Efficient Resource Allocation Mechanism Research Articles

Karma economies for sustainable urban mobility – a fair approach to public good value pricing

City road infrastructure can be considered a public good, and congestion is the intersection of demand for mobility with the limited available transportation supply. Congestion pricing is effective in reducing demand to sustainable levels below network capacity limits, but also controversial, as it introduces equity issues and systematically discriminates against lower-income groups. Karma is a non-monetary, fair, and efficient resource allocation mechanism that employs an artificial currency different from money, incentivises cooperation amongst selfish individuals, and achieves a balance between giving and taking. Where money does not do its job, Karma achieves socially more desirable allocations by being aligned with consumers’ needs rather than their financial power. This work highlights the value proposition of Karma, provides guidance on Karma mechanism design, and equips the reader with a useful software framework to model Karma economies and predict consumers’ behaviour. A case study of road pricing demonstrates Karma’s potential.

Wi‐Fi 6‐based home area network for demand response in smart grid

SummaryThe past decade has brought prolific developments to power systems that range from large‐scale renewable integration to digital twins. Modern power systems consist of highly variable generation clusters. Hence, monitoring and prediction of power generation has become a vital part of today's power systems. On the other hand, detailed real‐time information of power usage is also a key parameter for the control algorithms used for the demand side management. With this information flow, power systems become smart grids which offer improved service and enable many advanced operations. In this regard, the home area network (HAN), which collects the power consumption/generation of household devices, plays a key role. So far, a number of technologies such as ZigBee, Z‐Wave, and Power Line Communication (PLC) have been considered for the HAN. Yet, widely used wireless local area network (WLAN) technology, Wi‐Fi, was not identified as a prominent candidate due to the limitations in its latency and reliability. However, with the introduction of the IEEE 802.11ax standard, Wi‐Fi 6 was developed, and it demonstrates guaranteed latency and reliability for its users. Thus, with its widespread deployment in households, Wi‐Fi is becoming a natural candidate for HAN. In this article, we present a comprehensive HAN protocol based on Wi‐Fi 6 which can achieve guaranteed latency and reliability for HAN users. The results show that when an efficient resource allocation mechanism is in place, using Wi‐Fi for HAN does not adversely affect the other Wi‐Fi users in the normal WLAN.

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

Federated learning for resource allocation in vehicular edge computing-enabled moving small cell networks

Moving networks comprising of moving small cells (MoSCs) is an emerging technology that provide ubiquitous connectivity to the cellular users in vehicular environment. MoSCs are the small cells deployed on the top of vehicles (city buses, trains, trams etc.) to support the vehicular users with improved quality-of-service (QoS). However, the deployment of small cells in vehicular environment demands for an efficient resource allocation mechanism. This is due to high mobility of MoSCs resulting in dynamic interference between MoSCs, high computational cost, privacy issues, latency issues, and high data transmission requirement. To overcome these issues, gated recurrent unit (GRU)-based federated learning (FL) model is proposed for resource allocation in moving networks. In our proposed work, we investigate resource allocation in vehicular edge computing (VEC)-enabled MoSC network with MoSCs deployed on trams travelling with deterministic mobility. In the proposed MoSC network, road side units (RSUs) equipped with VEC servers use their computational power to train the resource allocation model in a distributed manner, in which each RSU exploits the training data of its associated MoSCs to generate a shared model. The proposed GRU-based FL model enables RSUs to cooperatively train a global model that can predict resource block (RB) allocation to MoSCs without transmitting the historical data to the central server. We have conducted extensive system level simulations to determine key performance comparison between FL and centralized learning-based resource allocation in MoSC network.

Virtual Network Function Migration Considering Load Balance and SFC Delay in 6G Mobile Edge Computing Networks

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration.

Priority-based task scheduling and resource allocation in edge computing for health monitoring system

New and innovative wearable IoT devices for health monitoring systems (HMS) have been invented one after another. However, most of these devices are resource-constrained with restricted energy and computation power. The HMS data need to be processed via mobile edge computing (MEC) to improve the response time to fulfill the latency-sensitive and computation-intensive applications and to reduce bandwidth consumption. This paper presents an efficient task scheduling and resource allocation mechanism in MEC to meet these demands in contemplating emergency conditions under HMS. We propose a priority-based task-scheduling and resource-allocation (PTS-RA) mechanism that can assign different priorities to different tasks by considering the tasks' emergency levels computed with respect to the data aggregated from a patient's smart wearable devices. The mechanism can optimally determine whether a task should be processed locally at the hospital workstations (HW) or in the cloud. This is aimed to reduce the total task processing time and the bandwidth cost as much as possible. The proposed approach is to ensure that tasks related to the emergency are given higher priorities and to run first. After the tasks’ computations, results are sent to the doctor to response promptly with quick decisions. The proposed PTS-RA was benchmarked against state-of-the-art algorithms concerning average latency, task scheduling efficiency, task execution time, network usage, CPU utilization, and energy consumption. The benchmarking results are promising as PTS-RA is capable to manage the emergency conditions and is meeting the latency-sensitive tasks' requirements with reduced bandwidth cost.

