Joe McKendrick| Zdnet
Arcitura Education, which has long been leading the way in service-oriented architecture and cloud thought leadership, has just launched CloudPatterns.org, a community site for documenting master patterns for cloud computing platforms and architectures. The site is similar to SOA patterns, which cataloges key design patterns for service oriented architecture. Arcitura president Thomas Erl is the organizer behind both efforts. The goal of both is the same: to identify patterns that map modular, technology-centric solutions to business problems.
The effort to document patterns and mechanisms “provides an extremely concrete view of cloud architecture layers and the individual building blocks that represent the moving parts that can be assembled in creative ways to leverage cloud environments for business automation,” according to Arcitura.
Patterns described at the site include the following. These patterns also are assembled together to provide “compound patterns” that tackle contemporary cloud delivery and deployment models (such as public cloud, IaaS, etc.). At the time of this posting, there were 39 design patterns and 13 compound patterns:
- Automated Administration: “How can common administrative tasks be carried out consistently and automatically in response to pre-defined events?”
- Bare-Metal Provisioning: “How can operating systems be remotely deployed on bare-metal servers?”
- Broad Access: “How can cloud services be made accessible to a diverse range of cloud service consumers?”
- Centralized Remote Administration: “How can diverse administrative tasks and controls be consolidated for central remote access by cloud consumers?”
- Cross-Storage Device Vertical Tiering: “How can the vertical scaling of data processing be carried out dynamically?”
- Direct I/O Access: “How can a virtual server overcome data transfer capacity thresholds imposed by its surrounding virtualization environment?”
- Direct LUN Access: “How can a virtual server overcome performance limitations imposed by emulated file-based storage?”
- Dynamic Data Normalization: “How can redundant data within cloud storage devices be automatically avoided?”
- Dynamic Failure Detection and Recovery: “How can the notification and recovery of IT resource failure be automated?”
- Dynamic Scalability: “How can IT resources be scaled automatically in response to fluctuating demand?”
- Elastic Disk Provisioning: “How can the billing of cloud storage be based on actual, fluctuating storage consumption?”
- Elastic Network Capacity: “How can network bandwidth be allocated to align with actual usage requirements?”
- Elastic Resource Capacity: “How can the processing capacity of virtual servers be dynamically scaled in response to fluctuating IT resource usage requirements?”
- Hypervisor Clustering: “How can a virtual server survive the failure of its hosting hypervisor or physical server?”
- Intra-Storage Device Vertical Data Tiering: “How can the dynamic vertical scaling of data be carried out within a storage device?”
- Load Balanced Virtual Server Instances: “How can a workload be balanced across virtual servers and their physical hosts?”
- Load Balanced Virtual Switches: “How can workloads be dynamically balanced on physical network connections to prevent bandwidth bottlenecks?”
- Multipath Resource Access: “How can an IT resource be accessed when its pre-defined path is lost or becomes unavailable?”
- Non-Disruptive Service Relocation: “How can cloud service activity be temporarily or permanently relocated without causing service interruption?”
- Pay-as-You-Go: “How can a cloud consumer be billed accurately for the actual amount of its IT resource usage?”
- Persistent Virtual Network Configuration: “How can a virtual server maintain its network connection when it is migrated to a new physical host?”
- Platform Provisioning: “How can cloud consumers build and deploy cloud solutions without the burden of having to create and manage the underlying infrastructure?”
- Rapid Provisioning: “How can the provisioning of IT resources be automated and made available to cloud consumers on-demand?”
- Realtime Resource Availability: “How can cloud consumers access current availability status information for IT resources?”
- Redundant Physical Connection for Virtual Servers: “How can a virtual server be kept connected when its physical connection fails?”
- Redundant Storage: “How can the reliability and availability of cloud storage devices survive failure conditions?”
- Resource Management: “How can a cloud consumer safely manage an IT resource without impacting neighboring IT resources?”
- Resource Pooling: “How can IT resources be organized to support dynamic sharing?”
- Resource Reservation: “How can shared IT resources be protected from conflicts that can arise from concurrent access?”
- Self-Provisioning: “How can cloud consumers be empowered to have IT resources provisioned on-demand?”
- Service Load Balancing: “How can a cloud service accommodate increasing workloads?”
- Service State Management: “How can stateful cloud services be optimized to minimize runtime IT resource consumption?”
- Shared Resources: “How can the capacity of physical IT resources be used to its potential?”
- Storage Maintenance Window: “How can access to data in a cloud storage device be preserved during a maintenance outage?”
- Storage Workload Management: “How can storage processing workloads be dynamically distributed across multiple storage devices?”
- Synchronized Operating State: “How can the availability and reliability of virtual servers be ensured when high availability and clustering technology is unavailable?”
- Usage Monitoring: “How can IT resource usage be measured?”
- Workload Distribution: “How can IT resource over-utilization be avoided?”
- Zero Downtime: “How can we accomplish a zero down time when both virtual and physical servers failures occur?”
