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@CloudExpo: Book Excerpt

Book Excerpt: Fundamental Cloud Architectures

Foundational cloud architectural models, exemplifying a common usage & characteristic of contemporary cloud-based environment

"This chapter is from the book, 'Cloud Computing: Concepts, Technology & Architecture' authored by Thomas Erl with Zaigham Mahmood and Ricardo Puttini, published by Pearson/Prentice Hall Professional, May 2013, ISBN 0133387526, Copyright 2013 Arcitura Education Inc. For more info please visit: http://www.informit.com/store/cloud-computing-concepts-technology-architecture-9780133387520"

This chapter introduces and describes several of the more common foundational cloud architectural models, each exemplifying a common usage and characteristic of contemporary cloud-based environments. The involvement and importance of different combinations of cloud computing mechanisms in relation to these architectures are explored.

Workload Distribution Architecture
IT resources can be horizontally scaled via the addition of one or more identical IT resources, and a load balancer that provides runtime logic capable of evenly distributing the workload among the available IT resources (Figure 1). The resulting workload distribution architecture reduces both IT resource over-utilization and under-utilization to an extent dependent upon the sophistication of the load balancing algorithms and runtime logic.

Figure 1: A redundant copy of Cloud Service A is implemented on Virtual Server B. The load balancer intercepts cloud service consumer requests and directs them to both Virtual Servers A and B to ensure even workload distribution.

This fundamental architectural model can be applied to any IT resource, with workload distribution commonly carried out in support of distributed virtual servers, cloud storage devices, and cloud services. Load balancing systems applied to specific IT resources usually produce specialized variations of this architecture that incorporate aspects of load balancing, such as:

  • The service load balancing architecture explained later in this chapter
  • The load balanced virtual server architecture covered in Chapter 12
  • The load balanced virtual switches architecture described in Chapter 13

In addition to the base load balancer mechanism, and the virtual server and cloud storage device mechanisms to which load balancing can be applied, the following mechanisms can also be part of this cloud architecture:

  • Audit Monitor - When distributing runtime workloads, the type and geographical location of the IT resources that process the data can determine whether monitoring is necessary to fulfill legal and regulatory requirements.
  • Cloud Usage Monitor - Various monitors can be involved to carry out runtime workload tracking and data processing.
  • Hypervisor - Workloads between hypervisors and the virtual servers that they host may require distribution.
  • Logical Network Perimeter - The logical network perimeter isolates cloud consumer network boundaries in relation to how and where workloads are distributed.
  • Resource Cluster - Clustered IT resources in active/active mode are commonly used to support workload balancing between different cluster nodes.
  • Resource Replication - This mechanism can generate new instances of virtualized IT resources in response to runtime workload distribution demands.

Resource Pooling Architecture
A resource pooling architecture is based on the use of one or more resource pools, in which identical IT resources are grouped and maintained by a system that automatically ensures that they remain synchronized.

Provided here are common examples of resource pools:

Physical server pools are composed of networked servers that have been installed with operating systems and other necessary programs and/or applications and are ready for immediate use.


Virtual server pools are usually configured using one of several available templates chosen by the cloud consumer during provisioning. For example, a cloud consumer can set up a pool of mid-tier Windows servers with 4 GB of RAM or a pool of low-tier Ubuntu servers with 2 GB of RAM.


Storage pools, or cloud storage device pools, consist of file-based or block-based storage structures that contain empty and/or filled cloud storage devices.


Network pools (or interconnect pools) are composed of different preconfigured network connectivity devices. For example, a pool of virtual firewall devices or physical network switches can be created for redundant connectivity, load balancing, or link aggregation.


CPU pools are ready to be allocated to virtual servers, and are typically broken down into individual processing cores.




Pools of physical RAM can be used in newly provisioned physical servers or to vertically scale physical servers.



Dedicated pools can be created for each type of IT resource and individual pools can be grouped into a larger pool, in which case each individual pool becomes a sub-pool (Figure 2).

Figure 2: A sample resource pool that is comprised of four sub-pools of CPUs, memory, cloud storage devices, and virtual network devices.

Resource pools can become highly complex, with multiple pools created for specific cloud consumers or applications. A hierarchical structure can be established to form parent, sibling, and nested pools in order to facilitate the organization of diverse resource pooling requirements (Figure 3).

