# Optimizing the Cost for Resource Subscription Policy in IaaS Cloud

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4 4) Resource Broker: This model which performs adaptive planning and delivers resource subscription to the IaaS Provider. IV. METHODOLOGY The optimization process has two phases, and their main functionalities are long-term resource reservation optimization and effective short-term resource allocation. In this section, we describe the proposed two-phase planning algorithms. A. Long term Resource Reservation In the long-term resource reservation, 1. Given set of demands we have to calculate the provisioning cost which includes the upfront fee, the usage charge for launching reserved VMs when demand is less than reservation capacity, the usage charge of launching all the reserved capacity when the demand exceeds the reserved capacity and the cost for on-demand allocated VMs to serve the exceeded demand. 2. The objective is to minimize the provisioning cost and to derive the optimal amount of long-term reserved resource with a model where the demand is a discrete random variable and only one single type of VM is considered. 3.Assume that r* is the optimal number of VMs to be reserved for long term planning and calculate an upper and lower bound of the optimal number of reserved VMs. 4. To show how to use the result of single type VM solution for the original problem with multiple VM types. (i)selecting the VM that has the best capacity/price (CP) ratio (ii)under constraint (i), the workload demand is transformed to demand of the number of best CP ratio VMs (iii)based on upper and lower bound we will obtain the optimal reservation (iv)we could do a search for the best combination of multiple VM types with capacity falls between the capacity of (r*-1).c bestcp and r*.c bestcp. C bestcp is the capacity of the VM with the best CP ratio. (v)each value in the range is regarded as the reserved demand of the Integer Linear Programming formulation. Minimize M R i n i * p i (1) Subject to M i n i * C i Reserved Demand (2) n i N o i M (3) (1) to minimize the upfront cost of reserved VMs n i number of type i VM that is subscribed in the long term lease contract. B. On Demand Resource Allocation A straight forward way to configure VMs for next shortterm planning interval is based on the measured demand of current I 0 configuring VMs based on some prediction mechanism will significantly reduce the operational cost. Our prediction mechanism is based on kalman filter because it has low computation complexity. SHORT-TERM PLANNING ALGORITHM (SPA) In the following,describe the proposed short-term planning algorithm (SPA). Depending on the values of rp, rc, and rr, the SPA classifies the resource planning scenarios into three cases which are illustrated as follows: Input: Output: r m,r p, r c, r r, Ic, Ir r m ->VM Capacity requirement for current demand r p ->Predictive VM Capacity r c ->Current launched VM Capacity r r ->Overall VM Capacity of reserved resources I c Current launched VM Configuration I r ->VM Configuration in reservation contract Initialization: The updated Ic, which is used for adaptive planning I O :={0}//VM Configuration subscribed via ondemand plan is empty I O -> The VM configuration subscribed via on-demand plan Procedure: 1 if r r < r p then 2 I o ILP1_OnDemand (r p - r r, ) 3 I c= I r + I o 4 else if r c <r p then //launching more reserved VMs is required 5I c ILP2_AdjustVMConfiguration (I r,,i c,r p ) 6 else if r c >r p then 7 I c ShutDownSpareVMs (I c, r p ) 8 end if 9 UpdatePredicationModel (r m ) 10 return I c End procedure Depending on the values of rp, rc, and rr, the SPA classifies the resource planning scenarios into three cases which are illustrated as follows: Scenario 1 (lines 1-3): On Demand Resource The predicted demand (rp) exceeds the capacity of all reserved VMs (rr), thus the Resource Broker must operate the on-demand option to subscribe more VMs. ISSN: Page 299

