SLA based Infrastructure resources allocation in Cloud computing to increase IaaS provider revenue

Transcription

1 SLA based Infrastructure resources allocation in Cloud computing to increase IaaS provider revenue Ateeqa Jalal NCBA Chenab College Mianwali, Pakistan & Afzal Badshah Khattak Education Department, Punjab, Pakistan & Tauseef-U-Rehman Faculty of Technology, Preston University Islamabad-Pakistan Abstract Cloud Computing is an emerging technology in which desktop services are delivered online on network.consumers pay for the service how much they use such as water, electricity telephone or gas.service consumer does not need to invest capital amount on infrastructure and IT experts. Revenue is the main focus of any business. In Cloud Computing revenue depends on proper VMs utilization and SLA.Cloud infrastructure provider have their own resources or they hire some resources to meet the customers requirements. Hiring other infrastructure, cost too much and performance depends on other hired systems which lead towards SLA violation. SLA violation in Cloud business minimizes the revenue of the Cloud provider. This paper discusses the IaaS model with SLA to maximize the Infrastructure provider revenue. Two types of SLAs are implemented in this model. One SLA is between service consumer and provider and second SLA is between hired external resources and IaaS provider. We propose such polices and algorithm which increase the company own infrastructure utilization, minimize the response time and SLA violation to enrich the revenue. This algorithm and policies will reduce the infrastructure cost and minimize the SLA violation. It will handle the dynamic changes in the customer request and will allot resources with dynamic pricing according to their dynamic requests. Keywords: Cloud Computing, Infrastructure as a Service, Service Level Agreement, Infrastructure Resources Allocation, Revenue Maximization 1. Introduction Cloud Computing is becoming very important for business. Instead of buying infrastructure, operator, license and software, customers simply hire cloud services on cheaper rates[1]. Cloud Computing is not a new technology but it is the combination of old technologies. Cloud Computing is selfdescriptive and self-corrective computer services. Cloud Computing use virtualization approach to provide different services. It is extremelysuccessful paradigm for utility computing [2][3]. Figure 1: Cloud Computing > RJSITM: Volume: 04, Number: 03, January-2015 Page 37

2 Cloud Computing provides three types of services. Software as a Service, provide online application such as application, monitoring finance and communication etc, Platform as a Service, provide online development tools such as testing, analysis and deployment service. Infrastructure as a Service provides online physical resources such as computing, storage and networking etc[3]. Cloud Computing services are provided in three model. Private Cloud resources are visible only for one organization users. Public Cloud computing resources can be hired by any person after getting registration. Hybrid cloud computing is the combination of private and public cloud computing [4]. Infrastructure as a Service provides physical resources of computer online on network such as computing, storage and bandwidth etc. First of all Infrastructure as a Service cost too much secondly it have too many problems such as configuration,management and maintenance etc [1]. Figure 2: Infrastructure as a Service Therefore small and medium level organization can t invest too much capital investment on IaaS at initial stages of business. They simply hire IaaS services to start their business [2]. Service Level Agreement is contract negotiated between service provider and service consumer. The need of SLA is to increase trust between both parties.sla provides benefits such as it enhances customer satisfaction in term of Quality of Service and improvesrelationship between two parties. Penalties are imposed on failure party [4]. Figure 3: Service Level Agreement In cloud computing three types of pricing mechanisms are used. Tiered pricing in which Cloud computing services are divided in different tires.each tire have different prices. Amazon use tiered pricing scheme. Unit pricing in which customers have been charged on unit of space or bandwidth > RJSITM: Volume: 04, Number: 03, January-2015 Page 38

