2016 IJSRSET Volume 2 Issue 4 Prit ISSN: 2395-1990 Olie ISSN : 2394-4099 Themed Sectio: Egieerig ad Techology Moitorig ad Normalizatio of the Loaded Servers By Load Balacig Dr. D. Ravidra, M. Ramya School of Computig Sciece, St. Joseph s College, Trichy, Tamil Nadu Idia ABSTRACT Cloud computig is a ew computatioal model which is primarily based o distributed computig. The services ad applicatios are accessible to the differet cliets usig proper iteret protocol suit ad etworkig stadards. Though it has may advatages the cliet has to cosider also the drawbacks of the services provided by the cloud. Some of the issues have to cosidered like security, scalability, cost, SLA, etc. Most of the cliet users have to otice the performace of the cloud that the provider is issuig to the cliet. This paper describes about oe of the factors whe cosidered for the performace of the services provided. Scalability is the commo factor which affects the satisfactio of the cloud users. By reviewig the cloud scalability factors, moitorig the server that are distributed over the etwork ad its load balacig issue of the servers. This paper greatly deals with the ormalizatio of the cloud servers. Keywords : Performace; Scalability; Load Balacig; Moitorig; Normalize; Status I. INTRODUCTION Cloud computig are outlied as a computig surroudigs wherever computig wats by oe party are ofte outsourced to a differet party ad oce would like be arise to use the computig power or resources like iformatio or emails, they will access them via web[1]. Cloud computig is a model for eablig uiversal, suitable, o demad etwork accesses to a shared pool of cofigurable computig resources like etworks, servers, storage, applicatios, ad services that ca be rapidly equipped ad released with less maagemet effort or service provider itervetio. Moitorig of Cloud is a task of paramout importace for both Providers ad Cosumers. O the oe side, it is a key tool for cotrollig ad maagig hardware ad software ifrastructures; o the other side, it provides iformatio ad Key Performace Idicators (KPIs) for both platforms ad applicatios. Moitorig is clearly istrumetal for all the activities covered by the role of Cloud Auditor. I more geeral terms, Cloud Computig ivolves may activities for which moitorig are a essetial task. II. METHODS AND MATERIAL A. Related Work M. Kriushath et al.(2015)[2] describes the auto scalig values ad that settig dyamic threshold values i a cloud eviromet should utilize the available resources completely ad prevets the physical server damage. It maipulates the provider to accommodate more users i a physical server ad also reduces the cost of the service. I this paper, the authors elaborate their cocept i the area to set a dyamic threshold value for the physical server, load balacer behavior idetifier mechaism is proposed to geerate the rule ad provide the resources dyamically. Major drawback is that it ca t be used i cloud data. Abhijit Aditya et al. (2015)[3] presets the basics of cloud computig like it s characteristics, deploymets models, service models. They are describig the each service delivery models characteristics, its vedor types their advatages ad disadvatages. The they describig about where the load problems are occurrig i the system ad so the challeges i keepig mid. The it describes each ad every types IJSRSET1624171 Received : 19 August 2016 Accepted : 25 August 2016 July-August 2016 [(2)4: 781-785] 781
of algorithm i load balacig separately. Their properties, advatages, disadvatages are also described. They specially described about these algorithms based o the time factor. Po-Huei Liag et al.(2015)[4] presets a framework for global server usig for load balacig of the web sites i a cloud with two-level load balacig model. The proposed framework is iteded for adjustig a ope-source load-balacig system ad while the customers eed more load balacers for icreasig the availability, this framework allows the etwork service provider to deploy the load balacer i differet data ceters dyamically. Further they described the load balacig algorithms with the various cloud service providers alog with its commuicatio iterface. C. Load Balacig Approaches Static ad dyamic are the two type of load balacig approaches used i cloud computig[7,8]. Static Approach This approach is maily defied i the desig or implemetatio of system. Static load balacig algorithm divides the traffic equivaletly amog all users. It uses oly iformatio about the average behavior of the system. These are much simpler ad igore the curret state or the load of the ode i the system. Dyamic Approach Radha Ramai (2015)[5] presets a cocept of Cloud Computig alog with load balacig. The mai thig is cosidered i this paper is load balacig algorithm. There are various metioed algorithms i cloud computig which cosists of may factors like scalability, ehaced resource utilizatio, high performace ad improved respose time. Further this paper provides the isight about the policies, characteristics, goals, curret state classificatio, eed for load balacig. They have proposed a frame work for givig the ew algorithm. B. Scalability Scalability is the capability of a system, etwork, or process to hadle a growig amout of work, or its potetial to be elarged i order to accommodate that growth[6]. For example, it ca refer to the capability of a system to icrease its total output uder a icreased load. Cloud balacig is a computer etworkig method to distribute work load across multiple computer or a computer cluster, etwork lik, cetral processig uits, disk drivers or other resources to achieve optimal resource utilizatio, maximize throughput, miimize respose time ad avoid overload. Load balacig helps i prevetig bottleeck of system. First step, algorithm is desiged based o performace i heterogeeous eviromet of hosts The ext process is to study the above algorithm with the effect of CPU utilizatio. The based o the algorithm result, the algorithm will make the decisio about the ormalizig factor. I this approach, the curret state of the system was cosidered durig