Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

Interference-aware coordinated power allocation in autonomous Wi-Fi environment

Self-managed access points (APs) with growing intelligence can optimize their own performances but pose potential negative impacts on others without energy ef ciency. In this paper, we focus on modeling the coordinated interaction among interest-independent and self-con gured APs, and conduct the power allocation case study in the autonomous Wi-Fi scenario. Speci cally, we build a `coordination Wi-Fi platform (CWP), a public platform for APs interacting with each other. OpenWrt-based APs in the physical world are mapped to virtual agents (VAs) in CWP, which communicate with each other through a standard request-reply process de ned as AP talk protocol (ATP).With ATP, an active interference measurement methodology is proposed re ecting both in-range interference and hidden terminal interference, and the Nash bargaining-based power control is further formulated for interference reductions. CWP is deployed in a real of ce environment, where coordination interactions between VAs can bring a maximum 40-Mb/s throughput improvement with the Nash bargaining-based power control in the multi-AP experiments.

Joint resource allocation for max-min throughput in multicell networks

We investigate the resource-allocation problem in multicell networks targeting the max-min throughput of all cells. A joint optimization over power control, channel allocation, and user association is considered, and the problem is then formulated as a nonconvex mixed-integer nonlinear problem (MINLP). To solve this problem, we proposed an alternating-optimization-based algorithm, which applies branch-and-bound and simulated annealing in solving subproblems at each optimization step. We also demonstrate the convergence and efficiency of the proposed algorithms by thorough numerical experiments. The experimental results show that joint optimization over all resources outperforms the restricted optimization over individual resources significantly.

Superimposed channel training for two-way MIMO relay systems

Two-way relaying systems are known to be capable of providing higher spectral efficiency compared with one-way relaying systems. However, the channel estimation problem for two-way relaying systems becomes more complicated. In this paper, we propose a superimposed channel training scheme for two-way MIMO relay communication systems, where the individual channel information for users-relay and relay-users links are estimated. The optimal structure of the source and relay training sequences are derived when the mean-squared error (MSE) of channel estimation is minimized. We also optimize the power allocation between the source and relay training sequences to improve the performance of the algorithm. Numerical examples are shown to demonstrate the performance of the proposed channel training algorithm.

Channel estimation for frequency-selective two-way MIMO relay systems

In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We propose a superimposed channel training algorithm to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the meansquared error (MSE) of channel estimation. We also derive the optimal power allocation between the source and relay training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.

Channel estimation for time-varying MIMO relay systems

In this paper, we investigate the channel estimation problem for multiple-input multiple-output (MIMO) relay communication systems with time-varying channels. The time-varying characteristic of the channels is described by the complex-exponential basis expansion model (CE-BEM). We propose a superimposed channel training algorithm to estimate the individual first-hop and second-hop time-varying channel matrices for MIMO relay systems. In particular, the estimation of the second-hop time-varying channel matrix is performed by exploiting the superimposed training sequence at the relay node, while the first-hop time-varying channel matrix is estimated through the source node training sequence and the estimated second-hop channel. To improve the performance of channel estimation, we derive the optimal structure of the source and relay training sequences that minimize the mean-squared error (MSE) of channel estimation. We also optimize the relay amplification factor that governs the power allocation between the source and relay training sequences. Numerical simulations demonstrate that the proposed superimposed channel training algorithm for MIMO relay systems with time-varying channels outperforms the conventional two-stage channel estimation scheme.

Channel estimation for two-way MIMO relay systems in frequency-selective fading environments

© 2002-2012 IEEE. In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We apply the method of superimposed channel training to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the mean-squared error (MSE) of channel estimation. Moreover, the optimal power allocation between the source and relay training sequences is derived to improve the performance of channel estimation. Numerical examples are shown to demonstrate the performance of the proposed superimposed channel training algorithm for two-way MIMO relay systems in frequency-selective fading environments.

Superimposed channel training algorithm for time-varying MIMO relay systems

Two-way relaying systems are known to be capable of providing higher spectral efficiency compared with one-way relaying systems. However, the channel estimation problem for two-way relaying systems becomes more complicated. In this paper, we propose a superimposed channel training scheme for two-way MIMO relay communication systems, where the individ-ual channel information for users-relay and relay-users links are estimated. The optimal structure of the source and relay training sequences are derived when the mean-squared error (MSE) of channel estimation is minimized. We also optimize the power allocation between the source and relay training sequences to improve the performance of the algorithm. Numerical examples are shown to demonstrate the performance of the proposed channel training algorithm.

Voltage stability analysis with optimum size and location based synchronous machine DG

Power loss of a distribution system can be reduced significantly by using optimum size and location of distributed generation (DG). Proper allocation of DG with appropriate size maximizes overall system efficiency. Moreover it improves the reliability and voltage profile of the distribution system. In this paper, IEEE 123 node test feeder has been considered to determine the optimum size and location of a synchronous machine based DG for loss reduction of the system. This paper also investigates the steady-state and dynamic voltage profile of that three phase unbalance distribution network in presence of DG with optimum size. This analysis shows that optimum size of DG at proper location minimizes the power loss as well as improves the dynamic voltage profile of the distribution system.

Optimum design and analysis study of stand-alone residential solar PV microgrid

Today’s power system network has become more complex and it has more responsibilities and challenges to provide secure, reliable and quality energysupply to the communities. A small entity of electrical network known as Microgrid (MG) is more popular nowadays to enhance reliablity and secure level of energy supply, in case of any energy crisis in the utility network. The MG can also provide clean energy supply by integrating renewable energy sources effectively. TheMG with small scale solar photovoltaic (PV) power system is more suitable to provide reliable and clean energy supply for remote or urban communities in residential level. This paper presents the basic analysis study of stand-alone solar photovoltaic (PV) MG power system which has been developed with the aid of Matlab - Simulink software, on the basis of residential load profile and solar exposure level in a particular area of Geelong, Victoria State. The simulation result depicts the control behavior of MG power system with optimum sizing of PV (4.385 kW)and battery storage (480Ah/48V) facility, fulfills daily energy needs in residential load level. This study provides a good platform to develop an effective and reliable stand-alone MG power system for the remote communities in the near future.

Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing

Fog computing, characterized by extending cloud computing to the edge of the network, has recently received considerable attention. The fog is not a substitute but a powerful complement to the cloud. It is worthy of studying the interplay and cooperation between the edge (fog) and the core (cloud). To address this issue, we study the tradeoff between power consumption and delay in a cloud-fog computing system. Specifically, we first mathematically formulate the workload allocation problem. After that, we develop an approximate solution to decompose the primal problem into three subproblems of corresponding subsystems, which can be independently solved. Finally, based on extensive simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.