Cloud Infrastructure and Services(2180712) lab manual PDF

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Cloud Infrastructure and Services

Laboratory Manual

INDEX PRACTICALS

Page No.

Sr. No From 1. 2. 3. 4. 5. 6. 7. 8. 9.

Study about the Cloud simulation tools. Install CloudSim and analyze the working of CloudSim simulator. Study about the Service models with real time examples. Study about Management of Cloud Computing Resources. Installation and Configuration of Oracle Virtual Box/VMware. A software study on Hadoop, MapReduce and HDFS. Installation and Configuration of Single Node Hadoop Cluster. Discuss performance evaluation of service over cloud. A case study on Google App Engine, Microsoft Azure, Amazon A case study on salesforce.com.

Create a scenario that uses Amazon 10. S3 as storage on cloud.

To

Date

Marks

Signature

Practical 1

AIM: Study about Cloud simulation tools. Install Cloud and analyze the working of CloudSim simulator. CloudSim is an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It is framework for modeling and simulation of cloud computing infrastructure and services. The main advantage of using CloudSim for initial performance testing includes: 1. Time effectiveness: it requires very less effort and time to implement Cloud-based application provisioning test environment and 2. Flexibility and applicability: developers can model and test the performance of their application services in heterogeneous Cloud environments (Amazon EC2, Microsoft Azure) with little programming and deployment effort.

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List of cloud tools 1. Open Stack Open Stack is a free and open-source software platform for cloud computing, mostly deployed as an infrastructure-as-a-service (IaaS). The software platform consists of interrelated components that control diverse, multi-vendor hardware pools of processing, storage, and networking resources throughout a data center. 2. Open QRM OpenQRM is a free and open-source cloud computing management platform for

managing

heterogeneous

data

center

infrastructures.

The openQRM platform manages a data center's infrastructure to build private, public and hybrid infrastructure as a service clouds.

3. Cloud Sim Cloud Sim is a framework for modeling and simulation of cloud computing infrastructures and services.

4. Cloud Foundry Cloud Foundry is an open source cloud platform as a service (PaaS) on which developers can build, deploy, run and scale applications on public and private cloud models. VMware originally created Cloud Foundry and it is now part of Pivotal Software

5. Open Shift Open Shift is Red Hat's Platform-as-a-Service (PaaS) that allows developers to quickly develop, host, and scale applications in a cloud environment. With Open Shift you have a choice of offerings, including

online,

on-premise,

and open source project options. 6. Docker Docker is an open-source project that automates the deployment of applications inside software containers.

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STEP BY STEP INSTALLATION OF CLOUD SIM INTO ECLIPSE 1. Open up Eclipse and go to Menu Section, then click File, keep on clicking New and finally select java project. It is shown as in the Figure1

Open up Eclipse and Click on java project 2. A new window will get open. Put a foot on to the following steps:2.1.Enter project name. (I have named it as CloudIntro) 2.2 In the next line you will see the path where your project will be created as it as shown in the Figure. 2.3 Next You need to select the JRE environment. 2.4 Finally Click Finish

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3. Once you hit finish. An empty project named CloudIntro will be created in the project List as shown in the Figure.

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4. Next step is to go the project CloudIntro, right click on it. Click Import as shown in Figure.

the

5. A new window will get open, now click File System as demonstrated in the Figure.

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6. Next Step is to go to the directory where you have extracted your cloud sim tool. Figure6 is

shown to guide you to get into the directory where your cloudsim folder is located.

7. Select the cloudsim and click Finish as shown in the Figure.

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8. Now go to the link http://commons.apache.org/proper/commonsmath/download_math.cgi .

Download

the file named as “commons-math3-3.4.1-bin.zip”. Unzip this file. We need jar files for math functions.

9.

Now go to the left side of the eclipse tool in the project bar. Go to jar and right click on it. Click import as shown in the Figure8.

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10. Now go to the folder where you have placed the downloaded and extracted file as described by point 8. Then all you have to do is select that jar file and hit finish as shown by the Figure

11. Finally the cloud sim is installed into your Eclipse environment.

EVALUATION: Involvement (4)

Understanding Problem solving

/ Timely Completion (3)

Total (10)

(3)

Signature with Date: Page | 8 Cloud Computing

Practical 2

AIM: Study about the Services models with real time example. There basically three type of services models in cloud computing. 1. SAAS (Software as a services) 2. PAAS (Platform as a services) 3. IAAS (Infrastructure as a services)