An Efficient Multi-Dimensional Resource Allocation Mechanism for Beam-Hopping in LEO Satellite Network

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

Adaptive priority-aware LoRaWAN resource allocation for Internet of Things applications

LoRaWAN is the most adopted communication technology for the Internet of Things (IoT) applications for enabling flexible, cost-effective long-range communication with low power consumption. The deployment of a wide range of IoT devices brings many benefits to many applications, but new challenges arise from the network point of view with this deployment. For instance, IoT applications have different requirements in terms of Quality of Service (QoS) and energy efficiency in dense IoT scenarios. In this context, it is crucial to design an efficient resource allocation mechanism that enables real-time adjustments of radio-related parameters to improve the system’s scalability while providing QoS and reducing IoT devices’ energy consumption depending on the application requirements. This article proposes an adaptive priority-aware resource allocation mechanism to improve LoRaWAN scalability and energy consumption in a dense IoT scenario, called APRA. The simulation results show the benefits of APRA in improving the energy consumption by 95% and increasing the battery discharge time of the end device up to 5 years while yielding high packet delivery and low delay to high priority applications.

Fault tolerant resource allocation in fog environment using game theory‐based reinforcement learning

AbstractRealtime decision making is associated with an on‐demand, latency aware resource allocation. Fog nodes along with cloud infrastructure, when used effectively can ensure real‐time decision making. In this article, we propose an efficient resource allocation and fault tolerance mechanism for the fog layer. Our work takes the advantage of game theory, where Nash equilibrium is the initial allocation strategy, which is then passed on to the reinforcement learner. The allocation is done proactively based on the network status and traffic history. The performance of our system is compared with the existing open shortest path first and neural network algorithms. Besides, fault tolerance mechanism has also been proposed which takes the advantage of the fail‐over cluster formation to find the link failure and provide an alternate path in the smart switch, which is the networking component of the fog network. The proposed work gives an improved recovery time and average service time in case of failure with a recovery time of 32 ms. The experimental results are justified in terms of improved service time, lower delay, and optimal energy utilization.

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

With the development of the IoT (Internet of Things), sensors networks can bring a large amount of valuable data. In addition to be utilized in the local IoT applications, the data can also be traded in the connected edge servers. As an efficient resource allocation mechanism, the double auction has been widely used in the stock and futures markets and can be also applied in the data resource allocation in sensor networks. Currently, there usually exist multiple edge servers running double auctions competing with each other to attract data users (buyers) and producers (sellers). Therefore, the double auction market run on each edge server needs efficient mechanism to improve the allocation efficiency. Specifically, the pricing strategy of the double auction plays an important role on affecting traders’ profit, and thus, will affect the traders’ market choices and bidding strategies, which in turn affect the competition result of double auction markets. In addition, the traders’ trading strategies will also affect the market’s pricing strategy. Therefore, we need to analyze the double auction markets’ pricing strategy and traders’ trading strategies. Specifically, we use a deep reinforcement learning algorithm combined with mean field theory to solve this problem with a huge state and action space. For trading strategies, we use the Independent Parametrized Deep Q‐Network (I‐PDQN) algorithm combined with mean field theory to compute the Nash equilibrium strategies. We then compare it with the fictitious play (FP) algorithm. The experimental results show that the computation speed of I‐PDQN algorithm is significantly faster than that of FP algorithm. For pricing strategies, the double auction markets will dynamically adjust the pricing strategy according to traders’ trading strategies. This is a sequential decision‐making process involving multiple agents. Therefore, we model it as a Markov game. We adopt Multiagent Deep Deterministic Policy Gradient (MADDPG) algorithm to analyze the Nash equilibrium pricing strategies. The experimental results show that the MADDPG algorithm solves the problem faster than the FP algorithm.

