Dynamic Allocation Scheme Research Articles

Queue-based and learning-based dynamic resources allocation for virtual streaming media server cluster of multi-version VoD system

Nowadays, video-on-demand (VoD) providers offer multiple-quality video streaming services to users, called as multi-version VoD. Unlike traditional VoD, multi-version VoD providers should consider to allocate bandwidth resource and transcoding computation resource simultaneously. However, most of existing resource allocation works only focused on cost reduction or bandwidth optimization, and they did not consider to allocate transcoding computation resources for multi-version VoD systems. Therefore, how to allocate bandwidth resource and transcoding computation resource simultaneously for multi-version VoD systems is still one major challenge. In this paper, we propose a queue-based and learning-based dynamic resources allocation strategy (QLRA) for virtual streaming media server cluster of multi-version VoD system. First, we analyze the user behavior habits and build the virtual streaming media server cluster as an M/G/n queue system. Based on queueing theory, we can allocate initial resources for virtual streaming media server cluster of multi-version VoD system. Second, taking the changes of the user arrival rate and the workload of multi-version VoD system as feedbacks, we introduce learning automaton to allocate resources dynamically for virtual streaming media server cluster. Third, we evaluate QLRA with other methods, and results show the correctness and effectiveness of our strategy.

On Dynamic Spectrum Allocation in Geo-Location Spectrum Sharing Systems

Spectrum sharing is a key technology to relieve the ever-increasing spectrum demand and realize the full potential of radio spectrum. In this paper, we study spectrum sharing between higher priority users and lower priority users under geo-location based spectrum sharing systems. We consider a dynamic spectrum allocation scheme that allocates spectrum to a higher priority user based on its spectrum need that can be determined by its traffic load. The lower priority users utilize the unallocated spectrum. In addition to studying the performance of higher priority users with this dynamic spectrum allocation scheme, we also investigate the impact of this scheme on spectrum availability and stability of lower priority users. We develop a mathematical model to analyze the performance. The simulation results indicate that spectrum sharing is efficient, and the spectrum is abundant and relatively stable to lower priority users, even when the system is moderately loaded with higher priority users.

The Application of Antenna Diversity to NOMA With Statistical Channel State Information

In this paper, antenna diversity is used to improve the outage performance of downlink non-orthogonal multiple access (NOMA) systems combined with single-user multiple-input multiple-output (SU-MIMO) due to the statistical channel state information (CSI), that the transmitter only knows the statistical characteristic associated with each user's channel matrix. We design the precoding and detection vectors, which makes the proposed MIMO-NOMA system reduce to a single-antenna NOMA system with statistical CSI. Next, a dynamic power allocation scheme is proposed to maximize the ergodic sum rate of the considered NOMA group with the minimum ergodic rate constraints of the weaker users. Under this power allocation, we analyze the impact of user clustering and then develop a novel user clustering algorithm. Also, the outage analysis indicates that the proposed system only obtains receive diversity and under the proposed power allocation, the users' diversity gains are linear with their successive interference cancellation decoding orders. Simulation results confirm our proposed analysis.

An efficient spatiotemporal chaotic image cipher with an improved scrambling algorithm driven by dynamic diffusion phase

In recent years, with the rapid development of chaotic technology, many chaotic image cryptosystems based on pixel/bit-level techniques have been investigated. However, the drawbacks of a cryptosystem are as follows: (1) the security and efficiency are merely based on an investigation into standard test images and are not suitable for certain unique images in which the existence of “1s” or “0s” is significant; (2) the processing of bits requires more chaotic variables that elevate the computational complexity; (3) computational redundancy occurs when encrypting certain unique images. The first comprehensive analysis of different types of existing bit/pixel-level permutation techniques is carried out to show the inapplicability of such algorithms for popular cloud computing technology. To address the aforementioned security pitfalls of the state-of-the-art approaches and promote efficiency, an improved scrambling algorithm (ISA) and a reverse diffusion based on a dynamic keystream allocation strategy (DKAS) have been proposed. The present study adopts a spatiotemporal mixed linear-nonlinear coupling with the logistic-sine system (SMLNLC-LSS) for chaotic orbit generation. This system uses coveted cryptographic properties contrasted with the existing chaotic systems. The employed strategies such as ISA and reverse diffusion strongly enhance the relationship between the plaintext and encryption process to fortify the breaking of the proposed algorithm under known attacks. Because the ISA and DKAS mechanism share the same keystream, the efficiency of the proposed algorithm is effectively improved. Extensive security evaluation methods such as statistical, sensibility, visual-assessment, and efficiency analyses were imposed on the proposed methodology. The corresponding results accentuate the efficiency of the image cryptosystem while maintaining the superior security performance provided by the assurance of the desirable cryptographic properties.

