Adaptive Resource Allocation Scheme Research Articles

The optimization and operation of multi-energy-coupled microgrids by the improved fireworks algorithm-shuffled frog-leaping algorithm.

This study aims to address optimization and operational challenges in multi-energy coupled microgrids to enhance system stability and reliability. After analyzing the requirements of such systems within comprehensive energy systems, an improved fireworks algorithm (IFWA) is proposed. This algorithm combines an adaptive resource allocation strategy with a community genetic strategy, automatically adjusting explosion range and spark quantity based on individual optimization status to meet actual needs. Additionally, a multi-objective optimization model considering active power network losses and static voltage is constructed, utilizing the shuffled frog-leaping algorithm (SFLA) to solve constrained multi-objective optimization problems. Through simulation experiments on a typical northern comprehensive energy system, conducted with a scheduling period of T = 24, the feasibility and superiority of IFWA-SFLA are validated. Results indicate that IFWA-SFLA performs well in optimizing microgrid stability, managing electrical energy flow effectively within the microgrid, and reducing voltage fluctuations. Furthermore, the circuit structure and control strategy of microgrid energy storage bidirectional inverters based on IFWA are discussed, along with relevant simulation results.

Data-driven adaptive testing resource allocation strategies for real-time monitoring of infectious diseases

Since 2002, with the SARS outbreak, infectious diseases, including the ongoing COVID-19 pandemic, have continued to be a major global public health threat. It is critical to develop effective data science methods to quickly detect disease outbreaks and contain their rapid globalized spread. However, in practice, limited testing availability, and thus insufficient testing data poses significant challenges in effective analysis and real-time monitoring of infectious diseases, especially during early stages of a novel disease outbreak. To tackle these challenges, this article proposes adaptive testing resource allocation strategies integrated with a physics-informed model to dynamically allocate limited testing resources across different communities. The physics-informed model accounts for transmission dynamics and health disparities, enabling effective health risk assessment despite limited data. By integrating nonstationary Multi-Armed Bandit techniques that strike superior balance between exploring the communities with high uncertain risks and exploiting those with high risk levels, the proposed methodology facilitates test allocation to collect high-quality testing data for early outbreak detection. Theoretical analysis is carried out to evaluate the performance of the proposed allocation strategies, ensuring either sublinear or linear dynamic pseudo-regret under regularity assumptions. A comprehensive simulation study is conducted under three transmission scenarios to thoroughly evaluate the proposed methodology.

Multi-Objective Optimization Algorithm With Adaptive Resource Allocation for Truck-Drone Collaborative Delivery and Pick-Up Services

To efficiently implement the truck-drone collaborative logistics system, we introduce a multi-objective truck-drone collaborative routing problem with delivery and pick-up services (MCRP-DP). A truck collaborating with a fleet of drones serves three types of customers that require delivery, pick-up, and simultaneous delivery & pick-up services, respectively. Different from most of the existing studies where the drone visits only one customer in a flight, we allow the drone to serve another customer requiring pick-up service when it completes a delivery service. Meanwhile, we simultaneously optimize three objectives: transportation costs, waiting time of vehicles (i.e., truck and drone), and service reliability. To solve MCRP-DP, we propose an objective space decomposition-based multi-objective evolutionary algorithm with adaptive resource allocation (ODEA-ARA) In ODEA-ARA, an objective space decomposition strategy is used to maintain the diversity while an adaptive resource allocation strategy is designed to improve convergence. We design an ensemble of relative improvement and relative contribution to assist the resource allocation and a variable neighborhood Pareto local search integrating 7 problem-specific neighborhood structures to improve the solution. Extensive computational experiments are carried out to evaluate the performance of ODEA-ARA. The experimental results show that ODEA-ARA outperforms its competitors. Meanwhile, several useful managerial insights are presented.

EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.

Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building "smart libraries" to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.