Sibling resource pools are usually drawn from physically grouped IT resources, as opposed to IT resources that are spread out over different data centers. Sibling pools are isolated from one another so that each cloud consumer is only provided access to its respective pool.

In the nested pool model, larger pools are divided into smaller pools that individually group the same type of IT resources together (Figure 11.4). Nested pools can be used to assign resource pools to different departments or groups in the same cloud consumer organization.

After resources pools have been defined, multiple instances of IT resources from each pool can be created to provide an in-memory pool of "live" IT resources.

Figure 3: Pools B and C are sibling pools that are taken from the larger Pool A, which has been allocated to a cloud consumer. This is an alternative to taking the IT resources for Pool B and Pool C from a general reserve of IT resources that is shared throughout the cloud.

In addition to cloud storage devices and virtual servers, which are commonly pooled mechanisms, the following mechanisms can also be part of this cloud architecture:

  • Audit Monitor - This mechanism monitors resource pool usage to ensure compliance with privacy and regulation requirements, especially when pools contain cloud storage devices or data loaded into memory.
  • Cloud Usage Monitor - Various cloud usage monitors are involved in the runtime tracking and synchronization that are required by the pooled IT resources and any underlying management systems.
  • Hypervisor - The hypervisor mechanism is responsible for providing virtual servers with access to resource pools, in addition to hosting the virtual servers and sometimes the resource pools themselves.

Figure 4: Nested Pools A.1 and Pool A.2 are comprised of the same IT resources as Pool A, but in different quantities. Nested pools are typically used to provision cloud services that need to be rapidly instantiated using the same type of IT resources with the same configuration settings.

  • Logical Network Perimeter - The logical network perimeter is used to logically organize and isolate resource pools.
  • Pay-Per-Use Monitor - The pay-per-use monitor collects usage and billing information on how individual cloud consumers are allocated and use IT resources from various pools.
  • Remote Administration System - This mechanism is commonly used to interface with backend systems and programs in order to provide resource pool administration features via a front-end portal.
  • Resource Management System - The resource management system mechanism supplies cloud consumers with the tools and permission management options for administering resource pools.
  • Resource Replication - This mechanism is used to generate new instances of IT resources for resource pools.

Dynamic Scalability Architecture
The dynamic scalability architecture is an architectural model based on a system of predefined scaling conditions that trigger the dynamic allocation of IT resources from resource pools. Dynamic allocation enables variable utilization as dictated by usage demand fluctuations, since unnecessary IT resources are efficiently reclaimed without requiring manual interaction.

The automated scaling listener is configured with workload thresholds that dictate when new IT resources need to be added to the workload processing. This mechanism can be provided with logic that determines how many additional IT resources can be dynamically provided, based on the terms of a given cloud consumer's provisioning contract.

The following types of dynamic scaling are commonly used:

  • Dynamic Horizontal Scaling - IT resource instances are scaled out and in to handle fluctuating workloads. The automatic scaling listener monitors requests and signals resource replication to initiate IT resource duplication, as per requirements and permissions.
  • Dynamic Vertical Scaling - IT resource instances are scaled up and down when there is a need to adjust the processing capacity of a single IT resource. For example, a virtual server that is being overloaded can have its memory dynamically increased or it may have a processing core added.
  • Dynamic Relocation - The IT resource is relocated to a host with more capacity. For example, a database may need to be moved from a tape-based SAN storage device with 4 GB per second I/O capacity to another disk-based SAN storage device with 8 GB per second I/O capacity.

Figures 5 to 7 illustrate the process of dynamic horizontal scaling.

Figure 5: Cloud service consumers are sending requests to a cloud service (1). The automated scaling listener monitors the cloud service to determine if predefined capacity thresholds are being exceeded (2).

Figure 6: The number of requests coming from cloud service consumers increases (3). The workload exceeds the performance thresholds. The automated scaling listener determines the next course of action based on a predefined scaling policy (4). If the cloud service implementation is deemed eligible for additional scaling, the automated scaling listener initiates the scaling process (5).

Figure 7: The automated scaling listener sends a signal to the resource replication mechanism (6), which creates more instances of the cloud service (7). Now that the increased workload has been accommodated, the automated scaling listener resumes monitoring and detracting and adding IT resources, as required (8).

