A Dynamic Feedback-based Load Balancing Methodology

Transcription

1 .J. Modern Educaton and Computer Scence, 2017, 12, Publshed Onlne December 2017 n MECS ( DO: /jmecs A Dynamc Feedback-based Load Balancng Methodology Xn ZHANG, Jnl L*, Xn FENG School of Computer Scence and Technology Changchun Unversty of Scence and Technology Emal: qanxaweguang@126.com Receved: 08 October 2017; Accepted: 08 November 2017; Publshed: 08 December 2017 Abstract Wth the recent growth of nternet-based applcaton servces, the concurrent accessng requests arrvng at the partcular servers offerng applcaton servces are growng sgnfcantly. t s one of the crtcal strateges that employng load balancng to cope wth the massve concurrent accessng requests and mprove the access performance s. To buld up a better onlne servce to users, load balancng solutons acheve to deal wth the massve ncomng concurrent requests n parallel through assgnng and schedulng the work executed by the members wthn one server cluster. n ths paper, we propose a dynamc feedback-based load balancng methodology. The method analyzes the real-tme load and response status of each sngle cluster member through perodcally collectng ts work condton nformaton to evaluate the current load pressure by comparng the learned load balancng performance wth the preset threshold. n ths way, snce the load arrvng at the cluster could be dstrbuted dynamcally wth the optmzed manner, the load balancng performance could thus be mantaned so that the servce throughput capacty would correspondngly be mproved and the response delay to servce requests would be reduced. The proposed result s contrbuted to strengthenng the concurrent access capacty of server clusters. Accordng to the experment report, the overall performance of server system employng the proposed soluton s better. ndex Terms Load balancng, dynamc feedback, server cluster, dstrbuted computng. NTRODUCTON n recent years, varous nternet-based applcaton servces have gradually been employed n all the areas of human lfe. Through the response procedures, there are a consderable number of servce requests arrvng at the server wthn a short tme and demandng to be answered wth less delay [1]. Therefore, t s the server throughput that would drectly affect the qualty of applcaton servces. t drectly reflects the parallel processng capablty of servers (clusters) that support servce capabltes. The hgher parallel processng capablty means that, wthn a fxed perod, the number of servce requests to be dealt wth can be relatvely larger [2]. As a soluton to mprove the parallel processng capablty, the load balancng technology groups a large number of servces requests wth mult-pont dstrbuton to averagely assgn the requests to each of the member servers (accordng to ther responsbltes n the cluster, we respectvely categorze the member servers as "master control node" and "workng node") wthn the same cluster [3]. Thus each of the workng servers only needs to cope wth a smlar number of the assgned requests, and then the server cluster s enabled to provde the parallel processng capablty [4]. The access effcency of servce requests s thus mproved. n ths paper, we propose a load balancng method based on dynamc feedback. By dynamc feedback mechansm, we collect the workng condtons of each workng node n real tme, to dynamcally adjust the workload dstrbuton scheme accordng to operatng condtons durng servce request access, so that each workng node Undertake smlar workloads to the extent feasble and avod unbalanced load dstrbuton. The method can effcently adapt to dynamc operatng condtons durng concurrent processng and has good real-tme capablty and flexblty.. RELATED WORK The earlest balancng mechansm s performed by employng DNS (Doman Name System) whch confgures a host wth multple mappng addresses through DNS confguraton [5]. After the nteractons wth the host from the arrved user servce requests, the requests are redrected by the host to the dfferent mappng addresses accordng to a preset prncple [6]. The servers at the mappng addresses would deal wth the requests n parallel,.e., the prelmnary load balancng operatons. However, n ths way, the load balancng effcency s relatvely low and the workng performance s lmted by the network topology. The load balancng doesn t gan much progress. Accordng to the research of Nkolaou et al., the actual resource utlzaton rate of workng nodes s the core factor to acheve a better load balancng performance [7]. One of the man objectves of the load balancng scheme

2 58 A Dynamc Feedback-based Load Balancng Methodology s to ratonally plan the workload dstrbuton and equalze the resources of all workng nodes. Cardelln and Bryhn et al. compared and studed a varety of load balancng methods, whch suggests the advantages and dsadvantages of load balancng strateges based on clents, servers, DNS and central allocators [8]. Wth comparng the performance of varous algorthms, the contrbuton provdes the sold support to load balancng research. Referrng to the commercal load balancng products, a lot of Chnese and nternatonal companes are makng an effort to develop the software-based and hardware-based load balancng products, such as LVS, Lander Balance, Check Pont, etc [9].. DYNAMC FEEDBACK-BASED LOAD BALANCNG METHODOLOGY When facng a large number of concurrent servce requests from users, the core task of mplementng load balancng s to arrange the strategy