A Hybrid Algorithm for Optimized Resource Allocation under Constrained Task Schedule

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

The basic purpose of resource allocation is to make the most efficient allocation of available resources. It contains resources and the number of tasks. The proposed methodology has two types there are resource discovery and resource allocation. The Multiple Kernel Fuzzy C Means Clustering Algorithm (MKFCM) is utilized for resource discovery process. Depends on the MKFCM algorithm the recommended method is group the available resources. Thereafter the resources are allocated with the help of a hybrid optimization technique. Here, resource provisioning algorithm is hybrid with bat algorithm for hybridization approach. The experimental analysis of the proposed mechanism is evaluated based on cost value, memory utilization and time. The prospective strategies have been experimented using the Cloud simulator with Java as the working platform.

In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

Sparse resource allocation to shape a network dynamical process is studied. Specifically, we consider allocating limited distributed control resources among a subset of a network's nodes, to minimize the dominant eigenvalue of a linear dynamical spread process associated with the network. Structural characterizations of the closed-loop dynamics at the optimum are obtained. These results are then used to 1) develop constructive algorithms for optimal resource allocation, 2) identify limits on the control performance, and 3) understand the relationship between the network's graph and the optimal resource profile. While the focus here is on a simplified linear model, an exploratory study of the design's applicability to realistic stochastic and nonlinear spread processes is undertaken, via simulation examples. As a whole, this study advances a research thrust on disease spread control in networks, toward the realistic paradigm that control resources can only be allocated at a subset of network locations.

We consider the allocation of spectral and power resources every subframe (1~ms) on the uplink of the Long Term Evolution (LTE) wideband orthogonal frequency division multiple access (OFDMA) cellular network. System bandwidth is divided into multiple sub-bands. The fractional power control to manage interference is allowed to be a function of the sub-band. Moreover, the channel gains from the base-station to the mobiles (user equipment (UE) in LTE) can vary with the sub-band. The power and spectral resources are to be allocated among different UEs in a cell based on channel conditions, desired fairness/QoS, and constraints on power due to interference and power limitation at a UE. For $N$ UEs and $M$ sub-bands, we exploit the structure of the underlying optimization problem to obtain an $O(NM)$ algorithm for optimal resource allocation when a UE can be allocated resources in a non-contiguous manner in frequency. To implement this in an OFDMA system, we delineate a simple control signalling structure where the UE computes in $O(M)$ time a waterfilling solution every subframe it is assigned resources. When the allocation is restricted to contiguous allocation in frequency due to single carrier constraint in LTE, we convert the solution computed above in $O(NM)$ time into a feasible solution. For typical network scenarios, the solution thus computed is numerically shown to be very close to the optimal solution with no restrictions.

Fog Computing (FC) paradigm offers significant potential for hosting diverse delay-sensitive Internet of Things (IoT) applications. However, the limited resources of fog devices pose significant challenges for deploying multiple applications, particularly in heterogeneous and dynamic IoT scenarios, due to the absence of effective mechanisms for resource estimation and discovery. An efficient resource allocation strategy is crucial for meeting the Quality of Service (QoS) requirements of IoT applications while enhancing overall system performance. Identifying the optimal allocation strategy for IoT applications with multiple QoS parameters is a complex and computationally intensive challenge, classified as an NP-complete problem. This paper proposes a Multi-Objective Optimization Algorithm (MOOA) for optimal resource allocation using the Osprey Optimization Algorithm (OOA) to efficiently allocate available resources. The proposed algorithm was evaluated against existing approaches, including the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), under varying task loads ranging from 100 to 500 tasks. The simulation results demonstrate significant performance improvements, including an average reduction in execution time by 12.45% compared to PSO and 22.97% compared to GA, response time by 32.57% compared to GA and 24.45% compared to PSO, and completion time by 44.39% compared to GA and 33.23% compared to PSO. These findings highlight the proposed algorithm’s ability to efficiently handle task allocation in dynamic FC environments and its potential to address complex QoS requirements in real-world IoT applications.

Virtual MIMO, also known as Multiuser-MIMO, has been widely investigated to improve data transmission rate of various wireless uplink systems. However, virtual MIMO transmission leads to performance degradation due to the multiuser interference. To obtain good trade-off between the system throughput and transmission performance, we investigate joint user grouping and resource allocation under the consideration of system throughput and average MSE performance in uplink SC-FDMA systems. Based on linear MIMO detection, we first develop MSE-oriented user grouping criterion for evaluation of transmission performance, then propose a dynamic user grouping and optimal resource allocation algorithm with elastic average MSE constraint. We evaluate the proposed joint resource allocation algorithms in LTE uplink scenarios and the results show that it achieves alterable trade-off between system throughput and average MSE.

