CES: A FRAMEWORK FOR EFFICIENT INFRASTRUCTURE UTILIZATION THROUGH CLOUD ELASTICITY AS A SERVICE (CES)

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1 International Journal of Computer Engineering & Technology (IJCET) Volume 6, Issue 8, Aug 2015, pp , Article ID: IJCET_06_08_004 Available online at ISSN Print: and ISSN Online: IAEME Publication CES: A FRAMEWORK FOR EFFICIENT INFRASTRUCTURE UTILIZATION THROUGH CLOUD ELASTICITY AS A SERVICE (CES) Swaroop J GITAM University, Rushikonda, Visakhapatnam, INDIA Dr. Poosapati Padmaja GITAM University, Rushikonda, Visakhapatnam, INDIA ABSTRACT In today s world Enterprise, medium and small scale organizations started adopting cloud computing at rapid pace instead of traditional datacenters. Cloud computing has been so popular for cost reduction, pay for use model and ease of management tasks. A key problem faced by cloud providers in managing scaled up and down of cloud resources automatically. Traditional datacenter management capacity models clearly indicate that heavily provisioned is important to keep up with peak loads of usage while the overprovisioning capacity involves lot of manual management overhead cost and also the resources that are heavily provisioned are under-utilized during offpeak hours. Such heavily-provisioned and under-utilization of resources can be overcome by moving to a scaling up and down of cloud resources automatically and allowing the Cloud Admin for providing configurable parameters. To overcome this problem we are proposing an approach, where user can trigger a policy template for proactively scaled-up and down of the resources on demand basis as per the end-user needs. Key words: Cloud Computing, Policy Template, QoS, Auto scale up/down, infrastructure Cite this Article: J. Swaroop and Dr. Poosapati Padmaja. CES: A Framework for Efficient Infrastructure Utilization through Cloud Elasticity as a Service (CES). International Journal of Computer Engineering and Technology, 6(8), 2015, pp editor@iaeme.com

2 Swaroop and Dr.Poosapati Padmaja 1. INTRODUCTION Adoption of Cloud Computing increasing rapidly and constantly most of the IT companies are moving towards Cloud Computing instead of traditional data center approaches. Cloud Computing has a been a new paradigm because of the increasing demand of Cloud adoption by Enterprises and Small/Medium scale Business (SMB). SMB doesn t need to have the datacenter in-house and the infrastructure (resources) can be provisioned by Cloud Service Provider. And the number of Cloud Service Providers (CSP) increasing rapidly and this will enable the end customers to build the Application rather than bothering about infrastructure or datacenter or real estate space. This enables pay-by-use [1] model and facilitates ease of managing datacenters. Cloud Service Providers are continuously adding new infrastructure to their datacenters to meet the needs of the workload request of the customers. Major challenges [2] the cloud administrators facing today s is automatically scaled up/down of infrastructure management based on the end-user QoS parameters. Managing the resources automatically scaled-up and down in a Cloud [3] is tedious tasks and also to ensure SLA specified by end-user is intact. Therefore, in this paper, we are proposing a framework for proactively managing a policy template for automatically scale-up and down of the resources on demand basis as per the user QoS parameters for effective infrastructure management. Cloud auto-scaling decisions are made based on infrastructure metrics which are static in nature. However, in a cloud environment, static metric based scaled-up or scale-down may not be reliable and enough for making auto-scaling up/down of the resources. For example consider Customer-A, who needs to perform high intensive operations which require more CPU cycles. Where as in case of Customer-B who needs better network latency throughput to route the packets in stipulated time. In such scenarios scaling up/down of cloud resources cannot be static in nature based on the predefined parameters. To overcome this problem we are proposing a new service called Cloud Elasticity as a Service (CES). CES responsible for scaleup/down of resources through template orchesteration by allowing cloud provider to define the Qos parameters based on the end-user needs and if the Qos parameters thresholds met, then the resources are scale-up/down based on end-user conditions. 2. CLOUD COMPUTING MANAGEMENT SERVICES Cloud services [4] are distributed shared pool of resources comprising compute, network and storage that are abstracted using Infrastructure as a Service. Templates provides the configuration details of compute, network and storage from the end user perspective to the Cloud Administrator but doesn t provide which infrastructure the work load request to be provisioned. We considered Open source community driving Cloud Computing management platforms [5] such as Openstack ( CloudStack, Cloud Foundry, etc. We have considered Openstack as the Cloud management platform, since Openstack community contributing a large way and most of the companies, end users are deploying the Openstack services. Details of Openstack are described as below: Openstack ( is a set of software services for managing and provisioning cloud computing platforms. OpenStack is managed by a non-profit organization which oversees both development and community-building around the project. Openstack is an open source Cloud management Service [6] platform which 25 editor@iaeme.com

