobility, and interoperability challenges. The development of fog markets and new beneficial business paradigms such as fog services considerably affect the future of fogonomics (which is defined as the economics of fog computing). Thus, it is a challenging issue to design a market-oriented model to deal with the economics-based resource management in fog computing, which adjusts the resource supply of Fog Resource Providers (FRPs) and the demands of User Services (USs) based on users' requirements while satisfying all parties' utilities. This paper proposes the Fog-Marketing scheme consisting of a Resource Propagation Component (RPC) and a Contract-aware Market Selection Component (CMSC) for resource provisioning in multi-tier fog computing. The RPC is deployed in each fog node to perform hierarchical resource propagation of FRPs to auction-based Fog Decentralized Markets (FDMs) close to USs to increase revenue opportunities, and embedded CMSC in the user agent applies a matching strategy to assign USs and FDMs aims to promote USs' utility. The simulation results under extensive experiments confirmed that the proposed scheme succeeded in reducing the Round-Trip-Time, increasing the revenue of FRPs, and improving the utility of USs compared to existing models. [ABSTRACT FROM AUTHOR]

The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum Honey Badger Algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters. [ABSTRACT FROM AUTHOR]

This paper presents a simulation approach to enhance the performance of heuristics for multiproject scheduling. Unlike other heuristics available in the literature that use only one priority criterion for resource allocation, this paper proposes a structured way to sequentially apply more than one priority criterion for this purpose. By means of simulation, different feasible schedules are obtained to; therefore, increase the probability of finding the schedule with the shortest duration. The performance of this simulation approach was validated with the MPSPLib library, one of the most prominent libraries for resource-constrained multiproject scheduling. These results highlight the proposed method as a useful option for addressing limited time and resources in portfolio management. [ABSTRACT FROM AUTHOR]

In this research, a hot module location-aware heuristic mapping algorithm for placement of hot modules is proposed, which aims to prevent the negative effects of tasks on the performance metrics of 2D network-on-chip. This method uses modular systematic and exact search optimization techniques to obtain an optimal solution to the mapping problem. Significant performance degradation and unfair allocation of system resources can occur with one or more hot modules on the network on the wormhole-base network system. By defining a specific access system for hot modules, the proposed approach optimizes system latency and throughput and minimizes energy consumption under bandwidth limitations. The efficiency and effectiveness of the proposed approach have been compared in simulation environments and similar traffic conditions. Saving energy consumption of the proposed approach compared to the equivalent algorithms for embedded systems, up to 81.69% less delay and 68% energy saving have been obtained. [ABSTRACT FROM AUTHOR]

Fog computing is a paradigm that allows the provisioning of computational resources and services at the edge of the network, closer to the end devices and users, complementing cloud computing. The heterogeneity and large number of devices are challenges to obtaining optimized resource allocation in this environment. Over time, some surveys have been presented on resource management in fog computing. However, they now lack a broader and deeper view about this subject, considering the recent publications. This article presents a systematic literature review with a focus on resource allocation for fog computing, and in a more comprehensive way than the existing works. The survey is based on 108 selected publications from 2012 to 2022. The analysis has exposed their main techniques, metrics used, evaluation tools, virtualization methods, architecture, and domains where the proposed solutions were applied. The results show an updated and comprehensive view about resource allocation in fog computing. The main challenges and open research questions are discussed, and a new fog computing resource management cycle is proposed. [ABSTRACT FROM AUTHOR]

With the evolution of 5G and Internet of Things technologies, Mobile Edge Computing (MEC) has emerged as a major computing paradigm. Compared to cloud computing, MEC integrates network control, computing, and storage to customizable, fast, reliable, and secure distributed services that are closer to the user and data site. Although a popular research topic, MEC resource management comes in many forms due to its emerging nature and there exists little consensus in the community. In this survey, we present a comprehensive review of existing research problems and relevant solutions within MEC resource management. We first describe the major problems in MEC resource allocation when the user applications have diverse performance requirements. We discuss the unique challenges caused by the dynamic nature of the environments and use cases where MEC is adopted. We also explore and categorize existing solutions that address such challenges. We particularly explore traditional optimization-based methods and deep learning-based approaches. In addition, we take a deeper dive into the most popular applications and use cases that adopt MEC paradigm and how MEC provides customized solutions for each use cases, in particular, video analytics applications. Finally, we outline the open research challenges and future directions. [ABSTRACT FROM AUTHOR]

Wireless Powered Mobile Edge Computing (WPMEC) is an integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies, to both improve computing capabilities of mobile devices and energy compensation for their limited battery capabilities. Generally, energy transmitters, mobile devices, and edge servers form a WPMEC system that realizes a closed loop of sending and collecting energy as well as offloading and receiving task data. Due to constraints of time-varying network environments, time-coupled battery levels, and the half-duplex character of mobile devices, the joint design of computation offloading and resource allocation solutions in WPMEC systems has become extremely challenging, and a great number of studies have been devoted to it in recent years. In this article, we first introduce the basic model of the WPMEC system. Then, we present key issues and techniques related to WPMEC. In addition, we summarize solutions for computation offloading and resource allocation to solve critical issues in WPMEC networks. Finally, we discuss some research challenges and open issues. [ABSTRACT FROM AUTHOR]