Q-CSF: Quantum-Aware Compositional Scheduling Framework for Hierarchical Real-Time Systems

Component-based design has received considerable attention owing to its advantages in terms of security and safety when developing modern embedded systems. To effectively allocate computing resources to components in these systems, real-time component-based scheduling theory has been studied from various perspectives. The main advantage of component-based scheduling theory is that it guarantees the schedulability of an independent component and composability of multiple components. However, the existing component scheduling theory cannot be directly applied to real hardware platform due to an impractical assumption that resource allocation must be conducted across the continuum of real numbers, whereas actual operating systems (or virtualization systems) allocate resources in units of scheduling time quantum. In this study, we proposed a new efficient resource allocation and supply mechanism for quantumized hardware platforms while using real-number-based component interface. In simulation results with randomly-generated workloads, our approach reduced the overhead of existing approaches by up to 97.1% in an individual component. In composition of multiple components, our approach has up to 0.41 better acceptance ratio than existing approaches.

QoS controlled capacity offload optimization in heterogeneous networks

An efficient resource allocation mechanism in the physical layer of wireless networks ensures that resources such as bandwidth and power are used with high efficiency in spite of low delay and high edge user data rate. Microcells in the network are typically set with bias settings to artificially increase the Signal-to-Interference-Plus-Noise Ratio, thus encouraging users to offload to the microcell. However, the artificial bias settings are tedious and often suboptimal. This work presents a low complexity algorithm for maximization of network capacity with load balancing in a heterogeneous network without the need for bias setting. The small cells were deployed in a grid topology at a selected distance from macrocell to enhance network capacity through coverage overlap. User association and minimum user throughput were incorporated as constraints to enable closer simulation to real word Quality of Service requirements. The results showed that the proposed algorithm was able to maintain less than 10% user drop rate. The proposed algorithm can increase user confidence as well as maintain load balancing, maintain the scalability, and reduce power consumption of the wireless network.

How Governance Shapes Emergency Management: China's Mixed Records in Responding to COVID-19

A nation's governing system shapes its capacity for emergency preparedness and management. Designed to maintain the central government's absolute authority, China's governing system limits local governments’ initiative and capacity in responding to mass emergencies. By examining China's fight against COVID-19, this essay dynamically demonstrates how the country's governing regime constrains the initial response of local officials and other non-state entities to the virus but facilitated large-scale mobilization once the crisis was recognized by the central leadership. Four essential factors for an adaptive emergency management system are identified: 1) raising the central government's ability to recognize mass emergencies, 2) changing political incentives of local cadres, 3) creating a flexible and efficient ad-hoc resource allocation mechanism, and 4) embracing the participation of non-governmental actors. This study provides insights into how political realities explain the disparity in pandemic control performance across nations. It also shows how the resilience of a mass emergency management system can be enhanced within the constraints of existing governing institutions.

Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges.

The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising technology. Network virtualization is an enabler for network slicing, where the physical network can be partitioned into different configurable slices in the multi-domain heterogeneous converged optical networks. An efficient resource allocation mechanism for multiple virtual networks in network virtualization is one of the main challenges referred as virtual network embedding (VNE). This paper is a survey on the state-of-the-art works for the VNE problem towards multi-domain heterogeneous converged optical networks, providing the discussion on future research issues and challenges. In this paper, we describe VNE in multi-domain heterogeneous converged optical networks with enabling network orchestration technologies and analyze the literature about VNE algorithms with various network considerations for each network domain. The basic VNE problem with various motivations and performance metrics for general scenarios is discussed. A VNE algorithm taxonomy is presented and discussed by classifying the major VNE algorithms into three categories according to existing literature. We analyze and compare the attributes of algorithms such as node and link embedding methods, objectives, and network architecture, which can give a selection or baseline for future work of VNE. Finally, we explore some broader perspectives in future research issues and challenges on 5G scenario, field trail deployment, and machine learning-based algorithms.

EX ANTE EFFICIENT MECHANISM WITH PRIVATE ENTRY COSTS

This paper studies ex ante efficient resource allocation mechanism in an environment where agents endowed with their private values must incur private entry costs to participate. Due to the nature of this bidimensional screening problem and the difficulty in fully characterizing all implementable endogenous entries, the domain of the related social planner's problem that is essential for establishing the existence of efficient mechanism needs to be carefully designed. We find that a second‐price auction among the entrants with a reserve price equal to the seller's valuation is ex ante efficient, and any ex ante efficient mechanism must be ex post efficient. (JEL D44, D61, D82)

Energy-Latency Tradeoff for Dynamic Computation Offloading in Vehicular Fog Computing