5 Scenario 2(lines 4-5): Adjust VM configuration The predicted demand can be served by reserved VMs, but it exceeds the capacity of current VM configuration (rc). Therefore, reconfiguring launched VMs from the reserved VM pool (Ir) is necessary. Scenario 3 (lines 6-7): Shutdown spare VMs The predicted demand is less than the currently configured VM capacity. Therefore, the corresponding action is to shut down some launched VMs, which had nearly a full hour of operation first, until the provisioning capacity is just above the predicted demand. V. RESULTS The Results for long-term reservation and on-demand cost is shown in following screen shots. Fig. 5 Monitoring Engine Fig. 2 Resource Computation and Resource Configuration of Virtual Server1 Fig. 3 Resource Computation and Resource Configuration of Virtual Server2 Fig. 4 Service Provision Details Fig. 5 On Demand Service VI. EVALUATION In this section, the experimental evaluation of the proposed algorithm is presented. We first present the parameter settings of a cloud computing environment used in this performance evaluation. We adopt the pricing models set by Amazon EC2. Since we re-configure VMs every shortterm planning interval, we present the normalized upfront and usage costs per short-term planning interval. In order to evaluate the performance of Resource Subscription policy in cloud, the following schemes are used:1)long-term Cost Computation, and 2)VM Allocation. A. Long-Term Cost Computation In this scheme Customers are requested to use their service based on duration and the resource will be allotted for that particular duration and the upfront cost is collected from the customer in order to get their service. Each user can upload their file and get their requested Service and the service provisioning cost will be collected from the customer. When the resource exceeds while using the service during their duration, normally resource will be allotted from the ondemand. But in proposed algorithm, based on the user needs resource will be extended normally and the cost will be computed for that particular resource usage. This is generally cheaper than on-demand Resources. The following graph which represents the comparison of cost based on extended resources and based on on-demand resources. ISSN: Page 300

6 Fig. 6 Long Term Cost Computation B. Comparing VM Allocation In this scheme Resource capacity is calculated first and then resources are configured automatically based on CPU usage and memory capacity. Here we considered two VMs and we calculated service for different users and also the allocation of VM for these different users. The following graph shows the comparison of VM allocation. Fig. 7 VM Allocation VII. CONCLUSION IaaS infrastructure becomes a popular platform for application providers to deploy their applications. However, IaaS providers offer many types of VM configuration and price them differently. Furthermore, they also offer several pricing models. It raises an interesting issue to application providers on how to effectively provision or subscribe VM resources from an IaaS provider. In this paper, we formulated the resource provisioning problem as a two phase resource planning problem. In the first phase, focused on determining the optimal long term resource provisioning.in the second phase, proposed a Kalman filter prediction model for predicting resource demand. Several issues had also been considered in our work, including impact of latency of VM reconfiguration, and minimum rental time constraint for launching a VM. Our numerical results showed that the proposed long term resource planning algorithm was able to yield near optimal operational cost. The results also showed that the proposed on-demand planning algorithm significantly reduced the operational cost. In future, we plan to evaluate our solutions with larger resource demand from some real web application system and were able to cope with the latency of VM reconfiguration. REFERENCES [1] Ren-Hung Hwang, Chung-Nan Lee, Yi-Ru Chen and Da-Jing Zhang- Jian, Cost Optimization of Elasticity Cloud Resource Subscription Policy,in IEEE Transaction on Service Computing,18 June [2] K. Tsakalozos, H. Kllapi, E. Sitaridi, M. Roussopoulos, D. Paparas, and A. Delis, Flexible Use of Cloud Resources through Profit Maximization and Price Discrimination, in Proc. 27th IEEE International Conference on Data Engineering (ICDE 2011), April 2011, pp [3] Y. Hu, J. Wong, G. Iszlai, and M. Litoiu, Resource Provisioning for Cloud Computing, in Proc. Conference of the Center for Advanced Studies on Collaborative Research, 2009, pp [4] R.N. Calheiros, R. Ranjan, and R. Buyya, Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments, in Proc. International Conference on Parallel Processing, Sept. 2011, pp [5] M. Mao, J. Li and M. Humphrey, Cloud Auto-Scaling with Deadline and Budget Constraints, in Proc. 11th ACM/IEEE International Conference on Grid Computing (Grid 2010), 2010, pp [6] S. Chaisiri, R. Kaewpuang, B. S. Lee, and D. Niyato, Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute Cloud, in Proc. International Symposium on Modeling, Analysis and Simulation, of Computer and Telecommunication Systems (MASCOT), July 2011, pp [7] C.C.T. Mark, D. Niyato, and C. Tham, Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing, in Proc. IEEE International Conference on Advanced Information Networking and Applications (AINA), 2011, pp [8] S. Islam, J. Keung, K. Lee, and A. Liu, An Empirical Study into Adaptive Resource Provisioning in the Cloud, Future Generation Computer Systems, vol. 28, pp , Jan [9] E. Caron, F. Desprez, and A. Muresan, Forecasting for Grid and Cloud Computing OnDemand Resources Based on Pattern Matching, in Proc. Cloud Computing Technology and Science (CloudCom), 2010, pp [10] G. Welch and G. Bishop, An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill, Chapel Hill, NC, ISSN: Page 301

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