Review of Load Balancing Mechanisms in SDN-Based Data Centers

With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones.Mosques are the type of buildings that have a unique energy usage pattern.Nevertheless,these types of buildings have minimal consideration in the ongoing energy efficiency applications.This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks.Therefore,this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh,Saudi Arabia.In this study,and by harvesting the load consumption of the mosque and meteorological datasets,the performance of four forecasting algorithms is investigated,namely Artificial Neural Network and Support Vector Regression(SVR)based on three kernel functions:Radial Basis(RB),Polynomial,and Linear.In addition,this research work examines the impact of 13 different combinations of input attributes since selecting the optimal features has a major influence on yielding precise forecasting outcomes.For the mosque load,the(SVR-RB)with eleven features appeared to be the best forecasting model with the lowest forecasting errors metrics giving RMSE,nRMSE,MAE,and nMAE values of 4.207 kW,2.522%,2.938 kW,and 1.761%,respectively.

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

Microscopic defects formation and dynamic mechanical response analysis of Q345 steel plate subjected to explosive load

As the basic protective element, steel plate had attracted world-wide attention because of frequent threats of explosive loads. This paper reports the relationships between microscopic defects of Q345 steel plate under the explosive load and its macroscopic dynamics simulation. Firstly, the defect characteristics of the steel plate were investigated by stereoscopic microscope(SM) and scanning electron microscope(SEM). At the macroscopic level, the defect was the formation of cave which was concentrated in the range of 0-3.0 cm from the explosion center, while at the microscopic level, the cavity and void formation were the typical damage characteristics. It also explains that the difference in defect morphology at different positions was the combining results of high temperature and high pressure. Secondly, the variation rules of mechanical properties of steel plate under explosive load were studied. The Arbitrary Lagrange-Euler(ALE) algorithm and multi-material fluid-structure coupling method were used to simulate the explosion process of steel plate. The accuracy of the method was verified by comparing the deformation of the simulation results with the experimental results, the pressure and stress at different positions on the surface of the steel plate were obtained. The simulation results indicated that the critical pressure causing the plate defects may be approximately 2.01 GPa. On this basis, it was found that the variation rules of surface pressure and microscopic defect area of the Q345 steel plate were strikingly similar, and the corresponding mathematical relationship between them was established. Compared with Monomolecular growth fitting models(MGFM) and Logistic fitting models(LFM), the relationship can be better expressed by cubic polynomial fitting model(CPFM). This paper illustrated that the explosive defect characteristics of metal plate at the microscopic level can be explored by analyzing its macroscopic dynamic mechanical response.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

L2-LBMT:A Layered Load Balance Routing Protocol for Underwater Multimedia Data Transmission

Providing highly efficient underwater transmission of mass multimedia data is challenging due to the particularities of the underwater environment. Although there are many schemes proposed to optimize the underwater acoustic network communication protocols, from physical layer, data link layer, network layer to transport layer, the existing routing protocols for underwater wireless sensor network(UWSN) still cannot well deal with the problems in transmitting multimedia data because of the difficulties involved in high energy consumption, low transmission reliability or high transmission delay. It prevents us from applying underwater multimedia data to real-time monitoring of marine environment in practical application, especially in emergency search, rescue operation and military field. Therefore, the inefficient transmission of marine multimedia data has become a serious problem that needs to be solved urgently. In this paper, A Layered Load Balance Routing Protocol(L2-LBMT) is proposed for underwater multimedia data transmission. In L2-LBMT, we use layered and load-balance Ad Hoc Network to transmit data, and adopt segmented data reliable transfer(SDRT) protocol to improve the data transport reliability. And a 3-node variant of tornado(3-VT) code is also combined with the Ad Hoc Network to transmit little emergency data more quickly. The simulation results show that the proposed protocol can balance energy consumption of each node, effectively prolong the network lifetime and reduce transmission delay of marine multimedia data.

Research on Rolling Load Distribution Method based on Data Mining

A new method of establishing rolling load distribution model was developed by online intelligent information-processing technology for plate rolling. The model combines knowledge model and mathematical model with using knowledge discovery in database （KDD） and data mining （DM） as the start. The online maintenance and optimization of the load model are realized. The effectiveness of this new method was testified by offline simulation and online application.

