• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4320-4333
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• 2017

Energy-efficient virtual resource allocation algorithm has become a hot research topic in cloud computing. However, most of the existing allocation schemes cannot ensure each type of resource be fully utilized. To solve the problem, this paper proposes a virtual machine (VM) allocation algorithm on the basis of multi-dimensional resource, considering the diversity of user's requests. First, we analyze the usage of each dimension resource of physical machines (PMs) and build a D-dimensional resource state model. Second, we introduce an energy-resource state metric (PAR) and then propose an energy-aware multi-dimensional resource allocation algorithm called MRBEA to allocate resources according to the resource state and energy consumption of PMs. Third, we validate the effectiveness of the proposed algorithm by real-world datasets. Experimental results show that MRBEA has a better performance in terms of energy consumption, SLA violations and the number of VM migrations.

• The Korean Journal of Ecology
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• v.15 no.4
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• pp.345-354
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• 1992

Allocation patterns of energy and mineral elements were investigated with phaseolus multiflorus grown in the environmental gradients. The result showed different energy allocation patterns according to relative light intensities and nutrients. The optimal switching time of energy allocation from vegetative to resproductive growth was delated as decreasing relative light intensity. The switch of the shift to reproduction was timed earlier in phosphorus treatment and delayed in nitrogen treatment. Analyzing the mineral elements to various organs, patterns of energy allocation were different from those of mineral allocation. There was no significant difference for allocation patterns in relative light intensity gradients. it was shown that n and p were distributed over the reproductive organs, k mainly in stems, ca in leaves and na in roots. mg was evenly distributed in each organs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4848-4863
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• 2016

The explosive wireless data service requirement accompanied with carbon dioxide emission and consumption of traditional energy has put pressure on both industria and academia. Wireless networks powered with the uneven and intermittent generated renewable energy have been widely researched and lead to a new research paradigm called green communication. In this paper, we comprehensively consider the total generated renewable energy, QoS requirement and channel quality, then propose a utility based renewable energy allocation policy. The utility here means the satisfaction degree of users with a certain amount allocated renewable energy. The energy allocation problem is formulated as a constraint optimization problem and a heuristic algorithm with low complexity is derived to solve the raised problem. Numerical results show that the renewable energy allocation policy is applicable not only to soft QoS, but also to hard QoS and best effort QoS. When the renewable energy is very scarce, only users with good channel quality can achieve allocated energy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• ETRI Journal
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• v.36 no.3
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• pp.506-509
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• 2014

This paper studies the energy efficiency power allocation for cognitive radio networks based on uplink orthogonal frequency-division multiplexing. The power allocation problem is intended to minimize the maximum energy efficiency measured by "Joule per bit" metric, under total power constraint and robust aggregate mutual interference power constraint. However, the above problem is non-convex. To make it solvable, an equivalent convex optimization problem is derived that can be solved by general fractional programming. Then, a robust energy efficiency power allocation scheme is presented. Simulation results corroborate the effectiveness of the proposed methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.33-39
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• 2020

A smart grid is a next-generation power grid that can improve energy efficiency by applying information and communication technology to the general power grid. The smart grid makes it possible to exchange information about electricity production and consumption between electricity providers and consumers in real-time. Advanced metering infrastructure (AMI) is the core technology of the smart grid. The AMI provides two-way communication by installing a modem in an existing digital meter and typically include smart meters, data collection units, and meter data management systems. Because the AMI requires data collection units to control multiple smart meters, it is essential to ensure network availability under heavy network loads. If the load on the work done by the data collection unit is high, it is necessary to allocation new data collection units to ensure availability and improve energy efficiency. In this paper, we discuss the allocation scheme of data collection units for the energy efficiency of the AMI.

• Journal of Energy Engineering
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• v.24 no.4
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• pp.1-10
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• 2015

For successful operation of waterflooding which is one of secondary recovery methods, suitable water injection allocation is important to increase oil recovery. Well allocation factor(WAF) which is one way to quantify the injector and producer connectivity, is utilized to allocate water injection of waterflooding. Static WAF cannot represent the field condition and can induce incorrect value. To compensate for limitation of static WAF, modified WAF which includes several parameters that affect patterns including well radius, distance between wells, and injection rates is proposed. In this study, static and modified WAFs were applied to injection optimization of waterflooding and results by each WAF were compared. In case of modified WAFs, produced water were less and produced oil were more than case of static WAF especially in big change of distance between producer and injector. Therefore, modified WAFs can allocate water injection more efficiently than static WAF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5943-5962
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• 2017

A resource allocation algorithm based on simultaneous wireless information and power transfer (SWIPT) to maximize the system throughput is proposed in orthogonal frequency division multiplexing (OFDM) relay networks. The algorithm formulates the problem under the peak power constraints of the source and each subcarrier (SC), and the energy causality constraint of the relay. With the given SC allocation of the source, we give and prove the optimal propositions of the formulated problem. Then, the formulated problem could be decomposed into two separate throughput maximization sub-problems by setting the total power to transfer energy. Finally, several SC allocation schemes are proposed, which are energy priority scheme, information priority scheme, balanced allocation scheme and exhaustive scheme. The simulation results reveal that the energy priority scheme can significantly reduce computational complexity and achieve approximate performance with the exhaustive scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1255-1261
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• 2016

Recently, the energy harvesting technology in which energy is collected from the wireless signal which is transmitted by mobile communication devices, has been considered as a novel way to improve the life time of wireless sensors by mitigating the lack of power supply problem. In this paper, we consider the optimal sensing time and power allocation problem for cognitive radio systems, where the energy efficiency of secondary user is maximized while the constraint are satisfied, using the optimization technique. Based on the derived optimal solutions, we also have proposed an iterative resource allocation algorithm in which the optimal power and sensing time allocation can be found without excessive computations. The simulation results confirm that the proposed scheme achieves the optimal performance and it outperforms the conventional resource allocation schemes in terms of energy efficiency while the constraints are guaranteed to be satisfied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3529-3550
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• 2016

In heterogeneous networks (HetNets), energy saving is vital for a sustainable network development. Many techniques, such as spectrum allocation, network planning, etc., are used to improve the network energy efficiency (EE). In this paper, micro BSs utilizing cell range expansion (CRE) and spectrum allocation are considered in multi-channel heterogeneous networks to improve EE. Hotspot region is assumed to be covered by micro BSs which can ensure that the hotspot capacity is greater than the average demand of hotspot users. The expressions of network energy efficiency are derived under shared, orthogonal and hybrid subchannel allocation schemes, respectively. Particle swarm optimization (PSO) algorithm is used to solve the optimal ratio of subchannel allocation in orthogonal and hybrid schemes. Based on the results of the optimal analysis, we propose three service control strategies on the basis of large-scale user behaviors, i.e., adjust micro cell rang expansion (AmCRE), adjust micro BSs density (AmBD) and adjust micro BSs transmit power (AmBTP). Both theoretical and simulation results show that using shared subchannel allocation scheme in AmBD strategies can obtain maximal EE with a very small area ratio. Using orthogonal subchannel allocation scheme in AmCRE strategies can obtain maximal EE when area ratio is larger. Using hybrid subchannel allocation scheme in AmCRE strategies can obtain maximal EE when area ratio is large enough. No matter which service control strategy is used, orthogonal spectrum scheme can obtain the maximal hotspot user rates.