Investigating the Pricing Impact on the QoS of a Server Farm Deployed on the Cloud

Transcription

1 Investigating the Pricing Impact on the QoS of a Server Farm Deployed on the Cloud A.M.D Aljohani 1,2, D.R.W Holton 2 and Irfan Awan 2 1 Tabuk university, Saudi Arabia 2 university of Bradford, England Abstract. One of the attractive characteristics of the cloud computing model is scalability. Web servers deployed in the cloud can scale computing resources upward and downward to match the demand. This achieved because the cloud model is provided in pay-as-you-go manner. However, some of the cloud providers have rules in the pricing manner which makes the decision to scale difficult such as Amazon Elastic Compute Cloud (Amazon EC2) when an instance terminated will be charged for each hour and each partial instance-hour consumed will be billed as a full hour. In this paper a discrete event simulation used to examine the impact of using this method of billing on the cost of the cloud and the QoS. Performance metrics of throughput, delay and utilization are obtained via simulation. Keywords: Cloud Computing, QoS, Discrete event simulation

2 Investigating the Pricing Impact on the QoS of a Server Farm Deployed on the Cloud Authors: Abdulaziz Aljohani, D.R.W Holton, I Awan HET-NETs 2013

3 Outline: Introduction Problem statement Model description Simulation Conclusion Future work WP05 2 of 14

4 Introduction: Many businesses and organisation have been using the web server farms locally or hosted into web hosting provider. However, With emerge of cloud computing many have moved to the cloud to harvest the cloud benefit. WP05 3 of 14

5 Introduction: What is Cloud Computing? One of the best definitions is presented in (A break in the clouds: towards a cloud definition): Clouds are a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). These resources can be dynamically reconfigured to adjust to a variable load (scale), allowing also for an optimum resource utilization. This pool of resources is typically exploited by a pay-per-use model in which guarantees are offered by the Infrastructure Provider by means of customized SLAs WP05 4 of 14

6 Introduction Cloud benefits: 1- Scaling up and down to meet demands. 2- Paying only for what you use. 3- eliminating the need for plan far ahead for provisioning WP05 5 of 14

7 Problem statement: Web server which deployed on the cloud can scale up to a very large number of virtual machine (VM) to improve the QoS and meet the demand. Also, it can release the resource to reduce cost when it no longer required. WP05 6 of 14

8 Problem statement: The ideal cloud computing should charge the customer for exact time it deployed the server. However, that is not the case in reality. Cloud providers such as Amazon they pricing is per instance-hour consumed for each instance from the time it launched until it is terminated. Also, each partial instance-hour consumed will be billed as a full hour. WP05 7 of 14

9 Problem statement: In the ideal cloud when an instance is not need it will be terminated to reduce the cost. However, if the user will be charged for the full hour that instance should remained on until the full hour is finished since the user will pay for it. WP05 8 of 14

10 Model description: In this work a model has been presented to investigate the effect of the pricing method. We present a simple web server farm contains front-end load balancer and a number of back-end servers. WP05 9 of 14

11 Problem statement: The server farm starts with one server and when the demand increase a server will be added to the pool. The number of request in the pool is used as trigger to add server using the upper threshold. When a server switched ON the time will be recorded in a table. A the end of each hour for each server it will check the number of request in the pool. If the number of request is less than the lower threshold the server will be terminated otherwise it will remain ON. WP05 10 of 14

12 Simulation: A discrete-event simulation (DES), were built using Java to investigate the model. In this model we assumed the inter-arrival time and the service time is exponentially distributed. Also, each server modelled as a node with finite buffer space (K) and all servers are identical. WP05 11 of 14

13 Conclusion: This work introducing the cloud pricing method and it impact on cost and QoS. A discrete-event simulation used to examine the model. WP05 12 of 14

14 Future work: We intend to include extra parameters such as the time between requesting a server until it deployed and fully function. Also, we planning to simulate the same model using a network simulator such as OPNET. WP05 13 of 14

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