Book Excerpt: Fundamental Cloud Architectures
Foundational cloud architectural models, exemplifying a common usage & characteristic of contemporary cloud-based environment
Jul. 1, 2013 08:30 AM
"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
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:
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:
Resource Pooling Architecture
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:
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.
Dynamic Scalability Architecture
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:
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:
Elastic Resource Capacity Architecture
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:
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).
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