3 CES: A Framework For Efficient Infrastructure Utilization Through Cloud Elasticity As A Service (CES) facilitates different types of services [7] for managing and which are easy to plugin with others service. Figure-1 Openstack [8] deployment diagram provides overview how the cloud resources will be managed through various services [5]. Since Openstack provides different type of deployment methodologies devstack (which is for developers can run as a sandbox for developing the services without need of physical hardware) and Openstack management software can be deployed as highly available services on physical servers as well. Openstack [9] consists of below services for managing the cloud: Keystone is a services for authenticating and authorization of cloud users. Nova is the major computing engine of OpenStack. Nova is mainly for deploying and managing large numbers of virtual machines and other instances to handle computing tasks. Neutron is for managing different type of networks between the computes, storage and also cloud management software. Cinder is a services for managing SAN storage typically block storage management of SAN arrays. HOT is an orchestration service of Openstack which provides a template based provisioning and the template supports heterogeneous resources configuration stored in the standard format YAML or json or XML format. HOT also known as Heat Orchestration Template which provisions resources invoking other services such as Nova, Neutron, Cinder, Swift and Glance. Ceilometer is a metering and measuring service of openstack cloud management platform for collecting the capacity and performance data of cloud resources Cinder Keystone Cloud Administrator Nova Neutron Servers (Computes) Networks Storages Storage Group HEAT Figure-1 Openstack deployment Cloud Elasticity as a Service (CES) talks to multiple services for cloud resources automatically scale-up and down of the resources based on the user defined QoS parameters in the policy. 3. CLOUD ELASTIC POLICY SERVICE WORKFLOW From Fig-1 depicts cloud management with Open stack Cloud Management software. Managing cloud resources elastic effectively is a herculean task, to overcome we are proposing a new service Cloud Elasticity as a Service. Cloud Elasticity as a Service consists of multiple components for Policy Manager, Cloud Data Collection Manager. Design and workflow of Cloud Elastic as a Service is depicted as below: 26 editor@iaeme.com

4 Swaroop and Dr.Poosapati Padmaja 1. Cloud Admin creates a policy with different conditions and the policy can be stored as XML or json or YAML file. 2. Based on the Policy schedule interval time, CES periodically checks the policies that required to be triggered and invokes Cloud Data Collection Manager to collect metering and monitoring data for the resources 3. Policy Manager invokes the Validation Rule Engine to check the condition in policy are met with metering and monitoring data collected from Cloud Data Collection Manager. 4. If the conditions are satisfied, CES services invokes the HEAT service of Openstack for scale-up or scale-down the resources based on template specified in the Policy. 5. CES checks whether the resources are scaled-up or down and periodically monitors the resources through Cloud Data Collection Manager. Cloud Elasticity as a Service Policy Manager CloudDataCollectionMa nager Cloud Elastic as a Service Validates Policy based on the data collected Yes No If policy condition doesn t met continue to run the policy validation as per user scheduling If Policy condition meets the measured data, invoke HEAT Service with template specified by the user Collects monitoring data from Ceilometer service Openstack Service HEAT (Template Provisioning) Scaled up/down the resource based on the template provided by the end-user during policy creation time Nova (Compute Neutron (Network Cinder (Storage Ceilometer (Meter & Measuring Cloud Infrastructure Figure 2 Architecture diagram of Cloud Elasticity as a Service 27 editor@iaeme.com