Shared control with a novel dynamic authority allocation strategy based on game theory and driving safety field

Abstract This paper proposes a novel dynamic control authority allocation strategy based on the elliptic driving safety field for the game-based shared control, which aims to deal with human-automation conflicts during obstacle avoidance. The noncooperative game theory is used for modeling the interaction between a human driver and the trajectory-following controller, where the Nash equilibrium solution is derived by distributed model predictive control (DMPC) approach. In addition, the elliptic driving safety field is introduced to evaluate the driving risk by considering the driver-vehicle-road interactions under different driving contexts. The authority allocation ratio, denoting the ratio between the lateral position error weight of the human driver’s cost function and the trajectory-following controller’s, is determined by the shared dynamic authority allocation strategy designed based on the evaluated driving risk. More importantly, two case studies are provided to verify the elliptic driving safety field and the proposed dynamic authority allocation strategy on a straight or curve road. The results show that the proposed strategy can help the vehicle obtain great performance for obstacles avoidance, and the control authority is dynamically shifted between a human driver and the trajectory-following controller according to the evaluated driving risk of current driving condition.

A Decentralized Dynamic Load Power Allocation Strategy for Fuel Cell/Supercapacitor-Based APU of Large More Electric Vehicles

In this paper, an enhanced mixed droop control strategy is proposed for a fuel cell/supercapacitor-based auxiliary power unit (FC/SC-APU), which is usually used in large electric vehicles, to achieve the dynamic load power allocation in a decentralized way. By applying an adjustable virtual impedance droop scheme to the SC converter and a virtual resistor droop scheme to the FC converter, the SC is able to buffer the fast-changing or pulsating load power flow, leaving the FC only providing the average load power. Since this method is fully decentralized, it features high flexibility and scalability. Furthermore, the proposed strategy is very suitable for extending the system's service life because the recovery and operational limit of the state-of-charge of SC have been considered in the power allocation. The operational principle of the proposed control strategy and the system design are elaborated, followed by which real-time simulations are conducted to verify theoretical analyses.

On the Achievable Sum-Rate of NOMA-Based Diamond Relay Networks

Non-orthogonal multiple access (NOMA) is an effective solution to improve spectral efficiency since it oversteps the limit of the orthogonality imposed on the transmitted data flows. In this paper, a NOMA-based dual-hop diamond relay network where a source communicates with a destination via two decode-and-forward (DF) relays is considered. It is assumed that either the statistical channel state information (S-CSI) or instantaneous channel state information (I-CSI) is acquired to the network. Specifically, first, for the S-CSI assumption, closed-form expression of the achievable sum-rate in the high signal-to-noise ratio region is derived, and two effective power allocation schemes are proposed accordingly; second, for the I-CSI assumption, the achievable sum-rate is analyzed and the dynamic power allocation scheme is proposed; third, a promising hybrid power allocation (HPA) scheme that resorts to both S-CSI and I-CSI is proposed to improve the performance under the S-CSI assumption and to reduce the computational complexity and feedback overhead under the I-CSI assumption. Simulation results demonstrate that, first, NOMA is superior to the conventional schemes in terms of achievable sum-rate, especially for the case that the near-far effect is severe and, second, the HPA strategy can be an effective solution with moderate computational complexity and superior performance.

Analysis and comparison of the two sectoring approaches in elevator traffic systems

Sectoring is a group control algorithm that is used in elevator traffic control systems by grouping passengers that have common destinations or common origins into elevator cars that serve these floors. The building is split into sectors usually comprising contiguous floors. Two different alternative algorithms for sectoring are discussed in this paper. The first approach is based on dynamic allocation with equal sector allocation. The second approach is based on static allocation with unequal sector sizes. Under static allocation, the same elevator car is allocated to the same sector in every round trip. Under dynamic allocation, each elevator car is allocated to a different sector in each round trip. Under the dynamic allocation scheme suggested in this paper, the elevator cars are sequenced to the various sectors in the buildings in a round-robin fashion. It is important to note that under both schemes, the provided (relative) handling capacity of different sectors is equalised. Five different buildings have been analysed using the two suggested sectoring algorithms. The building is first designed by finding the required number and speed of elevators assuming conventional control. Each building is then analysed using one of the two suggested sectoring algorithms. In order to compare the performance of the two sectoring algorithms, the provided (relative) handling capacity is calculated. The provided (relative) handling capacity of the two suggested algorithms is then compared. Very little difference was found between the two algorithms. The dynamic sectoring with equal sector sizes offers the convenience of having equal sector sizes. The static sectoring with unequal sector sizes is more convenient for passengers that are familiar with the building. Practical application: This paper analyses two different options for sectoring the control system of an elevator system in a building. Each of the two sectoring methods is suitable for different situations. The dynamic allocation method is more suitable for destination group control systems and offers the group controller more flexibility. The static allocation method with unequal sector sizes ensures that the passengers remain familiar with the elevators that they use to get to their floors. The programmer of the elevator group controller can programme both methods in the controller and allow it to switch to the most suitable algorithm depending on the prevailing conditions.