The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, and so on is increasing by means of cloud computing, which provides on-demand storage and computation facilities over the Internet. To meet specific requirements of IoT applications, the cloud has also shifted its service offering platform to its next-generation models, such as fog, mist, and dew computing. As a result, the cloud and IoT have become part and parcel of smart applications that play significant roles in improving the quality of human life. In addition to the inherent advantages of advanced cloud models, to improve the performance of IoT applications further, it is essential to understand how the resources in the cloud and cloud-influenced platforms are managed to support various phases in the end-to-end IoT deployment. Considering this importance, in this article, we provide a brief description, a systematic review, and possible research directions on every aspect of resource management tasks, such as workload modeling, resource provisioning, workload scheduling, resource allocation, load balancing, energy management, and resource heterogeneity in such advanced platforms, from a cloud perspective. The primary objective of this article is to help early researchers gain insight into the underlying concepts of resource management tasks in the cloud for IoT applications. [ABSTRACT FROM AUTHOR]

Compared to cloud computing, mobile edge computing (MEC) is a promising solution for delay-sensitive applications due to its proximity to end users. Because of its ability to offload resource-intensive tasks to nearby edge servers, MEC allows a diverse range of compute- and storage-intensive applications to operate on resource-constrained devices. The optimal utilization of MEC can lead to enhanced responsiveness and quality of service, but it requires careful design from the perspective of user-base station association, virtualized resource provisioning, and task distribution. Also, considering the limited exploration of the federation concept in the existing literature, its impacts on the allocation and management of resources still remain not widely recognized. In this paper, we study the network and MEC resource scheduling problem, where some edge servers are federated, limiting resource expansion within the same federations. The integration of network and MEC is crucial, emphasizing the necessity of a joint approach. In this work, we present NAFEOS, a proposed solution formulated as a two-stage algorithm that can effectively integrate association optimization with vertical and horizontal scaling. The Stage-1 problem optimizes the user-base station association and federation assignment so that the edge servers can be utilized in a balanced manner. The following Stage-2 dynamically schedules both vertical and horizontal scaling so that the fluctuating task-offloading demands from users are fulfilled. The extensive evaluations and comparison results show that the proposed approach can effectively achieve optimal resource utilization. [ABSTRACT FROM AUTHOR]

Cloud organizations now face a challenge in managing the enormous volume of data and various resources in the cloud due to the rapid growth of the virtualized environment with many service users, ranging from small business owners to large corporations. The performance of cloud computing may suffer from ineffective resource management. As a result, resources must be distributed fairly among various stakeholders without sacrificing the organization's profitability or the satisfaction of its customers. A customer's request cannot be put on hold indefinitely just because the necessary resources are not available on the board. Therefore, a novel cloud resource allocation model incorporating security management is developed in this paper. Here, the Deep Linear Transition Network (DLTN) mechanism is developed for effectively allocating resources to cloud systems. Then, an Adaptive Mongoose Optimization Algorithm (AMOA) is deployed to compute the beamforming solution for reward prediction, which supports the process of resource allocation. Moreover, the Logic Overhead Security Protocol (LOSP) is implemented to ensure secured resource management in the cloud system, where Burrows–Abadi–Needham (BAN) logic is used to predict the agreement logic. During the results analysis, the performance of the proposed DLTN-LOSP model is validated and compared using different metrics such as makespan, processing time, and utilization rate. For system validation and testing, 100 to 500 resources are used in this study, and the results achieved a make-up of 2.3% and a utilization rate of 13 percent. Moreover, the obtained results confirm the superiority of the proposed framework, with better performance outcomes. [ABSTRACT FROM AUTHOR]

In order to manage the load on dispersed data centers and cut down on energy established on time usage, agent-based resource allocation is given attention. Using a targeted load balancer (TLB), we suggest an energy-aware agent-based resource allocation in this research to enhance quality of service in a cloud setting. This agent is first set up to keep track of the resource load resulting from the request that has been assigned a job. Cloud watch also keeps an eye on energy levels to determine the typical payload size of resource execution. The TLB establishes new instance state to assign the resource based on the payload weight. To shorten the execution time, the dynamic hyper switching model develops a balancing mechanism. The suggested system achieves high performance in resource management by creating load balancer that is efficiently targeted to cut down on computation time and cost depending on energy levels. In comparison to existing techniques, the suggested parallelized homogeneous job in the cloud environment produces greater performance up to 95.5% while maintaining the time execution utilizing switching state of execution. This maintains the reduced CPU consumption, which dependent on the lowering of temporal complexity. [ABSTRACT FROM AUTHOR]