through proper task schedulng, so that the server cluster can meet the requrements of the overall servce duraton, task throughput, resource utlzaton effcency, scalablty and many other constrants [10]. The dynamc feedbackbased load balancng method proposed n ths paper focuses on the load condtons and the dynamcs of access tasks wthn each work node, and then t provdes the task schedulng plan and the near-optmal executon soluton under the concurrent request workng condtons. Ths paper elaborates the load balancng method wth consderng the followng factors [11] : memory occupancy of the tasks [14]. Through consderng the three factors, the strategy and the relevant parameters of dealng wth tasks are adjusted. Thrd, collectng the parameters of tasks arrvng at the workng nodes to determne the current processng performance. n ths way, the node and the feasble moment to start the load balancng process are settled [15]. Then confgurng and executng the load balancng method,.e., selectng the task tems to be moved out, the workng node where the tems are located and the destnaton nodes to move n the tasks. Ffth, determnng whether there s a new task to be moved n. f there s a new task, then dentfyng and collectng the avalable resource as the procedures n the prevous step. f there s no new ncomng task, then completng the current load balancng tasks (Seen n Fgure 1). (1) Node parameter: t refers to the real-tme load status of each workng node, ncludng the number of tasks n the runnng queue, the speed of the system call, the sze of the free memory, etc. (2) Evaluaton strategy: t refers to assess whether to re-transfer the follow-up tasks to other nodes for further processng accordng to the current workload of the workng node. n ths paper, we select to employ a threshold-based approach to determne the task transfer. (3) Transfer locaton: gven the tasks that are sutable for transferrng to other work nodes, t s necessary to specfy the target work node for task transfer. (4) Mantenance means: t refers to determne the lst of tasks to be transferred. Then the load schedulng plan for transferrng tasks s executed mantan load balancng. Through analyzng the above four factors, we can suggest the procedures for load balancng process. Frst, collectng and dentfyng the relevant avalable resources durng the balancng executon [12]. The resource concerns the avalable workng nodes, processng capabltes of nodes, avalable storage space and memory [13]. Second, analyzng and determnng the executon condton of dealng wth the arrved tasks. f all the tasks have been completed, then the node contnues wth watng for new tasks. f there are some pendng tasks, then the tasks are analyzed by concernng the arrval rate of tasks, the number of the tasks and the Fg.1. The executon flow of load balancng method 3.1 Dynamc feedback strategy Gven the actual stuaton of parallel processng, ths paper proposes a global load balancng qualty-orented dynamc feedback strategy wth consderng the key factors under the load balancng scenaro and the nterrelatons among the factors. Durng the process of accessng the servce requests and makng the response,

3 A Dynamc Feedback-based Load Balancng Methodology 59 the load condton of all the nodes s collected and analyzed contnuously. Combned wth the dynamc feedback strategy, the balancng procedures are kept avalable and useful. Ths paper proposes to dynamcally analyze the current status of the workng nodes relyng on the feedback control theory for evaluatng the load ndcators to adjust and arrange the load balancng scheme, wth whch the load balancng soluton s thus arranged and planned to gan the contnuous effectveness. (Seen Fgure 2). Fg.2. The evaluatng scheme of the dynamc feed n ths paper, the workng status of each node s taken as the startng pont to contnuously evaluate the resource occupancy and then to analyze varous characterstc data durng the task executon. Through contnuously assessng the performance of the server cluster system, the task allocaton plan s adjusted accordngly. Here are two specfc tasks: (1) to evaluate the load status of the work node; (2) to formulate the load threshold settng strategy accordng to the load assessment and to dynamcally adjust the threshold value correspondngly Evaluatng load status of node Node load assessment manly concerns evaluatng the workng node's stuaton and the nteractons between the nodes. The correspondng workng parameters are: (1) Workng node operaton ndcator (denoted as S N, refers to the dentty of the current work node, N represents the ndcator s used to specfy the parameter of a workng node): Ths ndcator reflects the performance of the current workng node under the correspondng load condton and manly covers the status of CPU (denoted as p), memory (denoted as r) and network card (denoted as t) that are partcpatng n the task executon. Ths ndcator reflects the performance of the current workng node under the correspondng load condton and manly covers the status of the core components partcpatng n the task executon, such as CPU (denoted as p), memory (denoted as r) and network card (denoted as t). Meanwhle, the status (denoted as S ) of the other component (denoted as c) that could c E affect the performance would also be concerned f necessary. Because the nstantaneous CPU load cannot truly reflect the real workng condtons durng the contnuous executon of computng tasks, ths paper selects to use the average CPU workng condton assessment method based on movng nspecton tme wndow to calculate the CPU work when calculatng CPU performance. Wth ths method, the CPU usage condtons are collected at the start tme pont and end tme pont of the nspecton tme wndow. Then the overall CPU usage s calculated as the percentage of the CPU runtme durng the nspecton wndow out of the total CPU runtme. We take the percentage value as the assessng result of nvestgatng usagecpu the CPU (denoted as and formulated as eq.