The previous article talked about workflows and multitenant systems. This article talks about innovations in cloud-based multitenant systems or solutions for short which include those for industry specific solutions, authoritative marketplace offerings, fleet deployment of agents, and solution-based automation.

In this section, we focus on transitions specifically. When control passes from the tenant workload to the multitenant infrastructure and back, there is an opportunity to add routines that can not only safeguard the state of the caller but also improve the statistics and bookkeeping withing the solution. It is even possible to introduce a tag or inject color into the data stream so that its propagation throughout the cloud can be made visible. This improves forensics as well as the detection of resources that are underutilized for advice towards application optimization.  

Similarly adding headers before and after data segments from specific callers enables the study of those data manipulations by all parties during its lifetime. This study could include stack captures that are authoritative and comprehensive. 

A lot of information can be obtained when specific bookkeeping is added to sequences or patterns of usages or by specific callers. Since this additional bookkeeping might introduce regressions in performance optimizations for the application, it becomes important to turn it on for as granular a session as possible and for the duration specified by the administrator. In this regard, feature flags, variables, and dynamic behavior from the code will be helpful in the isolation of the control and data path under investigation.  

Finally, system performance and behavior capture has traditionally been curated for manual inspection. With the advent of AI and the popularity of data mining techniques, this machine data could automate analysis that draws insights and makes recommendations to the application authors. This strategy could involve cross-application comparisons and associations, historical trends, and collaborative filtering. Some common scenarios are described here.

Conventional software engineering practice involves the use of profiling as a means of studying avenues for application optimizations. A multitenant solution hosted in the public cloud is uniquely positioned toward this goal. Not only does the public cloud have complete visibility into the utilization of cloud resources and profiling, but it can also draw insights into application performance with the help of models that weren’t possible earlier on-premises. The cloud-based solution can introduce a significant number of resources for short periods of time on bursts of analysis activities, so they remain unparalleled in elasticity elsewhere. By incorporating the control and feedback loop directly within the cloud-based solution on behalf of the tenant and their applications, the solution can offer better impact in terms of shaping the tenant resources and the workloads they host, for the future.  Finally, public cloud provides an excellent paradigm for multitenant solutions to imbibe as an infrastructure provider for their tenants