Exploiting Private and Hybrid Clouds for Compute Intensive Web Applications

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1 Exploiting Private and Hybrid Clouds for Compute Intensive Web Applications Aleksandar Draganov August 17, 2011 MSc in High Performance Computing The University of Edinburgh Year of Presentation: 2011

2 Abstract Cloud computing changes the way software and hardware are purchased and used. Increasing number of applications are becoming web based since these are available from anywhere and from any device. Such applications are using the infrastructures of large scale data centres and can be provisioned efficiently. Hardware, on the other side, representing basic computing resources, can be also delivered to match specific demands without the consumer having to actually own them. Cloud computing model provides benefits for private enterprise environments where a significant physical infrastructure already exists. Private cloud management platforms have been emerging in the last several years providing new opportunities for efficient management of internal infrastructures leading to high utilization. The resources in a private cloud may become insufficient to satisfy the demands for higher computational power or storage capacities. In cases where the local provisioning is not sufficient, the private resources can be extended with resources from other public remote infrastructures. The resulting infrastructure appears as one hybrid entity. In other words, a private cloud can be extended to a hybrid cloud by adding resources to their capacity from public cloud providers where required. This work investigates the usage of an open source cloud management platform (OpenNebula [1]) to create private cloud and using it for hosting compute intensive web application by managing a farm of virtual web servers to meet its demands. The benefits of using such an approach along with the issues it raises are explained. The chosen algorithm for the web application (LU factorisation) represents a generalized case of applications where the complexity is O(N 3 ) and the input data size is N 2. An approach where the computational load is increased is also tested. The results and the requests per second (i.e. throughput). Two scenarios are covered utilizing a small existing private infrastructure (i.e. a private cloud) and extending the private infrastructure with external resources (i.e. a hybrid cloud) on Amazon Web Services [2]. OpenNebula proved to be easy to use and robust software. Its capabilities ensured convenient management of virtual web server farm within private and hybrid clouds. The network bandwidths appeared to be the most significant limiting factor for the effective use of the farm influenced by the heterogeneous character of the setup, the virtualization of the network interfaces, their sharing between virtual machines and the size of the input data. However, increasing the execution time (i.e. heavier problems) proved to lessen the impact of those issues. i

3 Contents Chapter 1 Introduction... 1 Chapter 2 Background Cloud computing Main benefits of cloud computing Virtualization role Choice of cloud management system OpenNebula Amazon Web Services (AWS) Web applications Load balancing Benchmarking web servers Chapter 3 Web server farm design Cloud software stack Intel VT-X Scientific Linux 6 (Host OS) KVM kernel modules Libvirt OpenNebula Web server software stack ii

4 3.2.1 Java Load balancing/proxy server Private cloud web server farm setup Networking Hybrid cloud web server farm setup VM performance comparison System of linear equations solver The algorithm The web application Running the cloud Preparations Resources allocation Running the application Load Balancer Web server Benchmarking Benchmarking tool Benchmarking Chapter 4 Results and Analysis Results Private cloud web server farm LB deployed on VM LB deployed on a physical machine Increased load - LB deployed on VM iii

5 4.2.4 Increased load - LB deployed on a physical machine Hybrid cloud web server farm Cloud management platform role Centralized control EC2 instances managing User management Cloud API Monitoring Chapter 5 Conclusions and Future Work Conclusions Future work Appendix A Experimental Data A.1 Standard Load Application VM LB A.2 Standard Load Application Physical Machine LB A.3 Increased Load Application VM LB A.4 Increased Load Application Physical Machine LB Appendix B Results of iperf tests B.1 From ph-cplab to VM LB B.2 From ph-cplab to physical machine LB B.3 To local worker (NAT-based network virbr0) from LB VM B.4 To public worker (physical network br0) from LB VM B.5 To public worker from physical machine LB B.6 To local worker (virbr0) from physical machine LB B.7 To EC2 medium instance from VM LB iv