Vehicular Fog Computing (VFC) provides solutions to relieves overload cloudlet nodes, reduces service latency during peak times, and saves energy for battery-powered cloudlet nodes by offloading user tasks to a vehicle (vehicular node) by exploiting the under-utilized computation resources of nearby vehicular node. However, the wide deployment of VFC still confronts several critical challenges: lack of energy-latency tradeoff and efficient resource allocation mechanisms. In this paper, we address the challenges and provide an Energy-efficient dynamic Computation Offloading and resources allocation Scheme (ECOS) to minimize energy consumption and service latency. We first formulate the ECOS problem as a joint energy and latency cost minimization problem while satisfying vehicular node mobility and end-to-end latency deadline constraints. We then propose an ECOS scheme with three phases. In the first phase, we propose an overload cloudlet node detection policy based on resource utilization. In the second phase, we propose a computational offloading selection policy to select a task from an overloaded cloudlet node for offloading, which minimizes offloading cost and the risk of overload. Next, we propose a heuristic approach to solve the resource allocation problem between the vehicular node and selected user tasks for energy-latency tradeoff. Extensive simulations have been conducted under realistic highway and synthetic scenarios to examine the ECOS scheme's performance. In comparison, our proposed scheme outperforms the existing schemes in terms of energy-saving, service latency, and joint energy-latency cost.

Economic and mathematical modeling as an effective tool of the analysis of economic processes in industry

In conditions of market volatility, an important issue for industrial enterprises is the issue of creating an efficient resource allocation mechanism. The article gives an example about using of individually adapted economic and mathematical model for forecasting the cost of materials and purchased products, that takes into account both internal and external factors affecting the planning figures. In order to create an effective predictive model, an analysis was conducted of statistical data for the period from 2009 to 2016, data was represented by high-tech enterprises of the radioelectronic industry. As a result of analysis it was revealed the presence of statistical regularities in the nature of the distribution of the analyzed data.On the basis of the calculated distribution parameters, a prediction procedure was performed using the exponential smoothing method and the total projected cost of materials and purchased products was obtained. The use of elements of probability theory and mathematical statistics, as well as methods for forecasting time series as basic methods of the model allows to take into account probabilistic economic factors, such as, for example, a change in the exchange rate of a foreign currency, as well as the presence of defects in the production process. Application of a special mathematical apparatus provides an ability to create a flexible, individually-adapted forecasting model. As a result of application of the model intended for forecasting the cost of materials and purchased products at one of industry enterprises it was revealed that the developed model has lover calculation error than the method that is used at the enterprise at present. Thus economic and mathematical model allows increasing the efficiency of the enterprise’s planned system and ensuring a rational resource allocation by increasing the accuracy of the forecasting process.

Efficient admission control and resource allocation mechanisms for public safety communications over 5G network slice

The deployment of a broadband public safety (PS) mobile network can be undertaken in different ways. One method involves combining commercial networks with private ones to reduce their deployment cost and time to market. In shared commercial networks, priorities should be defined to differentiate traffic not only between consumers and PS users but also between different PS organizations and type of services. Prioritization must also ensure that emergency calls are always served under normal conditions and during disasters. The recent advent of the fifth generation (5G) wireless standard introduces new technologies, such as network slicing (NS), which allows the provision of logical PS networks in a shared 5G system wherein each slice can be dedicated to an organization or to a type of service. However, 5G management and orchestration become a challenging task with NS, e.g., in handling resource allocation between slices with diverse requirements. Therefore, efficient solutions for slice resource allocation are required to facilitate this task. In this paper, we present a review of adaptive and dynamic resource allocation leveraging on heuristic and reinforcement learning-based algorithms that have been proposed in the recent literatures. The challenge in implementing these algorithms is to find the most suitable one for our problem, i.e., an algorithm that is highly scalable, able to solve problems immediately, and exhibits the best convergence properties in terms of speed and ability to find the global optimum.

An adaptive model for resource selection and allocation in fog computing environment

Fog computing is a computing infrastructure that supports data distribution between cloud and source in an efficient manner. Building an effective and generalized model for efficient resource selection and allocation mechanism seems to be complex in fog computing. Allocation and selection mechanisms in such environment fall into a class of multi-criteria decision-making (MCDM) problems. The existing MCDM methods may not be suitable for fog computing environment as these problems are distributed, scalable and dynamic. We propose an adaptive multi-criteria decision-making (A-MCDM) model to obtain an optimal ranking of alternatives in dynamic and scalable environments. The time complexity of the proposed A-MCDM is O(nm) in general, and it takes only O(m) time to assign a rank to an alternative where n is the number of criteria and m is the number of alternatives. The performance of the proposed model has been analyzed and found to be better than other MCDM methods.

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark (MPB) and show its nonseparable nature irrespective of its number of peaks. We then propose a composite MPB suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of large-scale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decomposition-based coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower-dimensional components. A novel aspect of the framework is its efficient bi-level resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200-D, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems.