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The proliferation of data-intensive mobile applications is causing latency to become an issue in mobile edge computing (MEC) systems. In this work, we propose a novel methodology that optimizes communication, computation, and caching configurations in MEC to minimize the mean latency experienced by mobile devices. Transmission and computation processes are modeled using M/G/1 queues to account for service rates and warm-up times. Our caching scheme includes time variables for each file at each edge server in determining when to discard files from storage. We theoretically analyze the latency experienced by mobile devices due to communication, computation, and caching, showing how MEC system latency depends on the offloading decisions of mobile devices, bandwidth and CPU resources, and expiration times of files in the storage of edge servers. Our method for solving the latency minimization problem consists of two main components: iNner cOnVex Approximation (NOVA) to deal with non-convexity in the optimization, and an online algorithm for preventing cache storage violations as new tasks arrive and are serviced by the MEC system. Simulation results show that our algorithm outperforms several baselines in minimizing latency, and verify the benefit of including different resource allocation variables in our optimization.

As network complexity increases because of the diversification of applications and services based on internet of autonomous things, it is difficult for humans to design the optimal control rule for software-defined networking controllers. Intelligence-defined networking, called IDN, is proposed to overcome this limitation through machine learning algorithms. Since the existing IDN approaches are mostly designed to optimize only the network quality of services, including throughput, jitter, and latency, the controllers don’t consider the importance of data in the applications and the services. This causes the controller to allocate insufficient resources to the crucial data flow, which leads critical problems, such as self-driving car accidents. To prevent this problem, we propose an importance-based IDN architecture that enables network controllers to manage network traffic with importance levels of data flows. Firstly, we devise an importance estimation scheme to set the importance level for the flows. Secondly, a dynamic resource allocation model of the controllers is developed by means of deep learning algorithms in order to make the optimal network resource. Additionally, an online learning mechanism based on weighted auto-labeling is adopted to continue enhancing the adaptability of the resource allocation model on runtime as the network conditions change. The evaluation results of the proposed architecture under various autonomous things scenarios show that the loss rate for data flows of higher importance is reduced by one-quarter compared to the case of a network controller without importance level and that the bandwidth waste ratio is reduced by ten percent compared to the rule-based model.

Edge computing (EC) offers cloud-like services at the edge of mobile networks to satisfy the delay-sensitive and rapid computation applications in meeting the demands of rapidly increasing mobile devices and other IoTs. EC is known to be constrained with limited resources that its efficacy greatly depends on an effective and efficient resource allocation to provide optimal resources utilization. Focusing on the fact, this paper presents an adaptive resource allocation mechanism, abbreviated as A-PBRA, for effective resources utilization in the EC paradigm. To realize optimal utilization, the available resources are allocated dynamically (adaptability) by considering the nature of the incoming requests. The proposed scheme shall adapt to the resource demands and priorities of the incoming requests. After identifying the received request which can be either priority-based or normal request, each of them is processed with three possibilities. The available resources are thus allocated as per the priorities of the incoming requests to satisfy the constraints accordingly. The proposed mechanism is adaptable to a maximum number of incoming requests along with optimizing the utilization of limited resources at the edge node. Extensive simulations were performed through ifogsim to evaluate the performance of the proposed method. Critical comparisons were made against closely related algorithms and techniques i.e., the NBIHA and the CORA-GT. The simulation results from the proposed scheme optimistically showing that it performed better in terms of resources utilization, average response time, task execution time, and energy consumption.

Edge computing has been recently introduced as a way to bring computational capabilities closer to end users of modern network-based services, in order to support existent and future delay-sensitive applications by effectively addressing the high propagation delay issue that affects cloud computing. However, the problem of efficiently and fairly manage the system resources presents particular challenges due to the limited capacity of both edge nodes and wireless access networks, as well as the heterogeneity of resources and services' requirements. To this end, we propose a techno-economic market where service providers act as buyers, securing both radio and computing resources for the execution of their associated end users' jobs, while being constrained by a budget limit. We design an allocation mechanism that employs convex programming in order to find the unique market equilibrium point that maximizes fairness, while making sure that all buyers receive their preferred resource bundle. Additionally, we derive theoretical properties that confirm how the market equilibrium approach strikes a balance between fairness and efficiency. We also propose alternative allocation mechanisms and give a comparison with the market-based mechanism. Finally, we conduct simulations in order to numerically analyze and compare the performance of the mechanisms and confirm the theoretical properties of the market model., Comment: Corrected typos and figure orientation. in IEEE Transactions on Mobile Computing

We design a novel encoding model based on Lagrange coded computing (LCC) for private, secure, and resilient distributed mobile edge computing (MEC) systems, where multiple base stations (BSs) act as masters offloading their computations to edge nodes acting as workers. A two-fold objective of the scheme is: i) efficient allocation of computing tasks to the workers; ii) providing the workers with appropriate incentives to complete their tasks. As such, each master must decide on its offloading requests to the workers including the allocated tasks and service fees to be paid. This problem is complex due to the following reasons: i) masters can be privately-owned or managed by different operators, i.e., there is no communication and no coordination among them; ii) workers are heterogeneous non-dedicated nodes with limited and nondeterministic transmission and computing resources. As a result, the masters must compete for constrained resources of workers in a stochastic partially-observable environment. To address this problem, we define the interactions between masters and workers as a direct stochastic first-price-sealed-bid (FPSB) auction. To analyze the auction, we represent it as a stochastic Bayesian game and develop a Bayesian learning framework to perfect the auction solution.