6 B.8 To EC2 medium instance from physical machine LB Appendix C Work plan modifications References v

7 List of Tables Table 2.1: EC2 instance types Table 3.1: Theoretical IP addresses organization for private cloud Table 5.1 Standard load application throughputs with LB on VM. Private cloud server farm Table 5.2 Standard load application throughputs with LB on physical machine. Private cloud server farm Table 5.3 Increased load application throughputs with LB on VM. Hybrid cloud server farm Table 5.4 Increased load application throughputs with LB on physical machine. Hybrid cloud server farm vi

8 List of Figures Figure 2.1: OpenNebula main components. Reproduced from [1] Figure 2.2: Horizontally scaled web servers Figure 3.1: Standard OpenNebula cloud organization Figure 3.2: The actual OpenNebula cloud organization used Figure 3.3: Private cloud web server farm Figure 3.4: Standard private cloud network configuration Figure 3.5: The actual private cloud network configuration Figure 3.6: Hybrid cloud web server farm. Reproduced from [54] Figure 3.7: Performance comparison between EC2 instances and local VM. Single executions Figure 3.8: Performance comparison between EC2 instances and local VM. Thread pool with size Figure 3.9: Size of data transferred in relation to problem size Figure 3.10: Throughput generated by 1 web server for N = 50, 150, 250 for different number of running threads Figure 4.1: Throughput generated by private cloud web server farm for different problem sizes with LB deployed on VM Figure 4.2: Network bandwidths for private cloud web server farm with VM LB Figure 4.3: Throughput speedup for private cloud web server farm with VM LB Figure 4.4: Network bandwidths for private cloud web server farm with LB on a physical machine vii

9 Figure 4.5: Throughput generated by private cloud web server farm for different problem sizes with LB deployed on a physical machine Figure 4.6: Throughput speedup for private cloud web server farm with LB deployed on a physical machine Figure 4.7: Throughput generated by private cloud web server farm for different problem sizes with LB deployed as VM for increased load application Figure 4.8: Throughput speedup for private cloud web server farm with VM LB for increased load application Figure 4.9: Throughput generated by private cloud web server farm for different problem sizes with LB deployed on a physical machine for increased load application Figure 4.10: Throughput speedup for private cloud web server farm with LB deployed on a physical machine for increased load application Figure 4.11: Network bandwidths for hybrid cloud web server farm with LB on a physical machine Figure 4.12: Throughput generated by hybrid cloud web server farm for different problem sizes with LB deployed as VM for increased load application Figure 4.13: Throughput speedup for hybrid cloud web server farm with LB deployed on a physical machine for increased load application viii

10 Abbreviations AMI AWS CLI CRM DNS EBS EC2 ECU ERP HTTP IaaS JVM KVM LB MAC NAT NIC OS PaaS QoS RDS RR S3 SaaS SLA SQS SSL VM VMM Amazon Machine Image Amazon Web Services Command Line Interface Customer Relationship Management Domain Name System (Amazon) Elastic Block Storage (Amazon) Elastic Compute Cloud (Amazon) Elastic Compute Unit Enterprise Resource Planning Hypertext Transfer Protocol Infrastructure as a Service Java Virtual Machine Kernel-based Virtual Machine Load Balancer Media Access Control Network Address Translation Network Interface Card Operating System Platform as a Service Quality of Service (Amazon) Relational Database Service Round -Robin (Amazon) Simple Storage Software as a Service Service Level Agreement (Amazon) Simple Queue Service Secure Sockets Layer Virtual Machine Virtual Machine Manager ix

11 Acknowledgements I would like to thank my supervisor, Dr Charaka J. Palansuriya, for the time spent and his tireless and patient guidance and support during the project. I am also very grateful to Mr. Maciej Olchowik for all his helpful advices and suggestions. I would as well like to acknowledge the efforts of Mr. Craig Morris and Ms. Fiona Bisset from EPCC support to provide me with the required for the project hardware resources and help. Finally I would like to thank my family and my friends for their infinite support throughout the last 12 months. x