This work proposes a new model of reconfigurable intelligent surface (RIS) called cognizable RIS (CRIS) that is specifically designed to meet the unique demands of users who require extreme-ultra-reliable and low-latency Communication (xURLLC) in the sixth generation (6G) wireless networks. The programmable elements in the proposed CRIS unit can adapt to different modes of operation to provide significant performance gain. To improve reliability at the receiver, we integrate unmanned aerial vehicles with the CRIS module, which enhances network performance through beamforming and mobility. Our study focuses on maximizing the sum throughput in a multiple-input multiple-output scenario using the rate-splitting multiple access communication system. To achieve this, we introduce a novel hybridized multi-agent-based deep reinforcement learning (DRL) algorithm for optimal resource allocation that maximizes the sum throughput. We incorporate long-short-term memory (LSTM) networks into our proposed DRL to address the temporal dependencies due to stochastic channel conditions. By utilizing the proposed LSTM-based multi-agent DRL (MA-DRL) algorithm, we achieve notable gains of 11.7% and 26.9% in sum throughput over widely recognized DRL benchmark algorithms, all while adhering to xURLLC’s stringent maximum packet error probability constraint of 10−9.

: This project considers the dynamic spectrum management (DSM) problem whereby multiple users sharing a common frequency band must choose their transmit power spectra jointly in response to physical channel conditions including the effects of interference. The goal of the users is to maximize a system-wide utility function (e.g., weighted sum-rate of all users), subject to individual power constraints. The proposed work will focus on a general DSM problem formulation which allows correlated signaling rather than being restricted to the conventional independent orthogonal signaling such as OFDM. The general formulation will exploit the concept of 'interference alignment' which is known to provide substantial rate gain over OFDM signalling for general interference channels. We have successfully analyzed the complexity to characterize the optimal spectrum sharing policies and beamforming strategies in interfering broadcast networks and developed efficient computational methods for optimal resource allocations in such networks.

In intelligent transportation systems, wireless connected vehicles moving in platoons can improve roads' throughput. For managing driving status of the platoon, a lead vehicle transmits driving information to following autonomous vehicles by using multi-hop data dissemination. We study a novel data dissemination protocol which investigates a chain-based transmit rate control to reduce data dissemination latency. The optimal resource allocation algorithm is formulated to minimize the total dissemination latency of the platoon under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimization techniques. A novel dynamic programming algorithm is presented to solve the platooning resource allocation optimization, which uses backward induction to significantly reduce the resource allocation complexity. In addition, we interpret the vehicular platoon as one-dimensional Markov chain, and derive a closed form of dissemination latency. Simulations are carried out to evaluate the performance of the proposed dynamic programming algorithm. The numerical results show that our algorithm achieves optimal solutions with cutting off the complexity by orders of magnitude, while improving dissemination rate in the vehicular platoon.

The deployment of inference services at the network edge, called edge inference, offloads computation-intensive inference tasks from mobile devices to edge servers, thereby enhancing the former’s capabilities and battery lives. In a multiuser system, the joint allocation of communication-and-computation (C2) resources (i.e., scheduling and bandwidth allocation) is made challenging by adopting efficient inference techniques, batching and early exiting, and further complicated by the heterogeneity in users’ requirements on accuracy and latency. Batching groups multiple tasks into a single batch for parallel processing to reduce time-consuming memory access and thereby boosts the throughput (i.e., completed task per second). On the other hand, early exiting allows a task to exit from a deep-neural network without traversing the whole network, thereby supporting a tradeoff between accuracy and latency. In this work, we study optimal C2 resource allocation with batching and early exiting, which is an NP-complete integer programming problem. A set of efficient algorithms are designed under the criterion of maximum throughput by tackling the challenge. First, consider the case with batching but without early exiting. The target problem is solved optimally using a proposed best-shelf-packing algorithm that nests a threshold-based scheme, which selects users with the best channels and meeting the computation-time constraints, in a sequential search for the maximum batch size. Next, consider the general case with batching and early exiting. A low-complexity sub-optimal algorithm for C2 resource allocation is developed by modifying the preceding algorithm to exploit early exiting for latency reduction. On the other hand, the optimal approach is developed based on nesting a depth-first tree-search with intelligent online pruning into a sequential search for the maximum batch size. The key idea is to derive pruning criteria based on the simple greedy solution for the target problem without a bandwidth constraint and apply the result to designing an intelligent online pruning scheme. Experimental results demonstrate that both optimal and sub-optimal C2 resource allocation algorithms can leverage integrated batching and early exiting to double the inference throughput compared with conventional schemes.