Due to the rapid expansion of heterogeneous mobile networks, there has been a significant increase in the need for a network, processing, and caching resources. A dynamic vehicular network requires the ability to effectively and efficiently manage multiple resources. This is in accord with the majority of previous research on resource management. Due to this, it is imperative to focus on dynamic networks that change rapidly over time and require a large number of connections. Using curiosity-enabled learning, this study investigates how distributed resource allocation mechanisms for mobile vehicle-to-infrastructure (V2I) communications can be applied to medium and dense traffic scenarios. By utilizing the distributed resource allocation mechanism, an autonomous "agent" such as a vehicle can determine the optimal sub-band and power level for transmission without relying on global information. It is believed that the environment learns from extrinsic rewards because they can provide high levels of repetition and heavy fluctuations in decisions. We model curiosity in accordance with exit locations in mobility models. In addition to providing intrinsic rewards to the environment, a variety of curiosities will improve the performance of the system. We find that each agent can satisfy latency constraints with resource allocation for V2I links with a minimum prediction error when learning from intrinsic rewards. [ABSTRACT FROM AUTHOR]

Fog computing has risen into a popular topic in recent years with the idea of deploying computation and communication closer to end-users. The capability of the fog model to face serious challenges like high latency and power consumption is of paramount research significance, notably in the context of the Internet of Things (IoT), given the volume and requirements of IoT applications. Accordingly, issues such as using the capacity of the fog layer maximally, minimizing latency while maintaining reliability, and efficiently distributing the workload across the network can be well explored. This motivated us to propose a scheme for partial computation offloading and resource allocation in the fog-based IoT network with the goal of optimizing latency and power consumption (ROGI). In this research, the nodes in all network layers are involved in processing the workload. Also, the power consumption in the end-users layer is reduced. Additionally, the fog nodes collaborate to improve system reliability and provide more resources for handling users' requests. Furthermore, the concept of partial offloading is adopted, which would potentially lead to higher flexibility in resource management and provide the opportunity to leverage more resources in each layer of IoT architecture. Moreover, the whole model is decomposed into three problems, each of which is solved via optimization techniques. Extensive simulations are carried out to show the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Cloud computing provides different types of resources to users on-demand which are hosted in cloud data centers. Aforesaid services are provided at the expense of large energy consumption. Energy consumption increases the expenditure budget, greenhouse gases, and CO2 emissions. To handle this issue, researchers have come up with various server-level energy-efficient techniques. Though the proposed techniques attempt to reduce energy consumption, they only consider the energy consumption of the CPU during the task placement process. However, researchers have recently noted that memory is also one of the higher energy consumption components and it should be considered in task placement. Moreover, existing techniques ignore the SLA violations that are encountered due to workload. To address the aforementioned issues, we propose two novel nature-inspired techniques which consider the energy consumption of both CPU and memory during the VM placement process. Proposed novel techniques are based on artificial bee colony and particle swarm optimization which haven't been used to place VM while considering energy consumption of CPU and memory. Moreover, to handle the issue of resultant SLA violations, we also provide the SLA-aware variants of the proposed energy-efficient techniques, which try to lower SLA violations faced because of excessive task consolidation. The results depict that the proposed energy-efficient techniques perform better than the existing state-of-the-art techniques, whereas proposed SLA variants also reduce the SLA violations. [ABSTRACT FROM AUTHOR]

Computation-intensive vehicle tasks sharply increase with the rapid growth of intelligent vehicles. The technology of Mobile Edge Computing (MEC) has the possibility of assisting vehicles with computation offloading. To solve the problem of computation resource management and guarantee the security of resource transaction, we jointly combine the MEC network and the blockchain networks to build a blockchain based MEC offloading model. The non-cooperative interactions between MEC server and vehicles formulate a two-stage Stackelberg game in an aim to maximize their benefits and information security. We theoretically demonstrate the unique existence of Nash equilibrium, which enables participants to decide their optimal strategies. Finally, the performance of the proposed model is analyzed by conducting simulation experiments. Our proposed model optimizes resource allocation and also improves the security of the whole network. [ABSTRACT FROM AUTHOR]

Summary: Various opportunities aimed at the employment of delay‐sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)‐centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion‐centered bacteria foraging optimization (BM‐BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K‐means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM‐CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC. [ABSTRACT FROM AUTHOR]

Satellite network resource management and scheduling technology are significant to constructing integrated information networks in heaven and earth. The difficulty in realizing this technology lies in improving resource utilization efficiency while ensuring the service quality of satellites and efficiently coordinating complex satellite network systems and services. This paper proposes a model, A Dynamic task scheduling method based on a UNified resource Management architecture(DUNM), based on the designed resource management architecture supported by dynamic scheduling algorithms to address the problems of low resource utilization, resource allocation, and task completion rate. First, with sufficient resources, the task execution time to complete a task is calculated based on the number of resources, task transmission time, task waiting time, etc. Secondly, based on the tasks assigned to satellites, the execution time of all functions with different transmission rates of communication links between satellites is calculated, and the total sum of all time consumption is analyzed. Finally, after simulation experiments and comparison with various baseline algorithms, about a 40% reduction in time to complete scheduled tasks and an almost 25% reduction in the average cost to finish a scheduling task, our method has higher scheduling efficiency and lower task completion revenue. It also guarantees a higher task completion rate while completing the tasks. Our approach attained a nearly 100% completion rate for scheduling tasks, which means that our algorithm can achieve the scheduling tasks faster and at high task revenue, thus improving the efficiency and economic efficiency of the whole system. Therefore, it validates the advantages of our method, such as high efficiency and high revenue. [ABSTRACT FROM AUTHOR]