3 used.this pricing mechanism is more flexible than tired pricing mechanism. Subscription base pricing in which customers are charged according to their subscription [4]. Cloud Computing is very emerging and hot topic in industry and academic damp terminal were connected with main frame computer, so resources sharing idea was exist that time too. In 1960 Mr. John Mclarthy gives an idea that computing resources can be delivered at home as other utility resources [5]. In 1970 the concept of virtualization has come. VM machines share one physical resource for too many users.virtualization is the core of Cloud Computing [6]. In 1990 telecommunication companies started virtual private network, before that dedicated lines were used for each customer. In1999 salesforce.com was the first Cloud services provider. Now days Amazon is the leading Cloud Computingservices provider. Google also has stared his Cloud business in 2009 [5][6]. 2. Related Work Mario Macfas, J.OriolFito and JordiGuitart (2003) worked on revenue maximization in cloud computing using EERM. This used bidirectional data between market and resources to increase revenue. They used dynamic pricing mechanisms. Price will be higher if the workload is higher. When provider is unable to fulfill customers request, SLA violation list is created then those SLA are violated which cause lower lose.if new customer is coming with new SLA which profit is more than one other SLA penalties then the older SLA is cancelled. There are different QoS SLAs. Combination of these SLAs are adopted and those are violated or cancelled which cause lower lose. If one machine is overloaded then some more VMs are taken to reduce the performance reduction. This SLA gives good suggestion about increasing revenue but they are rejecting the proposal which workload is higher then their capacity and they are also rejecting some running SLAs after finding some profitable SLA. These steps will create doubtful situation in business [7]. Linlin Wu, Saurabh Kumar Garg and Rajkumar Byya (2011) worked on SLA and algorithm to maximize the revenue of SaaS provider. The main objective of this work is to maximize the profit for provider by minimizing the costs of virtual machines. By providing individual VM to every users then no QoS degradation will occur so no penalties and more number of customers. SaaS Company increases their profit by providing new VM to new request and upgraded VM to upgraded requests. The main focus of this work is on the dynamic changing requests of customers. Software provider tries to increase their profit and to ensure QoS to expand their business. Software is delivered in standard, professional and enterprise. Costumer s accounts are created in group, team and department. Contract is signed between Software provider and customer. If any one violate then defaulter pay his penalties. This work is good step to increase the revenue of Software provider but the main focus of this research work is only SaaS. There is no alternative way if customers request load is higher than their resources capacity. So they will reject such like proposal which have very bed effects on business[8]. Hong Xu and Bachun Li (2011) worked on revenue maximization model to increase the revenue of the Cloud computing provider. For better utilization of computing resources and to increase the revenue of provider now a days dynamic pricing mechanism is used for dynamic customer s requests. Amazon EC2 is also offering dynamic pricing sine Dynamic pricing mechanism in IaaS causing too many problems.they formulated a program which deal with such like problem and handle infinite horizon cases[9]. Qi Zang, ErenGijrses,Raouf Boutaba and Jin Xiao (2012) developed a framework to maximize the revenue of cloud provider. They are using market analyzer which analyzes the market incoming request briefly. Secondly they are using capacity planner which prepare the machines and resources capacity according to the reports of market analyzer. This model is using both price mechanism, dynamic and static price mechanism. Different algorithms are used to predict the situation to use suitable price mechanism technique which mechanism would be suitable for certain situations. [10]. 3. Proposed Model Three types of parties are participating in this proposed model. One is IaaS Provider party which is providing his physical resources. Second is consumer party which hires the resources from IaaS provider party. Third is external cloud service. IaaS hire some resources from third party to minimize SLA violation and to give reliable services to the consumers. > RJSITM: Volume: 04, Number: 03, January-2015 Page 39

4 Figure 4: Proposed IaaS Provider Model Two types of SLAs are established in this model. One SLA is between IaaS provider and consumers and second SLA is between IaaS and external Cloud Services. The End users can dynamically change their request and there is no need to upgrade account. They will be charged according to their usage of resources. IaaS will give resources according to their dynamic request. IaaS does not hire complete resources from external cloud service but it pay to external Cloud services according to the usage of service. External cloud provider charged the IaaS provider with Cost Total =Cost Perunit * t, so we will try to use external resources only in emergency to minimize the external payments. Algorithm 1: Pseudo Code 1. Comp_resources 2. Hired_resources 3. Sort(Sort VMs according to their available resources) 4. Customer_request(request_resources) 5. If(Deploy the VM to the Job using first fit policy){ 6. If(VM has enough resources){ 7. Put job on that VM 8. } 9. } 10. If(Utilization of VM increase from its available resources){ 11. Shift the task to furthervm having enough resources 12. } 13. If(request_resources increases from Comp_resouces){ 14. Shift the portion of this job to external cloud services 15. } 16. If(Comp_resources is enough to run portion of the job running on hired resourcs){ 17. Copy it back to Comp_resources 18. } In algorithm 1 Comp_Resource shows the IaaS provider own physical infrastructure. Hired_Resources show those resources which are hired from any external Cloud provider. The working of this model is described in steps. 1. All resources (VMs) available with the IaaS provider are sorted according to their capacity. 2. Proposed IaaS model receive request from customers. Request is checked with SLA. 3. All VMs are already sorted. VM is allotted to the job with first fit mechanism. If the VM have enough space then job is put on it. 4. If the utilization of the current VM is increasing from its capacity then job is shifted to any other machine using algorithm steps from 3 to 9. > RJSITM: Volume: 04, Number: 03, January-2015 Page 40