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in orthogonal frequency division multiple access (OFDMA)- non-orthogonal multiple access (NOMA) based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between a macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterward, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in OFDMA-NOMA based H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.

The performance of smart vehicle (SV) applications like autonomous driving and in-vehicle augmented reality based traffic information system depends on the Field of View (FoV) and the timely processing of the SVs sensor data. Vehicular networking (VN) technology can enhance the performance of these applications by enabling a SV to access the sensing and processing capabilities of other neighbouring SVs. The processing and storage capacity of a SV is limited compared to cloud servers and the communication link between two SVs is unreliable due to their mobility and the nature of wireless channels. Hence, developing efficient processing and sensing schemes for SVs and VNs can help in optimizing the performance of SV applications. In this paper, we propose Contextual Bandits (CB), Markov decision process (MDP) and deep Q-network (DQN) based sensing and processing schemes for VNs. Simulation results show that the proposed schemes outperform the baseline schemes in a variety of scenarios.

This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

In a public blockchain system applying Proof of Work (PoW), the participants need to compete with their computing resources for reward, which is challenging for resource-limited devices. Mobile blockchain is proposed to facilitate the application of blockchain for mobile service, in which the lightweight devices can participate mining by renting resources from the Cloud Computing Service Provider (CCSP), but CCSP usually does not have the information about the demand preference of users. In this paper, a contract model is adopted to address the cloud computing resource allocation and pricing problem in the mobile blockchain. In particular, an adverse selection contract solution is proposed to overcome the information asymmetry problem, and resource pooling is introduced to improve the stability of users rewards. Simulation results show that the information asymmetry problem is well overcome by adverse selection contract so that CCSP can obtain more utility than linear pricing contracts. Furthermore, the resource pooling could effectively improve the users and CCSPs utilities. When the size of the mining pool is large enough, it can achieve an improvement effect of more than 10 times. The effect of pool size and user type distribution on CCSPs utility is also studied.

The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research currently. In this work, we focus on addressing the computation migration oriented resource allocation problem among mobile edge devices. Specifically, we first construct a framework for Mobile Social Cloud(MSC), in which the mobile devices with rich resources are abstracted as resource suppliers and those resource-lacking devices are abstracted as resource demanders. Then, a mathematical model is formulated and an evolutionary algorithm is proposed to effectively solve this model based on decomposition, dominance and genetic operations. Moreover, the parallel computing is introduced to further improve the efficiency of the proposed algorithm. The experimental results indicate that the proposed algorithm outperforms the other state-of-the-art methods and it improves the calculation efficiency by about 178% (2 cores) and 262% (3 cores) by introducing parallel computing.

Apache Spark as a popular in-memory data analytic framework has been employed by various applications---such as machine learning, graph computation, and scientific computing, which benefit from the long-running process (e.g. executor) programming model to avoid system I/O overhead. Since the resource usages of long-running applications like iterative computation vary significantly over time, we find that peak demand based resource allocation policies lead to low cloud utilization in production environments. In this paper, we present a utilization aware resource provisioning approach for iterative workloads on Apache Spark (iSpark). iSpark aims to timely scale up or scale down the number of executors in order to fully utilize the allocated resources while taking the dominant factor into consideration. Testbed evaluations show that iSpark averagely improves the resource utilization of individual executors by 35.2% compared to vanilla Spark. Furthermore, we have extended iSpark to multi-tenancy cloud environments. Specifically, we extend the two-dimensional resource constraints (i.e., CPU and MeM) in iSpark to three-dimensional resource constraints (i.e., CPU, MeM and I/O) to include I/O performance in the cloud environment. Experimental results on virtual clusters with varying interferences show that iSpark with cloud extension improves the average job completion time by 68% compared to the default policy.

Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this paper, we design a differentially private combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time., 12 pages, 6 figures

Short-range wireless technologies will enable vehicles to communicate and coordinate their actions, thus improving people's safety and traffic efficiency. Whereas IEEE 802.11p (and related standards) had been the only practical solution for years, in 2016 a new option was introduced with Release of 14 long-term evolution (LTE), which includes new features to enable direct vehicle-to-vehicle (V2V) communications. LTE-V2V promises a more efficient use of the channel compared to IEEE 802.11p, thanks to an improved PHY layer and the use of orthogonal resources at the MAC layer. In LTE-V2V, a key role is played by the resource allocation algorithm and increasing efforts are being made to design new solutions to optimize the spatial reuse. In this context, an important aspect still little studied, is therefore that of identifying references that allow, first, to have a perception of the space in which the resource allocation algorithms move and, second, to verify the performance of new proposals. In this paper, we focus on a highway scenario and identify two algorithms to be used as a minimum and maximum reference in terms of the packet reception probability (PRP). The PRP is derived as a function of various parameters that describe the scenario and settings, from the application to the physical layer. Results, obtained both in a simplified Poisson point process scenario and with realistic traffic traces, show that the PRP varies considerably with different algorithms and that there is room for the improvement of current solutions.