The dynamic scalability architecture can be applied to a range of IT resources, including virtual servers and cloud storage devices. Besides the core automated scaling listener and resource replication mechanisms, the following mechanisms can also be used in this form of cloud architecture:

  • Cloud Usage Monitor - Specialized cloud usage monitors can track runtime usage in response to dynamic fluctuations caused by this architecture.
  • Hypervisor - The hypervisor is invoked by a dynamic scalability system to create or remove virtual server instances, or to be scaled itself.
  • Pay-Per-Use Monitor - The pay-per-use monitor is engaged to collect usage cost information in response to the scaling of IT resources.

Elastic Resource Capacity Architecture
The elastic resource capacity architecture is primarily related to the dynamic provisioning of virtual servers, using a system that allocates and reclaims CPUs and RAM in immediate response to the fluctuating processing requirements of hosted IT resources (Figures 8 and 9).

Resource pools are used by scaling technology that interacts with the hypervisor and/or VIM to retrieve and return CPU and RAM resources at runtime. The runtime processing of the virtual server is monitored so that additional processing power can be leveraged from the resource pool via dynamic allocation, before capacity thresholds are met. The virtual server and its hosted applications and IT resources are vertically scaled in response.

This type of cloud architecture can be designed so that the intelligent automation engine script sends its scaling request via the VIM instead of to the hypervisor directly. Virtual servers that participate in elastic resource allocation systems may require rebooting in order for the dynamic resource allocation to take effect.

Some additional mechanisms that can be included in this cloud architecture are the following:

  • Cloud Usage Monitor - Specialized cloud usage monitors collect resource usage information on IT resources before, during, and after scaling, to help define the future processing capacity thresholds of the virtual servers.
  • Pay-Per-Use Monitor - The pay-per-use monitor is responsible for collecting resource usage cost information as it fluctuates with the elastic provisioning.
  • Resource Replication - Resource replication is used by this architectural model to generate new instances of the scaled IT resources.

Figure 8: Cloud service consumers are actively sending requests to a cloud service (1), which are monitored by an automated scaling listener (2). An intelligent automation engine script is deployed with workflow logic (3) that is capable of notifying the resource pool using allocation requests (4).

Figure 9: Cloud service consumer requests increase (5), causing the automated scaling listener to signal the intelligent automation engine to execute the script (6). The script runs the workflow logic that signals the hypervisor to allocate more IT resources from the resource pools (7). The hypervisor allocates additional CPU and RAM to the virtual server, enabling the increased workload to be handled (8).

More Stories By Thomas Erl

Thomas Erl is a best-selling IT author and founder of Arcitura Education Inc., a global provider of vendor-neutral educational services and certification that encompasses the Cloud Certified Professional (CCP) and SOA Certified Professional (SOACP) programs from CloudSchool.com™ and SOASchool.com® respectively. Thomas has been the world's top-selling service technology author for nearly a decade and is the series editor of the Prentice Hall Service Technology Series from Thomas Erl, as well as the editor of the Service Technology Magazine. With over 175,000 copies in print world-wide, his eight published books have become international bestsellers and have been formally endorsed by senior members of many major IT organizations and academic institutions. To learn more, visit: www.thomaserl.com

More Stories By Ricardo Puttini

Ricardo Puttini, PhD is a Senior Professor with the University of Brasília who has spent the past several years researching and documenting the evolution of cloud computing security and assembling a comprehensive compendium on this topic area. Ricardo lectures and teaches undergraduate and graduate level courses on cloud computing, SOA and other IT topics with academic institutions in Sweden, France and Brazil. Ricardo is a speaker at international events and acted as the conference chair for the 4th International SOA + Cloud Symposium in Brasilia in 2011.

More Stories By Zaigham Mahmood

Dr. Zaigham Mahmood is an IT author that has authored and co-authored several books and papers dedicated to cloud computing, including Cloud Computing for Enterprise Architectures that was released in 2011. Zaigham is the associate editor of the Journal of Cloud Computing and the editor-in-chief of the Journal of E-Government Studies and Best Practices, as well as the Reader in Applied Computing and Assistant Head of Distributed and Intelligent Systems (DISYS) research group at the University of Derby UK.

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