Efficient Dynamic Resource Allocation Research Articles

Energy efficient dynamic resource allocation in C-RAN using tunicate swarm and whale optimization algorithm

Energy efficient dynamic resource allocation in C-RAN using tunicate swarm and whale optimization algorithm

Efficient Trajectory Planning and Dynamic Resource Allocation for UAV-Enabled MEC System

Efficient Trajectory Planning and Dynamic Resource Allocation for UAV-Enabled MEC System

Sequential dynamic resource allocation in multi-beam satellite systems: A learning-based optimization method

Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources. One of the primary challenges accompanying with Multi-Beam Satellites (MBS) is an efficient Dynamic Resource Allocation (DRA) strategy. This paper presents a learning-based Hybrid-Action Deep Q-Network (HADQN) algorithm to address the sequential decision-making optimization problem in DRA. By using a parameterized hybrid action space, HADQN makes it possible to schedule the beam pattern and allocate transmitter power more flexibly. To pursue multiple long-term QoS requirements, HADQN adopts a multi-objective optimization method to decrease system transmission delay, loss ratio of data packets and power consumption load simultaneously. Experimental results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.

RETRACTED ARTICLE: Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.

Efficient dynamic resource allocation method for cloud computing environment

The dynamic resource allocation is a good feature of the cloud computing environment. However, it faces serious problems in terms of service quality, fault tolerance, and energy consumption. It was necessary, then, to find an effective method that can effectively address these important issues and increase cloud performance. This paper presents a dynamic resource allocation model that can meet customer demand for resources with improved and faster responsiveness. It also proposes a multi-objective search algorithm called Spacing Multi-Objective Antlion algorithm (S-MOAL) to minimize both the makespan and the cost of using virtual machines. In addition, its impact on fault tolerance and energy consumption was studied. The simulation revealed that our method performed better than the PBACO, DCLCA, DSOS and MOGA algorithms, especially in terms of makespan.

Energy efficient dynamic resource allocation for wireless systems using receiver puncturing technique

Given the massive increase in network infrastructure required to provide the high capacities expected in current wireless systems due to the exponential growth in mobile data consumption, substantial improvement in energy efficiency of the current 4G systems is necessary. High energy efficiency results in low operational cost and sustainable network deployment. In this paper, we propose a new receiver puncturing technique and a novel energy efficient dynamic resource allocation scheme. Improvements in BER performance and 20%~25% transmit-energy expenditure reduction, as compared to conventional proportional fair resource allocation schemes, are shown by both link level and system level simulations of a multi-cell downlink orthogonal frequency-division multiple access (OFDMA) system. Finally, the complexity issues inherent to the proposed technique are also evaluated.

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided.

ARAM: A New Auction-based Resource Allocation Model in Cloud Computing

Cloud computing is an on-demand network access model to a shared pool of configurable computing resources. One of the main challenges in cloud computing is the efficiency of the pricing and resource allocation models adopted by cloud providers. In this paper, we proposed a new resource allocation model called ARAM, which is based on reverse auctions and provides dynamic pricing policies. In this model, independent reverse auctions are hold for each cloud service request. ARAM has been compared with other previously proposed approaches, considering several issues such as the types of the services it supports, the necessity for configuring service instances, and also those related to load balancing. The main advantage of ARAM, compared to previous forward auction based models, is that it eliminates the necessity for configuring service instances. Therefore, the utilities of both consumers and providers are enhanced, in terms of cost and time. Also by adopting a dynamic pricing approach, it moderates the shortcomings of fixed pricing strategies in cloud computing, such as resource wastage and lack of fairness. Furthermore, contrary to most of the previous approaches, ARAM supports dynamic pricing for all types of services and considers load balancing issues in allocating resources. Today, most of the providers in the industry use fixed pricing models ignoring the market condition. Therefore, it seems necessary to use efficient dynamic pricing and resource allocation models, such as ARAM, in cloud computing.

Utility-based efficient dynamic distributed resource allocation in buffer-aided relay-assisted OFDMA networks

In this paper, we study resource allocation in buffer-aided relay-assisted OFDMA networks. We consider utility-based stochastic optimization framework where there are constraints to be met either instantaneously or in average sense. Using the well-known Lyapunov drift-plus-penalty policy, we extract the instantaneous problem that needs to be solved in each slot to control the data admission and allocate the time slots, power, and subchannels. We propose the parameters that should be taken into account in utilizing the drift-plus-penalty policy in relay-assisted cellular networks, for providing fair data admission and satisfying the average power constraints. We introduce a low-complexity strategy for power and subchannel allocation and propose distributed and centralized algorithms to utilize it. Specifically, the proposed efficient dynamic distributed resource allocation (EDDRA) scheme is suitable for use in practice as it imposes less overhead on the system and splits the resource allocation tasks among the base station (BS) and the relays. Extensive simulation results show the effectiveness of the proposed parameters in meeting the objective and the constraints of the studied problem. We also show that the proposed EDDRA scheme has close performance to the proposed centralized one and outperforms an existing centralized scheme.