(1)). usage (1 ( dle dle ) / ( cpu cpu )) 100% (1) cpu n eq.(1), the varable denoted as dle represents the dle tme of CPU at the correspondng tme pont, and the varable denoted as cpu represents the correspondng total CPU runtme. r n calculatng the memory work condton (.e., S E ), the percentage of the actual memory cost spent n the calculaton out of the total memory capacty s used as the memory assessment result (denoted as calculated by eq.(2)). usage mem usage ( MemTotal MemFree ) / MemTotal (2) mem

4 60 A Dynamc Feedback-based Load Balancng Methodology n eq.(2), MemTotal represents the total physcal capacty of memory and MemFree represents the memory that s not used yet. Durng calculatng the network card work condton, we employ a network test tool to measure the data flow wthn the nspecton tme wndow. The network card work condton s assessed as a percentage of the maxmum data flow value out of the network card bandwdth parameter. Based on the workng status assessments onto all the components wthn one workng node, the workng condton of the workng node s expressed as: S S S S S p + r t c 1 p r t c N p E r E t E c E n eq.(3), represents the weght of the correspondng component durng the measurement and t s called component performance weght. All the workng condtons of all the components are notated n form of percentage (from 0% to 100%). 0% ndcates that the components are not used, whereas 100% means the component has reached the full load workng condton. (2) The node nteracton ndcator (denoted as (3) S, refers to the dentty of the current work node, represents the ndcator s used to specfy the nteracton among the workng nodes): the ndcator reflects the communcaton performance between the current workng node and the master node (.e., the node n responsble for managng the dstrbuton of tasks to all the other work nodes). The nteracton ndcator s expressed as: s S k 0 1 m k 1 m s k dsconnected connected n eq.(4), m represents the sum number of the lnks from the current node to the other ones; s represents the specfc communcaton status and condtons from the current node (whose dentty s k) to the other ones. f the nteracton exsts (.e., the lnk s connected), then the value of s s assgned wth 1. Otherwse, the value s k assgned wth 0. Wth the above ndcator, the load status assessment of each workng node s expressed as: L N N k (4) S S S (5) n eq.(5), represents the weght of one ndcator durng the assessment and s called node performance weght The strategy of confgurng load threshold Through the computng process mentoned above, the strategy of schedulng tasks to the workng nodes can be establshed wth the above load ndcators, durng whch we employ two thresholds as the schedulng decson parameters,.e., 1 and 2 ( 1 2). (1) When SL 1, t means that the workng load of the current workng node s relatvely small. The workng node can complete the scheduled even earler and t could be assgned wth new tasks n advance. (2) When 1 S L 2, t means that the workng load of the current workng node s normal. The workng node can fnsh the tasks as planned and t could be assgned wth the new tasks requrng small load. (3) When SL 2, t means that the workng load of the current workng node s too large. And the workng node should not be assgned wth new tasks n order to prevent the overload that mght lead to server crash. n ths case, after completng the current task, the load assessment ndcator would be reset as 0. The node should re-partcpate n the load nformaton feedback process to obtan the new tasks n the further schedulng. Through analyzng eq.(3), eq.(4) and eq.(5), the load ndcator denoted as S reflects the load status and workng condtons of the nodes as well as ther nteractons. The component performance weght denoted as and the node performance weght denoted as reflects the crtcalty of ether load condton (.e., node workng condton or component workng condton) durng feedng back the assessment. Therefore, the confguraton of the weght parameters can ntroduce the load balancng scenaro nto the process of formulatng the schedule scheme. The weght value mans orgns relyng on the experences from the data center engneers and they would be gradually turnng to stable through long-term runnng. Moreover, the paper recommends to ntalze the two schedulng decson parameters (.e., 1 and 2 ) as 0.5 and 0.8 respectvely based on the authors practces. Durng load balancng process, the load parameters are contnuously collected to form the perodc assessment onto the load ndcators. Meanwhle, the perod of collectng parameters can also be adjusted correspondng to the scenaros where to run load balancng soluton. The adjustment can prevent the larger consumpton of the system performance and the energy. Accordng to the practcal experence, we recommend to confgure the collectng perod between 10 seconds and 90 seconds correspondng to the servce request frequency.