Knowledge-driven adaptive evolutionary multi-objective scheduling algorithm for cloud workflows

Workflow scheduling in cloud platforms is a highly challenging issue because it faces multiple conflicting optimization objectives and large-scale decision variables. Most of the existing multi-objective workflow scheduling algorithms regard the focused problems as black boxes, and optimize large-scale decision variables as a whole. This leads to inefficiency in searching solution spaces that grow exponentially with the increase of decision variables. To compensate the above deficiency, this paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm, KAMSA for short, to optimize makespan and cost of workflow execution in cloud platforms. Specifically, we excavate the knowledge that adjustment of a task’s execution only affects its successor tasks to divide large-scale decision variables into a series of groups, so as to give play to the strengths of divide-and-conquer technology to improve the evolutionary search efficiency. Moreover, we develop an adaptive resource allocation scheme to reward more evolution opportunities for groups with high contributions to further improve the evolutionary search efficiency. We compare the proposed KAMSA with five state-of-the-art competitors in the context of 20 real-world workflows and the Amazon elastic compute cloud (EC2). The comparison results verify the KAMSA’s advantages by prevailing over the five competitors on 18 out of the 20 test cases with respect to the metric hypervolume.

Deep Reinforcement Learning for Online Resource Allocation in IoT Networks: Technology, Development, and Future Challenges

The growing number of complex and heterogeneous Internet of Things (IoT) applications has imposed a high demand for scarce communications and computing resources. To meet this stringent requirement, it is desirable to develop large-scale highly adaptive online resource allocation strategies to streamline existing network operations. Deep reinforcement learning (DRL), which combines the merits of reinforcement learning and deep learning, is capable of addressing complex decision-making tasks, thus enabling efficient online resource allocation. In this article, we present a DRL-based resource allocation framework. We begin a discussion on DRL basics and review its several recent applications. Then, we develop two new DRL algorithms that facilitate unlocking the potential of DRL and offer viable solutions to many more complex resource allocation problems. The first one tackles an optimization problem exposed to mixed (discrete and continuous) action spaces and bound by a number of highly non-linear quality-of-service (QoS) constraints. The second one extends the single-agent DRL to a more challenging multi-agent DRL by introducing a novel semi-distributed architecture. Finally, we discuss the challenges and future visions of applying DRL to real-world IoT networks.

Adaptive Cache Server Selection and Resource Allocation Strategy in Mobile Edge Computing

The enormous increase of data traffic generated by mobile devices emanate challenges for both internet service providers (ISP) and content service provider (CSP). The objective of this paper is to propose the cost-efficient design for content delivery that selects the best cache server to store repeatedly accessed contents. The proposed strategy considers both caching and transmission costs. To achieve the equilibrium of transmission cost and caching cost, a weighted cost model based on entropy-weighting-method (EWM) is proposed. Then, an adaptive cache server selection and resource allocation strategy based on deep-reinforcement-learning (DRL) is proposed to place the cache on best edge server closer to end-user. The proposed method reduces the cost of service delivery under the constraints of meeting server storage capacity constraints and deadlines. The simulation experiments show that the proposed strategy can effectively improve the cache-hit rate and reduce the cache-miss rate and content access costs.

Deep-Reinforcement-Learning-Based Resource Allocation for Cloud Gaming via Edge Computing

Compared with cloud computing, edge computing is capable of effectively solving the high latency problem in cloud gaming. However, there are still several challenges to address for optimizing system performance. On the one hand, the unpredictable bursts of game requests can cause server overload and network congestion. On the other hand, the mobility of players makes the system highly dynamic. Although existing research has studied game fairness and latency separately to improve the Quality of Experience (QoE), a tradeoff between fairness and latency has been largely ignored. Furthermore, how to balance network and computing load is identified as another constraint during optimization. Focusing on latency, fairness, and load balance simultaneously, we propose an adaptive resource allocation strategy through deep reinforcement learning (DRL) for a dynamic gaming system. The experimental results have demonstrated that the proposed algorithm outperforms the traditional optimization methods and classical reinforcement learning algorithms in solving complex multimodal reward problems.

