## 3 Proposed Cost-Effective Auto-Scaling (CEAS) Framework  
The primary objective of the scaling method is to improvise the scalability for cloud computing. In this section, the conventional auto-scaling framework for cloud computing in Hadoop system is presented outlining key components that enable the scalability of virtual resources on the cloud. To process a large volume of data efficiently in real time, it is crucial to improvise auto-scaling and sentiment analysis of the big data on the cloud. This section initially presents our proposed CEAS framework for collecting and aggregating data on the AWS platform. The configuration used for setting up the EMR clusters for efficient data processing is then discussed in detail along with our proposed solution for implementing the CEAS framework for sentiment analysis of Twitter data based on cost effectiveness, high throughput and high reliability on the cloud environment. Figure 1 illustrates the proposed CEAS framework which consists of the following six components:  
1. _Cloud computing platform_: The cloud computing offers scalability of virtual resources on either public or private clouds (e.g., Amazon EC2, Amazon EMR, Microsoft Azure, OpenStack and Eucalyptus). 2. _Covering HDFS-enabled Hadoop cluster_: Covering HDFS of Hadoop cluster on the EMR cloud has provisioned computing resources to be able to manage, store and process a large volume of data. 3. _Cluster monitor (or cloud watch)_: Monitoring clusters plays a vital role in auto-scaling framework, which allows us to capture the information about all the resources on the cluster by usingApplication Programming Interface (API) of Hadoop, such as CPU use, the performance of map and reduce tasks, and the status of the jobs. 4. _Auto-scaler_: The auto-scaler provisions the virtual resources on the cloud platform using cloud's API (e.g., Amazon EMR API) which is dependent on the information gathered by the cloud watch. When the data on the cluster increases, more nodes are added to the Hadoop cluster, while the idle nodes are shut down when the data load is less. 5. _Ganglia_: Ganglia is a front-end open-source monitoring system which is scalable and distributed to monitor the clusters or grids. This monitoring system offers a complete solution for archiving, monitoring and visualisation of the system performance. 6. _Sentiment analysis_: The sentiment analysis is performed on the auto-scaling cluster by taking the data from multiple universities via Twitter app. In the CEAS framework, the above-mentioned Hadoop's auto-scaling framework is adopted on the AWS cloud to automatically scale the Hadoop clusters so as to allocate the Amazon Machine Images (AMIs) (The AMIs provide the information required to launch EC2 instances, which includes the volume of instances and permissions that control these instances.) based on the dynamic processing workload. Amazon AWS allows for multiple scaling groups within a scaling plan that is based on optimisation for availability, cost or balance of both. However, the scaling plan would be enabled for all scalable targets, instead of individual targets.\" Thus, based on the scaling options available in AWS, in the scope of this work, an auto-scaling framework is defined as effective if it meets the following requirements:  
1. A cloud environment should be cost-effective, while still being able to perform auto-scaling and sentiment analysis. The framework also supports deployment, configuration and management of Hadoop clusters. 2. A cloud platform should be able to monitor all tasks in an efficient way for all workload in real time. Moreover, when the task is completed, it would be able to shut down the cluster. Also, it should provide elasticity for the cloud computing by either expanding or shrinking the virtual cluster to handle the data. 3. An efficient sentiment analysis should be performed via the auto-scaling cluster with low cost and short scaling time. Figure 1: Cost-Effective Auto-scaling (CEAS) framework.