Monolithic approaches to data management, and the management of big data in particular, are no longer sufficient. Organizations have entirely too many storage and computing environments to account for, including bare metal on-premise servers, virtualization options, the cloud, and any combination of hybrid implementations.
Capitalizing on this distributed data landscape requires the ability to seamlessly shift resources between hosts, machines, and settings for real-time factors affecting workload optimization. Whether spurred by instances of failure, maintenance, surges, or fleeting pricing models (between cloud providers, for example), contemporary enterprises must react nimbly enough to take advantage of their infrastructural and architectural complexity.
Furthermore, regulatory mandates such as the EU General Data Protection Regulation and others necessitate the dynamic positioning of workflows in accordance with critical data governance protocols. A careful synthesis of well-planned governance policy, Artificial Intelligence techniques, and instance-level high availability execution can create the agility of a global data fabric in which âif [organizations] can get smart and let the automation take place, then everythingâs done the way they want it done, all the time, right,â DH2i CEO Don Boxley said.
The automation of intelligent workload routing is predicated on mitigating downtime and maximizing performance. Implicit to these goals is the notion of what Boxley referred to as Smart Availability in which âyouâre always able to pick the best execution venue for your workloads.â Thus, the concept of high availability, which relies on techniques such as clustering, failovers, and other redundancy measures to ensure availability, is enhanced by dynamically (and even proactively) moving workloads to intelligently improve performance. Use cases for doing so are interminable, but are particularly acute in instances of online transaction processing in verticals such as finance or insurance. Whether processing insurance claims or handling bank teller transactions, âthose situations are very sensitive to performance,â Boxley explained. âSo, the ability to move a workload when itâs under stress to balance out that performance is a big deal.â Of equal value is the ability to move workloads between settings in the cloud, which can encompass provisioning workloads to âspan cloudsâ, as Boxley mentioned, or even between them. âThe idea of using the cloud for burst performance also becomes an option, assuming all the data governance issues are aligned,â Boxley added.
The flexibility of automating intelligent workloads is only limited by data governance policy, which is a crucial piece of the success of dynamically shifting workload environments. Governance mandates are especially important for data hosted in the cloud, as there are strict regulations about where certain information (pertaining to industry, location, etc.) is stored. Organizations must also contend with governance protocols about who can view or access data, while also taking care to protect sensitive and personally identifiable information. In fact, the foresight required for comprehensive policies about where data and their workloads are stored and enacted is one of the fundamental aspects of the intelligence involved in routing them. âThatâs what the key is: acting the right way every time,â Boxley observed. âThe burden is on organizations to ensure the policies they write are an accurate reflection of what they want to take place.â Implementing proper governance policies about where data are is vitally important when automating their routing, whether for downtime or upsurges. âOne or two workloads, okay I can manage that,â Boxley said. âIf Iâve got 100, 500 workloads, that becomes difficult. Itâs better to get smart, write those policies, and let the automation take place.â
Artificial Intelligence Automation
Once the proper policies are formulated, workloads are automatically routed in accordance to them. Depending on the organization, use case, and the particular workload, that automation is tempered with human-based decision support. According to Boxley, certain facets of the underlying automation are underpinned by, âAI built into the solution which is constantly monitoring, getting information from all the hosts of all the workloads under management.â AI algorithms are deployed to denote substantial changes in workloads attributed to either failure or spikes. In either of these events (or any other event that an organization specifies), alerts are generated to a third-party monitor tasked with overseeing designated workflows. Depending on the specifics of the policy, that monitor will either take the previously delineated action or issue alerts to the organization, which can then choose from a variety of contingencies. âA classic use case is if a particular process isnât getting a certain level of CPU power; then you look for another CPU to meet that need,â Boxley said. The aforementioned governance policies will identify which alternative CPUs to use.
The governance requisites for shifting workloads (and their data) between environments is a pivotal aspect of holistically stitching together whatâs termed a comprehensive data fabric of the enterpriseâs computational and data resources. âIn terms of the technology part, weâre getting there,â Boxley acknowledged. âThe bigger challenge is the governance part.â With regulations such as GDPR propelling the issue of governance to the forefront of data-driven organizations, the need to solidify policies prior to moving data around is clear.
Equally unequivocal, however, is the need to intelligently shift those resources around an organizationâs entire computing fabric to avail itself of the advantages found in different environments. As Boxley indicated, âThe environments are going to continue to get more complex.â The degree of complexity implicit in varying environments such as Windows or Linux, in addition to computational settings ranging from the cloud to on-premise, virtualization options to physical servers, seemingly supports the necessity of managing these variations in a uniform manner. The ability to transfer workloads and data between these diverse settings with machine intelligent methods in alignment with predefined governance policies is an ideal way of reducing that complexity so it becomes much more manageableâand agile.