Optimal resource allocation and adaptive robust control of technology innovation ecosystems based on cooperative game theory

This paper addresses a problem of optimal resource allocation for technology innovation ecosystem. Two layers goal are realized. First, an uncertain dynamical system is constructed with the Lotka–Volterra model for the description of the dynamics of the concerned innovation ecosystem, in which the growth rate and the symbiosis coefficient are specially treated as possibly (fast) time-varying uncertain parameters. Second, an adaptive robust resource allocation strategy is designed to drive the errors between the populations and the desired populations to be uniformly bounded and uniformly ultimately bounded, such that the first layer (basic) goal of resource allocation is achieved. Third, a two-player cooperative game is formulated for the seeking of the optimal control parameters, in which two (static) cost functions and a comprehensive performance index are constructed by performance analysis, and then the optimal control parameters (i.e., the Pareto-optimality) are obtained by minimizing the comprehensive performance index, such that the second layer (further) goal of optimal balance between the control inputs and the system performance is achieved. This work should be the first effort that addresses the optimal resource allocation and control for technology innovation ecosystem through a creative combination of dynamic modeling, control theory and game theory.

Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement Learning

The ever-expanding scale of cloud datacenters necessitates automated resource provisioning to best meet the requirements of low latency and high energy-efficiency. However, due to the dynamic system states and various user demands, efficient resource allocation in cloud faces huge challenges. Most of the existing solutions for cloud resource allocation cannot effectively handle the dynamic cloud environments because they depend on the prior knowledge of a cloud system, which may lead to excessive energy consumption and degraded Quality-of-Service (QoS). To address this problem, we propose an adaptive and efficient cloud resource allocation scheme based on Actor-Critic Deep Reinforcement Learning (DRL). First, the actor parameterizes the policy (allocating resources) and chooses actions (scheduling jobs) based on the scores assessed by the critic (evaluating actions). Next, the resource allocation policy is updated by using gradient ascent while the variance of policy gradient is reduced with an advantage function, which improves the training efficiency of the proposed method. We conduct extensive simulation experiments using real-world data from Google cloud datacenters. The results show that our method can obtain the superior QoS in terms of latency and job dismissing rate with enhanced energy-efficiency, compared to two advanced DRL-based and five classic cloud resource allocation methods.

Adaptive Access Control and Resource Allocation for Random Access in NGSO Satellite Networks

In non-geostationary orbit (NGSO) satellite networks, users perform random access (RA) to establish connections. Due to the non-uniform traffic demands of RA in time and space, dynamic access control and resource allocation methods with low signaling overhead and high RA efficiency are required, where the RA efficiency is denoted as the number of successfully accessed users per resource block. Existing dynamic adjustment methods provide a feasible approach to improve RA efficiency with short update intervals, but the frequent updates of these methods result in high signaling overhead. Compared with the short-term update, update with a given longer interval could reduce update frequency, while it will lead to a reduction in RA efficiency for the accuracy of long-term prediction decreases. Hence, to improve RA efficiency and decrease signaling overhead at the same time, we propose a new adaptive access control and resource allocation scheme in this paper, where the length of update interval is adaptive to the changes in traffic. First, average random access efficiency (ARAE) is defined to evaluate the RA efficiency between each update. Then, to decrease signaling overhead while keeping high ARAE, adaptive access control and resource allocation scheme is proposed, where the adaptive traffic prediction step is introduced to forecast future traffic with adaptively selected update interval, and the access control and resource allocation step is designed to maximize ARAE based on forecasted traffic. Finally, the effectiveness of the proposed scheme is corroborated by simulations, which indicate our scheme is adaptive to varying demands and outperforms other methods with high ARAE and low signaling overhead.

