Allocation algorithms for multicore partitioned mixed-criticality real-time systems by Luis Ortiz, Ana Guasque, Patricia Balbastre, José Simó

“…This is achieved through the implementation of a Mixed-Integer Linear Programming (MILP) algorithm. The second phase involves the allocation of tasks to cores, employing both, an additional MILP algorithm and a modified worst fit decrease utilisation approach. …”
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SPEEDING ALGORITHM FOR MINIMAX OPTIMIZATION OF ALLOCATION PROBLEM SOLUTIONS IN HOMOGENEOUS SYSTEMS by Rudolf Anatolyevich Neydorf, Artem Alexandrovich Zhikulin

“…(Such problems cannot be solved by the classical algorithm due to the limited timing budgets.) The results of processing these solutions have shown that the new modification does not solve the problem of NP-complete allocation tasks, but it provides a resource-time gain associated with the significant reduction in the exponential model index of the average solution time.…”
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Improved genetic algorithm based on rule optimization strategy for fibre allocation by Feng Tan, Zhipeng Yuan, Yong Zhang, Sheng Tang, Feng Guo, Shuai Zhang

“…Comparative simulations with the unimproved genetic algorithm and a stepwise greedy algorithm underscore the superiority of the improved genetic algorithm in resolving the fibre allocation problem.…”
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Q-LME: Q-learning-based local mutual exclusion algorithm for flying ad hoc networks by Sindhuja S, Mary Anita Rajam V

“…However, resource allocation and token management in FANETs remain unexplored. …”
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A survey on AI algorithms applied in communication and computation in Internet of vehicles by Yu KANG, Yaqiong LIU, Tongyu ZHAO, Guochu SHOU

Subjects: “…communication resource allocation;communication security;computation offloading;communication-computation integration;AI algorithm;deep reinforcement learning…”
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A survey on AI algorithms applied in communication and computation in Internet of vehicles by Yu KANG, Yaqiong LIU, Tongyu ZHAO, Guochu SHOU

Subjects: “…communication resource allocation…”
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Joint Optimization of Cache Placement and Bandwidth Allocation in Heterogeneous Networks by Weiqi Sun, Yong Li, Chunjing Hu, Mugen Peng

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GLOPS: A Hybrid Approach for Enhanced Scheduling in Cloud Computing Environments via Machine Learning-Based Process Prediction by Syed Abu Ammar Muhammad Zarif, Layla Kader Suma, Md. Sadat Rahman, Md. Motaharul Islam, Phuoc Hung Pham, Phong Dung Thi Nguyen, Nguyen Thanh Binh

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Cloud edge end network resource allocation for thermostatically controlled load aggregation regulation by Yi LIU, Xin WU

“…Thermostatically controlled load is a flexible load that controls temperature regulation, such as air conditioning and electric water heaters.As a crucial demand side resource, flexible aggregation and regulation of load clusters can fully mobilize clean energy consumption capacity and ensure the balance between supply and demand of the power grid.Due to the common occurrence of thermostatically controlled loads in commercial office buildings and residential areas, a relatively stable control and transmission method can be adopted.Therefore, an efficient hierarchical transmission network is introduced to achieve data transmission and information interaction between loads and the power grid, and to flexibly, real-time, and accurately utilize the adjustable potential of load clusters.Firstly, an information interaction architecture of load IoT which structured “central cloud-edge cloud-regional load controller-thermostatically controlled load”was proposed.Then, for the “end edge”part, considering the requirements of different aggregation control tasks, an improved clustering algorithm was used to classify the tasks and reduce transmission overhead.For the “end-side” part, an improved clustering algorithm was used to optimize the transmission distance.For the edge-cloud collaboration part, a subchannel resource allocation algorithm was designed based on stable matching and water injection algorithms.The binary particle swarm optimization algorithm was used to solve the task upload decision problem.Finally, the effectiveness of the proposed model and algorithm is verified through simulation, and comparative experiments are also conducted.…”
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Edge computing offloading policies and resource allocation considering system fairness in trusted environments by Shouyi YANG, Fukang LI, Ruimin REN

“…Considering the fairness problem in edge computing in an unreliable trust environment, the offloading decision and resource allocation problem of edge computing was studied for a multi-user multi-server scenario.Based on the proposed real-time trust model, system fairness was defined from the perspective of user satisfaction, a joint optimization model considering trust and fairness was established, and a trust-based task offloading and resource allocation (TOA) algorithm was proposed on this basis.The original NP-hard problem was converted into multiple deterministic optimization subproblems, the offloading strategy was determined based on the backtracking algorithm, and the optimization of resource allocation was accomplished by using the Lagrange multiplier method, the convex optimization toolbox, and the bifurcated iterative search method, so as to achieve system fairness and enhance user satisfaction.Simulation experimental results show that the proposed algorithm outperforms other benchmark algorithms.…”
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User-cooperative dynamic resource allocation for backscatter-aided wireless-powered MEC network by Huaiwen He, Chenghao Zhou, Feng Huang, Hong Shen, Yihong Yang, Shangsong Liang, Xinyuan Jin

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Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform by Sida Huang, Matilda Isaac, Yuan Gao, Yuji Dong, Jie Zhang, Bintao Hu