Understanding mobile data traffic and forecasting future traffic trend is beneficial to wireless carriers and service providers who need to perform resource allocation and energy saving management. However, predicting wireless traffic accurately at large-scale and fine-granularity is particularly challenging due to the following two factors: the spatial correlations between the network units (i.e., a cell tower or an access point) introduced by user arbitrary movements, and the time-evolving nature of user movements which frequently changes with time. In this paper, we use a time-evolving graph to formulate the time-evolving nature of user movements, and propose a model Graph-based Temporal Convolutional Network (GTCN) to predict the future traffic of each network unit in a wireless network. GTCN can bring significant benefits to two aspects. (1) GTCN can effectively learn intra- and inter-time spatial correlations between network units in a time-evolving graph through a node aggregation method. (2) GTCN can efficiently model the temporal dynamics of the mobile traffic trend from different network units through a temporal convolutional layer. Experimental results on two real-world datasets demonstrate the efficiency and efficacy of our method. Compared with state-of-the-art methods, the improvement of the prediction performance of our GTCN is 3.2\% to 10.2\% for different prediction horizons. GTCN also achieves 8.4 $\times$ faster on prediction time.

Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider's and users' perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider's cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.

Federated Learning (FL) is an emerging technology for empowering various applications that generate large amounts of data in Intelligent Cyber-Physical Systems (ICPS). Though FL can address users’ concerns about data privacy, its maintenance still depends on efficient incentive mechanisms. For long-term incentivization to participants in data federation under dynamic environments, deep reinforcement learning as a promising technology has been extensively studied. However, the non-stationary problem caused by the heterogeneity of ICPS devices results in a serious effect on the convergence rate of existing single-agent reinforcement learning. In this paper, we propose a multi-agent learning-based incentive mechanism to capture the stationarity approximation in FL with heterogeneous ICPS. First, we formulate the secure communication and data resource allocation problem as a Stackelberg game in FL with multiple participants. Then, to tackle the heterogeneous problem, we model this multi-agent game as a partially observable Markov decision process. In particular, a multi-agent federated reinforcement learning algorithm is proposed to learn the allocation policies efficiently by dwindling variances in policy evaluation caused by interaction among multiple devices without the requirement of sharing privacy information. Moreover, the proposed algorithm is proved to attain convergence at an expected rate. Lastly, extensive experimental results demonstrate that our proposed algorithm significantly outperforms baseline approaches.

In the era of the sixth generation (6G), the deployment of massive Internet of Things (IoT) generates and processes large amounts of data, resulting in high energy demand and huge challenges to the energy-limited IoT devices. To achieve green and sustainable communication, energy harvesting is a feasible technology to prolong the lifetime of IoT. However, the existing energy harvesting architecture cannot guarantee the privacy of energy users while improving the intelligence and effectiveness of energy transmission. To solve these issues, we propose a differential privacy and intelligent reflecting surface empowered privacy-preserving energy harvesting framework for 6G-enabled IoT. First, a secure and intelligent energy harvesting framework is designed, which includes an intelligent reflecting surface-aided radio frequency power transmission mechanism and a differential privacy-based energy harvesting mechanism. Secondly, an exponential mechanism-based privacy-preserving energy harvesting scheme is established, where we analyze the adversary mode, propose the differential privacy-enabled location-preserving algorithm, and provide security analysis and proof. Third, we quantify the user satisfaction for energy harvesting and propose a deep reinforcement learning empowered resource allocation scheme to maximize the weighted satisfaction of all system users. Finally, simulation results show the effectiveness of the proposed secure and intelligent energy harvesting architecture for 6G IoT.

The development of Intelligent Cyber-Physical Systems (ICPSs) in virtual network environment is facing severe challenges. On the one hand, the Internet of things (IoT) based on ICPSs construction needs a large amount of reasonable network resources support. On the other hand, ICPSs are facing severe network security problems. The integration of ICPSs and network virtualization (NV) can provide more efficient network resource support and security guarantees for IoT users. Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources and security constraints to ensure the rationality and security of resource allocation in ICPSs. In particular, we use reinforcement learning (RL) method as a means to improve algorithm performance. We extract the important attribute characteristics of underlying network as the training environment of RL agent. Agent can derive the optimal node embedding strategy through training, so as to meet the requirements of ICPSs for resource management and security. The embedding of virtual links is based on the breadth first search (BFS) strategy. Therefore, this is a comprehensive two-stage RL-VNE algorithm considering the constraints of computing, storage and security three-dimensional resources. Finally, we design a large number of simulation experiments from the perspective of typical indicators of VNE algorithms. The experimental results effectively illustrate the effectiveness of the algorithm in the application of ICPSs.

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism.