The fog-assisted cloud computing gives better quality of service (QoS) to Internet of things (IoT) applications. However, the large quantity of data transmitted by the IoT devices results in the overhead of bandwidth and increased delay. Moreover, large amounts of data transmission generate resource management issues and decrease the system's throughput. This paper proposes the optimized task scheduling and preemption (OSCAR) model to overcome the limitations and improve the QoS. The dataset used for the study is a real-time crowd-based dataset which provides task information. The processes involved in this paper are as follows: (i) Initially, the tasks from the IoT devices are clustered based on the priority and deadline by implementing expectation–maximization (EM) clustering to decrease the computational complexity and bandwidth overhead. (ii) The clustered tasks are then scheduled by implementing a modified heap-based optimizer based on the QoS and service level agreement (SLA) constraints. (iii) Distributed resource management is performed by allocating resources to the tasks based on multiple constraints. The categorical deep Q network is the deep reinforcement learning model is implemented for this purpose. The dynamic nature of tasks from the IoT devices is addressed by performing preemption of tasks using the ranking method, where the tasks with higher priority, with a short deadline replaces less priority task by moving it into the waiting queue. The proposed model is experimented with in the iFogsim simulation tool and evaluated in terms of average response time, loss ratio, resource utilization, average makespan time, queuing waiting time, percentage of tasks satisfying the deadline and throughput. The proposed OSCAR model outperforms the existing model in achieving the QoS and SLA with maximal throughput and reduced response time. [ABSTRACT FROM AUTHOR]

Resource optimization is becoming a prime factor in the progress of Internet-based technology, Cloud Computing. A resource management model is highly required in cloud data center paradigms to utilize available resources effectively. Bin-Packing problem is an applicable combinatorial optimization for Virtual Machine (VM) to Physical Machine (PM) allocation to minimize the required PMs. In this paper, we have proposed an efficient resource allocation and management algorithm in two phases. During the first phase, a Load Balanced Multi-Dimensional Bin-Packing (LBMBP) heuristic for Virtual Machine (VM) to Physical Machine (PMs or host) allocation is introduced, considering multidimensional resources: CPU, RAM, and Network Bandwidth. In the Second Phase, to perform VM migration, a mechanism to detect overloaded and underloaded hosts based on outliers has been described. The proposed work illustrated the simulation results using CloudSim Plus Simulator and observed a reduction in the number of active PMs. Energy consumption and the number of migrations with improved resource utilization. [ABSTRACT FROM AUTHOR]

Fifth-generation mobile communication networks (5G)/Beyond 5G (B5G) can achieve higher data rates, more significant connectivity, and lower latency to provide various mobile computing service categories, of which enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low latency communications (URLLC) are the three extreme cases. A symmetrically balanced mechanism must be considered in advance to fit the different requirements of such a wide variety of service categories and ensure that the limited resource capacity has been properly allocated. Therefore, a new network service architecture with higher flexibility, dispatchability, and symmetrical adaptivity is demanded. The cloud native architecture that enables service providers to build and run scalable applications/services is highly favored in such a setting, while a symmetrical resource allocation is still preserved. The microservice function in the cloud native architecture can further accelerate the development of various services in a 5G/B5G mobile wireless network. In addition, each microservice part can handle a dedicated service, making overall network management easier. There have been many research and development efforts in the recent literature on topics pertinent to cloud native, such as containerized provisioning, network slicing, and automation. However, there are still some problems and challenges ahead to be addressed. Among them, optimizing resource management for the best performance is fundamentally crucial given the challenge that the resource distribution in the cloud native architecture may need more symmetry. Thus, this paper will survey cloud native mobile computing, focusing on resource management issues of network slicing and containerization. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) is one of the promising and effective approaches to establishing network virtualization by providing a central controller to monitor network bandwidth and transmission devices. This paper studies resource allocation in SDN by mapping virtual networks on the infrastructure network. Considering mapping as a way to distribute tasks through the network, proper mapping methodologies will directly influence the efficiency of infrastructure resource management. Our proposed method is called Effective Initial Mapping in SDN (EIMSDN), and it suggests writing a module in the controller to initialize mapping by arriving at a new request if a sufficient number of resources are available. This would prevent rewriting the rules on the switches when remapping is necessary for an n-time window. We have also considered optimizing resource allocation in network virtualization with dynamic infrastructure resources management. We have done it by writing a module in OpenFlow controller to initialize mapping when there are sufficient resources. EIMSDN is compared with SDN-nR, SSPSM, and SDN-VN in criteria such as acceptance rates, cost, average switches resource utilization, and average link resource utilization. [ABSTRACT FROM AUTHOR]