5 A Dynamc Feedback-based Load Balancng Methodology 61 Durng operatng the dynamc feedback-based load balancng method, we fnd that the stuatons that lead to the dynamc adjustment of tasks n the cluster and nvoke the feedback actons manly occur n three scenaros,.e., new tasks are loaded n real-tme, real-tme dynamc changes of network bandwdth s dynamcally changed n real-tme, and nstant load capacty of workng nodes s beng adjusted. Durng loadng the new task, the jonng of newly added workng node(s) wll not have a negatve mpact on overall msson plannng and cluster performance f the new workng nodes are ncluded n the load balancng soluton. The newly added work nodes wll be gven the prorty to perform tasks wth a relatvely short tme and are confgured to complete and mplement the correspondng feedback measures accordng to the above strateges. 3.2 Task schedulng n load balancng n the server cluster that mplements the load balancng soluton, the load balancng algorthm deployed at the master node consders the real-tme load condtons and processng performance of all workng nodes, and t constantly adjusts the proporton of task dstrbuton to avod varous problems caused by unbalanced task dstrbuton. Because the load balance dynamcally changes wth tme, the workload of each workng node s roughly equal to the load threshold through the dynamc adjustment of the threshold, so that the tasks can be evenly dstrbuted and the workng effcency of each workng node can be brought nto full performance. Fg.3. Workng process of load balancng The load balancng process ncludes (Seen n Fg.3): (1) Load balancng management s carred out on the master control node, and the load of the system s analyzed accordng to the workng status of the exstng nodes and the load balancng threshold. (2) Through the connecton between the nodes, the master control node dstrbutes the work tasks to each workng node. (3) Each workng node processes the receved tasks. (4) The real-tme load status nformaton for each worker node s collected. (5) The workng condton of each node s calculated and evaluated accordng to the collected load status nformaton. (6) Through comparng the workng status of the node wth the load balancng threshold, the load balancng threshold s adjusted accordng to the preset balancng strategy n the further load balancng management. Durng the nformaton collecton phase of load balancng, the master control node collects nformaton about the real-tme operaton of other work nodes. The collected nformaton manly ncludes the load of the node, the task allocaton, and the response speed. The collecton phase s executed perodcally accordng to the strategy and the actual stuaton. The more commonly used mechansm s that the workng node perodcally sends nformaton to the master control node. The workng node perodcally sends state nformaton to the master control node by usng the heartbeat mechansm to ensure that the master control node has a newer workng node status and enables the former to use ths Judge whether the workng node exsts. Perodc nformaton collecton s relatvely smple to mplement, but the problems are relatvely obvous. Frst, the perodc nformaton transmsson wll ncrease the load of the master control node. Snce all the workng nodes send messages to the master control node, t wll cause greater communcaton overhead. Second, the length of the message transmsson s not easy to determne. Too short perod wll cause the ncrease of communcaton load whereas too long perod wll cause the update s not executed n tme. Thrd, f we send the new tasks wthn a task allocaton cycle for many tmes, then t wll cause the further tasks cannot be effcently processed. Conversely, when there are no new tasks arrvng between the two updatng actons, the system resources are wasted. n vew of the above stuaton, the perodc nformaton collecton of the master node s not entrely sutable for varous stuatons occurrng n practcal applcatons. Therefore, ths paper extends the method of collectng perodc nformaton so that the master control node collects the status nformaton of the workng nodes accordng to the needs of the current task data along collectng the perodc nformaton. The dea of collectng nformaton accordng to the number of tasks s that when the master control node receves a new task, t actvely sends a status query request to the workng node and collects the workload of the workng node. When the workng node receves the status query request from the master control node, t wll send the current load nformaton, resource occupaton nformaton and task allocaton nformaton to the master control node. The schedulng process s stated as follows: f (new task arrves) (a new perod starts) reset clock; the master node sends out query request and receves the load nformaton from the workng nodes f (the current node denoted as satsfes the schedulng constrants) assgn task to node or add the node nto queue end f end f