A Strategy for Loading Oblique Photogrammetry Models and Multilayer Basemap Data

With the development of drone technology and oblique photogrammetry technology, the acquisition of oblique photogrammetry models and basemap becomes more and more convenient and quickly. The increase in the number of basemap leads to excessively redundant basemap tiles requests in 3D GIS when loading oblique photogrammetry models, which slows down the system. Aiming at improving the speed of running system, this paper proposes a dynamic strategy for loading basemap tiles. Different from existing 3D GIS which loading oblique photogrammetry models and basemap tiles inde-pendently, this strategy dynamically loads basemap tiles depending on different height of view and the range of loaded oblique photogrammetry models. We achieve dynamic loading of basemap tiles by predetermining whether the basemap tiles will be covered by the oblique photogrammetry models. The experimental results show that this strategy can greatly reduce the num-ber of redundant requests from the client to the server while ensuring the user’s visual requirements for the oblique photogrammetric model.

Research on data load balancing technology of massive storage systems for wearable devices

Because of the limited memory of the increasing amount of information in current wearable devices,the processing capacity of the servers in the storage system can not keep up with the speed of information growth,resulting in low load balancing,long load balancing time and data processing delay.Therefore,a data load balancing technology is applied to the massive storage systems of wearable devices in this paper.We first analyze the object-oriented load balancing method,and formally describe the dynamic load balancing issues,taking the load balancing as a mapping problem.Then,the task of assigning each data node and the request of the corresponding data node’s actual processing capacity are completed.Different data is allocated to the corresponding data storage node to complete the calculation of the comprehensive weight of the data storage node.According to the load information of each data storage node collected by the scheduler in the storage system,the load weight of the current data storage node is calculated and distributed.The data load balancing of the massive storage system for wearable devices is realized.The experimental results show that the average time of load balancing using this method is 1.75h,which is much lower than the traditional methods.The results show the data load balancing technology of the massive storage system of wearable devices has the advantages of short data load balancing time,high load balancing,strong data processing capability,short processing time and obvious application.

Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.

Load Shedding Strategy Based on Combined Feed-Forward Plus Feedback Control over Data Streams

In data stream management systems (DSMSs), how to maintain the quality of queries is a difficult problem because both the processing cost and data arrival rates are highly unpredictable. When the system is overloaded, quality degrades significantly and thus load shedding becomes necessary. Unlike processing overloading in the general way which is only by a feedback control (FB) loop to obtain a good and stable performance over data streams, a feedback plus feed-forward control (FFC) strategy is introduced in DSMSs, which have a good quality of service (QoS) in the aspects of miss ratio and processing delay. In this paper, a quality adaptation framework is proposed, in which the control-theory-based techniques are leveraged to adjust the application behavior with the considerations of the current system status. Compared to previous solutions, the FFC strategy achieves a good quality with a waste of fewer resources.

Load Balancing Fat-Tree on Long-Lived Flows:Avoiding Congestion in a Data Center Network

In a data center network （DCN）, load balancing is required when servers transfer data on the same path. This is necessary to avoid congestion. Load balancing is challenged by the dynamic transferral of demands and complex routing control. Because of the distributed nature of a traditional network, previous research on load balancing has mostly focused on improving the performance of the local network; thus, the load has not been optimally balanced across the entire network. In this paper, we propose a novel dynamic load-balancing algorithm for fat-tree. This algorithm avoids congestions to the great possible extent by searching for non-conflicting paths in a centralized way. We implement the algorithm in the popular software-defined networking architecture and evaluate the algorithm＇ s performance on the Mininet platform. The results show that our algorithm has higher bisection band- width than the traditional equal-cost multi-path load-balancing algorithm and thus more effectively avoids congestion.

Energy-balanced clustering protocol for data gathering in wireless sensor networks with unbalanced traffic load

Energy-efficient data gathering in multi-hop wireless sensor networks was studied,considering that different node produces different amounts of data in realistic environments.A novel dominating set based clustering protocol (DSCP) was proposed to solve the data gathering problem in this scenario.In DSCP,a node evaluates the potential lifetime of the network (from its local point of view) assuming that it acts as the cluster head,and claims to be a tentative cluster head if it maximizes the potential lifetime.When evaluating the potential lifetime of the network,a node considers not only its remaining energy,but also other factors including its traffic load,the number of its neighbors,and the traffic loads of its neighbors.A tentative cluster head becomes a final cluster head with a probability inversely proportional to the number of tentative cluster heads that cover its neighbors.The protocol can terminate in O(n/lg n) steps,and its total message complexity is O(n2/lg n).Simulation results show that DSCP can effectively prolong the lifetime of the network in multi-hop networks with unbalanced traffic load.Compared with EECT,the network lifetime is prolonged by 56.6% in average.

