When you work in I/O intensive environments with large workloads of varying sizes there are unique management challenges. Anticipation of peak loads and good planning will help you work at maximum efficiency throughout the year.
Just because a storage environment is very large does not mean that it’s I/O intensive. Small and medium-sized environments can also be highly I/O intensive so size is not directly relevant to I/O volume. Online transaction processing for CRM and ERP can also be I/O intensive even though they are relatively small. Other I/O intensive systems are employed in healthcare and imaging, geosciences and energy exploration, for paging and journaling and in security video surveillance. For overall administration, backup and recovery along with data migration are routinely used in numerous IT environments.
I/O intensive environments can be optimized to support many transactions, a large amount of bandwidth or both.
Your application must meet the availability requirements of your users and customers. An application manager can help monitor a heterogeneous database server environment consisting of any combination of Oracle, MS SQL, Sybase and MySQL databases. A monitoring component can help IT administrators by notifying them about potential performance problems. Performance statistics are tracked and can help you to plan your resource requirements.
Application performance management software can monitor applications across traditional, mobile, virtual and cloud environments. Details of individual transactions are visible enabling speedy resolution of application issues. You can monitor end-user experience and utilize this feedback for coordinating IT performance with business goals. You can enhance application quality by acting on detailed diagnostics and real-time analytics.
Here are some ways of dealing with bottlenecks caused by high I/O operations:
- Transform random I/O into sequential I/O. You can update long-term structures off-peak.
- Read caching retains existing disk infrastructure as-is and relies on a single persistent data source supporting validity.
- Separate access patterns by spindle. Separating sequential I/O and random I/O with the sequential I/O head on the right track at your file’s head or tail is helpful.
- High end controllers can reorder random I/O eliminating seek time as a bottleneck.
- Rotational latency can cause throughput bottlenecks. These can be reduced by using faster disks.
- Raw block I/O might be preferable over a file system.
- Additional disks for replication and partitioning can also ease bottlenecks.
You can get consistently reliable storage and I/O for your cloud apps with cloud block storage. This relatively low cost route gives you the scalability and usability that can let you focus on your enterprise functions without spending valuable resources on analysis and planning of infrastructure maintenance.
Data archive implementation, data sets and on-demand dynamic approaches for provisioning data sets are currently being investigated by The National Science Foundation.
San Diego Super Computer Center researchers will explore the use of compute clouds to populate and manage scientific data sets on a large scale. In current Big Data environments a traditional, parallel, RDBMS architecture, which is more structured and static is the norm. The SDSC team will pursue the feasibility of a cloud computing approach.
A service approach to data intensive computing providing a high degree of usability and configurability may be best served in most cases by choosing cloud data implementations. A well-planned administrative interface will provide the usability that you seek and performance can be monitored and controlled to a great extent by an experienced team. Scalability is also an inherent part of cloud services. When you choose cloud services, place an emphasis on testing and vendor research. It’s important to select a vendor that will deliver.