A Load Balancing Algorithm in OpenFIow Networks

The goal of load balancing algorithm in OpenFlow networks is to combine the load balancing technology with the Open lqow technology, so that load balancing can adapt to the characteristics of the OpenFlow networks.The study of this paper focused on load balancing in OpenFlow networks, proposed a new load balancing algorithm.The algorithm considered the status of servers and network comprehensively, and provided the metrics for them.To distribute the network traffic to the servers and links reasonably, the goal of load balancing is reached.     

Research on scheduling algorithms in Web cluster servers

This paper analyzes quantitatively the impact of the load balance scheduling algorithms and the locality scheduling algorithms on the performance of Web cluster servers,and brings forward the Adaptive_LARD algorithm.Compared with the representative LARD algorithm,the advantages of the Adaptive_LARD are that:(1)it adjusts load distribution among the back-ends through the idea of load balancing to avoid learning steps in the LARD algorithm and reinforce its adaptability;(2)by distinguishing between TCP connections accessing disks and those accessing cache memory,it can estimate the impact of different connections on the back-ends‘load more precisely.Performance evaluations suggest that the proposed method outperforms the LARD algorithm by up to 14.7%.     

基于负载平衡的并行JOIN关系划分粒度研究

Based on the analysis of the task sizes and the load, this paper discussps the granularity ofrelation spliting in the spliting phase taking acount of task load being less than average load,andprobes into the relationship between the granularity and load balancing. The minimum number ofbuckets is determined on the basis of relation spliting granularity,and the maximum number ofproduct tuples of each node is induced under the prerequisite for ensuring load balance in paralleljoin.     

基于规则的分层负载平衡调度模型

On a massively parallel and distributed system and a network of workstations system, it is a critical problem to increase the utilization efficiency of resources and the answer speed of tasks by using effective load balancing scheduling strategy. This paper analyzes the scheduling strategy of dynamic load balancing and static load balancing,and then proposes a hierarchical load balancing scheduling model based on rules. Finally,making somecomparisons with Other scheduling models.     

A Dynamic Load Balancing Mechanism for Distributed Systems

It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized.In this paper,we present a dynamic load balancing mechanism(DLB).It adopts a cntralized approach and is network topology independent.The DLB mechanism employs a set of threscholds which are automatically adjusted as the system load changes.It also provides a simple mechanism for the system to switch between periodic and instantaneous load balancing policies with ease.The performance of the proposed algorithm is evaluated by intensive simulations for various parameters.Te simulation results show that the mean job response time in a system implementing DLB algorithm is significantly lower than the same system without load balancings.Furthermore,compared with a previously proposed algorithm,DLB algorithm demonstrates improved performance,especially when the system is heavily loaded and the load is unevenly distributed.     

Power system aggregate load area modelling by particle swarm optimization

This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.     

Strategy and Simulation of Adaptive RID for Distributed Dynamic Load Balancing in Parallel Systems

Dynamic load balancing schemes are significant for efficiently executing nonuniform problems in highly parallel multicomputer systems.The objective is to minimize the total exectuion time of single applications.This paper has proposed an ARID strategy for distributed dynamic load balancing.Its principle and control protocol are described,and te communication overhead,the effect on system stability and the performance efficiency are analyzed.Finally,simulation experiments are carried out to compare the adaptive strategy with other dynamic load balancing schemes.     

A new technique for islanding operation of distribution network connected with mini hydro

An islanding operation of a distribution network is a topic of interest due to the significant penetration of distributed generation(DG) in a power system network. However, controlling the frequency of an islanded distribution system remains an unresolved issue, especially when the load exceeds the generation. This paper presents a new technique for a successful islanding operation of a distribution network connected with multiple mini hydro based DGs. The proposed technique is based on three main parts. The first part uses an islanding detection technique to detect the islanding event correctly. The second part consists of a power imbalance estimation module(PIEM), which determines the power imbalance between the generation and load demand. The third part consists of a load shedding controller, which receives the power imbalance value and performs load shedding according to load priority. The proposed technique is validated on an 11 k V existing Malaysia distribution network. The simulation results show that the proposed technique is effective in performing a successful islanding operation by shedding a significant number of loads.     

Design and Implementation of the Federation Load Simulator

The importance and necessity of emulating the federation load during simulating was analyzed firstly. Then, the special federation load emulation tool, federation load simulator （ FLS ）, was designed and implemented, by which all of the vital essential characteristics of a simulation could be tested, e. g. the amount of federates, the joint/resigned speed of federate, the amount of object instances, the registered/deleted speed of instance in one single program, etc.. The applications proved that FLS could provide a convenient, effective and adjustable simulation load testing environment during the procedure of run-time infrastructure（RTI） and interrelated tool federates researching, developing and performance testing. Furthermore, the FLS utilized all kinds of resources with high efficiency.     

Load Shedding for Window Joins over Streams

We address several load shedding techniques over sliding window joins. We first construct a dual window architectural model including aux-windows and join-windows, and build statistics on aux-windows. With the statistics, we develop an effective load shedding strategy producing maximum subset join outputs. In order to accelerate the load shedding process, binary indexed trees have been utilized to reduce the cost on shedding evaluation. When streams have high arrival rates, we propose an approach incorporating front-shedding and rear-shedding, and find an optimal trade-off between them. As for the scenarios of variable speed ratio, we develop a plan reallocating CPU resources and dynamically resizing the windows. In addition, we prove that load shedding is not affected during the process of reallocation. Both synthetic and real data are used in our experiments, and the results show the promise of our strategies.     

