Energy-aware Resource Allocation Algorithm Research Articles

Optimized Energy Aware Resource Allocation Algorithm Using Software Defined Network Technology

The number of data centers (DCs) used for storing and processing data has evolved rapidly in recent years. However, the operations held by DCs may relate to a number of disadvantages, primarily presuming in excessive energy and power consumption due to the poor management standards applied. This may lead to a situation in which many devices within the DC operate at full capacity without any tasks assigned for actual execution. A Software Defined Network (SDN) is a network architecture where the control plane is an independent entity from the data plane, yielding to a higher controllability and flexibility over the network. Through the utilization of SDN architecture, a highly functional energy aware network may be established. In this paper, we propose a heuristic algorithm that monitors the current status of an SDN network (in addition to all ingoing and outgoing traffic), in order to dynamically and efficiently allocate network resources by ensuring that only the necessary network devices are active and by turning the idle ones off. The results show that the proposed algorithm reduces energy consumption of the network compared to existing solutions.

Energy‐aware resource allocation for OFDMA wireless networks with hybrid energy supplies

In this study, the authors investigate the resource allocation for orthogonal frequency-division multiple access (OFDMA) wireless networks, where the base station is powered by renewable energy and electric grid. To fully exploit the renewable energy, the authors propose an energy-aware resource allocation (EARA) algorithm to maximise the network utility, which captures the tradeoff between the system throughput and the grid energy consumption. Specifically, the EARA algorithm only has to track the current system states (e.g. channel and queueing conditions) without requiring a relevant priori distribution knowledge, making it applicable for practical OFDMA wireless networks with unpredictable channel dynamics, renewable energy arrivals and stochastic traffics. Moreover, the performance achieved by the EARA algorithm is theoretically characterised. Most importantly, the authors develop an implementation architecture to take the EARA algorithm into practice, and also analyse the low implementation costs (e.g. low computational complexity, trivial signalling overhead etc.). Finally, simulation results verify the theoretical analysis and also demonstrate the advantages of the EARA algorithm.

<italic>EnReal</italic>: An Energy-Aware Resource Allocation Method for Scientific Workflow Executions in Cloud Environment

Scientific workflows are often deployed across multiple cloud computing platforms due to their large-scale characteristic. This can be technically achieved by expanding a cloud platform. However, it is still a challenge to conduct scientific workflow executions in an energy-aware fashion across cloud platforms or even inside a cloud platform, since the cloud platform expansion will make the energy consumption a big concern. In this paper, we propose an Energy-aware Resource Allocation method, named EnReal, to address the above challenge. Basically, we leverage the dynamic deployment of virtual machines for scientific workflow executions. Specifically, an energy consumption model is presented for applications deployed across cloud computing platforms, and a corresponding energy-aware resource allocation algorithm is proposed for virtual machine scheduling to accomplish scientific workflow executions. Experimental evaluation demonstrates that the proposed method is both effective and efficient.

Energy Aware Resource Allocation for Incremental AF-OFDM Relaying

This letter first presents an incremental amplify-and-forward orthogonal frequency division multiplexing relaying scheme to promote the spectrum efficiency of cooperative transmission. Then, the energy efficiency problem is thoughtfully investigated in order to prolong the lifetime for such battery-operated wireless networks. As will be seen, the original optimization problem incorporating incremental policy, relay selection and power loading is formulated as a mixed-integer nonlinear programming problem that is quite hard to solve even numerically, thereby necessitating our derivation of a suboptimal approach for implementation feasibility. Specifically, by exploiting energy pricing concept as well as max-min energy efficiency criterion, a stepwise energy aware resource allocation algorithm is proposed. Finally, numerical results verify the benefits of our proposals.