12 Chapter 1 Introduction There is an ever increasing movement towards adoption of cloud computing within IT departments of academic and business environments. Based on the advances of the virtualization technologies developed so far, the concept of the Cloud offers a convenient way of efficient organization and usage of different computing resources. Many cloud services have been introduced in the recent years some of them available networks (private clouds). Public cloud services are available in three types of service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) [3]. IaaS provides control over running Virtual Machines (VMs), networks and any other basic computing resources. PaaS offers a platform where users can deploy their applications if they use specific tools and technologies of the PaaS provider. SaaS is normally a web application that is running on the top of a cloud infrastructure. The consumer only pays for what he uses an hour of CPU, a gigabyte of memory for the first two servicemodels and a monthly fee per person for SaaS. The cloud providers often offer different fee schemes so they can match most of the requirements. Private clouds are deployed within the organization and managed by its system administrators. They require more work to set up and to maintain, but provide better flexibility and can be used for different purposes virtual clusters, hosting web applications or deploying VMs used as desktops. Private clouds can be extended to hybrid clouds by adding resources to their capacity from public providers. There are several open source platforms that can be used for managing private clouds. One of the main characteristics of a cloud platform is its elasticity. From a perspective the cloud seems to have unlimited resources that can be provisioned just for the time period required to accomplish a task and then can be released with minimum effort when they are no longer required. This feature allows quick scaling and better utilization and provisioning of the resources. This project focuses on the installation process and usage of an open source cloud platform for private cloud management for creating and managing a private cloud for hosting web applications that require significant computational time. An important aspect of this work is to investigate how the private cloud can be extended to a hybrid 1

13 cloud when the internal resources are no longer adequate. The focus is on web applications since this is an ever more popular way of providing software and thus making it available for PCs, tablets, smartphones and any other device that can access it. The project has 4 main objectives: To show that a private cloud platforms provide benefits for hosting compute intensive web applications; To show that public clouds can be used seamlessly for extending the private resources thus transforming a private cloud into hybrid (i.e. cloud bursting); To identify issues that can arise in usage of private and hybrid clouds for web applications; To investigate the possible usage of existing resources for cloud infrastructure. The OpenNebula [1] software will be utilized for creating a small private cloud that will be used for performing various experiments with a simple web application. The steps of the deployment process will be discussed along with the major issues arisen during the setup. Some of the important capabilities OpenNebula provides will be described. One of these features is the logic that offers control over VMs within Amazon Web Services [2]. The cloud has to be configured to use those external resources in addition to the local setup and increase its capacity in this way. The web application itself will be deployed on running VMs. The project does not aim to outline the advantages and disadvantages of any specific web technology or programming language or platform, but some performance aspects of the chosen setup will be discussed. 2

14 Chapter 2 Background 2.1 Cloud computing Cloud computing model has become an important concept in the last few years. Many different major companies and independent individuals have given definitions for Cloud and cloud computing according to their strategies, visions and products [4], but most of them focus on several important characteristics resources are provided rapidly, on demand, they are highly scalable, and in case of public services the consumer pays only for what he uses. The resources might be computational power, storages, networks, applications. Therefore IT resources are now elastic - they can be provisioned for the exact duration they are necessary and released when the consumer no longer needs them. As described in [4] some definitions include other aspects as Internet-driven service, monitoring and (self-) management, Service Level Agreement (SLA) management, controllable through API, guaranteed Quality of Service (QoS). But those are mostly specific for some platforms or services. As mentioned earlier there are three main models of cloud computing: Software as a Service In general SaaS is a normal web site service often pay a fee to gain access to specific features of the system for a fixed period of time, usually per week, month or year. SaaS software is running in a cloud environment hence it should be able to serve a number of users together. Popular areas where SaaS software is available are accounting, project management, ERP, CRM, document management and many others [5]. Platform as a Service The consumers of the service can deploy their applications on top of the cloud platform supported by the provider. The applications must be developed using APIs and programming languages made available by the PaaS provider. Therefore software development is restricted to a specific platform and limited set of technologies. Since 3