6 62 A Dynamc Feedback-based Load Balancng Methodology The process of the current workng node (denoted as ) f the node receves the query request from the master control node compose the load nformaton to the master control node end f n the nteracton process among all the nodes, TCP protocol can be used for message transmsson to ensure relablty. Accordng to the transmsson characterstcs, the workng node accepts the task assgnment from the master control node and reples to the master control node. n order to ensure the correctness of the nformaton replyng to the master control node, After recevng the task, the workng node reples a message to the master control node, whch contans the workng node's own nformaton and the partner node nformaton used n the load balancng adjustment process, and each workng node adds the carred tme n the reply message. The message s nsert wth the tmestamp to ensure that the nformaton receved by the master node s up to date. The master control node updates the stored correspondng workng node nformaton accordng to the carred nformaton, whch can not only catch the workng node load nformaton accurately but also reduce the communcaton load between the nodes and keep the schedulng algorthm smple and feasble. When the system s dle, the workng node wats for the master control node to collect nformaton and make decsons. When the system s runnng, the workng nodes process the actual tasks and reples n parallel. The workng nodes do not contnue to wat for the allocaton of tasks n order to take full advantage of system resources. The master control node collects nformaton only when t submts a task. When the workng pressure of the system load balancng s relatvely hgh and the number of tasks performed durng a specfc tme perod s too large, the master control node consumes more resources and more tme n task allocaton. Accordng to the algorthm, the workng node would send the latest ts own load nformaton to the master control node after recevng the task, the latter only updates the nformaton of the nodes that have accept the tasks. Referrng to the workng nodes that haven t receved the tasks, they would not send the updated nformaton to the master control node. n ths way, t s possble to effectvely reduce the possblty of dle nodes recevng new tasks. n combnaton wth the perodc collecton of node load nformaton, the master control node perodcally collects the nformaton of all the nodes whle mantanng the on-demand collecton manner, and ncreases the possblty of acceptng new tasks for nodes that have not been assgned tasks n the near future. n summary, the combnaton of perodc collecton and on-demand collecton of work patterns, to ensure real-tme load nformaton and reduce the amount of nteracton between nodes to mprove the overall performance of the system at the same tme, better balanced load of each node. V. VERFCATON EXPERMENTS n ths paper, we adopt a cloud computng smulaton platform CloudSm to verfy load balancng solutons (verson 4.0). Based on the dscrete event model, CloudSm s an applcaton system for smulaton, whch s developed by Java language. Thus, t has unque features of Java language such as cross-platform deployment. t can run on dfferent operatng systems lke Wndows, Lnux or MacOS. n addton, t can be used for modelng and smulatng for system archtectures and deployment plans n a cluster envronment. n ths work, we utlze some core components of CloudSm to buld the smulaton envronment, whch ncludes the followng steps: (1) DataCenter class: used for smulatng the solutons of data center located n the cluster nfrastructure, and encapsulatng the related methods to confgure the correspondng work nodes. (2) DataCenterBroker class: used for encapsulatng the related methods to manage the nsde work nodes. (3)Host class: used for smulatng the mappng relatons between the physcal hosts and the vrtual hosts n cluster envronment. (4) VrtualMache class: used for smulatng the deployed vrtual hosts n cluster. The vrtual host acts as a member n Host class to smulate the