Emergence of Modularity in System of Systems: Complex Networks in Heterogeneous Environments

Effective use of modularity in distributed systems is key to accommodating the complexity arising from having diverse requirements and stakeholders and increased environmental uncertainties. Moreover, modularity facilitates efficient dynamic resource allocation in the system. In this paper, a formal framework that links various classes of systems modularity to distributed decision complexities is presented by formulating modularity as an emergent phenomenon arising from environmental heterogeneity. This paper also demonstrates behavioral and mathematical formulations of the problem and agent-based simulation of sample networked systems to verify the results.

Cooperative ARQ via auction-based spectrum leasing

A novel distributed scheme that combines cooperative ARQ with the spectrum leasing paradigm is proposed and analyzed. The strategy harnesses the opportunistic gains of cooperative communications, while inherently providing a spectrum-rewarding incentive for the otherwise non-cooperative relays to assist the source's transmission. As in cooperative ARQ, the source might decide to hand over the possible retransmission slots to nearby stations that were able to decode the original transmission. In the proposed scheme, however, in exchange for the cooperation, the relaying station is also awarded an opportunity to exploit the retransmission slot for its own traffic. Arbitration of relays' retransmissions is performed via an auction mechanism, with the source, the competing relays and the transmission slot acting as the auctioneer, the bidders and the bidding article, respectively. Auction theory (more generally, the theory of Bayesian games) is applied to analyze the scheme performance. It is noted that the setting here can be alternatively seen as a practical framework for implementation of property-rights cognitive radio networks. Numerical results and analysis show that the proposed scheme enables an efficient dynamic resource allocation that provides relevant gains (e.g., transmission reliability) for both the original source (primary) and the cooperating nodes (secondary users).

An efficient dynamic resource allocation algorithm for packet-switched communication networks based on Hopfield neural excitation method

Dynamic Resource Allocation (DRA) algorithms set up different connections over the same resources and perform a scheduling policy to distribute the resources usage. Recently, intelligent DRA techniques based on Hopfield Neural Networks computational methods have been proposed, showing their potential for solving this kind of complex optimization problems. However, the initial algorithms suffer from severe instability problems impacting performance. This paper addresses these specific limitations stressing the proper neuron dynamics and proposing an efficient energy formulation and an optimum calculation of the weighting coefficients. These changes result in a maximum resource utilization together with an optimized neural network convergence.

Dynamic Downlink OFDM Resource Allocation With Interference Mitigation and Macro Diversity for Multimedia Services in Wireless Cellular Systems

This paper presents efficient dynamic resource allocation schemes with interference mitigation techniques for multimedia services in downlink orthogonal frequency-division multiplexing mobile cellular systems. Performance of the proposed algorithms is evaluated in terms of user quality-of-service and system spectral efficiency. It is shown that the best subcarrier allocation scheme with interference mitigation and macrodiversity techniques gives significant performance gains in terms of system spectral efficiency. Furthermore, sharing the system bandwidth among real-time stream-type voice and bursty data services can support much larger system loads than having a hard division

Power Allocation in a Wireless Network Using Scheduling and Stochastic Programming with Penalty Functions

This paper studies the optimal allocation of transmit power in a wireless communication network. First, a stochastic programming formulation is introduced, based on penalizing violations of quality-of-service constraints. The maximization of the certainty equivalent signal-to-interference ratio under Rayleigh fading corresponds to a penalty model where (max-min) fairness is explicitly taken into consideration. Second, optimum dynamic power allocation is discussed. Efficient dynamic resource allocation under both linear and logarithmic utility functions is addressed. The dynamic model studies the optimal trade-off between instantaneous quality-of-service and a delay-penalized reliable quality-of-service. Related work on optimal stochastic power control is summarized.

Adaptive Resource Allocation for Multimedia QoS Management in Wireless Networks

Adaptive resource allocation for multimedia quality of service (QoS) support in broadband wireless networks is examined in this work. A service model consisting of three service classes with different handoff-dropping requirements is presented. Appropriate call-admission control and resource-reservation schemes are developed to allocate resources adaptively to the real-time service classes with a stringent delay bound. Moreover, we propose an effective and efficient measurement-based dynamic resource allocation scheme to meet the target handoff-dropping probability. The nonreal-time applications, serviced by the best-effort model, are supported. The system accommodates adaptive multimedia applications to further reduce the blocking and dropping probabilities of real-time applications. Based on a multidimensional model analysis, simulations are conducted to evaluate the system performance. The simulation results show that the proposed system can satisfy the desired QoS of multimedia applications under different traffic loads, while achieving high utilization.