The occurrence of a sudden, calamitous, and unfortunate event requires a quick response that can reduce damage and save lives, fulfilling the basic humanitarian needs of the affected population. National or international organizations and agencies may be initiated to provide assistance but local agencies need to act as soon as possible, because they are familiar with the geographical location as well as the local conditions of the affected areas. Effective coordination of disaster assistance is often crucial, particularly when resources are limited in the local agencies and the demands are more. In such conditions, it becomes a challenging job for the agencies to distribute resources so that the resources are optimally used. This article is initiated to address the challenging fact by introducing the concept of priority measure for demands. In this regard, ranking of the affected areas based on resource requirement for a disaster scenario is evaluated through a multicriteria decision-making process. A formula is developed to find the numerical value of priority factor collecting the responses of requirement of relief materials using information crowdsourcing. To this contingent, a three-echelon emergency solid transportation problem is formulated, in which the demand with the most priority is fulfilled by the local agencies in echelon-I, and the remaining demand is fulfilled by the other national or international agencies in echelon-II along with a restoration of local agencies in echelon-III. A numerical example is presented for the sake of model performance and to test the convergence using LINGO and CPLEX optimization solvers. The model is also designed using a genetic algorithm to obtain the optimal solution. Moreover, a brief analysis of the model is presented by considering 11 different numerical instances to enhance the efficacy of the model in real-life applications.

Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, called MULTISCALER, to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions. MULTISCALER is mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that the MULTISCALER approach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines, MULTISCALER produces significantly better reaction to changes in workloads even under the presence of noisy neighbors.

This paper proposes a primary and backup resource allocation model that provides a probabilistic protection guarantee for virtual machines against multiple failures of physical machines in a cloud provider to minimize the required total capacity. A physical machine allocates both primary and backup computing resources for virtual machines. When any failure occurs, the survived physical machines with preplanned backup resources recover the virtual machines on the failed physical machines and take over the workloads. The probability that the protection provided by a physical machine does not succeed is guaranteed within a given number. Providing the probabilistic protection can reduce the required backup capacity by allowing backup resource sharing, but it leads to a nonlinear programing problem in a general-capacity case against multiple failures. We apply robust optimization with extensive mathematical operations to formulate the primary and backup resource allocation problem as a mixed integer linear programming problem, where capacity fragmentation is suppressed. We prove the NP-hardness of considered problem. A heuristic is introduced to solve the optimization problem. The results reveal that the proposed model saves about one-third of the total capacity in our examined cases; it outperforms the conventional models in terms of both blocking probability and resource utilization.

Mobile edge computing (MEC) can significantly reduce latency by pushing resources away from remote clouds to distributed base stations (BSs) equipped with MEC servers, which are closer to users and deployed by service providers (SPs) at the edge of cellular networks. To improve user experience and increase their own revenue, SPs tend to use resources in their deployed BSs to provide services instead of using resources in BSs deployed by other SPs. We envision a densely-deployed multi-SP MEC network where a user equipment (UE) is covered by multiple BSs from different SPs. As the resource in BSs and MEC servers is limited, it is a challenging problem for SPs to reasonably allocate resources in the edge computing (EC) layer to improve the quality of service. In this paper, we propose a novel resource allocation scheme, Decentralized Multi-SP Resource Allocation (DMRA), which aims to maximize the total profit of all SPs at the EC layer and provide high-quality services. Then, we extend our design to the online scheme. The algorithm Decentralized Multi-SP Online Resource Allocation (DMORA) is proposed to fit the dynamic network environment. Simulation results indicate that our proposed schemes can effectively maximize the total profit of all SPs at the EC layer while improving user experience.

Wireless virtualization has emerged as one of the efficient ways to overcome numerous wireless networking challenges such as RF resource sharing. RF spectrum sharing in wireless networks focuses on increasing the usage efficiency of radio frequency (RF) spectrum with its increasing demand. To reduce uncertainties that accompany the spectrum sharing process caused by RF sensing, database-based spectrum sharing allows opportunistic spectrum access through occupancy information query of a spectrum database. In this paper, we propose a 2-level hierarchical spectrum allocation framework for multiple service-based virtual networks (VN) by leasing wireless resources from Wireless Infrastructure Providers (WIPs). The software defined network (SDN)-enabled virtualization configures the operating environment on the fly. This paper focuses on the competition between multiple VNs to get best RF channels to serve their users while eliminating the need for one-to-one contracts between VNs and WIPs. We develop a mathematical model for the spectrum allocation scheme which allows heterogeneous network configurations with better economic benefit. A trust-based security mechanism is also proposed for early detection and prevention of security attacks to maintain the availability attribute of the resource allocation process. Simulation results show that spectrum sharing is efficient, and assures service availability to perceived legitimate VN users.

In this article, a two-hop simultaneous wireless information and power transfer relay network using a decode-and-forward strategy is considered. In order to improve the spectral efficiency, the relay node performs in full-duplex (FD) mode, and each node is equipped with multiple antennas. First, the end-to-end achievable rate is formulated for FD and half-duplex (HD) modes under imperfect channel state information (ICSI). Then, the optimal closed-form expressions for power allocation at the source and relay, as well as the energy harvesting ratios, are derived for primal variables associated with the dual variables through analytical solutions. We propose an iterative primal-dual algorithm based on the ellipsoid method so as to obtain final answers. Numerical results demonstrate the FD achievable rate superiority over the HD mode under proper self-interference cancellation (SIC). According to the existing results, there is an immense discrepancy between the achievable rates in perfect CSI and ICSI cases. Also, the impact of the relay distance from the source node, the number of antennas, and different SIC modes are investigated on the end-to-end achievable rate.

