A Convex Optimization Framework for Service Rate Allocation in Finite Communications Buffers

Ghamkhari, Mahdi; Mohsenian‐Rad, Hamed

doi:10.1109/lcomm.2015.2499740

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Meanwhile, it is easy to prove that the solution spaces constructed by the constrains of two problems are convex spaces, and the functions of two problems are the cumulative sums after taking the logarithm of two objective functions. Therefore, two functions of the optimization problems are concave; then, these two problems are convex optimization problems according to convex optimal theory [26], which means the distance between solutions of dual problems and original problems can be regarded as 0 [27], and these two problems can be transferred into dual problems and solved in their dual domain.…”

Section: Theorem 1 the Objective Function Fmentioning

confidence: 99%

Traffic Load Optimization for Multi-Satellite Relay Systems in Space Information Network: A Proportional Fairness Approach

Zhong

Ren

Gong

et al. 2022

Sensors

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Backbone satellites in a space information network (SIN) can be used as air base stations or data relay satellites (DRSs) to realize cross-system, cross-network and long-distance relay transmission. In this paper, a traffic load optimization problem for multi-satellite relay systems in SIN is considered to achieve highly efficient cooperative transmission and improve resource utility. A model of SIN based on a distributed satellite cluster (DSC) is considered, and the characteristics of the model are analyzed. Based on this, a hybrid resource management architecture combining distributed and central resources control schemes is proposed to realize a centrally controllable and distributed optimization of resources to meet various comprehensive service requirements. Two scenarios of multi-satellite relay systems in SIN are given, and traffic load optimization problems with joint bandwidth and power allocation for these two scenarios are formulated based on proportional fairness (PF) criterion to achieve traffic load balancing with considerable system capacity. The optimization problems in these two scenarios are proved to be a convex optimization problem with mathematical analysis, and the closed-form solutions of two problems in their dual domain are derived by dual transformation. With the closed-form solutions, two iterative algorithms based on the subgradient method are designed under the proposed hybrid resource management architecture to solve the problems in this paper. Simulation results show that the proposed schemes can effectively improve the upper bound of system capacity by resource sharing and cooperative relay, and it can balance the traffic load well with guarantees of a reasonable level system capacity compared with existing methods.

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Section: Theorem 1 the Objective Function Fmentioning

confidence: 99%

Traffic Load Optimization for Multi-Satellite Relay Systems in Space Information Network: A Proportional Fairness Approach

Zhong

Ren

Gong

et al. 2022

Sensors

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“…Moreover, when ignoring the inequality constraints, there is a linear function relationship between λ i and x i,t , which can be represented as φ i (λ i ) = x i,t . When each DG is operating in the optimal consistent condition λ i * , the optimal power output x * i,t can be obtained [20].…”

Section: Optimization Solutionmentioning

confidence: 99%

“…Based on the definition of communication matrices, it is clear to gain that W i,j is a row stochastic matrix and U i,j is a column stochastic matrix. Note that the convergence will not be affected by the communication weights selection process when the above stochastic characteristics are satisfied [20]. Note ρ(W) = ρ(U) = 1, where ρ(•) represents the spectral radius of the matrix.…”

Section: The Design Of Communication Weightmentioning

confidence: 99%

A Decentralized Optimization Strategy for Distributed Generators Power Allocation in Microgrids Based on Load Demand–Power Generation Equivalent Forecasting

Zhang

Zhou

et al. 2020

Energies

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In order to guarantee the economic and reliable operation of renewable Distributed Generators (DGs) in microgrids, a decentralized optimization strategy for DGs power allocation is proposed in this paper. According to the method, all processes and parameters are designed in a fully distributed way. To achieve decentralization and to maintain the balance between power supply and load demand, a load demand–power generation equivalent forecasting method is proposed to improve the strategy through replacing information of load demand by predicted power output, which removes the load prediction center and load sensor devices. The data of historical power generation, which is used for prediction, has already satisfied the balance constraint between power supply and load demand. Therefore, when the balance between the real power output and the predicted power output is gained, the balance constraint of power supply and load demand is achieved. Meanwhile, the uncertainty and forecasting errors of renewable generation are taken into account in the cost functions to optimize the expense of DG operation comprehensively. Then, the proposed algorithm is expounded in detail and the convergence is proved by eigenvalue perturbation theory. Finally, various cases are simulated to verify the accuracy and effectiveness of the proposed method. In summary, the proposed method are effective tools for DGs economic power allocation and the decentralization of microgrid system.

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“…An extension of (11) when this assumption is relaxed is given in [39]. As for the model in (10), the first term is the reserve capacity payment and the second term is the reserve call payment.…”

Section: F Operational Revenuementioning

confidence: 99%

“…For the case studies that involve only one time slot, the data is from 3:30 PM to 3:45 PM, which is one of the ten time intervals at which PJM sent out a reserve capacity signal during the studied period. For simulations that include one data center, we use the loss probability model in [39], which is an extension of the model in (11) to the entire range of service rate.…”

Section: A Simulation Settingmentioning

confidence: 99%

Energy Portfolio Optimization of Data Centers

Ghamkhari

Wierman

Mohsenian‐Rad

2017

IEEE Trans. Smart Grid

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Abstract-Data centers have diverse options to procure electricity. However, the current literature on exploiting these options is very fractured. Specifically, it is still not clear how utilizing one energy option may affect selecting other energy options. To address this open problem, we propose a unified energy portfolio optimization framework that takes into consideration a broad range of energy choices for data centers. Despite the complexity and nonlinearity of the original models, the proposed analysis boils down to solving tractable linear mixed-integer stochastic programs. Using experimental electricity market and Internet workload data, various insightful numerical observations are reported. It is shown that the key to link different energy options with different short-and long-term profit characteristics is to conduct risk management at different time horizons. Also, there is a direct relationship between data centers' servicelevel agreement parameters and their ability to exploit certain energy options. The use of on-site storage and the deployment of geographical workload distribution can particularly help data centers in utilizing high-risk energy choices, such as offering ancillary services or participating in wholesale electricity markets.

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A Convex Optimization Framework for Service Rate Allocation in Finite Communications Buffers

Cited by 6 publications

References 12 publications

Traffic Load Optimization for Multi-Satellite Relay Systems in Space Information Network: A Proportional Fairness Approach

Traffic Load Optimization for Multi-Satellite Relay Systems in Space Information Network: A Proportional Fairness Approach

A Decentralized Optimization Strategy for Distributed Generators Power Allocation in Microgrids Based on Load Demand–Power Generation Equivalent Forecasting

Energy Portfolio Optimization of Data Centers

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