Resource planning algorithm for reconfigurable information system development in the case of several sources of resources

Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but alsoconcave programming significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness.

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.

Resource allocation is an important part of non-orthogonal multiple access technology. How to allocate resources reasonably to achieve high service quality has become an important research topic. The existing resource allocation algorithms have the disadvantages of low system performance improvement, inability to achieve dynamic resource allocation, and high complexity. In view of the limitations of the existing resource allocation methods, this paper proposes to minimize the power consumption of the base station by obtaining the minimum power required by the terminal to reach the target rate under certain fairness constraints, and at the same time obtain the channel state through the base station. The information calculates the actual rate reached by the terminal in real time. If the rate is lower than a certain value, the resources are reallocated to achieve dynamic resource allocation. Finally, the simulation shows that the performance of the proposed algorithm is improved when the terminal is fixed. When the terminal moves, the system performance is greatly improved and the base station energy consumption is reduced.

УДОСКОНАЛЕННЯ НОРМАТИВНО-ПРАВОВОГО ЗАБЕЗПЕЧЕННЯ ПІДГОТОВКИ ФАХІВЦІВ ІТ-ПІДРОЗДІЛІВ ОРГАНІВ ДЕРЖАВНОЇ ВЛАДИ УКРАЇНИ

In this paper, we propose a dynamic event-based distributed reinforcement learning algorithm to address the resource allocation problem in a smart grid. The main challenge lies in the presence of unknown resource transfer functions and cost functions. Our objective is to discover a set of resource allocation solutions that minimise the total cost functions over a specified period. Our approach involves re-constructing a new convex optimisation problem to facilitate the approximation of Q-values in the reinforcement learning training process. Additionally, to avoid continuous communication, we integrate an event-based distributed optimisation approach to approximate the action-value function. We support our methodology with theoretical proofs that establish the validity of the event-triggered conditions and demonstrate the algorithm's convergence. We further validate the algorithm's feasibility through an illustrative example involving output power allocation in a smart grid.

In this paper, we present an energy-per-bit minimized radio resource allocation scheme in heterogeneous networks equipped with multi-homing capability, simultaneously connecting to different wireless interfaces. Specifically, we formulate an optimization problem related to minimization of energy-per-bit which takes a form of nonlinear fractional programming. Then we derive a parametric optimization problem out of that fractional programming and solve the original problem by using a double-loop iteration method. In each iteration, we derive the optimal resource allocation policy by applying Lagrangian duality and an efficient dual update method. In addition, we present suboptimal resource allocation algorithms using the properties of the optimal resource allocation policy. Numerical results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency.

In this letter, we investigate the adaptive resource allocation with Lyapunov optimization for time-varying wireless networks. The transmit power minimization is characterized by a stochastic optimization model. A dynamic resource allocation (DRA) algorithm is developed to accommodate the wireless network dynamics, i.e., time-varying wireless channels and as well the queuing dynamics. We investigate the tracking error between the DRA algorithm output and the target optimal resource allocation solution. Based on these results, we further develop an adaptive-compensation resource allocation (ACRA) algorithm, which iterates only once when the network state changes for saving the huge iteration overheads. Finally, we determine a sufficient condition that the ACRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis of our proposed scheme.

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

It is proposed that resource allocation algorithm considers rate proportion among the multicast groups in multicast orthogonal frequency division multiplexing (OFDM) downlink systems in this paper. However, it is minor significance to enforce the rate proportion strictly in actual mobile communication system. To solve this problem, the resource allocation algorithm is proposed to satisfy the rate proportion approximately in this text, including sub carrier allocation algorithm and power allocation algorithm. The former is achieved by modifying the existing unicast sub carrier assignment algorithm, the latter is Water-filling algorithm, which is capable of maximize the capacity when sub carrier assignment is determined. Simulation results show that compared to conventional algorithms, the proposed resource allocation algorithm has a good performance in capacity as well as rate proportion, which is a realistic method to solve the resource allocation in actual mobile communication system. In addition, we also discussed the capacity has a close relation with the number of multicast group.