Background: Worldwide, different strategies have been chosen to face the COVID-19-patient surge, often affecting access to health care for other patients. This observational study aimed to investigate whether the standard of burn care changed globally during the pan-demic, and whether country acute accent s income, geographical location, COVID-19-transmission pat-tern, and levels of specialization of the burn units affected reallocation of resources and access to burn care.Methods: The Burn Care Survey is a questionnaire developed to collect information on the capacity to provide burn care by burn units around the world, before and during the pandemic. The survey was distributed between September and October 2020. McNemar`s test analyzed differences between services provided before and during the pandemic, chi 2 or Fishers exact test differences between groups. Multivariable logistic regression analyzed the independent effect of different factors on keeping the burn units open during the pandemic.Results: The survey was completed by 234 burn units in 43 countries. During the pandemic, presence of burn surgeons did not change (p = 0.06), while that of anesthetists and dedi-cated nursing staff was reduced (< 0.01), and so did the capacity to manage patients in all age groups (p = 0.04). Use of telemedicine was implemented (p < 0.01), collaboration be-tween burn centers was not. Burn units in LMICs and LICs were more likely to be closed, after adjustment for other factors.Conclusions: During the pandemic, most burn units were open, although availability of standard resources diminished worldwide. The use of telemedicine increased, suggesting the implementation of new strategies to manage burns. Low income was independently associated with reduced access to burn care.(c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

In this paper, Interference Reduction Scheme for Device-to-Device (D2D) Mobile Groups Using Uplink NOMA (ISHU) is proposed to maximize the throughput of the network. To achieve this goal, we integrated uplink non-orthogonal multiple access (NOMA) in the D2D mobile groups (DMGs). DMGs improve the spectral efficiency by sharing the resources with cellular mobile users (CMUs) whereas, uplink NOMA in DMGs associate the large number of D2D mobile users (DMUs) with the D2D transmitter (DDT). ISHU jointly optimizes the user group association and resource allocation in the uplink NOMA-enabled DMGs. The problem of joint user association and resource allocation is formulated as a mixed integer non-linear programming. To address this problem, we divided the problem of interference mitigation in two sub-problems and solved it independently. Firstly, for joint user group association and subcarrier assignment, a 3-D matching game is designed between the DMUs, DDT, and sub-carriers, respectively. Secondly, to optimize the power of DMUs across each sub-carriers, the branch and bound (BB) technique is used. Also, to reduce the complexity of ISHU scheme, the successive convex approximation low complexity (SCALE) technique is used across each subcarrier.

Video surveillance in intelligent transportation systems (ITS) is in the rapid growth stage, where video analytics is a potential technology to improve the safety of the Internet of Autonomous Vehicles (IoAV). However, massive video data transmission and computation-intensive video analytics bring an overwhelming burden for vehicular networks. Moreover, owing to the unstable network connection, the video data are not always reliable, which makes data sharing lack of security and scalability in IoAV. In this work, we first propose a video analytics framework, where the multi-access edge computing (MEC) and blockchain technologies are integrated into IoAV to optimize the transaction throughput of the blockchain system as well as reducing the latency of the MEC system. Furthermore, based on deep reinforcement learning, the joint optimization problem is modeled as a Markov decision process (MDP), and the asynchronous advantage actor-critic (A3C) algorithm is adopted to solve this problem. Simulation results demonstrate that our approach can fast converge and significantly improve the performance of blockchain-enabled IoAV with MEC.

Under the paradigm of Edge Computing, the enormous data generated at the network edge can be processed locally. To make full utilization of these widely distributed data, we focus on an edge computing system that conducts distributed machine learning using gradient-descent based approaches. To ensure the system's performance, there are two major challenges: how to collect data from multiple data source nodes for training jobs and how to allocate the limited resources on each edge server among these jobs. In this paper, we jointly consider the two challenges for distributed training (without service requirement), aiming to maximize the system throughput while ensuring the system's quality of service (QoS). Specifically, we formulate the joint problem as a mixed-integer non-linear program, which is NP-hard, and propose an efficient approximation algorithm. Furthermore, we take service placement into consideration for diverse training jobs and propose an approximation algorithm. We also analyze that our proposed algorithm can achieve the constant bipartite approximation under many practical situations. We build a test-bed to evaluate the effectiveness of our proposed algorithm in a practical scenario. Extensive simulation results and testing results show that the proposed algorithms can improve the system throughput 56%-69% compared with the conventional algorithms.

As a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for the accurate and efficient processing of on-board data in vehicular edge computing (VEC). However, FL involves the training and transmission of model parameters, which consumes the vehicles' precious energy resources and takes up much time. It is a departure from many applications with severe real-time requirements in VEC. And the capabilities and data quality of each vehicle are distinct that will affect the performance of training the model. Therefore, it is crucial to select the appropriate vehicles to participate in learning tasks and optimize resource allocation under learning time and energy consumption constraints. In this paper, taking the vehicle position and velocity into consideration, we formulate a min-max optimization problem to jointly optimize the on-board computation capability, transmission power, and local model accuracy to achieve the minimum cost in the worst case of FL. Specifically, we propose a greedy algorithm to select vehicles with higher image quality dynamically, and it keeps the system's overall cost to a minimum in FL. The formulated optimization problem is a nonlinear programming problem, so we decompose it into two subproblems. For the resource allocation problem, we use the Lagrangian dual problem and the subgradient projection method to approximate the optimal value iteratively. For the local model accuracy problem, we develop an adaptive harmony algorithm for heuristic search. The simulation results show that our proposed algorithms have well convergence and effectiveness and achieve a tradeoff between cost and fairness.