5 CES: A Framework For Efficient Infrastructure Utilization Through Cloud Elasticity As A Service (CES) 3.1. Policy Policy defined in CES services is a flexible and combination of multiple rules, a rule is a condition defined by the Cloud Admin as per the user needs. Cloud Provider can request the end user on what basis the infrastructure can be scaled-up or down and what criteria that are need to be satisfied and what different metering and monitoring parameters should be validated. Typically by default the cloud providers monitors only used metrics of CPU, memory or storage. But the customer s requirement might be varying based on the load, demand and billing cost. Policy defined in the CES services provides provision to monitor not only used metrics of CPU, memory or storage, but can also include monitoring of resources that are underutilized based on free, unused metrics, so that the user is aware of billing cost for under/heavily utilized resources and can take appropriate action. Below is the Policy XML format with condition: <?xml version="1.0" encoding="utf-8"?> <ces-policy> <policy> <meta-data> </meta-data> <resource> <uuid>bca e5-885d-feff819cdc9f</uuid> <name>app-usage</name> <schedule-interval-mins>15</schedule-interval-mins> <meta-data> <uuid>01fa990e e5-885d-feff819cdc9f</uuid> <name>vm-mysql-webapp</name> <scale-up> <heat-template>952cb76e e5-885dfeff819cdc9f</heat-template> </scale-up> </meta-data> <rule> <criteria name="cpu_loaning"> 28 editor@iaeme.com

6 Swaroop and Dr.Poosapati Padmaja <condition> GREATER_THAN_EQUAL</condition> </criteria> <metric-name>cpu.loan_perc</metric-name> <value>60</value> <criteria name="memory"> <metric-name>swap_used</metric-name> <condition>greater_than_equal</condition> <value>2048</value> </criteria> <criteria name="network"> <metric-name>incoming_bytes_sec</metricname> </policy> </ces-policy> 4. RESULTS <condition>greater_than_equal</condition> </rule> </resource> </criteria> <value>1024</value> Our experimental goal is to check the resource utilization and CES services periodically validates the policy rules against the conditions specified. We started increasing the load on the cloud to trigger for testing applications. From the Figure- 3, depicts the Virtual Machine CPU load and Network throughput over a period of time. But the Virtual Machine CPU is heavily utilized for processing the data constantly, CES service identified this trend based on the policy conditions and scaleup the resources as per the template provided in the policy editor@iaeme.com

7 CES: A Framework For Efficient Infrastructure Utilization Through Cloud Elasticity As A Service (CES) Figure 3 Depicts the Virtual Machine CPU load and Network throughput over a period of time 5. CONCLUSION In this paper we proposed a new service Cloud Elastic Policy Service in Openstack cloud management, this service identifies the policies that exceeds the thresholds defined by end-user QoS parameters and automatically scaled up/down of cloud resources with no human intervention. We experimented with scaling up/down the virtual machine configurations based on the policy template defined. In this research work few guidelines have been proposed that could be efficiently employed for assisting cloud administrator for creating policies with configurable QoS parameters. Experimental results show the feasibility and effectiveness of our algorithm especially for datasets related to applications deployed in IaaS Clouds. Although the initial evaluation results are satisfactory, we have several ideas to improve the framework. Further we will expand the applicability of our approach to different Cloud deployment scenarios. REFERENCES [1] J. Greenberg et al., The Cost of a Cloud: Research Problems in Data Center Networks, Computer Communication Rev., 39(1), 2009, pp [2] Q. Zhang, L. Cheng, and R. Boutaba, Cloud Computing: State-of-the-Art and Research Challenges, J. Internet Services and Applications, 1(1), 2010, pp [3] L.M. Vaquero-Gonzalez et al., A Break in the Clouds: Towards a Cloud Definition, Computer Communication Rev., 39(1), 2009, pp [4] J. Cardoso, K. Voigt, and M. Winkler, Service Engineering for the Internet of Services, Enterprise Information Systems, Lecture Notes in Business Information Processing, 19(1), Springer, 2009, pp [5] A. Li et al., Cloud Cmp: Comparing Public Cloud Providers, Proc. 10th Ann. Conf. Internet Measurement, ACM, 2010, pp [6] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. D. Rose, R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and Experience,2011, 41(1), pp [7] M. Smit, P. Pawluk, B.Simmons and M.Litoiu, A web service for cloud metadata in Proceedings International Conference on Cloud Computing, 2011, pp [8] R. Buyya, C.S Yeo, S.Venugopal, J Broberg and I. Brandic, Cloud computing and emerging its platforms: Vision, hype and reality for delievering computing as the utility, Future Generation Comp. System. 25(6), pp , 2009 [9] editor@iaeme.com