Summary: Over‐The‐Top broadcasts a huge number of medias that mobile network operators have to manage efficiently before to deliver it to their subscribers. We propose an economic pricing approach to address caching resource management issues in the 5G wireless networks and to overcome limitations in terms of throughput, latency, and reliability. Moreover, we consider this approach based on an oligopolistic multi‐market deducted from Cournot, Stackelberg, and Bertrand models. For simulation purpose, we consider the routing protocol (Ad‐hoc On‐Demand Distance Vector [AODV]) commonly used for the wireless network. We use the NS‐2 package, and we analyze results in terms of End‐to‐End delay representing latency, throughput, packet delivery ratio, and normalized network load. [ABSTRACT FROM AUTHOR]

Vehicular Edge Computing (VEC), based on the Edge Computing motivation and fundamentals, is a promising technology supporting Intelligent Transport Systems services, smart city applications, and urban computing. VEC can provide and manage computational resources closer to vehicles and end-users, providing access to services at lower latency and meeting the minimum execution requirements for each service type. This survey describes VEC’s concepts and technologies; we also present an overview of existing VEC architectures, discussing them and exemplifying them through layered designs. Besides, we describe the underlying vehicular communication in supporting resource allocation mechanisms. With the intent to overview the risks, breaches, and measures in VEC, we review related security approaches and methods. Finally, we conclude this survey work with an overview and study of VEC’s main challenges. Unlike other surveys in which they are focused on content caching and data offloading, this work proposes a taxonomy based on the architectures in which VEC serves as the central element. VEC supports such architectures in capturing and disseminating data and resources to offer services aimed at a smart city through their aggregation and the allocation in a secure manner. [ABSTRACT FROM AUTHOR]

In this article, we address the online resource allocation problem in service queuing systems under uncertainty. In particular, the optimal control policy is derived by using the real-time data, i.e., without fully knowing information about the system state. We employ the conditional value-at-risk to achieve the minimum long-run average cost subject to the constraints on the risk of instability. Then, we can ensure the service ability of the system at a high level under such on-the-fly uncertainty. We show that the proposed model naturally leads to a minmax saddle point optimization problem. We first present an intuitive offline primal–dual learning method, which can achieve a desirable convergence rate. Then, we further improve the algorithm by learning the optimal Lagrange multiplier concerning the instantaneous system state, i.e., online primal–dual learning algorithm, to achieve the same convergence rate as the offline algorithm. Besides, we demonstrate that the proposed method outperforms classical models in providing the lower average delay under uncertainty, which means it can improve the stability and provide a better quality of service under the ambiguity of uncertainty. A real case of inpatient bed allocation for hospital operations is presented to show the performance in applications. The results show that our model can reduce the average wait with certain total available beds under uncertain arrivals. [ABSTRACT FROM AUTHOR]

We investigate a novel multi-user mobile Virtual Reality (VR) arcade system for streaming scalable 8K 360° video with low interactive latency, while providing high remote scene immersion fidelity and application reliability. This is achieved through the integration of embedded multi-layer 360° tiling, edge computing, and wireless multi-connectivity that comprises sub-6 GHz and mmWave (millimeter wave) links. The sub-6 GHz band is used for broadcast of the base layer of the entire 360° panorama to all users, while the directed mmWave links are used for high-rate transmission of VR-enhancement layers that are specific to the viewports of the individual users. The viewport-specific enhancements can comprise compressed and raw 360° tiles, decoded first at the edge server. We aim to maximize the smallest immersion fidelity for the delivered 360 content across all VR users, given rate, latency and computing constraints. We characterize analytically the rate-distortion trade-offs across the spatiotemporal 360° panorama and the computing power required to decompress 360° tiles. The proposed solution consists of geometric programming algorithms and an intermediate step of graph-theoretic VR user to mmWave access point assignment. The results reveal a significant improvement (8–10 dB) in delivered VR user immersion fidelity and spatial resolution (8K vs. 4K) compared to a state-of-the-art method based on sub-6 GHz transmission only. We also show that an increasing number of raw 360° tiles are sent, as the mmWave network link data rate or the edge server/user computing power increase. Finally, we demonstrate that in order to hypothetically deliver the same immersion fidelity, the reference method would incur a much higher (2.5-4.5x) system latency. [ABSTRACT FROM AUTHOR]