In this paper, we propose a two-level adaptive resource allocation (TARA) framework to support vehicular safety message transmissions. In particular, three types of safety messages are considered in urban vehicular networks, i.e., event-triggered messages for urgent condition warnings, periodic messages for vehicular status notifications, and messages for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmissions for each vehicle group at the first level. To learn the implicit relationship between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, SRA scheme offline optimizes the allocation of sensing resources, i.e., choosing vehicles to provide perception data, and communication resources. With resources reserved for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real-time. Extensive simulation results demonstrate the effectiveness of TARA framework in terms of the high packet delivery ratio and low latency for message transmissions, and the high quality of collective environmental perception.

User-facing services are now evolving towards the microservice architecture where a service is built by connecting multiple microservice stages. Since the entire service is heavy, the microservice architecture shows the opportunity to only offload some microservice stages to the edge devices that are close to the end users. However, emerging techniques often result in the violation of Quality-of-Service (QoS) of microservice-based services in cloud-edge continuum, as they do not consider the communication overhead or the resource contention between microservices and external co-located tasks. We propose Nautilus, a runtime system that effectively deploys microservice-based user-facing services in cloud-edge continuum. Nautilus ensures the QoS of microservice-based user-facing services while minimizing the required computational resources, which is comprised of a communication-aware microservice mapper, a contention-aware resource manager and an IO-sensitive and load-aware microservice migration scheduler. The mapper divides the microservice graph into multiple partitions based on the communication overhead and maps the partitions to appropriate nodes. On each node, the resource manager determines the optimal resource allocation for its microservices based on reinforcement learning that may capture the complex contention behaviors. Once the microservices are suffered from external IO pressure, the IO-sensitive microservice scheduler migrates the critical one to idle nodes. Furthermore, when the load of microservices changes dynamically, the load-aware microservice scheduler migrates microservices from busy nodes to idle ones to ensure the QoS goal of the entire service. Our experimental results show that Nautilus can guarantee the required QoS target under external shared resources contention while the state-of-the-art suffers from QoS violations. Meanwhile, Nautilus reduces the computational resource usage by 23.9% and the network bandwidth usage by 53.4%, while achieving the required 99%-ile latency.

Various collision-avoidance assistance (CAA) systems, such as automatic emergency braking (AEB) and lane-keeping assistance (LKA), have been developed in the last decades to enhance the active safety of ground vehicles. Meanwhile, more electronic computing units (ECUs) have been embedded inside a vehicle to support the diversified CAA systems, which complicate the automotive electrical/electronic architecture and increase the cost. Instead of adding extra ECUs, we propose to allocate the existing implementation resources, i.e., the available processor time and memory space to the CAA systems, per individual driver's maneuver capabilities. As an illustrative example, we first show that two drivers can exhibit distinct maneuvers in a pre-crash situation on highway, according to which they can be classified as either steering-oriented or braking-oriented. Then, we design two CAA systems: an AEB and an LKA, based on the ultra-local model predictive control method. Furthermore, we show that by adjusting the prediction horizons of the two controllers, the implementation resources can be allocated to the two CAA systems in different fashions, which yields three control modes: standard mode, steering-enhanced mode, and braking-enhanced mode. Finally, by comparing the control performance of each driver-type/control-mode pair through both CarSim-Simulink joint simulations and driver-in-the-loop simulator experiments, we demonstrate that by allocating more resources to compensate for the weakness of a driver's maneuver, the CAA systems can provide enhanced driving safety by consuming the same overall amount of the implementation resources.

Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Vehicles require to make task offloading decisions in dynamic network conditions to obtain maximum computation efficiency. In this paper, we analyze computation efficiency in a VEC scenario, where a vehicle offloads its tasks to maximize computation efficiency as a tradeoff between computation time and energy consumption. Although, it is quite a challenge to ensure the quality of experience of the vehicle due to diverse task requirements and the dynamic wireless conditions caused by vehicle mobility. To tackle this problem, a computation efficiency problem is formulated by jointly optimizing task offloading decision and computation resource allocation. We propose a Mobility-Aware Computational Efficiency based Task Offloading and Resource Allocation (MACTER) scheme and develop a distributed MACTER algorithm that provides the best solution. We further consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to enhance the system performance. The simulation outcomes demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.

Recent years have witnessed a rapid growth of distributed machine learning (ML) frameworks, which exploit the massive parallelism of computing clusters to expedite ML training. However, the proliferation of distributed ML frameworks also introduces many unique technical challenges in computing system design and optimization. In a networked computing cluster that supports a large number of training jobs, a key question is how to design efficient scheduling algorithms to allocate workers and parameter servers across different machines to minimize the overall training time. Toward this end, in this paper, we develop an online scheduling algorithm that jointly optimizes resource allocation and locality decisions. Our main contributions are three-fold: i) We develop a new analytical model that considers both resource allocation and locality; ii) Based on an equivalent reformulation and observations on the worker-parameter server locality configurations, we transform the problem into a mixed packing and covering integer program, which enables approximation algorithm design; iii) We propose a meticulously designed approximation algorithm based on randomized rounding and rigorously analyze its performance. Collectively, our results contribute to the state of the art of distributed ML system optimization and algorithm design., Comment: IEEE Transactions on Network Science and Engineering (TNSE), accepted in July 2021, to appear

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various computation spots, each with very limited capacity. In this paper, we consider Function-as-a-Service (FaaS) edge-clouds where application providers deploy their latency-critical functions that process user requests with strict response time deadlines. In this setting, we investigate the problem of resource provisioning and allocation. After formulating the optimal solution, we propose resource allocation and provisioning algorithms across the spectrum of fully-centralized to fully-decentralized. We evaluate the performance of these algorithms in terms of their ability to utilize CPU resources and meet request deadlines under various system parameters. Our results indicate that practical decentralized strategies, which require no coordination among computation spots, achieve performance that is close to the optimal fully-centralized strategy with coordination overheads.