15 every PaaS provider has his own special features and mechanisms, the developers are creating software intended to run only on a particular platform (i.e., vendor lock-in). The most popular platforms at the moment are Google App Engine [6] and Microsoft Windows Azure Platform [7]. Infrastructure as a Service The provider of the service delivers basic resources computational power, networks, storages, operating systems. Those resources are scalable and elastic. They can be provisioned on demand and made accessible for a minimum time. The consumers of the service have full control over the running virtual machines (VM) hence being able to choose all the elements of the software stack that their applications require. But the providers cannot guarantee that the physical resources such as VMs or storage will not fail and they do not provide back up for the data. That is a responsibility of the user. Other research [4] even differentiates some other services such as Computing, Storage, and Database that usually fit into the models mentioned above, but they are offered as separate products by some cloud providers. 2.2 Main benefits of cloud computing One of the main features that differentiate cloud computing from the traditional computing is that clouds are usually programmable. With PaaS the user is supplied with API that allows starting, stopping and other operations over workers and storages [8] [9]. In IaaS the API can be used to run, stop, migrate, clone, delete, etc VMs [8] [11] [12]. In such a context the cloud can indeed be called self-manageable, but it only provides API that can be used to achieve this. Private cloud management platforms also offer API for the same purposes as IaaS. A cloud system can run a number of VMs within a physical machine in a complete isolation from each other. As a result applications can be easily provisioned only with the resources they require. In case of under provisioning another VM can be started in order to handle part of the load generated towards the application and later on destroyed if not necessary so the resources can be used for other purposes or applications. Better utilization is an advantage that comes from the virtualization technologies, but cloud platforms provide mechanisms to control many physical nodes from a centralized point and thus organizing the nodes into server farms improving the process of their management. other details as virtualization software, hosts OS, hardware organization, etc are hidden to the consumer of the service providing a new level of abstraction. Undoubtedly better utilization and server consolidation have financial benefits when considering the existing infrastructure that many organizations have. But public IaaS providers ensure that an organization can rent significant external resources instead of purchasing its own. Small- or medium-sized business can considerably decrease their 4

16 expenses with such an approach. No capital investments for building an infrastructure, moderate ongoing costs, provisioning new computational power requires minimum effort, no staff dedicated entirely to maintain the hardware setup and high availability (eg., Amazon Web Services) are the main befits for organizations that use IaaS services. Detailed cost comparison between internal IT, managed services, and IaaS approaches is published in chapter 1 in [13]. For bigger organizations that can invest in data centre infrastructure and have the expertise to build and maintain it (e.g., a university) it might be more advantageous to have the resources internally. That is to have a private cloud. 2.3 Virtualization role The increasing popularity of the cloud computing approach is driven by the advancement of the virtualization technology. It allows different logical machines to share the same hardware but to run isolated from each other. As a result physical hosts can be utilized better and the computing resources can be allocated easily in a very flexible manner. These isolated running operating systems (OS) are called virtual machines (VMs). A number of virtualization software solutions are available. They usually include a hypervisor also called virtual machine manager (VMM). The hypervisor assign resources to the VMs and lets them operate as if they were running on different machines independently. It runs on the host hardware directly (bare-metal) or as a part of the OS by modifying the kernel or by using the system services. Based on this and on the way the VMs communicate with the physical devices the virtualization is either full virtualization or para-virtualization [14]. Recently the hardware vendors added a new virtualization feature (Intel VT-x [15], AMD-V [16]) which allows guest OS to directly interact with the VMM layer [14]. Popular virtualization hypervisors are VMware vsphere [14], XEN [18], KVM [19], and Microsoft Hyper-V [20]. The last one cannot be used on Linux machines. Red Hat has cut the support for XEN in their new RHEL 6 and has replaced it with KVM. Therefore the support for XEN for Scientific Linux which is the OS used for the current project is also being dropped. As KVM packages for the latest Scientific Linux 6 are prebuilt and ready to install that makes it a suitable choice for the scenarios in the current dissertation. Virtualization also applies to other physical resources as storage and networks. Though virtualization software abstracts the hardware and provides flexible and agile way to control it, it is not a cloud itself. A layer that controls it has to be deployed so it can access the entire infrastructure within a data centre and to manage all the resources. This layer is called cloud management platform. 5