In this article, we consider a class of decentralized resource allocation problems over directed networks, where each node only communicates with its in-neighbors and attempts to minimize its own cost when network-wide resource constraints as well as local capacity limits are satisfied. Decentralized optimization to solve this problem has been a significant focus within engineering research due to its advantages in scalability, robustness, and flexibility. Most existing methods are synchronous while few works are devoted to asynchronously solving the problem. The problem becomes even more challenging when the networks are directed. To address the resource allocation problem when the above issues are considered, we propose a novel decentralized asynchronous algorithm based on the gossip-based communication protocol and epigraph strategy. An important feature of the algorithm is that it is implemented in a completely decentralized manner in the case of asynchronous communication and directed networks. We provide theoretical proof to guarantee the convergence of the proposed algorithm, which indicates that it can successfully allocate the optimal resource. When solving the resource allocation problem over time-varying directed networks, we further discuss a related decentralized asynchronous algorithm according to the random sleep protocol. Numerical examples are given to demonstrate the viability and performance of the algorithms. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah, known as Wi-Fi HaLow, is envisioned for long-range and low-power communication. It is sub-1 GHz technology designed for massive Internet of Things (IoT) and machine-to-machine devices. It aims to overcome the IoT challenges, such as providing connectivity to massive power-constrained devices distributed over a large geographical area. To accomplish this objective, IEEE 802.11ah introduces several unique physical and medium access control layer (MAC) features. In recent years, the MAC features of IEEE 802.11ah, including restricted access window, authentication (e.g., centralized and distributed) and association, relay and sectorization, target wake-up time, and traffic indication map, have been intensively investigated from various aspects to improve resource allocation and enhance the network performance in terms of device association time, throughput, delay, and energy consumption. This survey paper presents an in-depth assessment and analysis of these MAC features along with current solutions, their potentials, and key challenges, exposing how to use these novel features to meet the rigorous IoT standards. [ABSTRACT FROM AUTHOR]

Cloud providers tend to support dynamic computing resources reallocation (e.g., Autoscaling) to handle the bursty workload for web applications (e.g., e-commerce) in the cloud environment. Nevertheless, we demonstrate that directly scaling a bottleneck server without quickly adjusting its soft resources (e.g., server threads and database connections) can cause significant response time fluctuations of the target web application. Since soft resources determine the request processing concurrency of each server in the system, simply scaling out/in the bottleneck service can unintentionally change the concurrency level of related services, inducing either under- or over-utilization of the critical hardware resource. In this paper, we propose the Scatter-Concurrency-Throughput (SCT) model, which can rapidly identify the near-optimal soft resource allocation of each server in the system using the measurement of each server’s real-time throughput and concurrency. Furthermore, we implement a Concurrency-aware autoScaling (ConScale) framework that integrates the SCT model to quickly reallocate the soft resources of the key servers in the system to best utilize the new hardware resource capacity after the system scaling. Based on extensive experimental comparisons with two widely used hardware-only scaling mechanisms for web applications: EC2-AutoScaling (VM-based autoscaler) and Kubernetes HPA (container-based autoscaler), we show that ConScale can successfully mitigate the response time fluctuations over the system scaling phase in both VM-based and container-based environments. [ABSTRACT FROM AUTHOR]

At present, great changes are taken place in the internal production management and resource allocation model of manufacturers. Under the premise of rational resource allocation, the completion period of products largely depends on the timeliness of resource allocation. The related studies mostly tackle the allocation of a single type of production resources in a single workshop, without considering much about the mutual influence between workshops. Through in-depth research on workshop manufacturing practices, this paper chooses to explore the planning, allocation, and demand prediction of manufacturing resources, which has long been a difficulty in workshop production. The research has great scientific research significance and practical value. The authors designed an algorithm based on the difference of the mean stagnation time of different production processes in the execution process, and used the algorithm to predict the number of production resources required in each period, before formulating the optimal configuration plan. This method is highly reasonable and applicable. After presenting a prediction method for the allocation demand of workshop manufacturing resources, the authors discussed whether the manufacturing resource allocation between different workshops is balanced in a fixed period. Then, a new idea was proposed for collaborative production between machines of different workshops in a specific environment, and an optimization algorithm was put forward to optimize the manufacturing resource allocation to machines facing the operation execution process. Through experiments, the authors compared the utilization rate of material, technological or human production resources in each period, and thereby verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Network resources and traffic priorities can be utilized to distribute requested tasks across edge nodes at the edge layer. However, due to the variety of tasks, the edge nodes have an impact on data accessibility. Resource management approaches based on Virtual Machine (VM) migration, job prioritization, and other methods were used to overcome this problem. A Minimized Upgrading Batch VM Scheduling (MSBP) has recently been developed, which reduces the number of batches required to complete a system-scale upgrade and assigns bandwidth to VM migration matrices. However, due to poor resource sharing caused by suboptimal VM utilization, the MSBP was unable to effectively ensure the global best solutions. In order to distribute resources and schedule tasks optimally during VM migration, this paper proposes the MSBP with Multi-objective Optimization of Resource Allocation (MORA) method. The major goal of this proposed methodology is to take into account different objectives and solve the Pareto-front problem to enhance lifetime of the fog-edge network. First, it formulates an NP-hard challenge for MSBP by taking into account a variety of factors such as network sustainability, path contention, network delay, and cost-efficiency. The Multi-objective Krill Herd optimization (MoKH) algorithm is then used to address the NP-hard issue using the Pareto optimality rule and produce the best solution. First, it introduces an NP-hard challenge for MSBP by accounting in network sustainability, path contention, network latency, and cost-efficiency. The Pareto optimality rule is then implemented to overcome the NP-hard problem and provide the optimum solution employing the Multi-objective Krill Herd optimization (MoKH) algorithm. This increases network lifetime and improves resource allocation cost efficiency. Finally, the simulation results show that the MSBP-MORA distributes resources more efficiently and hence increases network lifetime when compared to other traditional algorithms. [ABSTRACT FROM AUTHOR]