Stochastic game-based dynamic information delivery system for wireless cooperative networks

The haptic communications is considered as the prime application running on the Tactile Internet. Therefore, Tactile Internet required to be highly reliable, provide a very low latencies, and required sufficient capacities at intermediate nodes to allow a large number of devices to communicate with each other simultaneously and autonomously. Moreover, the wireless cooperative network (WCN), is considered as one of the major component of the 5G technologies due to it promising advantages, such as improving wireless transmission capacity and reliability. However, the selfish nature of relay nodes may depress such enhancement and is not favored by the source node. In this paper, we propose an incentive-based dynamic flow allocation (FA) and forwarding strategy selection (FSS) scheme under time-varying selfishness. In the proposed scheme, the source node determines the FA to maximize the average network throughput under the constraints of network stability and selfishness boundaries, while each selfish relay executes the FSS to optimize its own profit with regard to the dynamic network state. Moreover, to cope with the conflicting interests between selfish relays a stochastic game model is employed to design a competition for haptic information forwarding and Nash equilibrium is proven also a combined Q-learning-based algorithm is proposed to guide the relays’ forwarding strategies. Furthermore, by considering the stochastic property of the network state, the FA for the source is formulated as a stochastic optimization problem. Finally, by exploiting the concept of virtual selfishness queue, the problem is solved by using the Lyapunov optimization theory. Performance of the proposed scheme is evaluated with traditional FA approach and data queue-based FA approach. Numerical results exhibit that our scheme not only sustains a large network throughput but also achieves low latency and avoids the occurrence of a completely selfish relay in the long term.

Group layer MU-MIMO for 5G wireless systems

Multiple-input multiple-output (MIMO) and nonorthogonal multiple access technologies are considered as fundamental components to meet the high spectral efficiency requirements of the forthcoming 5G wireless systems and beyond. In this context, group layer multiuser MIMO (GL-MU-MIMO) scheme has been proposed by the authors with linear group multiuser detection and receive antenna selection (RAS) to increase the number of simultaneously and reliably connected users more than the utilized number of radio frequency chains at the base station. In this paper, we derive the sum rate and capacity region expressions for GL-MU-MIMO uplink Rayleigh fading channels to demonstrate the impact of power allocation on the system performance and user-fairness. In addition, two low complexity RAS algorithms are proposed to maximize the sum rate of designed users’ groups and overall channel capacity. These techniques are utilized for new dynamic user grouping, RAS, and power allocation strategy to optimize the system performance under total received power and minimum user rate constrains. Compared with the generic MU-MIMO, numerical simulations demonstrate valuable tradeoffs between user overloading, complexity, and achieved performance through efficient utilization of groups’ power allocation. The new outcomes of GL-MU-MIMO extends the state-of-the-art towards diverse multi-antenna applications for future communications.

A dynamic resource allocation scheme based on cognition in LTE system

In this paper, we propose a dynamic spectrum allocation scheme based on cognition in the LTE system. The new scheme is to address the poor communication service problem caused by co-channel interference between the cell-edge users in the LTE system. The scheme leverages the cognitive characteristic in the LTE system to enable the base station to dynamically obtain the idle resource status information of the adjacent cells and integrates the idle resource information of the target cell, and then employs the ant colony optimisation technique to allocate the resource blocks to users. Experimental results show that the proposed scheme allows the cell-edge users to dynamically utilise the idle resource blocks of adjacent cells, which enhances resource blocks utilisation and system throughput while ensuring fairness among users, thus may improve the quality of user service at the cell-edge.