17 2.4 Choice of cloud management system The choice of an IaaS platform to be used for this project presents a challenge since software in this field is relatively new and new versions are released in short cycles. For example, OpenNebula has three beta releases between July 2010 and July The other widely used cloud platform is called Eucalyptus [21]. Eucalyptus and OpenNebula share their most important features: Both projects had first versions in 2008 and are equally mature nowadays; Both can be installed and run on the majority of the Linux distributions available; Both can use XEN, KVM, and VMware virtualization; Both are EC2 compatible; Both can be configured to cloudburst using resources into Amazon Web Services (AWS); Both have user management, etc. In [22] different cloud management systems are compared, but they have all been changed dramatically after the publication of the article. For example, both OpenNebula and Eucalyptus can exploit EC2 resources and there is Graphical User Interface (GUI) available now for OpenNebula. 4caaSt project cloud analysis [23] provides up to date detailed descriptions of existing platforms including Open Stack [24] which is still very new and not as mature as OpenNebula and Eucalyptus. Eucalyptus is dual licensed and some of the features are only available in the commercial version as VMWare support and Windows guest OS. Creators of OpenNebula maintain a blog [25] where practical experience is shared between all the users. OpenNebula is also proved to work effectively in large scale environments [26]. Based on its proved capabilities and big and active community OpenNebula was chosen for the experimental work in this project. 2.5 OpenNebula OpenNebula is a fully open-source toolkit to build IaaS Private, Public and Hybrid Clouds [1]. Figure 2.1: OpenNebula main components. Reproduced from [1]. 6

18 The platform consists of two running processes on a front-end or a central machine as shown on Figure 2.1. The first process is called OpenNebula daemon and it is responsible for controlling the cloud platform modules and the virtual machines. The other process called scheduler decides where to place each virtual machine by accessing the database OpenNebula holds on the front-end. The information stored in the database includes the available resources on the nodes based on the requirements for previously submitted VMs so the scheduler does not need to contact the hypervisors. For each host added to the list of hosts 3 drivers have to be specified so that the daemon can use it. The virtualization driver (VMM driver) is used for communication with the hypervisor installed on the node and to control the VMs running on it. The daemon uses the Transfer Manager (TM) driver to perform operations with the storage system on the host in order to control the VM images. The last driver called the Information Manager (IM) driver allows the daemon to monitor the hosts. Different drivers can be specified and used with different nodes. Working with OpenNebula requires describing images, virtual networks and VMs in template files that can be later submitted to the daemon. Management of the platform is usually performed through the command line, but there is also a GUI that includes the majority of the functions and replaces the template files OpenNebula Sunstone. OpenNebula works with several basic objects through the command line: User represents a user that has access to the system. The command used is oneuser. Host represents a host/node that is controlled by the front-end. Group of drivers have to be specified for each host. The command used is onehost. Image represents an OS image. Its basic parameters are name of the image, whether it is public and available to all users or not, short description and path to the image. Once submitted OpenNebula copies the image to a new location and changes its name. Every image is described with a template file. The command used is oneimage. Virtual network represents a network shared by the VMs. They are either ranged or fixed. For the range networks the only required parameters are size (B, C), network address and a bridge. OpenNebula assigns IPs to the VM automatically. The fixed networks are defined with a set of IPs, possibly MAC addresses and a bridge. The number of addresses specified is the maximum number of VMs that can use it. Networks are also described with a template file. The command is onevnet. Virtual machine represent a guest OS or a VM. Also described with template file. The basic parameters are CPU, memory, image and network. OpenNebula automatically assigns IP (if available) to the instance and places it on a host of its choice. Every VM may be in several states. The most significant of them are pending (waits for the scheduler to place it somewhere), running, stopped, failed. The command is onevm. 7