As resources are valuable assets, organizations have to decide which resources to allocate to business process tasks in a way that the process is executed not only effectively but also efficiently. Traditional role-based resource allocation leads to effective process executions, since each task is performed by a resource that has the required skills and competencies to do so. However, the resulting allocations are typically not as efficient as they could be, since optimization techniques have yet to find their way in traditional business process management scenarios. On the other hand, operations research provides a rich set of analytical methods for supporting problem-specific decisions on resource allocation. This paper provides a novel framework for creating transparency on existing tasks and resources, supporting individualized allocations for each activity in a process, and the possibility to integrate problem-specific analytical methods of the operations research domain. To validate the framework, the paper reports on the design and prototypical implementation of a software architecture, which extends a traditional process engine with a dedicated resource management component. This component allows us to define specific resource allocation problems at design time, and it also facilitates optimized resource allocation at run time. The framework is evaluated using a real-world parcel delivery process. The evaluation shows that the quality of the allocation results increase significantly with a technique from operations research in contrast to the traditional applied rule-based approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated movement in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time scales. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multi-objective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multi-objective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resource allocation. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes. [ABSTRACT FROM AUTHOR]

Mutualistic cooperative ambient backscatter communications (AmBC) have been proposed to improve the spectrum and energy efficiencies of Internet-of-Things (IoT) systems, where a primary link (from a primary transmitter to a primary receiver) and an AmBC link (from an IoT device to the same primary receiver) form a mutualism relationship. We note that hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we study a mutualistic cooperative AmBC network with HIs at all the active transceivers and a non-linear energy harvesting circuit at each IoT device. We derive closed-form rate expressions for both the AmBC and primary links and theoretically prove that the mutualism relationship between the AmBC and primary links is maintained under HIs, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. To maximize the weighted sum rate of all links in a cooperative AmBC network under HIs, we propose two resource allocation schemes for two scenarios with a single link and multiple AmBC links, respectively. For the single AmBC link case, we derive the optimal transmit power of the primary transmitter and the optimal power reflection coefficient of the IoT device in closed forms. For the scenario with multiple AmBC links, the weighted-sum-rate maximization problem is transformed into a convex one and solved with convex optimization tools. Computer simulations validate our theoretical results and that our proposed schemes outperform the benchmark schemes in terms of the weighted sum rate. [ABSTRACT FROM AUTHOR]

Dynamic resources management in reconfigurable processors often manifests as a hard online decision-making task, which should yield premier solutions that must meet Quality-of-Service (QoS) requirements while maximizing the system’s efficiency. Most prior works rely on a hard-to-train predictor to model the complicated relationships between processor configurations and performance. To decide the proper resource allocation, the predictor needs to tentatively evaluate a group of possible configurations, and then decide the best configuration for the workload. This tedious process has an expensive runtime overhead for resource configuration in processors. Besides, prior works focus on improving the prediction accuracy, however, higher performance prediction cannot guarantee a good system outcome. Inspired by recent advances in adversarial learning, we present a generative adversarial network (GAN)-based framework, Amphis, which can directly generate the on-demand processor configuration for any scheduled-in application. By evaluating Amphis on a reconfigurable processor with 18 different workloads, our results demonstrate that the GAN-based method provides tremendous overhead reduction (up to 90%) compared to the SOTA prediction-based method WNNM while providing higher resource utilization. [ABSTRACT FROM AUTHOR]

With the increasing complexity of modern design, mixed-cell-height designs have become more popular, which makes the legalization problem more challenging. In this article, a robust negotiation-based legalizer (NBLG) is proposed to reduce average displacement and maximum displacement for mixed-cell-height circuits with considering the fence region and technology constraints. By dissecting the main components of the negotiation-based method, we divide the placement grid in terms of placement sites and reformulate the legalization problem as a resource allocation task. We then allow all movable cells to gradually remove overlaps in a surrounding window with two individual techniques: 1) isolation point and 2) adaptive penalty function. We also adopt a deterministic multithreading technique to accelerate the convergence of our algorithm. Experimental results show that our legalizer achieved the minimal average displacement and maximum displacement in a reasonable runtime compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A cellular unmanned-aerial-vehicle (UAV)-enabled system is studied in this research, where multiple UAVs work cooperatively to upload the collected sensory data to the base station (BS). The UAVs form a two-tier network with two kinds of communication modes, i.e., UAV-to-Network (U2N) and UAV-to-UAV (UAV) communications. We investigate the communication resource allocation for UAVs to maximize the aggregate energy efficiency of the system. The formulated problem is mixed-integer and NP-hard. To make the formulated problem tractable, an efficient matching-based algorithm is proposed to complete the transmission mode selection and subchannel allocation, respectively. Then the transmission power is optimized based on game theory. The simulation results reveal that the uplink energy efficiency can be improved with the proposed scheme, compared with the maximum power strategy. [ABSTRACT FROM AUTHOR]