A dynamic resource allocation scheme based on cognition in LTE system

In this paper, we propose a dynamic spectrum allocation scheme based on cognition in the LTE system. The new scheme is to address the poor communication service problem caused by co-channel interference between the cell-edge users in the LTE system. The scheme leverages the cognitive characteristic in the LTE system to enable the base station to dynamically obtain the idle resource status information of the adjacent cells and integrates the idle resource information of the target cell, and then employs the ant colony optimisation technique to allocate the resource blocks to users. Experimental results show that the proposed scheme allows the cell-edge users to dynamically utilise the idle resource blocks of adjacent cells, which enhances resource blocks utilisation and system throughput while ensuring fairness among users, thus may improve the quality of user service at the cell-edge.

UFMC-Based Interference Management for Heterogeneous Small-Cell Networks

In this paper, we apply the advanced waveform universal filtered multi-carrier (UFMC) to a heterogeneous small-cell network, where both the inner-cell interference and the inter-cell interference are considered. By employing UFMC, a dynamic spectrum allocation scheme and a precoding scheme are proposed to mitigate the interference. The proposed UFMC based interference management schemes are analyzed in detail, and the achievable data rate of the proposed scheme is derived. Simulation results show that the proposed UFMC based schemes can efficiently mitigate the interference, and reduce the effect of frequency offsets.

Prediction-Based Spectrum Management in Cognitive Radio Networks

Cognitive radio networks (CRNs) have been widely studied to improve spectrum utilization through a dynamic spectrum allocation scheme. However, many challenges remain and one big issue is how to provide an efficient spectrum management in a mobile environment. In this paper, we propose a new prediction-based spectrum management strategy to tackle this challenge. Both spectrum prediction and users’ mobility prediction are fully considered in the proposed strategy. Combining the prediction information and cooperative sensing, a CR base station can obtain the sensing information for future location of each secondary user, so as to prearrange high-quality channels for secondary users ahead of time. In addition, we propose a new channel selection scheme when multiple channels are available simultaneously. Besides traditional decision factors, the new channel selection algorithm takes the channel's future availability obtained from spectrum prediction into account. By properly integrating the spectrum prediction, user mobility prediction, and channel selection, the new spectrum management strategy is capable of allocating the spectrum resource more efficiently. Extensive simulations are conducted and results verify that the proposed spectrum management strategy significantly improves the system performance in terms of reducing handoff times and improving user satisfactory, connection reliability, and channel utilization.

The Construction and Application of a Personalized Teaching Space in Mobile English

This paper designs the construction dimension of College English individualized teaching space, and constructs its system architecture combining with the requirements of teaching and learning cloud space. Secondly, it expounds the four application strategies of learning cloud space in College English Teaching: individualized spatial dynamic allocation strategy, self-taught strategy under the support of experience, and based on the ubiquitous environment. Fragmented learning strategy and language learning oriented interactive dialogue strategy; finally, it introduces three application patterns in College English Teaching in the light of learning cloud space: overturning, subject inquiry learning and mixed teaching, and verifying the use effect of cloud space through case studies. The results show that customized learning cloud space and its application can improve the efficiency of College English teaching and promote students'mastery of English application skills.

Hi-DMM: High-Performance Dynamic Memory Management in High-Level Synthesis

High-level synthesis (HLS) of field programmable gate array (FPGA)-based accelerators has been proposed in order to simplify accelerator design process with respect to design time and complexity. However, modern HLS tools do not consider dynamic memory allocation constructs in high-level programming languages like C and limit themselves to static memory allocation. This paper proposes a dynamic memory allocation and management scheme, called Hi-DMM, for inclusion in commercial HLS design flows. Hi-DMM performs source-to-source transformation of user C code with dynamic memory constructs into C-source code with the dynamic memory allocator and management scheme developed in this paper. The transformed C-source code is amenable to synthesis by commercial tools like Vivado HLS. Relying on buddy tree-based allocation schemes and efficient hardware implementation of the allocators, Hi-DMM achieves 4x speed-up in both fine-grained and coarse-grained memory allocation compared to previous works. Experimental results obtained by including Hi-DMM with Vivado-HLS show that dynamic memory allocation of FPGA memory resources can be achieved at a much lower latency with minimal resource overhead, paving the way for synthesis of dynamic memory constructs in commercial HLS flows.