resource sharng and nternal schedulng. (5) VMScheduler class: used for smulatng the polces of dspatchng and managng among varous vrtual machnes. (6)VMProvsoner class: used for confgurng the mappng relatonshp between Host object and VrtualMachne object belongng to DataCenter objects. (7) Cloudlet calss: used for smulatng the tasks n cluster, and confgurng the resources. n ths work, we took advantage of three physcal hosts (dentfed by PH_LB_1601 PH_LB_1602 and PH_LB_1603) to confgure the load balancng smulaton envronment. Each physcal host nstalled and confgured JDK8.0, confgured CloudSm4.0, and set envronment varables. The load balancng smulaton envronment set the three physcal hosts mentoned above to one DataCenter object (dentfed by CS_DC_LB). The host PH_LB_1601 set two VrtualMachne objects (dentfed by VM_CL_1601_01 and VM_LB_CTL_1601_02 separately) n DataCenter object as a load balancng controller, used for dspatchng and managng the load balance. Physcal hosts PH_LB_1602 and PH_LB_1603 buld three VrtualMachne objects separately (dentfed by VM_LB_1602_01 VM_LB_1602_02 VM_LB_1602_03 VM_LB_1603_01 VM_LB_1603_02 and VM_LB_1603_03). The nsde VrtualMachne object creates a load balancng soluton consstng of seven nodes. The network topology of the above nodes s llustrated as Fgure 4:

7 A Dynamc Feedback-based Load Balancng Methodology 63 Fg.4. The network topology of the experment Based on the afore mentoned network topology, we called the Host object and VMScheduler object to deploy the load balancng strategy and schedulng algorthm based on Dynamc Feedback to the physcal hosts PH_LB_1601, PH_LB_1602 and PH_LB_1603. For testng the load balancng scheme, ths paper confgures the load task generated by Httperf n the VrtualMachne object VM_CL_1601_01 n order to test the accessng capablty of servce request. The test scenaro focuses on two performance measures: (1) the assocaton between the average response tme of servce requests and the number of concurrent connectons to reflect the servce access performance of the soluton; (2) the number of avalable concurrent connectons n a gven servce request scenaro to reflect the maxmum concurrent access capablty. n the expermental envronment, the average response tme of a cluster consstng of multple servers to multple concurrent connectons s relatvely stable, and the test data s shown n Table-1. From the above expermental results, t can be seen that the cluster-based load balancng scheme has good performance n handlng concurrent requests. When the number of concurrent connectons contnues to rse, the response tme remans wthn 7 ms. t can be seen that the load balancng scheme bult by clusterng can effectvely control the server to process the request access tme. Table-1. The response of load balancng soluton Concurrent connectons Response tme(ms) For evaluatng the load balancng strategy, we manly studed the actual number of concurrent connectons to analyze the performance of the load balancng algorthm based on dynamc feedback, and compared t wth the Ngnx-based P Hash algorthm. Among them, the core dea of P Hash algorthm s to hash map accordng to the P address of the servce request source, and to use the result of hash operaton as the bass to select the server node that actually answers the servce request, and then to allocate the servce request to the correspondng server node (See Table-2). Table-2. Actual concurrent connectons between the algorthms Dynamc feedbackbased P Hash algorthm load balancng Concurrent Actual concurrent algorthm connectons connecton amount Actual concurrent (access rate) connecton amount (access rate) (100%) 200(100%) (99.9%) 399.8(99.9%) (99.9%) 599.1(99.9%) (99.8%) 794.9(99.4%) (68.2%) 980.4(98.0%) (46.8%) 734.2(61.2%) (36.7%) 662.2(47.3%) Fgure 5 shows that when the number of concurrent connectons s low (lower than 800), the actual number of concurrent connectons s bascally the same as the number of concurrent requests. Wth occasonal small loss (the rate of loss of P Hash algorthm s 0.2% and the loss of load balancng algorthm based on the dynamc feedback s 0.6%), t can mantan the basc normal request access performance. When the number of concurrent connectons contnues to ncrease and reaches 1000, the P hash algorthm shows a sgnfcant loss of concurrent connecton requests, whch results n a loss