The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages. [ABSTRACT FROM AUTHOR]

Benefiting from the rapid progress of wireless communication and artificial intelligence, multi-agent collaboration opens up new opportunities for various fields. To facilitate multi-agent acting as a group, effective communication plays a crucial role. Recently, many efforts based on multi-agent reinforcement learning have been made to enable effective multi-agent communication under limited bandwidth or noisy channel. However, current methods do not explore wireless resource allocation strategy explicitly. Moreover, due to ignoring task-relevant significance of information, traditional wireless resource allocation schemes may fail to guarantee the transmission efficiency and reliability for multi-agent communication. To this end, in this paper, we propose a task-oriented communication principle for multi-agent communication. We model the task-oriented channel allocation problem as a decentralized partially observable Markov decision process and propose a multi-agent reinforcement learning framework as a solution. Specifically, we design a novel variational information bottleneck to extract task-relevant information from local observation. Furthermore, a task-oriented channel allocation mechanism is developed to choose the allocation pattern with maximum expected gain. Finally, a double attention mechanism is developed to motivate the efficient utilization of task-relevant information. Experimental results show that our method can improve the effectiveness and efficiency of multi-agent communication, enhancing collaboration performance compared to baselines. [ABSTRACT FROM AUTHOR]

The application of non-orthogonal multiple access (NOMA) to multi-layer multicast video streaming is envisioned to address the explosively growing demand for capacity which is dominated mostly by multimedia content. In this paper, a joint power allocation and subgrouping scheme is developed to enhance the performance of NOMA-based multi-layer multicast systems. An optimization problem is formulated to maximize the overall sum multicast rate whilst satisfying the maximum transmission power and proportional rate constraints. Due to the complexity of the optimization problem, we first derive two power allocation techniques for the 2-layer case considering arbitrary subgrouping which are based on the iterative implementation and closed-form analysis, respectively. We then generalize the low-complexity closed-form solution for the general multi-layer case. This scheme successively allocates power to each layer stream while assuring that the minimum target rate and proportionality are guaranteed, particularly for the transmission rate of the high-priority base layer stream. A sub-optimal joint power allocation and subgrouping scheme is also designed by incorporating the power allocation scheme into the proposed iterative subgrouping techniques. Simulation results show the effectiveness of the power allocation and subgrouping schemes in enhancing the sum multicast rate performance. In addition, the proposed power allocation scheme ensures substantially higher rates for the base layer stream, which is crucial in robust delivery of standard quality video to all users. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication has been designed to afford improvements in the traffic congestion and traffic accidents by directly exchanging information between nearby vehicles. However, V2V communication may have difficulty providing service reliability due to interference between V2V links and vehicle-to-cellular user equipment links. In this paper, we consider the transmit power control algorithm to minimize interference among cellular users and vehicles in the context of V2V communication underlaid uplink cellular networks. First, we formulate the problem, which is an NP-hard combinatorial optimization problem with linear constraints. Addressing this problem with traditional optimization methods is ineffective; therefore, we design and train a deep neural network to address this optimization problem. Computational complexity analyses and simulation results reveal that the proposed algorithm outperforms weighted minimum mean squared error (WMMSE), fixed transmit power, and Dinkelbach's methods, and achieves near-global optimum with lower computation complexity than the exhaustive search (ES). [ABSTRACT FROM AUTHOR]

Although Unmanned Aerial Vehicles (UAVs) are capable of significantly improving the information security by detecting the eavesdropper's location, their limited energy motivates our research to propose a secure and energy efficient scheme. Thanks to the common-message philosophy introduced by Rate-Splitting (RS), we no longer have to allocate a portion of the transmit power to radiate Artificial Noise (AN), and yet both the Energy Efficiency (EE) and secrecy can be improved. Hence we define and study the Secrecy Energy Efficiency (SEE) of a multi-carrier multi-UAV network, in which Cooperative Rate-Splitting (CRS) is employed by each multi-antenna UAV Base-Station (UAV-BS) for protecting their downlink transmissions against an external eavesdropper ($Eve$). Furthermore, we consider the challenging scenario in which CRS is employed by each multi-antenna UAV-BS to protect their corresponding downlink transmissions against an external $Eve$. We further consider a difficult scenario in terms of security in which only imperfect channel state information of $Eve$ is available at the Tx. Accordingly, we conceive a robust secure resource allocation algorithm, which maximizes the SEE by jointly optimizing both the user association matrix and the network parameter allocation problem, including the RS precoders, time slot sharing and power allocation. Due to the non-convexity of the problem, it is decoupled into a pair of convex sub-problems. Firstly, new two-tier intra-cell optimization problems are formulated for achieving $\xi$ -optimal solutions by iterative block coordinate decent programming. Then, the power of each sub-channel is optimized by formulating the associated power control problem. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [ABSTRACT FROM AUTHOR]