19 The users list on the OpenNebula home page [1] is often updated. The most significant companies and projects that control internal resources with it are CERN, China Mobile, ESA, KPMG, Fermilab, Telefonica, Reservoir, StratusLab, BonFIRE, etc. The creators of OpenNebula have suggested two ways [27] to increase the internal infrastructure with Amazon EC2 VMs for virtual cluster and web server farm. For both of their scenarios they use VPN channel in order to make the remote nodes a part of the private network. The network latencies caused by the Internet connection and especially by the VPN channel affect the performance, but the results clearly show improvement of the throughput when using EC2 instances. However for the web server benchmarking they have only performed tests for accessing static files. 2.6 Amazon Web Services (A WS) Amazon has described several use cases [28] covering many IT services as application hosting, backup and storage, databases, e-commerce, HPC, web hosting, search engines, on-demand workforce. For each case they offer a group of products that can be exploited for deployment of a high quality service. The major products and services available are: Elastic Compute Cloud (EC2) - provides compute capacity; Simple Storage (S3) - a scalable storage; Elastic Block Storage (EBS) represents block level storage that is designed to be utilized with EC2; SimpleDB non-relational database; Relational Database Service (RDS) highly scalable RDBMS in the cloud; Simple Queue Service (SQS) highly scalable queue; CloudWatch provides more detailed monitoring for the resources being used; CloudFront used for distributed delivery in order to make content easily accessible with low latencies and fast data transfers. The services exploited in the current project are EBS and EC2. EBS is only used to store Amazon Machine Image (AMI) so its part is insignificant. The AMI is used as a prototype of a VM. Amazon provides many images that are preconfigured and publicly available for common needs. Once the image is chosen, a key to access the running VM is provided, security group that acts as a firewall has to be specified, and the type of instance must be selected for the image. The fee paid for a running VM depends of the instance. 8

20 Instance type Memory ECUs I/O Perf. Storage Platform $ per hour Micro 613 MB Up to 2 Low EBS only 32/64 bit Small 1.7 GB 1 Moderate 160 GB 32 bit Large 7.5 GB 4 High 850 GB 64 bit 0.38 Extra Large 15 GB 8 High bit 0.76 High-Memory Extra Large High-Memory Double Extra Large High-Memory Quadruple Extra Large 17.1 GB 6.5 Moderate 420 GB 64 bit GB 13 High 850 GB 64 bit GB 26 High 1690 GB 64 bit 2.28 High-CPU Medium 1.7 GB 5 Moderate 350 GB 32 bit 0.19 High-CPU Large Extra Cluster Compute Quadruple Extra Large Cluster Quadruple Large GPU Extra 7 GB 20 High 1690 GB 64 bit GB 33.5 Very High 1690 GB 64 bit 1.60* 22 GB 33.5, 2 x NVIDIA M2050 GPUs Very High 1690 GB 64 bit 2.10* Table 2.1: E C2 instance types. All instance types available with EC2 service are shown on Table 2.1. Obviously there are configurations for any requirements including cluster with 10 Gigabit network and GPU cluster hence there are resources which are suitable even for HPC applications. The prices shown are for Linux usage for the data centre in Ireland, however clusters are only offered in the main data centre in the US. A free tier of services is offered for the newly registered users for their first months only including micro instances. One EC2 Compute Unit (ECU) provides the equivalent CPU capacity of a GHz 2007 Opteron or 2007 Xeon processor [2]. EC2 services are organized in different data centres called regions. They isolate the regions from each other to achieve greater fault tolerance, improve stability, and to help prevent issues within one region from affecting another [29]. A performance comparison between the basic instance types for Geosciences application is described in [30]. They have achieved good CPU utilization for small, medium, and large instances, but the problem does not efficiently make use of instances that are bigger. Though all their products are advertised as highly available, flexible, and scalable they do not fit into every problem. As shown in [31] both the financial model and the performance of S3 are not suitable for big scale scientific projects since they have different requirements for data usage. However benchmarking AWS and any other 9

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