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

The work reports the developed planning algorithms for optimum resource allocation over a dynamic 5G infrastructure, addressing all involved types of system resources, from the radio access domain to the MEC domain through the Optical Distribution Network (ODN). A modular modelling design is adopted considering these three domains. In that manner, allocation is composed of three optimization phases, which are executed sequentially and ensure optimal end-to-end allocation of resources. The first allocation phase focuses on the over-the-air resources, which are directly assigned to the 5G mobile users. The key entities for radio access are the Remote Radio Heads (RRHs), which are placed at the edge of the network fronthaul and provide wireless communication slots to the User Equipment (UE). Beamforming capabilities are also assumed allowing each RRH to support simultaneous and independent transmission of multiple directed beams to provide high quality targeted coverage. The resource allocation scheme is responsible for assigning subchannels within specific beams to address the bandwidth requirements of the requested services. The translation from the requested data-rate and the allocated bandwidth is based on the characteristics of 3GPP New Radio (NR) standard. Furthermore, the radio resource allocation process is responsible for assigning different bands for optimal frequency reuse within the available spectrum and avoids interference. The formulated ILP optimization problem, which is solvable in close to real-time due to its low computational complexity, allows dynamic adaptation to requests and resources, with a target to minimize energy consumption. The latter is achievable through the aggregation of wireless resources into individual beams and RRHs. In order to further enhance this process, an RRH prioritization algorithm is introduced for weighting the candidate allocations. With the completion of the first allocation phase, the optical resource allocation phase is initiated. The considered architecture follows that of the EU funded blueSPACE project and consists of a fully flexible ODN exploiting the cutting-edge capabilities of Spectrally Spatially Flexible Optical Networks (SS-FONs). In this context, the allocator considers the spectral and/or spatial multiplexing features of the optical nodes for performing elastic switching. The aim is to establish complete optical routes (lightpaths) from the radio access domain to the Central Office (CO). In particular, the output of this allocation phase is utilized to determine end-to-end fiber-based connections between BBUs and beams. The formulated optimization problem (a low complexity ILP) is able to model any type of network topology and provide optimal paths to dynamic demands according to the available optical domain resources. Power saving is achieved by minimizing the number of the optical elements used for building the required lightpaths. The last allocation phase assigns MEC resources within the blueSPACE virtual Content Delivery Network (vCDN) and Evolved Packet Core (vEPC). The services request MEC resources to offload computationally demanding tasks (such as real-time multimedia processing) and the allocator is responsible to make optimal decisions for the hosting of the required VM instances. The formulated ILP problem provides such a fast decision-making mechanism, while averting the scattering of allocated resources to numerous physical servers in order to conserve energy. With the termination of this third phase, all required network and computing resources are fully allocated for serving dedicated enhanced Mobile Broadband (eMBB) slices. The aforementioned resource allocation algorithms are implemented and simulated in MATLAB. For the evaluation process, two real-world simulation scenarios are considered to assess the allocation effectiveness and energy conservation capabilities for changing traffic requests. One scenario simulates a 5G deployment over a football stadium and the second scenario a deployment over a city park. Due to their coverage requirements the two scenarios lead to different RRH location and connectivity properties. Results show that in the “Stadium” scenario we achieve 50% savings in the number of activated RRHs and beams, while in the “City Park” scenario this metric reaches 67%. With respect to the allocation of optical networking resources, our optimization approach achieves conservation of up to 11% of optical ports and 14% of optical cores for the stadium scenario, while the corresponding results for the “City Park” scenario are 9% and 16%, respectively. Lastly, regarding the MEC resource allocation, the conducted simulations in the context of the “Stadium” scenario have shown 22% average reduction of the activated physical MEC servers (reaching up to 38%), while in the “City Park” scenario these savings average even higher to 68% (reaching up to 91%).

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.

Cloud based services have seen a steady increase in popularity in recent time due to high availability, low cost and ease of use. The performance of such services is determined by the resource allocation algorithms. Generic allocation algorithms like best fit or first fit are an adequate solution for generic services. When additional information is available about the applications that are deployed over the cloud, the information can be used by the resource allocator to make a more informed, and thus, more efficient, decision. We propose an alternative resource allocator for the miriaPOD platform, a distributed system for the virtualization of entire network topologies. The allocator exploits the unique memory sharing features of the virtualization backend. Although custom built for miriaPOD, the applied principles can be easily extended to other applications where memory sharing between guest systems is possible. A simulator is implemented to determine the performance of the proposed allocator compared to standard allocation algorithms.

This paper proposes a QoE-aware resource allocation (EARA) algorithm for D2D communication underlaying a cellular network. The proposed EARA algorithm takes into account the service completion time in spectrum resource allocation, and aims to complete the data service of a D2D user within its expected service completion time and meanwhile ensure a satisfying QoE of a D2D pair during the entire process of the data service. To this end, time is divided into a sequence of timeslots of fixed length and a parameter called k-QoE is introduced to measure the QoE of a D2D pair in the kth timeslot. The value of k-QoE is used to determine the priority of a D2D pair in resource allocation and a D2D pair with a smaller value of k-QoE is given a higher priority to obtain spectrum resources in order to ensure the QoE of the D2D user. Simulation results show the proposed EARA algorithm can achieve a better performance in terms of the average service completion satisfaction ratio, the average QoE of D2D pairs, and the overall throughput of the system as compared with a max-throughput resource allocation algorithm for D2D communication.

Survey of resource allocation schemes and algorithms in spectrally-spatially flexible optical networking