8 64 A Dynamc Feedback-based Load Balancng Methodology rate of 31.8%. n contrast, the rate of loss based on dynamc feedback load balancng algorthm s only 2%. n conclude, the latter has better servce request access performance. Fg.5. Comparson of actual concurrent connecton amount V. CONCLUSON n ths paper, the dynamc feedback-based load balancng method s ntroduced to optmze the dynamc feedback strategy n the process of dynamc load balancng. n the real case, load balancng task schedulng s optmzed and task queue schedulng n load balancng s analyzed n detal. The paper frst realzes an assessment of the load of the nodes and then optmzes the load balancng optmzaton strategy by settng the threshold of the load balancng. The expermental results are verfed by settng up an expermental envronment. t s proved that the optmzed load balancng algorthm based on dynamc feedback s better than P Hash algorthm n solvng hgh data concurrency. Wth the ncreasng amount of data n the network, the research on network load balancng optmzaton requres more optmzed algorthms n the process of data hgh concurrent processng. More research wll be conducted n ths respect n future work. ACKNOWLEDGE Ths work s supported by Jln Provncal Scence and Technology Department va nternatonal Jont Research Project ( GH). REFERENCE [1] Al-Anbag, Erol-Kantarc M, Mouftah H T. A low latency data transmsson scheme for smart grd condton montorng applcatons[c]// Electrcal Power and Energy Conference. EEE, 2013: [2] Zhong C, Ca D, Yang F. Dvsble loads schedulng usng concurrent sendng data on mult-core cluster[j]. Journal of Computer Research & Development, 2014, 51(6): [3] Wang S C, Yan K Q, Lao W P, et al. Towards a Load Balancng n a three-level cloud computng network[c]// EEE nternatonal Conference on Computer Scence and nformaton Technology. EEE, 2010: [4] Bhogal K S, Lews P D, Okunsende F O, et al. Computer data communcatons n a hgh speed, low latency data communcatons envronment[j] [5] Scharber J. CLENT-SELECTABLE ROUTNG USNG DNS REQUESTS:, US [P] [6] Stoltzfus A, O Meara B, Whtacre J, et al. Sharng and reuse of phylogenetc trees (and assocated data) to facltate synthess[j]. Bmc Research Notes, 2012, 5(1):1-15. [7] Leber A W, Knez A, Von Z F, et al. Quantfcaton of obstructve and nonobstructve coronary lesons by 64- slce computed tomography: a comparatve study wth quanttatve coronary angography and ntravascular ultrasound.[j]. Journal of the Amercan College of Cardology, 2005, 46(1):147. [8] Cardelln V, Colajann M, Yu P S. Dynamc Load Balancng on Web-Server Systems[J]. nternet Computng EEE, 1999, 3(3): [9] Andrews J, Sngh S, Ye Q, et al. An Overvew of Load Balancng n HetNets: Old Myths and Open Problems[J]. EEE Wreless Communcatons, 2013, 21(2): [10] Peng X. Research and Realzaton of Task Schedulng n Couplng Dstrbuted System[J]. Computer Technology & Development, [11] Bao-Tong D U, Bng L, Yang R. Concurrent servce composton approach based on QoS fuzzy domnance[j]. Computer Engneerng & Desgn, [12] Debankur M, Borst S C, Van L J S H, et al. Unversalty of load balancng schemes on the dffuson scale[j]. Journal of Appled Probablty, 2016, 53(4): [13] Ka H, Shen H. Localty-Preservng Clusterng and Dscovery of Resources n Wde-Area Dstrbuted Computatonal Grds[J]. EEE Transactons on Computers, 2012, 61(4): [14] Zhang N, Yan Y, Xu S, et al. A dstrbuted data storage and processng framework for next-generaton resdental dstrbuton systems[j]. Electrc Power Systems Research, 2014, 116(11): [15] Yang W C, Huang W T. A load transfer scheme of radal dstrbuton feeders consderng dstrbuted generaton[c]// Cybernetcs and ntellgent Systems. EEE, 2010: Authors Profles Xn ZHANG, Ph.D., lecturer at School of Computer Scence and Technology n Changchun Unversty of Scence and Technology. Hs nterests nclude: Moble nternet, desgn engneerng, software engneerng and system engneerng. Jnl L, Master canddate at School of Computer Scence and Technology n Changchun Unversty of Scence and Technology. Her research nterests nclude nternet of Thngs and applcatons, computer network, software engneerng.

9 A Dynamc Feedback-based Load Balancng Methodology 65 Xn FENG, Ph.D.,Assocate Professor of School of Computer Scence and Technology n Changchun Unversty of Scence and Technology. Hs research nterests nclude nternet of Thngs technology and applcatons, software engneerng and nformaton system, database and data mnng. How to cte ths paper: Xn ZHANG, Jnl L, Xn FENG, "A Dynamc Feedback-based Load Balancng Methodology", nternatonal Journal of Modern Educaton and Computer Scence(JMECS), Vol.9, No.12, pp , 2017.DO: /jmecs