Storage and Parallel Loading System Based on Mode Network for Multimode Medical Image Data

Since Multimode data is composed of many modes and their complex relationships,it cannot be retrieved or mined effectively by utilizing traditional analysis and processing techniques for single mode data.To address the challenges,we design and implement a graph-based storage and parallel loading system aimed at multimode medical image data.The system is a framework designed to flexibly store and rapidly load these multimode data.Specifically,the system utilizes the Mode Network to model the modes and their relationships in multimode medical image data and the graph database to store the data with a parallel loading technique.

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion strategies.Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload pro-cessing.Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains unexplored.In this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed DC.The primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy.On the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming workload.The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

Load Balance Strategy of Data Routing Algorithm Using Semantics for Deduplication Clusters

The backup requirement of data centres is tremendous as the size of data created by human is massive and is increasing exponentially.Single node deduplication cannot meet the increasing backup requirement of data centres.A feasible way is the deduplication cluster,which can meet it by adding storage nodes.The data routing strategy is the key of the deduplication cluster.DRSS（data routing strategy using semantics） improves the storage utilization of MCS（minimum chunk signature） data routing strategy a lot.However,for the large deduplication cluster,the load balance of DRSS is worse than MCS.To improve the load balance of DRSS,we propose a load balance strategy used for DRSS,namely DRSSLB.When a node is overloaded,DRSSLB iteratively migrates the current smallest container of the node to the smallest node in the deduplication cluster until this overloaded node becomes non-overloaded.A container is the minimum unit of data migration.Similar files sharing the same features or file names are stored in the same container.This ensures the similar data groups are still in the same node after rebalancing the nodes.We use the dataset from the real world to evaluate DRSSLB.Experimental results show that,for various numbers of nodes of the deduplication cluster,the data skews of DRSSLB are under predefined value while the storage utilizations of DRSSLB do not nearly increase compared with DRSS,with the low penalty（the data migration rate is only6.5% when the number of nodes is 64）.

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Live Virtual Machine(VM)migration is one of the foremost techniques for progressing Cloud Data Centers’(CDC)proficiency as it leads to better resource usage.The workload of CDC is often dynamic in nature,it is better to envisage the upcoming workload for early detection of overload status,underload status and to trigger the migration at an appropriate point wherein enough number of resources are available.Though various statistical and machine learning approaches are widely applied for resource usage prediction,they often failed to handle the increase of non-linear CDC data.To overcome this issue,a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory(CDB-LSTM)model is proposed.The CDB-LSTM adopts Helly property of Hypergraph and Savitzky–Golay(SG)filter to select informative samples and exclude noisy inference&outliers.The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computa-tional complexity during live VM migration.Further,the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration.A Hypergraph based CDB-LSTM was vali-dated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.

Mining Rules from Electrical Load Time Series Data Set

The mining of the rules from the electrical load time series data which are collected from the EMS (Energy Management System) is discussed. The data from the EMS are too huge and sophisticated to be understood and used by the power system engineer, while useful information is hidden in the electrical load data. The authors discuss the use of fuzzy linguistic summary as data mining method to induce the rules from the electrical load time series. The data preprocessing techniques are also discussed in the paper.

基于SDN的数据中心网络流量负载均衡研究

目前数据中心网络(data center network,DCN)的负载均衡方法存在对大小流的调度缺乏全局实时检测等不足,部分大流会造成拥塞、负载不均衡和带宽碎片等问题.针对上述问题,提出了一种SDN网络流量负载均衡算法—DSA-D.首先,对流量进行分类,为大流计算所有源至目的主机可达路径的最短跳数路径集;然后,根据LLDP和ECHO测量链路时延以求得时延最优路径集;最后,采用概率拟合算法分配路径,实现数据中心网络流量负载均衡.在相同场景下的实验结果表明,与ECMP、Hedera和DIFF算法相比,DSA-D算法具有更好的吞吐量、链路带宽利用率和平均往返时延.