In intelligent transportation systems (ITS), the vehicular ad-hoc network (VANET) is an enabling technology that can provide information exchange services among connected and autonomous vehicles (CAVs). Video streaming over VANETs is a potential application to ensure the safety of drivers and passengers and improve infotainment services. However, owing to the dynamic network topology, video transmission in VANETs is very challenging in terms of latency, reliability, and security. Therefore, a comprehensive summary of the state-of-art video streaming over VANETs is surveyed in this work. Firstly, related works and background knowledge are introduced. Then, a systematic survey on resource allocation (RA) scheme for video streaming in VANETs is provided, and some prevailing and feasible optimization tools are elaborated. Furthermore, enabling technologies of video streaming over VANETs are summarized with a special focus on the integration of video communication, caching, and computing. Finally, we give some challenges and future research directions.

We assess the effects of land markets on misallocation and productivity both empirically and quantitatively. Exploiting variation from a land certification reform across time and space in Ethiopia, we find that certification facilitates rentals and improves agricultural productivity. We calibrate a quantitative macroeconomic model with heterogeneous household farms facing institutional costs to land markets using the micro panel data. The effect of a counterfactual reallocation from no rentals to efficient rentals increases zone-level agricultural productivity by 43 percent on average. While our estimated institutional costs are strongly associated with land certification across zones, there are nontrivial residual frictions to rental market activity, implying that land certification only partially captures the overall effects of rentals. A full certification reform accounts for about one-fourth of the overall productivity gains from land rentals. This result highlights the importance of comprehensive reforms alleviating frictions to land transactions beyond the granting of certificates.

Containers are lightweight and provide the potential to reduce more energy consumption of data centers than Virtual Machines (VMs) in container-based clouds. The on-line resource allocation is the most common operation in clouds. However, the on-line Resource Allocation in Container-based clouds (RAC) is new and challenging because of its two-level architecture, i.e. the allocations of containers to VMs and the allocation of VMs to physical machines. These two allocations interact with each other, and hence cannot be made separately. Since on-line container allocation requires a real-time response, most current allocation techniques rely on heuristics (e.g. First Fit and Best Fit), which do not consider the comprehensive information such as workload patterns and VM types. As a result, resources are not used efficiently and the energy consumption is not sufficiently optimized. We first propose a novel model of the on-line RAC problem with the consideration of VM overheads, VM types and an affinity constraint. Then, we design a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to solve the RAC problem. The CCGP can learn the workload patterns and VM types from historical workload traces and generate allocation rules. The experiments show significant improvement in energy consumption compared to the state-of-the-art algorithms.

Cloud computing is a popular and powerful paradigm for leasing IT services over the Internet. It allows sharing the resources among a large number of consumers. This requires allocating and releasing resources dynamically over time. Since resources are paid as are used, ensuring a better level of service is a challenge for cloud providers. They must provide enough resources to meet user needs with maintaining the quality of service. Therefore, QoS is a serious problem that arises during the dynamic management of heterogeneous resources. This type of problem can be resolved by minimizing execution time and resource reservation prices. It is necessary to integrate a new method well enough to improve the performance of the cloud, in this area. This paper presents a dynamic resource allocation model for the cloud computing environment. In addition, we propose a meta-heuristic approach named Multi-Objectives Symbiotic Organism Search algorithm (MOSOS) for resource allocation. We specifically designed MOSOS to minimize both the makespan and cost. Simulation results revealed that MOSOS gave a better result compared to other methods. It shown significant adaptation with the dynamic change of the cloud, as well as minimizing the execution time. Hence, improving the QoS given to cloud users.

Technological advances in fog computing are precipitating an evolution in conventional vehicle networks to a new paradigm called fog-enabled connected vehicle networks (FCVNs). FCVNs provide communication efficiency for ensuring safe transportation through the massive Internet of vehicles. In FCVNs, massive vehicles tend to associate with roadside units and high power nodes, which act as fog nodes (FNs), when they have a good channel quality and/or popular contents. This circumstance may lead to a load imbalance among the FNs. This problem significantly decreases the resource utilization efficiency and service capability of the networks. In this paper, we propose a dynamic resource orchestration (DRO) scheme to harmonize resource allocation for connected vehicles by migrating the offloaded services among FNs. A graph-theoretic approach is utilized to transform the FCVN into a directed graph model, where the maximum resource reduction obtained by service migrations is considered the weight of the link between every two FNs. Subsequently, the maximum weight matching solution is used to determine optimal pairs of FNs for migrating services to maximize network resource utilization. Our simulation results reveal that the proposed DRO scheme achieves significant improvements in terms of service capability, throughput, and resource utilization efficiency as compared with existing algorithms.