“…To minimise the total service delay (which includes the transmission delay and processing delay) among the user equipment (UEs) and UAVs, we propose jointly optimising the communication resource allocation for all UEs and UAVs, the computation resource allocation at each UAV, and the transmission power. …”
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Dynamic resource allocation scheme for multiuser OFDM–UWB systems based on user's rate by Chao CHENG, Zhi-hong QIAN, Chun-lan LI, Xue WANG

“…According to original dynamic resource allocation algorithms,a new dynamic resource allocation algorithm for multiuser OFDM-UWB system based on users'rates was proposed to minimize the total transmitting power whil sa-tisfy requests for QoS and data rate of all users.Qual ty of the system was improved running the subcarrier allocation and bit allocation algorithms based on fairness of users.In subcarrier allocation process,rate impact factor was used to com-pare influence of different subcarriers to a user.In allocation process,bits were allocated to each user's subcarriers first,then bits on each subcarriers were adjusted according to rate request of each user.In or to reduce the complexity system of the furthermore,a subcarrier and bit allocation scheme is employed through subcarrier grouping.Simulation results indicate that the algorithm can lower power consume,BER and operation complexity.…”
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Service aware base station processing resource allocation for centralized radio access network by Xinping ZHANG, Yuanyuan WANG, Lin TIAN, Shuliang HAO

“…Current research on resource allocation algorithmsmainly focuses on data centers,and cloud computing environments.These resource allocation algorithms did not consideRthe diversity of processing resources(CPU,memory,network bandwidth,FPGA,DSP)and the diversity of service types in the centralized base station,resulting in the low processing resource utilization.In ordeRto solve this problem,a service oriented base station processing resource allocation algorithm was proposed.Firstly,the Fisher partitionmethod was used to classify the base station’s processing resource requirements according to the user’s service request.Then,the resource allocation balancing strategy was used to allocate the base station processing resources.Experimental results show that the algorithm is effective to reduce the number of physi cal servers and improves the resource utilization of the servers,realizing energy conservation and communications.…”
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Resource Allocation of Multi-Satellite Beam Hopping Technology Based on System Capacity Maximization by Qing SHU, Xiaoning ZHANG, Zesong FEI

“…Unlike medium and high earth orbit satellites, LEO satellites have the characteristics of small path loss, low propagation delay and high deployment fl exibility.LEO satellites are widely used in real time services, global internet of things, emergency communication and other fi elds.In order to solved the problems of large diff erences in service demand distribution of terrestrial users and low resource utilization in LEO satellite communication systems, a resource allocation algorithm of LEO multi-satellite beam hopping technology based on convex optimization was proposed, which aimed to maximized the system capacity.For multisatellite beam hopping scenarios in LEO satellite communication system, the beam-hopping technology could fl exibly allocated system resources accorded to user needs.Considered the factor of co-channel interference, the service region division scheme based on load balancing and the inter-satellite resource allocation priority scheme was proposed.In order to improved system capacity and resource utilization, it jointly designed a time and power resource allocation algorithm based on convex optimization.The simulation results showed that the proposed scheme could eff ectively reduced co-channel interference and realized the ondemand allocation of system capacity without causing waste of resources.…”
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Joint scheduling and resource allocation based on reinforcement learning in integrated access and backhaul networks by Joeun Kim, Youngil Jeon, Junhwan Lee, Moon-Sik Lee, Taesoo Kwon

“…Because of the inherent nature of IAB, access and backhaul links share the same resource pool causing cross-link interference. To address this challenge, this paper investigates an algorithm based on reinforcement learning (RL) for joint scheduling and resource allocation (RA) problem, aiming to mitigate interference and enhance user data rates. …”
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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…The study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. …”
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Robust QoT Assured Resource Allocation in Shared Backup Path Protection Based EONs by Venkatesh Chebolu, Sadananda Behera, Goutam Das

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Opportunistic spectrum sharing protocol and optimal resource allocation based on OFDM cooperation relaying by Wei-dang LU, Xuan-li WU, Xue-jun SHA, Nai-tong ZHANG

“…An opportunistic spectrum sharing protocol based on OFDM cooperation relaying was proposed.When the primary user experiences weak channel conditions,it could not achieve its target rate.If the cognitive user could help the primary user achieve its target rate,it accessed the pectrum of the primary user in the cooperation way.In this access way,the cognitive user used a fraction of the subcarriers to amplify-and-forward the signal of the primary user,to help the primary user achieve its target rate.And uses the remained subcarriers to transmit its own signal.The resource allocation was analyzed in this cooperation way.An optimal resource allocation algorithm based on dual ethod was proposed,which maximized the rate of the cognitive user while g anteeing the primary user achieve its target rate.If the cognitive user could not help the primary user achieve its target rate,to make use the spectrum efficiently,the cognitive user accessed the spectrum of the primary user in the non-cooperation way,and uses the whole accessed spectrum to transmit its own signal.The simulation demonstrates the efficiency of the proposed spectrum sharing protocol as well as its benefit to both primary and cognitive user.…”
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Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO by Qusay Alghazali, Husam Al-Amaireh, Tibor Cinkler

“…This paper introduces a comprehensive framework for reducing energy consumption in MEC environments by leveraging advanced optimization techniques and energy-efficient resource allocation algorithms. We propose a novel approach that dynamically adjusts the computational resources based on the current network load and the type of services requested, thus minimizing unnecessary energy consumption. …”
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