5G Hybrid System Design and Energy Efficient Resource Allocation Deployment

5G communication provide a promising platform for new, innovative and diverse enhanced mobile broadband (eMBB) and massive device connectivity applications, such as streaming media, machine vision and Internet of Things (IoT), real-time and dynamic data processing, intensive computation. However, 5G multimedia devices deployment relies on the coverage of base stations, which is inefficient and costly in wide-area coverage and physical penetration. In this paper, a 5G and wide-area Ad Hoc network fusion architecture is proposed to flexibly provide scalable 5G and extensible low-power devices interconnection liberated from geographical restriction, which consists of a low-power wide-area network and an edge processing gateway. Moreover, the intelligent edge gateway near a specific base station can support real-time ultra-high-definition video streams access and achieve traffic optimization by compressing, intelligent identification and preprocessing of the video streams to alleviate traffic congestion. The coverage capacity efficiency of wide-area Ad Hoc networks is restricted by the "funnel effect" in multihop cascading, and adaptive resource allocation strategies will present a promising approach to realize energy-efficient deployment. A non-convex optimization problem is formulated to maximize the energy-efficient deployment of Ad Hoc network. Then, a coordination and optimization strategy of internal resource allocation in deployed multihop nodes based on Lagrange relaxation algorithm was presented to solve the optimization problem. The actual system deployment and real measurement proved that the system function is running normally and stably. The experimental simulation test results show that the proposed 5G wide-area Ad Hoc network can effectively make up for the adaptive streaming needs of 5G coverage blind spots. Compared with static resource allocation, the proposed resource allocation and deployment scheme reduces energy consumption by 42.31%.

Parallel algorithm portfolios with adaptive resource allocation strategy

Parallel algorithm portfolios with adaptive resource allocation strategy

Joint Optimization for Mobile Edge Computing-Enabled Blockchain Systems: A Deep Reinforcement Learning Approach.

A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor–critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.

Controlling the Trade-Off between Resource Efficiency and User Satisfaction in NDNs Based on Naïve Bayes Data Classification and Lagrange Method

This paper addresses the fundamental problem of the trade-off between resource efficiency and user satisfaction in the limited environments of Named Data Networks (NDNs). The proposed strategy is named RADC (Resource Allocation based Data Classification), which aims at managing such trade-off by controlling the system’s fairness index. To this end, a machine learning technique based on Multinomial Naïve Bayes is used to classify the received contents. Then, an adaptive resource allocation strategy based on the Lagrange utility function is proposed. To cache the received content, an adequate content placement and a replacement mechanism are enforced. Simulation at the system level shows that this strategy could be a powerful tool for administrators to manage the trade-off between efficiency and user satisfaction.

Assessing Hospital Adaptive Resource Allocation Strategies in Responding to Mass Casualty Incidents – RETRACTION

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Decomposition based multiobjective evolutionary algorithm with adaptive resource allocation for energy-aware welding shop scheduling problem

Welding is an important industrial process that consumes a huge amount of energy. This paper addresses the energy-aware welding shop scheduling problem (EAWSSP) to minimize both makespan and energy consumption. A mathematical model is presented and a multiobjective evolutionary algorithm based on decomposition with adaptive resource allocation (MOEA/D-ARA) is proposed. Two initialization heuristics are designed to generate an initial population with certain quality and diversity. To effectively improve solutions located in different objective spaces, several objective-oriented search operators are designed. A cooperative search and a problem-specific local intensification are represented to balance the exploration and exploitation. An adaptive resource allocation strategy is developed to improve the computational efficiency of the algorithm. Computational experiments demonstrate the effectiveness of the adaptive resource allocation strategy, and statistical comparisons to the existing algorithms demonstrate the superiority of MOEA/D-ARA to solve EAWSSP. In addition, the application of MOEA/D-ARA to a real-world case also verifies the effectiveness of the proposed algorithm to minimize makespan and energy consumption of the EAWSSP.

Ensemble of Dynamic Resource Allocation Strategies for Decomposition-Based Multiobjective Optimization