➢ SAAS (Software as a services) Cloud application services, or Software as a Service (SaaS), represent the largest cloud market and are still growing quickly. SaaS uses the web to deliver applications that are managed by a third-party vendor and whose interface is accessed on the clients’ side. Most SaaS applications can be run directly from a web browser without any downloads or installations required, although some require plugins. Because of the web delivery model, SaaS eliminates the need to install and run applications on individual computers. With SaaS, it’s easy for enterprises to streamline their maintenance and support, because everything can be managed by vendors: applications, runtime, data, middleware, OS es, virtualization, servers, storage and networking. Popular SaaS offering types include email and collaboration, customer relationship management, and healthcare-related applications. Some large enterprises that are not traditionally thought of as software vendors have started building SaaS as an additional source of revenue in order to gain a competitive advantage. SaaS Real time Examples: Google Apps, Sales force, Workday, Concur, Citrix GoToMeeting, CiscoWebEx Common SaaS Use-Case: Replace straditionalon- device software

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➢ PAAS (Platform as a services) Cloud platform services, or Platform as a Service (PaaS), are used for applications, and other development, while providing cloud components to software. What developers gain with PaaS is a framework they can build upon to develop or customize applications. PaaS makes the development, testing, and deployment of applications quick, simple, and cost-effective. With this technology, enterprise operations, or a third-party provider, can manage OS es, virtualization, servers, storage, networking, and the PaaS software itself. Developers, however, manage the applications. Enterprise PaaS provides line-of-business software developers a self-service portal for managing computing infrastructure from centralized IT operations and the platforms that are installed on top of the hardware. The enterprise PaaS can be delivered through a hybrid model that uses both public IaaS and on-premise infrastructure or as a pure private PaaS that only uses the latter. Similar to the way in which you might create macros in Excel, PaaS allows you to create applications using software components that are built into the PaaS (middleware). Applications using PaaS inherit cloud characteristic such as scalability, high-availability, multi-tenancy, SaaS enablement, and more. Enterprises benefit from PaaS because it reduces the amount of coding necessary, automates business policy, and helps migrate apps to hybrid model. For the needs of enterprises and other organizations, Apprenda is one provider of a private cloud PaaS for .NET and Java. PaaS Examples: Apprenda Common PaaS Use-Case: Increases developer productivity and utilization rates while also decreasing an application’s time-to-market

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➢ IAAS (Infrastructure as a services) Cloud infrastructure services, known as Infrastructure as a Service (IaaS), are selfservice models for accessing, monitoring, and managing remote datacenter infrastructures, such as compute (virtualized or bare metal), storage, networking, and networking services (e.g. firewalls). Instead of having to purchase hardware outright, users can purchase IaaS based on consumption, similar to electricity or other utility billing. Compared to SaaS and PaaS, IaaS users are responsible for managing applications, data, runtime, middleware, and OS es. Providers still manage virtualization, servers, hard drives, storage, and networking. Many IaaS providers now offer databases, messaging queues, and other services above the virtualization layer as well. Some tech analysts draw a distinction here and use the IaaS+ moniker for these other options. What users gain with IaaS is infrastructure on top of which they can install any required platform. Users are responsible for updating these if new versions are released. IaaS Examples: Amazon Web Services (AWS), Cisco Metapod, Microsoft Azure, Google Compute Engine (GCE), Joyent. Common IaaS Use-Case: Extends current data center infrastructure for temporary workloads (e.g. increased Christmas holiday site traffic).

EVALUATION: Involvement

Understanding Problem solving

(4)

(3)

/ Timely Completion (3)

Total (10)

Signature with Date:

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Practical 3

AIM: Study about management of cloud computing resource. ➢ Resource management is a core function required of any man-made system. It affects the three basic criteria for system evaluation: performance, functionality and cost. Inefficient resource management has a direct negative effect on performance and cost. It can also indirectly affect system functionality. Some functions the system provides might become too expensive or ineffective due to poor performance. ➢ A cloud computing infrastructure is a complex system with a large number of shared resources. These are subject to unpredictable requests and can be affected by external events beyond your control. Cloud resource management requires complex policies and decisions for multi-objective optimization. It is extremely challenging because of the complexity of the system, which makes it impossible to have accurate global state information. It is also subject to incessant and unpredictable interactions with the environment. ➢ The strategies for cloud resource management associated with the three cloud delivery models, Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS), differ from one another. In all cases, the cloud services providers are faced with large, fluctuating loads that challenge the claim of cloud elasticity. In some cases, when they can predict a spike can be predicted, they can provision resources in advance. For example, seasonal Web services may be subject to spikes. ➢ For an unplanned spike, the situation is slightly more complicated. You can use Auto Scaling for unplanned spike loads, provided there’s a pool of resources you can release or allocate on demand and a monitoring system that lets you decide in real time to reallocate resources. Auto Scaling is supported by PaaS services such as Google App Engine. Auto Scaling for IaaS is complicated due to the lack of standards.For an unplanned spike, the situation is slightly more complicated. You can use Auto Scaling for unplanned spike loads, provided there’s a pool of resources you can release or allocate on demand and a monitoring system that lets you decide in real time to reallocate resources. Auto Scaling is supported by PaaS services such as Google App Engine. Auto Scaling for IaaS is complicated due to the lack of standards.