The progressive adaptability of today’s mobile communication services and the prompt progressions in the mobile phones industry have steered to a remarkable development in the wireless market capacity. Notwithstanding the incessant advancement in the radio access technologies to manage with that growth. However, many remaining challenges, i.e., inefficient allocation and management of wireless network resources in Heterogeneous Wireless Networks (HWNs) when using the modern cellular mobile communications radio access technologies such as Wireless Local Area Network (WLAN)/Wi-Fi, Long-TERA Evolution (LTE) and Fifth Generation (5G)) still continue and needs to be addressed. This problem emanates from the enormous number of users sharing these resources, random distribution of wireless channels, scarceness of the wireless spectral resources, and the dynamic behavior of generated traffic. Therefore, this paper solves the identified problem by designing an Efficient Resource Allocation (ERA) algorithm to address the inefficient allocation of available resources versus quality of service (QoS) challenges. The obtained simulation results demonstrate the effectiveness of the proposed model to meet the objectives subject to limitations and constraints compared to other published works. On average, the proposed ERA algorithm, Joint Power Bandwidth Allocation (JPBA) algorithm, and Dynamic Bandwidth Allocation (DBA) algorithm have shown 98.9%, 85%, 81% throughput performance, respectively at a time interval of 100s.

One of the drawbacks of an orthogonal frequency division multiplexing (OFDM) system is the larger peak to averaged power ratio (PAPR). The PAPR reduction scheme is a complicated problem, especially when a resource allocation is considered jointly. In this paper, a joint algorithm for determining an optimal resource allocation with PAPR awareness is proposed. Firstly, an adaptive tone reservation assignment is performed by optimally choosing a group of subcarriers, whose channel gains are sufficiently high, to be the data subcarriers and the remaining subcarriers will be used as the reserved tones for reducing PAPR to the desired level. Secondly, an adaptive power adjustment for PAPR reduction technique is introduced for determining the allocated power of data subcarriers by using the water-filling approach with the joint PAPR and capacity constraints. By using the proposed joint algorithm, the PAPR threshold level is satisfied while the desired capacity could be achieved. From the results, the proposed algorithm achieves the better results comparing with a conventional partial transmit sequence (PTS) technique and the tone reservation (TR) technique in terms of PAPR, and probability of error performances, in which the side information is not required, in contrast with the other techniques.

Power information is an important guarantee for energy security. As an important technical means of safety management and risk control, video monitoring is widely used in the power industry. Power video monitoring system uses efficient processing of multimodal video data and automatically identifies abnormal events and equipment status, replacing human monitoring with machine. Video monitoring data of power substations usually contain both visual information and auditory information, and the data types are diversified. The multimodal video data provides a rich underlying data source for the intelligent monitoring function, but it requires multiple service forms for efficient processing. Most intelligent edge monitoring equipment are only equipped with lightweight computing resources and limited battery supply, limited resources, and weak local processing data capabilities. Power video monitoring system has the characteristics of distribution, openness, interconnection, and intellectualization. Its intelligent edge video equipment is widely distributed, which also brings convenience and also brings security risks in terms of data security and reliability. For the outdoor multimodal power video monitoring system scenario, this paper adopts the edge-cloud distributed system architecture to solve the problem of resource shortage and adopts the first proposed service function virtualization (SFV) to solve the problem of multimodal video data processing. At the same time, the problem of security protection is solved by introducing blockchain to establish trust among intelligent video equipment and service providers. Under the security protection of virtualized service consortium blockchain (VSCB), virtualization technology is introduced into the service function chain (SFC) to realize SFV and solve the resource optimal allocation problem of multimodal video data processing. The work mainly involves the joint mapping of virtual resources, physical resources, and the joint optimization of computing and communication resources. Problems such as large state space and high dimensionality of action space have an impact on resource allocation. The stochastic optimization problem of resource allocation is established as a Markov decision process (MDP) model, and SFV technology is used to optimize cost and delay. The resource allocation optimization algorithm (RAOA-A3C) based on asynchronous advantage actor-critic algorithm (A3C) is proposed. Simulation experiments show that the RAOA-A3C proposed in this paper is more suitable for high-dynamic, multidimensional, and distributed power video monitoring system scenario and has achieved better optimization results in reducing time delay and deployment costs.

Higher vocational colleges and universities should realize the optimal allocation of teaching resources to provide the necessary guarantee for the improvement of talent cultivation quality. The study puts forward the evaluation index system of teaching resource allocation for teaching resource allocation in higher vocational education, constructs the multi-objective allocation optimization model of teaching resources on this basis, determines the index weights by using the objective combination assignment method combining the principal component analysis method and entropy weight method, and applies NSGA-II algorithm to solve the model. Simulation analysis is carried out with several higher vocational colleges and universities in a city as an example, and the allocation optimization results of multiple teaching resources in higher vocational colleges and universities are obtained. After the optimization of resource allocation, the utilization efficiency and allocation efficiency of teaching resources in each college and university as a whole have been improved by 16.6% and 3.4%, respectively, and all of them tend to be in the state of equilibrium of allocation. The constructed teaching resource allocation optimization model can realize the optimization of teaching resource allocation and promote the reasonable allocation and utilization efficiency of teaching resources in higher vocational education.