Multiple-input-multiple-output (MIMO) is a prom-ising technique in internet-of-things (IoT) Networks, which can effectively multiplex more IoT users and improve the spectrum efficiencies (SE) of the users. However, when the number of IoT users is large, the complexity becomes enormous. Moreover, perfect channel state information (CSI) is a basic assumption in MIMO systems, which is impractical with a large number of IoT users. In order to reduce the complexity and break the CSI limitation, an opportunistic beamforming (OBF) based IoT network is proposed, which can achieve high spectrum efficiency (SE) with low complexity and feedback under a large number of IoT users condition. Then, to solve the unfairness problem and further improve SE, a downlink multiple-weight and multiple receive antenna OBF system (MW-OBF-MRA) with proportional fairness (PF) strategy is proposed, where the transmitter and receiver are jointly designed. Moreover, a joint resource allocation scheme is provided to achieve the maximum SE with PF constraints. Numerical results show that our proposed system can achieve fairness with the minimum SE loss, and provide better SE than the other beamforming schemes with the limited feedback information.

For wireless caching networks, the scheme design for content delivery is non-trivial in the face of the following tradeoff. On one hand, to optimize overall throughput, users can associate their nearby APs with great channel capacities; however, this may lead to unstable queue backlogs on APs and prolong request delays. On the other hand, to ensure queue stability, some users may have to associate APs with inferior channel states, which would incur throughput loss. Moreover, for such systems, how to conduct predictive scheduling to reduce delays and the fundamental limits of its benefits remain unexplored. In this paper, we formulate the problem of online user-AP association and resource allocation for content delivery with predictive scheduling under a fixed content placement as a stochastic network optimization problem. By exploiting its unique structure, we transform the problem into a series of modular maximization sub-problems with matroid constraints. Then we devise PUARA, a Predictive User-AP Association and Resource Allocation scheme which achieves a provably near-optimal throughput with queue stability. Our theoretical analysis and simulation results show that PUARA can not only perform a tunable control between throughput maximization and queue stability, but also incur a notable delay reduction with predicted information.

This paper investigates intelligent reflecting surface (IRS) assisted wireless powered Internet of Things (IoT) networks. Specifically, multiple IoT devices first collect energy radiated from a power station (PS), then each device uses its harvested energy to support data transmission to an access point (AP) via frequency division multiple access (FDMA). In addition, an IRS aims to improve wireless energy transfer (WET) and wireless information transfer (WIT) capabilities using passive reflection beamformers. The system sum throughput, as a performance metric, is maximized evaluate the overall performance of the considered model, which is subject to the constraints of IRS phase shifts, transmission time scheduling, and bandwidth allocation. This problem is not convex with respect to multiple coupled variables, and cannot be directly solved. To circumvent this non-convexity, the transmission time scheduling and the bandwidth allocation are optimally designed in closed-form by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Moreover, an alternating optimization (AO) algorithm is used to optimally design the IRS’s phase shifts during the WET and WIT phases in an alternating fashion. Specifically, we propose element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) algorithms to iteratively derive the optimal phase shifts in closed-form. We also characterize the convergence behaviour of the proposed algorithms. Finally, numerical results are presented to validate the performance of the proposed scheme, where the benefits of the IRS are highlighted in terms of sum throughput, transmission time scheduling, and energy harvesting, compared with the benchmark schemes.

Resource allocation problems often manifest as online decision-making tasks where the proper allocation strategy depends on the understanding of the allocation environment and resource workload. Most existing resource allocation methods are based on meticulously designed heuristics which ignore the patterns of incoming tasks, so the dynamics of incoming tasks cannot be properly handled. To address this problem, we mine the task patterns from the large volume of historical allocation data and propose a reinforcement learning model termed IRDA to learn the allocation strategy in an incremental way. We observe that historical allocation data is usually generated from the daily repeated operations, which is not independent and identically distributed. Training with the partial dataset can make the strategy converged already, thereby wasting the remaining data. To improve the learning efficiency, we partition the whole historical big dataset into multi-batch datasets, which forces the agent continuously to ‘explore’ and learn on the distinct state spaces. We apply the proposed method to handle baggage carousel allocation at Hong Kong International Airport (HKIA). The experimental results show that IRDA improves the baggage carousel resource utilization by around 51.86% compared to the current baggage carousel allocation system at HKIA.

With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile (multi-access) edge computing (MEC), acquiring and processing data at network edge (like base station (BS)) via wireless links, has emerged as a promising technique for real-time applications. In this paper, we consider the scenario that multiple devices sense then offload data to an edge server/BS, and the offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) multiuser computation offloading. Particularly, we take the sequence of TDMA-based multiuser transmission/offloading into account. The studied problems are NP-hard and non-convex. A set of low-complexity algorithms are designed based on decomposition approach and exploration of valuable insights of problems. They are either optimal or can achieve close-to-optimal performance as shown by simulation. The comprehensive simulation results show that the sequence optimized TDMA scheme achieves better throughput performance than the NOMA scheme, while the NOMA scheme is better under the assumptions of time-sharing strategy and the identical sensing capability of the devices., Comment: To appear in IEEE Transactions on Wireless Communications