|By Patrick Carey||
|January 6, 2014 08:45 AM EST||
As the significant benefits of SaaS and hybrid cloud services become more evident, it's no surprise that organizations are moving more mission-critical applications - e.g., email, VoIP, online meetings, document storage/collaboration, etc., - to the cloud. This is different than CRM apps, which have been in the cloud for many years. If Salesforce.com is unavailable, the sales team is certainly impacted, but if email, IP, and/or VoIP communications are unavailable, the entire organization takes a productivity hit.
Moving your mission-critical apps to the cloud doesn't absolve IT of responsibility for the quality of service though. If users can't access email, they are not going to call Microsoft or Google or Amazon. They are going to call the IT help desk and the IT team will be expected to fix whatever problem exists.
That's a problem though. With SaaS applications, IT does not have direct access to the servers running the services. They may have access to a status dashboard, but those often do not provide anything close to real time information. Nor do they provide any information on the health and availability of the various networks (the customers, the ISPs, the regional backbone, etc.) connecting the users to the service.
To effectively monitor and manage mission-critical SaaS applications, IT needs end-to end visibility of the service delivery chain, from their own premise-based infrastructure, through their network service providers, to the application service provider. But how?
Bring on the Crowd
SaaS applications are by definition shared by a global community of customers. So it stands to reason that monitoring these services could and should be done in a shared manner as well. For example, if a service provider could aggregate data across a broader set of customers around the globe, organizations could get a global view of availability and performance metrics. They could quickly compare local performance measurements and alerts with data collected from other apps, locations, customers, and relevant service provider notifications.
This would not only enable IT to quickly determine the location (local, ISP, or SaaS provider) of any service impacting issues, but also provide a rich dataset from which to determine whether their own service delivery chain is optimally configured.
There are certainly examples of this "crowd-effect" already happening in informal ways through Twitter. It's not uncommon for users to check Twitter when they are having problems with the service. It sort of acts as an impromptu global network of monitors, watching the service from hundreds of thousands of access points. The problem with Twitter though is that it is primarily anecdotal and qualitative information and generally does not give you the fidelity needed to fix issues impacting your users.
Despite its limitations, there is a lot to be said for the "power of the crowd" that is so fundamental to Twitter. What if IT could take that same model and use it to proactively monitor SaaS applications? First, it would require some type of active monitoring behind your firewall at the locations where users access their SaaS applications. These "sensors" could act like Twitter users, constantly running transactions against the service and collecting data on transaction and network node performance. They would also allow you to proactively detect and notify an IT admin of any outages or performance anomalies before they impact users.
Then, what if we could collect and share real-time performance data from those sensors in a global database maintained as part of a cloud service. IT would then have visibility into the health of the complete service delivery chain between their organization and the SaaS provider. For example:
- Current status, alerts, network statistics, and performance trends for one or more access points to determine if there are service issues affecting a particular location or subnet.
- Comparative performance data with the rest of the "crowd" to determine whether service issues are systemic to the application provider or the result of downstream internet service provider problems. IT may not be able to fix these directly, but with this information they would know which service provider to call and help them speed time to resolution
- Detailed service level reports
For example, an IT admin might see where logon time in its own organization is significantly worse than the average of the crowd. If IT is getting reports of sluggish performance from its own users, this data tells them that the problem is probably not with the service. Likewise by comparing trend data from multiple sensors IT would be able to see whether the problem is more likely in the local network or at the network service provider. This could significantly reduce the time it takes to get the issue resolved.
The goal of every IT shop is to keep their application users online and happy. But with SaaS, that's more difficult to do because administrators don't have the same visibility that they do with on-premise applications. We, as a community, need to change that. Finding new ways to leverage the crowd seems like a great place to start.
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