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➢ In the cloud, where changes are frequent and unpredictable, centralized control is unlikely to provide continuous service and performance guarantees. Indeed, centralized control can’t provide adequate solutions to the host of cloud management policies you have to enforce. ➢ In cloud computing, a critical goal is minimizing the cost of providing the service. In particular, this also means minimizing energy consumption. This leads to a different meaning of the term load balancing. Instead of having the load evenly distributed among all servers, we want to concentrate it and use the smallest number of servers while switching the others to standby mode, a state in which a server uses less energy. In our example, the load from D will migrate to A and the load from C will migrate to B. Thus, A and B will be loaded at full capacity, whereas C and D will be switched to standby mode. ➢ Quality of service is that aspect of resource management that’s probably the most difficult to address and, at the same time, possibly the most critical to the future of cloud computing. Resource management strategies often jointly target performance and power consumption. ➢ Dynamic voltage and frequency scaling (DVFS) techniques such as Intel Speed Step and AMD Power Now lower the voltage and the frequency to decrease power consumption. Motivated initially by the need to save power for mobile devices, these techniques have migrated to virtually all processors, including those used in highperformance servers. As a result of lower voltages and frequencies, the processor performance decreases. However, it does so at a substantially slower rate than the energy consumption. ➢ Virtually all optimal or near-optimal mechanisms to address the five policy classes don’t scale up. Many require complex computations that can’t be done effectively in the time available to respond. Performance models are complex, analytical solutions are intractable, and the monitoring systems used to gather state information for these models can be too intrusive and unable to provide accurate data. ➢ Therefore, many techniques are concentrated on system performance in terms of throughput and time in system. They rarely include energy tradeoffs or QoS guarantees. Some techniques are based on unrealistic assumptions. For example, capacity allocation is viewed as an optimization problem, but under the assumption that servers are protected from overload.

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➢ Control the cloud Allocation techniques in computer clouds must be based on a disciplined approach, rather than ad hoc methods. The four basic mechanisms for implementing resource management policies are: 1. Control theory: Control theory uses feedback to guarantee system stability and predict transient behavior, but it can only predict local behavior. 2. Machine learning: A major advantage of machine-learning techniques is that they don’t need a performance model of the system. You could apply this technique to coordinating several autonomic system managers. 3. Utility-based: Utility-based approaches require a performance model and a mechanism to correlate user-level performance with cost. 4. Market-oriented/economic mechanisms: Such mechanisms don’t require a system model, such as combining auctions for bundles of resources. ➢ A distinction should be made between interactive and non-interactive workloads. The management techniques for interactive workloads (Web services, for example) involve flow control and dynamic application placement, whereas those for noninteractive workloads are focused on scheduling.

EVALUATION: Involvement (4)

Understanding Problem solving

/ Timely Completion (3)

Total (10)

(3)

Signature with Date:

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Practical 4

AIM: Installation and configuration of Oracle Virtual Box/VMware ➢ VMware Workstation is a computer emulator. It allows you to create virtual machines in which you can install operating systems as if they were physical machines. You might want to emulate an operating system because you want to run a program that isn't compatible with the host operating system (the operating system that you are installing VMware Workstation on), or because you want to test malware without putting an actual computer at risk.

1. Check the Specification of system

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2. Download a setup file for internet as u require

Setup is downloaded successfully.

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3. Run the setup file.

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4. Accept agreement of setup file.

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5. Select Folder or location where you want to install VMware.

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5. Select shortcut where you want more useful.

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6. Enter license key.

7. Finish and exit complete installation.

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8. Open VMware.

Using VMware Workstation is we can use multiple virtual OS in Single system and they all work same as individual system and give better performance

EVALUATION: Involvement (4)

Understanding Problem solving

/ Timely Completion (3)

Total (10)

(3)

Signature with Date:

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Practical 5

AIM: A software study on hadoop, Mapereduce and HDFS. The applications running on Hadoop clusters are increasing day by day. This is due to the fact that organizations have found a simple and efficient model that works well in distributed environment. The model is built to work efficiently on thousands of machines and massive data sets using commodity hardware. HDFS and Map Reduce is a scalable and fault-tolerant model that hides all the complexities for Big Data analytics.

HADOOP ➢ Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. ➢ Hadoop makes it possible to run applications on systems with thousands of commodity hardware nodes, and to handle thousands of terabytes of data. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating in case of a node failure. ➢ This approach lowers the risk of catastrophic system failure and unexpected data loss, even if a significant number of nodes become inoperative. ➢ Consequently, Hadoop quickly emerged as a foundation for big data processing tasks, such as scientific analytics, business and sales planning, and processing enormous volumes of sensor data, including from internet of things sensors. ➢ Hadoop components and supporting software packages in their local data center. However, most big data projects depend on short-term use of substantial computing resources. ➢ Public cloud providers often support Hadoop components through basic services, such as AWS Elastic Comp...