A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers

The rapid growth of computational power demand from scientific, business, and Web applications has led to the emergence of cloud-oriented data centers. These centers use pay-as-you-go execution environments that scale transparently to the user. Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment. Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective. Load classification before prediction is necessary to improve prediction accuracy. In this paper, a novel approach is proposed to forecast the future load for cloud-oriented data centers. First, a hidden Markov model （HMM） based data clustering method is adopted to classify the cloud load. The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers. Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load. With the data from this cluster, a genetic algorithm optimized Elman network is used to forecast future load. Experimental results show that our algorithm outperforms other approaches reported in previous works.     

EALBMA： A Load Balancing Model Based on Mobile Agent

In this paper, firstly, we analyze some problems in the traditional load balancing, such as the structure, collecting and updating load information, adjusting strategy, and the extensibility. Secondly, we propose EALBMA （Efficient and Adaptive Load Balancing based on Mobile Agent） and discuss its basic principles. Using mobile agent, which is intelligent and mobile, EALBMA can resolve these problems above well. Therefore, EALBMA can improve the performance, adaptability and extensibility greatly. Finally, we draw the conclusion that it is reasonable and necessary to improve the load balancing using mobile agent.     

Hybrid partial least squares and neural network approach for short-term electrical load forecasting

Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares （PLS） and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.     

Cost-Sensitive and Load-Balancing Gateway Placement in Wireless Mesh Networks with QoS Constraints

In wireless mesh networks(WMNs),gateway placement is the key to network performance,QoS and construction cost.This paper focuses on the optimization of the cost and load balance in the gateway placement strategy,ensuring the QoS requirements.Firstly,we define a metric for load balance on the gateways,and address the minimum cost and load balancing gateway placement problem.Secondly,we propose two algorithms for gateway placement.One is a heuristic algorithm,which is sensitive to the cost,selects the gate...     

Assessment of Numerical Models for Live Load Distribution in a Road Slab Bridge

This paper presents comparison of numerical models used in an analysis of a road bridge deck. The models were adapted for computing the live load distribution coefficients in composite concrete bridge deck. The load distribution method was chosen for assessment of the usability of different numerical model in slab bridge deck analysis. The goal of the study is to determine a simplest but still accurate numerical model to estimate live load effects on composite slab bridge. In the analysis, the well-established grillage approach was adapted for representation of the bridge deck as a basic model as well as more sophisticated three-dimensional models which was supposed to better represent the real behavior of the deck under concentrated wheel loads. The bridge deck was effectively modeled using beam and shell elements. The grillage method compares well with the finite-element method. This finding is allowed to establish simplification in numerical modeling of slab bridge decks for live load effect computations.     

A unified algorithm for mobility load balancing in 3GPP LTE multi-cell networks

3GPP long term evolution (LTE) is a promising candidate for the next-generation wireless network, which is expected to achieve high spectrum efficiency by using advanced physical layer techniques and flat network structures. However, the LTE network still faces the problem of load imbalance as in GSM/WCDMA networks, and this may cause significant deterioration of system performance. To deal with this problem, mobility load balancing (MLB) has been proposed as an important use case in 3GPP self-organizing network (SON), in which the serving cell of a user can be selected to achieve load balancing rather than act as the cell with the maximum received power. Furthermore, the LTE network aims to serve users with different quality-of-service (QoS) requirements, and the network-wide objective function for load balancing is distinct for different kinds of users. Thus, in this paper, a unified algorithm is proposed for MLB in the LTE network. The load balancing problem is first formulated as an optimization problem with the optimizing variables being cell-user connections. Then the complexity and overhead of the optimal solution is analyzed and a practical and distributed algorithm is given. After that, the proposed algorithm is evaluated for users with different kinds of QoS requirements, i.e., guaranteed bit rate (GBR) users with the objective function of load balance index and non-GBR (nGBR) users with the objective function of total utility, respectively. Simulation results show that the proposed algorithm leads to significantly balanced load distribution for GBR users to decrease the new call blocking rate, and for nGBR users to improve the cell-edge throughput at the cost of only slight deterioration of total throughput.     

小工具巧治宠物“怪眼症”

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A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.     

Dynamic load distribution with hop-by-hop forwarding based on max-min one-way delay

Load balancing can effectively improve network performance and scalability,but it may cause packet disorder,so worsening the performance.Additionally,without MPLS to establish the desired end-to-end paths,hop-by-hop routing load balancing is more difficult to achieve than the source routing;however,it can significantly improve network performance.In this paper,we propose a load balancing scheme with hop-by-hop routing,by using the burstiness features of flows to make sure that the packets of the same flow arrive at the receiving end in order.Simulation results show that our algorithm can adapt to the dynamic changes of the end-to-end delay and the routing vector,and also can achieve fine-gained load balancing.     

Minimum energy storage for power system with high wind power penetration using p-efficient point theory

Minimum energy storage （ES） and spinning reserve （SR） for day-ahead power system scheduling with high wind power penetration is significant for system operations. A chance-constrained energy storage optimization model based on unit commitment and considering the stochastic nature of both the wind power and load demand is proposed. To solve this proposed chance-constrained model, it is first converted into a deterministic-constrained model using p-efficient point theory. A single stochastic net load variable is developed to represent the stochastic characteristics of both the wind power and load demand for convenient use with the p-efficient point theory. A probability distribution function for netload forecast error is obtained via the Kernel estimation method. The proposed model is applied to a wind-thermal-storage combined power system. A set of extreme scenarios is chosen to validate the effectiveness of the proposed model and method. The results indicate that the scheduled energy storage can effectively compensate for the net load forecast error, and the increasing wind power penetration does not necessarily require a linear increase in energy storage.