EMA Radar for Workload Automation 2012 – Key Findings

Jun 14, 2012 10:57:43 AM

During this year's research for the 2012 EMA Workload Automation (WLA) Radar Report, we encountered a number of very interesting core findings. These research results mostly originated from dozens of conversations with end customers, who have been using WLA software for many years and sometimes even for decades. WLA, by definition, is a mature discipline, as it started during the beginning of mainframe times, then became more complex when organizations adopted distributed computing, and today is faced with a new challenge: the cloud. Please take a look at what our research showed as the most important aspects of a modern WLA solution. The following vendors were included in the report: Arcana, ASCI, ASG, BMC, CA Technologies, Cisco, Flux, MVP Systems, Network Automation, ORSYP, Stonebranch, UC4.

Scalability & Performance: Solid and scalable scheduling capabilities are the core requirement for any WLA project. All reviewed WLA packages offered advanced triggering, flexible alerts, and highly available setups. Most of them scaled well, without putting a significant strain on the processing hardware.

Ease-of-implementation: Especially, some of the newer vendors have focused on making their products easy-to-implement. We have talked with some of their customers and came across multiple large enterprises that implemented their WLA solution within less than one week.

Application Integration: Deployment times of less than one week allow many organizations to take advantage of WLA packages for rapid application integration. Most business processes require the involvement of multiple software applications. Centrally controlling these applications through WLA minimizes interface-issues between these applications and makes the entire process more transparent and controllable.

Workload-as-a-Service: Offering WLA to developers and business users through a central service portal allows organizations to leverage their existing WLA investment. WLA software contains many components that can be effectively reused, instead of recreated, by commercial or custom software applications.

SLA-Awareness & Analytics: In most use cases, workload has a direct impact on SLAs. Therefore, business service dashboards must be aware of workload performance in order to reliably predict business service health. Advanced analytics are required to dynamically manage the critical path, based on cost, resource availability, and historic performance.

Intelligent Workload Placement: Most vendors allow the creation of resource pools that can be made available to specific workloads. The WLA software distributes workloads to the pools where they can be processed in the most efficient manner. Intelligent placement decisions are based on advanced workload analytics and on performance requirements derived from applicable SLAs.

Ease-of-Use: WLA software typically offers multiple interfaces: GUI, API, CLI, mobile access. The quality of each one of these interfaces determines the extent to which the respective WLA software can be leveraged by developers, business users, and other enterprise software packages.

Mobile Access: The requirement for mobile access can be found as part of more and more RFPs. Many vendors have acknowledged this requirement and are offering mobile applications or web portals that are optimized for mobile use.

For many more research results and facts regarding the WLA market place in 2012, please take a look at the summary of the 2012 WLA Radar Report. You can also purchase the complete version here.

Torsten Volk

Written by Torsten Volk

With over 15 years of enterprise IT experience, including a two-and-a-half-year stint leading ASG Technologies' cloud business unit, Torsten returns to EMA to help end users and vendors leverage the opportunities presented by today's hybrid cloud and software-defined infrastructure environments in combination with advanced machine learning. Torsten specializes in topics that lead the way from hybrid cloud and the software-defined data center (SDDC) toward a business-defined concept of enterprise IT. Torsten spearheads research projects on hybrid cloud and machine learning combined with an application- and service-centric approach to hyperconverged infrastructure, capacity planning, intelligent workload placement, public cloud, open source frameworks, containers and hyperscale computing.

  • There are no suggestions because the search field is empty.

Lists by Topic

see all

Posts by Topic

see all

Recent Posts