Evolutionary algorithms via decomposition, namely, DEAs, decompose the original challenging problem and evolve a number of subproblems/subspaces concurrently in a cooperative fashion. Adaptive computational resource allocation (CRA) strategy is able to identify the efficiency of different subspaces and invest search effort on them accordingly in an online manner. A crucial issue for CRA is to measure the efficiency of subspaces. Unfortunately, existing approaches for efficiency measurement are either fitness improvement oriented or contribution oriented, which struggle to capture the potentials of subspaces accurately. To mitigate such drawback, we present an ensemble method for CRA, based on the recent fitness contribution rates (FCRs) and fitness improvement rates (FIRs) of subspaces simultaneously. In order to dynamically track the potential of each subregion, we adopt two memory matrices to record FIR and FCR for multiple subspaces over recent generations, respectively. Afterward, an aptitude vector indicating the potentials of subspaces is defined by exploiting FCR and FIR with memory and decaying scheme. On the basis of above strategies, an ensemble CRA (ECRA) scheme is designed, which is then embedded into an adaptive objective space partition-based DEA, termed ECRA-DEA, for solving the multi/many-objective optimization. Extensive experimental studies for ECRA-DEA on various types of challenging problems have been carried out and the results confirm that ECRA is effective. Besides, the competence of ECRA-DEA is empirically validated in comparison with state-of-the-art designs. The proposed ECRA paves a new way to leverage the capability of DEAs on handling complex problems.

Assessing Hospital Adaptive Resource Allocation Strategies in Responding to Mass Casualty Incidents.

Hospitals are expected to operate at a high performance level even under exceptional conditions of peak demand and resource disruptions. This understanding is not mature yet and there are wide areas of possible improvement. In particular, the fast mobilization and reconfiguration of resources frequently result into the severe disruption of elective activities, worsening the quality of care. This becomes particularly evident during the on-going coronavirus disease 2019 (COVID-19) pandemic. More resilient resource allocation strategies, that is, which adapt to the dynamics of the prevailing circumstance, are needed to maximize the effectiveness of health-care delivery. In this study, a simulation approach was adopted to assess and compare different hospital's adaptive resource allocation strategies in responding to a sudden onset disaster mass casualty incident (MCI). A specific set of performance metrics was developed to take into consideration multiple objectives and priorities and holistically assess the effectiveness of health-care delivery when coping with an MCI event. Discrete event simulation (DES) and system dynamics (SD) were used to model the key hospital processes and the MCI plan. In the daytime scenario, during the recovery phase of the disaster, a gradual disengagement of resources from the emergency department (ED) to restart ordinary activities in operating rooms and wards returned the best performance. In the night scenario, the absorption capacity of the ED was evaluated by identifying the current bottleneck and assessment of the benefit of different resource mobilization strategies. The present study offers a robust approach, effective strategies and new insights to design more resilient plans to cope with MCIs. It becomes particularly relevant when considering the risk of indirect damage of emergencies, where all the available resources are shifted from the care of the ordinary to the "disaster" patients, like during the on-going COVID-19 pandemic. Future research is needed to widen the scope of the analysis and take into consideration additional resilience capacities such as operational coordination mechanisms among multiple hospitals in the same geographic area.

Assessing Hospital Adaptive Resource Allocation Strategies in Responding to Mass Casualty Incidents.

Hospitals are expected to operate at a high-performance level even under exceptional conditions of peak demand and resource disruptions. This understanding is not mature yet, and there are wide areas of possible improvement. In particular, the fast mobilization and reconfiguration of resources frequently result into the severe disruption of elective activities, worsening the quality of care. More resilient resource allocation strategies, ie, which adapt to the dynamics of the prevailing circumstance, are needed to maximize the effectiveness of health-care delivery. In this study, a simulation approach was adopted to assess and compare different hospital's adaptive resource allocation strategies in responding to a mass casualty incident (MCI). A specific set of performance metrics was developed to take into consideration multiple objectives and priorities and holistically assess the effectiveness of health-care delivery when coping with an MCI event. Discrete event simulation (DES) and system dynamics (SD) were used to model the key hospital processes and the MCI plan. In the daytime scenario, during the recovery phase of the emergency, a gradual disengagement of resources from the emergency department (ED) to restart ordinary activities in operating rooms and wards, returned the best performance. In the night scenario, the absorption capacity of the ED was evaluated by identifying the current bottleneck and assessment of the benefit of different resource mobilization strategies. The present study offers a robust approach, effective strategies, and new insights to design more resilient plans to cope with MCIs. Future research is needed to widen the scope of the analysis and take into consideration additional resilience capacities, such as operational coordination mechanisms, among multiple hospitals in the same geographic area.