5 Resources utilization rate 5. If the requested job workload is higher than the company own resources then hired resources are utilized for that job to give the customer reliable service. External resources are only used in emergency. Job is shifted back to company own resources when they become free to minimize the external payments and to minimize the company own resources. 6. If any job is running on external hired cloud and there is enough capacity to run that job on company resources then job is shifted back to company own resources. 4. Experimental Design We used CloudSim to simulate proposed IaaS model. In simulation experiment we created n VM as company resources, n VM as hired resources which are used by the company during overloading requests by customers VMs and n VMs are created to send requests toward the proposed model. Figure 5: Simulation for experimental test of proposed IaaS model Large number of VMs are created to create real environment of Cloud Computing. The proposed model receives too much resources request created by customers VMs. Algorithm try fully utilized the company available VMs capacity. As the capacity increased than the available VMs capacity, workload or some of its portion is transferred to the hired VMs and so on. 5. Results In this IaaS model our main focus is to fully utilize the company own services, minimize the violation of the SLA and to minimize the usage of the hired resources and finally to increase the revenue of the cloud computing service provider. Experimentally we have checked our model for VMs utilization and SLA violation. In figure 6 graph is drawn between resource utilization and number of tasks. We have checked it for fixed and dynamic pricing. Results shows that dynamic pricing is showing very best result comparing to fixed pricing. Dynamic pricing is very reliable. Resources Utilization Number of Tasks Figure 6: Resource Utilization FP DP > RJSITM: Volume: 04, Number: 03, January-2015 Page 41

6 SLA Violation rate In figure 7 graph is drawn between number of tasks and SLA violation. We checked it for company own resources and hired resources. Results shows that hired resources are giving some SLA violation but due to these hired resources we are engaging heavy load customers. This is increasing our business.sla violation comparatively to this business is very low so we neglect it SLA Violation Rate HR CR Number of Customers request Figure 7: SLA violation rate 6. Conclusion Pricing and revenue have an important role in any business. In this paper our main focus was on to increase the revenue of IaaS provider by maximum utilization of company own resources and to minimize the SLA violation. This model use an algorithm which tries to fully utilize company resources and transfer some portion of the workload or completeworkload to external hired resources to minimize the SLA violation and to engaged the overload customers in this way to increase the company business. Algorithm shift the workload back to company resources as enough space become available to minimize the external payment and to maximize the utilization of company resources. References [1]. Cloud Standard customer council Cloud Service Level Agreement for Cloud Services ETSI TR V1.1.1(2012). [2]. Claude Baudoin, Jordan Flyn and John McDonland Public Cloud Service Level Agreement: what to expect and what to negotiate Cloud Standard Customer Council, March 30, [3]. Mohammad Firdhous, Suhaidi Hassan and Osman Ghjazali A comprehensive survey on Quality of Service implementation in Cloud Computing International Journal of Scientific & Engineering Research, Feb 10, 2014 [4]. En.m.wikipedia.org/wiki/service_level_agreementNobember [5]. Searchcio.techtarget.com/essentialguide/the-history-of-cloud-computing-and-whatscoming-next-a-cio-guide.Nobember [6]. Aerofs.com/blog/the-history-and-deployment-of-cloud-computing. Nobember [7]. Mario Macias, J.Oriol and JordiGuitart Rule-based SLA management for revenue maximization in Cloud Computing markets CNSM, [8]. Linlin Wu, Saurabh Kumar Garg and RajkumarByya SLA-based allocation for Software as a Service provider (SaaS) in Cloud Computing envoirnment. IEEE Computer socity, 2011 [9]. Hong Xu andbachun Li Maximizaing Revenue with dynamic Cloud Pricing: the infinite Horizon case In proceeding of ICC. 2012, Page > RJSITM: Volume: 04, Number: 03, January-2015 Page 42

7 [10]. Qi Zang, ErenGijrses,RaoufBoutaba and Jin Xiao Dynamic Resource Allocation for Spot markets in clouds [11]. October 2014 [12] [13]. Sarwan Singh and Manish Arora Monitoring and Controlling multi level SLA in Cloud Environment using agent International Journal of Advance Research in Computer and Software Engineering. July [14]. AalexandruIosup and RaduProdan Performance analysis of Cloud Computing services for many tasks scientific computing IEEE [15]. Alexander Keller and Heiko Ludwig The Web Service Level Agreement: Specifying and monitoring Service Level Agreement for Web Services Journal of Network and Systems Management, Vol. 11, No. 1, March 2003 [16]. ChristpherRedl, Ivan Breskovic, Ivona and Schahram Automatic SLA Matching and Provider selection in Grid and Cloud Computing Environments GRID 2012 [17]. Jennifer Ortizy and Victor Teixeira A Vision of Personalized Service Level Agreement DanaC 13, June 23, 2013, New York, NY, USA [18]. Vincent C. Emeakaroha, IvonaBrandic, Michael Maurer and SchahramDAustadar Low Level Metric to Highs Level SLAs- LoM2HiS Framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments HPCS 2010 page no [19]. Zhu, Tengchaun, Li, Hao and Lu A Service Level Agreement Framework of Cloud computing based on Cloud Bank Model Computer Science and Automation Engineering (CSAE), 2012 IEEE International Conference, May 25, 2012, page no [20]. Jisha S. Mangaly and Jisha S A Comparative study on open source Cloud Computing Framework International Journal of Communication System, > RJSITM: Volume: 04, Number: 03, January-2015 Page 43