Distributed power control in mobile wireless sensor networks

Controlling the transmission power level in a mobile wireless sensor network is an essential process to improve the energy efficiency and quality of service (QoS) at each wireless node. To achieve this goal, we propose to evaluate the QoS by an estimation of the signal-to-interference noise ratio (SINR) according to the IEEE 802.15.4 standard. For this purpose, the received signal strength indicator per packet (RSSIp), and general received signal strength indicator (RSSIg) metrics are considered in this research work followed by a filtering stage to reduce the variability in the SINR estimation. In addition, this research work aims to evaluate different dynamic power control algorithms through an experimental test-bed, where the wireless nodes are in motion. A comparative analysis is presented of five distributed power control algorithms: fixed-step, Foschini-Miljanic, proportional-integral-derivative control, variable structure control, and water-filling control. Moreover, a new distributed power control technique is proposed for mobile wireless sensor networks: modified fixed-step (MFS). All the power allocation algorithms use the tracking error between the estimated and objective SINRs as the driving mechanism to adjust the transmission power. In general, the results show that a dynamic power allocation scheme not only enables the efficient use of the battery, extending its lifetime, but also it is able to achieve the desired QoS despite multiple interference and mobility of the nodes in the network. Furthermore, the results of the experimental evaluation indicate that the modified fixed-step algorithm has the best performance as a function of the reference tracking of the QoS and power consumption.

Dynamic power allocation scheme with clustering based on physical layer security

Achieving large confidential capacity under the wiretap channel model is a challenge due to the narrow modulation bandwidth and total transmission power constraints. The confidential capacity of a system can be improved through a non-orthogonal multiple access technique that can obtain the highest transmission power in a downlink network. A clustering method is applied to network users who require data with similar contents. Based on the channel gain of each user, cluster heads are selected as agents for the corresponding clusters; then, the total transmission power is shared among the cluster heads. Before the power allocation process, the signal-to-interference-plus-noise ratio of the cluster heads is derived by considering clipping noise to ensure fairness. On this basis, an optimal power allocation scheme is proposed using Lagrangian dual theory. A case is presented to validate the performance of the proposed power allocation scheme. The comparison of the numerical results with those of other schemes shows that the proposed method achieves better performance regarding both secrecy sum capacity and outage probability.

A systematic analysis of multimodal transport systems with road space distribution and responsive bus service

A smart design of transport systems involves efficient use and allocation of the limited urban road capacity in the multimodal environment. This paper intends to understand the system-wide effect of dividing the road space to the private and public transport modes and how the public transport service provider responds to the space changes. To this end, the bimodal dynamic user equilibrium is formulated for separated road space. The Macroscopic Fundamental Diagram (MFD) model is employed to depict the dynamics of the automobile traffic for its state-dependent feature, its inclusion of hypercongestion, and its advantage of capturing network topology. The delay of a bus trip depends on the running speed which is in turn affected by bus lane capacity and ridership. Within the proposed bimodal framework, the steady-state equilibrium traffic characteristics and the optimal bus fare and service frequency are analytically derived. The counter-intuitive properties of traffic condition, modal split, and behavior of bus operator in the hypercongestion are identified. To understand the interaction between the transport authority (for system benefit maximization) and the bus operator (for its own benefit maximization), we examine how the bus operator responds to space changes and how the system benefit is influenced with the road space allocation. With responsive bus service, the condition, under which expanding bus lane capacity is beneficial to the system as a whole, has been analytically established. Then the model is applied to the dynamic framework where the space allocation changes with varying demand and demand-responsive bus service. We compare the optimal bus services under different economic objectives, evaluate the system performance of the bimodal network, and explore the dynamic space allocation strategy for the sake of social welfare maximization.

50 million atoms scale molecular dynamics modelling on a single consumer graphics card

This paper presents a dynamic cell-list method for realizing large scale molecular dynamics (MD) simulations with more than 50 million atoms on a single consumer graphics card. It adapts the cell-list algorithm by introducing an efficient two-step atom location scheme and a dynamic memory allocation scheme such that only those cells containing atoms consume device memory. In addition, a large amount of memory is saved since it does not use the neighbour list. The computational efficiency is improved by reducing the memory loading times and maximizing coalesced memory access as compared to methods utilizing neighbour lists, since memory bandwidth is becoming the bottle-neck of the latest GPUs. As a result, MD simulations with more than 50 million atoms utilizing advanced three-body interaction potential are made possible on a consumer graphics card with just 11 GB of graphics memory. The proposed framework is designed to run totally on the graphics card, with all the data stored in the graphics memory to avoid the time-consuming data transfer between host and device. It achieves 2.5 times the speed and 20 times the atom number of the latest Lammps GPU package on the NVIDIA GTX 1080Ti GPU. The proposed framework is expected to help adapt existing MD packages for supporting large scale MD simulations on personal desktops and thereby extend MD to a wider range of researchers and engineers.