load balacig decisio. It is more suitable for widely distributed system such a cloud computig Dyamic approach has two parts[7] Cetralized Approach: Oly a sigle ode is resposible for maagig ad distributio withi the whole system. Distributed Approach: Each ode idepedetly builds its ow load vector. Vector collectig load iformatio of other ode. All decisio is made locally usig local load vector. D. Factors To Be Cosidered Performace metrics also play a major role i moitorig techiques for resource maagemet. As per the survey, it is cosidered as secod issue i cloud computig. Poor performace ca be caused by lack of resources such as disk space, limited badwidth, lower CPU speed, memory, etwork coectios etc. The data itesive applicatios are more challegig to provide proper resources. There is a series of factors that affect the performace such as [9]: Security. Recovery ad Fault tolerace. Service level agreemets. Badwidth. Storage capacity. Physical memory. Disk capacity. Processor Power. 782
Availability. Number of users ad Workload. Usability. Scalability. Locatio, data ceters ad their distace from a user s locatio. Ad there is a series of criteria for evaluatig the performace such as[10]: Average respose time per uit time. Average waitig time per uit time. Workload to be serviced per secod (Mbps) or a uit of time. Throughput (Req / Sec). The average time of processig (exe / sec). Percetage of CPU utilizatio. The umber of requests executed per uit time. The umber of requests per uit time buffer. The umber of rejected requests per uit time. E. Workload Calculatio Workload coditio i load balacer varies from small to heavy request[11]. As per the performace details, the proposed algorithm is based o the percetage of CPU utilizatio. To predict the workload ad allocate the suitable resources, load balacig ad scalig mechaisms are used. Scalig ca be doe i two ways called reactive ad proactive respectively. Reactive techiques are always time cosumig also mislay user satisfactio[12]. Proactive techiques are always preferable to avoid such complicatios. Workload has bee predicted usig CPU usage, Memory usage ad Network usage. CU = CPU utilizatio V = Total CPU s used Memory Load Memory is aother importat part of the computer all the work by the cliet is stored ad retrieved o ad from the memory respectively. So without the memory usage the workig process of the whole system become dazed. Memory load is measured as where MEM USED = memory used TOT MEM = total memory Memory = MEM USED/TOT MEM Network Load Network is the techique by which the systems are itercoected. Network systems ca be loaded if there are too may systems i a coditio to execute. The cliet has to cosider the etwork badwidth whe the cliet is goig to calculate the load. Network load measured as Network = NET BANDWIDTH / TOT BANDWIDTH where NET BANDWIDTH = etwork badwidth used TOT BANDWIDTH = total amout of etwork badwidth. CPU Load: F. Dyamic Normalized Algorithm The system will have may processes to execute i a sigle system. The task maager will show may variables like the CPU utilizatio of the resources, memory usage, etc. The CPU load plays the major role i the executio of etire system. Its utilizatio has great impact o the performace of a system. The two distiguished algorithm has proposed for the cloud service providers. The system is maitaied o ormalized mode o these two proposals [13]. The CPU usage has got by the performace couter. From the each IP address the CPU load has bee calculated by the above algorithm[14,15]. CPU load is measured as where = the umber of odes, CPU = (CU/V) Aget1: Begi While all the servers are i ruig state Get CPU usage from each system 783
Calculate the CPU load The CPU load has bee aalyzed for the differet request If (CPU load>=upper threshold) Status is OVERLOAD Else if (CPU load <Upper threshold && CPU load>ormal value) Status is NORMAL Else Status is UNDERFLOW Ed Aget 2: If (Status==OVERFLOW) Server has to Sleep for a while Else Server works i balaced mode III. RESULTS AND DISCUSSION A. Process Diagram To make the system as i the ormalized state, a check has to be performed[16]. The process of makig the system as ormalized is give i a flow diagram as follows Figure 2. Chart for CPU load IV. CONCLUSION Load balacig is cosidered as the mai factor i performace issues. Here, load balacig is aalysed o the IaaS level. There may be differet load factors the user ca cosider. I this paper, the CPU load ad the way to ormalize the executio of the CPU are measured. The whole implemetatio improves the efficiecy of the distributed CPU s. Though it has good efficiecy, the request has to wait for the overloaded server to become load balaced. Therefore, the task legth ad waitig time is icreased. Here this cocept is implemeted oly o CPU load; i future, the authors ca try this algorithm usig all the workloads of the system. V. REFERENCES Figure 1. Process diagram B. Aalysis of The Load Balacer The resources are aalyzed i this paper ad it is based o oly oe factor i.e. CPU performace [16]. The followig performace chart is studied based upo the time ad the CPU load. The load has bee differetiated based o the above algorithm. This chart shows the CPU utilizatio of the various servers coected i the etwork. [1] Mohd Hairy Mohamaddiah, Azizol Abdullah, Shamala Subramaiam, Masida Hussi, A Survey O Resource Allocatio Ad Moitorig I Cloud Computig, Iteratioal Joural Of Machie Learig Ad Computig, Vol4, No1, February 2014, DOI: 10.7763/IJMLC.2014.V4.382 [2] M.Kriushath, DrLArockiam, Load Balacer Behavior Idetifier (Lobbi) For Dyamic Threshold Based Auto-Scalig I Cloud, ICCCI -2015, Ja08 10, 2015, Coimbatore, INDIA. [3] Abhijit Aditya, Uddalak Chatterjee, Sehasis Gupta, A Comparative Study Of Differet Static Ad Dyamic Load Balacig Algorithm I Cloud Computig With Special Emphasis O Time Factor, Iteratioal Joural Of Curret Egieerig Ad Techology E-ISSN 2277 4106, P-ISSN 2347 5161 2015. [4] Po-Huei Liag1 Ad Jia-Mi Yag, Evaluatio Of Two-Level Global Load 784
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