Workload Automation today is primarily focused on automating workload in mainframe and distributed environments that are of a batch or near real-time nature. While this form of workload will continue to remain prevalent in the data center for years to come, there are new forms of workload, made possible by new shifts in enterprise architecture that are emerging in parallel and will necessitate the integration of both existing and new platforms.
To more greatly concentrate computing power to reduce overall run time, applications have taken to greater balancing of workload to shorting data processing windows. Parallel processing emerged, and was later refined by Grid computing. Data centers now had greater capability of executing workload within their existing infrastructure, but could not optimize hardware resources. This led to large numbers of servers under utilized the vast majority of the time and on standby for peak operating periods.
The on-demand nature of virtualization technology and cloud computing changes that - though the vast majority of job scheduling and workload automation solutions deployed in the enterprise cannot leverage it.
|Technical Challenge||Consequence for Workload Automation|
|Virtualization and Cloud-based technologies mean inexpensive, on-demand computing resources can be made available at any time to accelerate processing of workload||Workload Automation solutions must be able to differentiate between the workload to the infrastructure, and be able to identify new resources on the fly as they become available. More importantly, solutions must be able to integrate and control provisioning tools of these environments.|
|Key components of an Enterprise's workload may exist in disparate data stores including in-house as well as SaaS-hosted applications in the cloud.||The Solution must simultaneously support corporate as well as the industry's standard security protocols. Must be able to easily extract and move large volumes of data between partners.|
|These new workload systems, such as Grid Computing, are also closed systems with limited awareness in terms of other workload management systems - but may have data and process dependencies||These systems need to be integrated with the rest of an enterprise's workload, which may span generations of technology. The WAB must be able to integrate with and create cross-platform, cross-application workflows to manage workload end-to-end and create an effective business process view|
Today, OpsWise helps you move forward to Workload Nirvana by providing a set of core services that moves the bar in what is achievable by a Job Scheduler and Workload Automation Broker today by providing:
- A state of the art user experience that's 100% web-based that allows users to build business-level views of the workload that's running
- Integrate all of your platforms - from z/OS to virtualized Linux servers
- Dynamically place workload on servers - and auto-discovery of new machine images containing OpsWise agents
- Load-balancing of servers based on CPU
- Broadcasting workload across a set of servers