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Pamela McNamaraPam McNamara is CEO and co-founder of Health Helm, Inc., a Boston-based mobile software company supporting Patient Reported Outcomes and engagement to improve outcomes and reduce costs through better care coordination and communications with physicians and clinicians. She was formerly CEO of CRF Inc., the world’s leading mobile health, electronic patient reported outcome mobile health platform supporting clinical trials. Under her leadership, CRF profitably scaled in the U.S. And globally.Before that, she was CEO of Arthur D.
Little, Inc., and led the firm’s Global Healthcare Practice. She was president of Cambridge Consultants, Inc. She sits on the boards of Southcoast Health System (trustee), Tufts School of Engineering (advisor), Brookhaven at Lexington (trustee), and was formerly an independent director of Omrix Biopharmaceuticals (NASDAQ:OMRI), GTC Biotherapeutics (NASDAQ:GTCB). McNamara has a B.S. In civil engineering from Tufts University.
In order to more fully understand this reality, we must take into account other dimensions of a broader reality.- John Archibald WheelerI explored the architectural styles we can use to stay nimble so we can react to customer and market needs in “” and further in “”. Figure 1 Scale cube derived from 'The Art of Scalability'Dimensions of Architecture Technical Architecture LayeringTechnical Application Layering represents the set of application patterns that support separation of concerns such as presentation, application logic and data storage. This separation includes externalizing protocol agents such as HTTP agents (web servers, IP Load Balancers, etc). Examples include three-tiered architectures used in first generation web applications. In this model, scale is achieved by simple load balancing across cloned hardware that host components such as web servers or application containers (application servers, autonomous Java applications, etc).
Figure 2 X-Axis –Horizontal Technical duplication - Traditional 3-Tier Web Application with component level cloning – Load Balancing to Clones from 'The Art of Scalability'Traditional monolithic web applications often follow this pattern, but have limited scale because of the coarse tuning restrictions of shared resources inherent in high functional coupling and a single data store.Great strides have been made in recent years that have served to extend the scale capabilities and cost efficiencies of such solutions. The virtualization of commuting, network and storage resources along with the availability of highly efficient (and cost effective) cache mechanisms combined with local data partitioning for storage and access scaling have contributed to the longevity of many systems. These advances will not avoid the inevitable limitations if used in isolation, but can contribute to ultimate longevity when used in concert with other scaling and availability techniques. Functional Segmentation – Components: Modules and MicroservicesFunctional Segmentation (decomposition) is the complete encapsulation of functional components that provide a single business capability. These capabilities are separated into modules or autonomous microservices shaped by bounded contexts – meaning there is no cross-sharing of models or data except through a published API.
Scale is achieved by intelligently routing requests for specific “things” (components) that are independently managed. Figure 3 Y-Axis - Functional Decomposition - Components: Modules and Microservices – Routing to ThingsEach component, module or microservice may in turn independently follow the relevant Technical Architectural Layering patterns to achieve a much higher fidelity of tuning based upon volume and load characteristics by function. In this approach, scaling by Technical Architecture Layering is greatly enhanced by combining Y-Axis scaling with X-Axis scaling so that we have smart routing to the “things” that are themselves load balanced clones. Figure 4 Y-Axis Scaling with X-Axis Scaling - Smart Routing to Things that are Load Balanced ClonesThis independent management by function allows for right sizing of clones on the X-Axis based upon unique load characteristics, but even more significantly, it also allows for right-choice selection of the application stack technology to match the specific need. For instance, all logic does not have to be Java JEE and all data management/storage does not have to be in an RDBM system – technology choices may be made based on the unique nature of the function and the skills of the team accountable for the function. Horizontal Data Partitioning - ShardsHorizontal Data Partitioning is a data-driven system management approach that is the value-based, horizontal or row-level separation of data to allow for storage (and ultimately, the related computational work) on separate servers for distributed load.
The separation is typically based on the value of key fields such as marketing channels, groups/clients, location, or even a consistent hashing model for pure load distribution. Scale is achieved by intelligently routing requests for specific contexts (shards) that are independently managed. Figure 6 Pods - Z-Axis Scaling with X-Axis Scaling – Smart Routing to Context that are Load Balanced ClonesThis independent management allows right sizing (the number and depth of clones) and unique tuning of application stack technology to match the specific load characteristics of the shard context, even though each shard is a logical clone of all others.
This model is often referred to as “Pods” when the technical architecture supporting the application logic that is operating on the shard is also included in each independent deployment – that is the stack along the X-axis. Not only is scale achieved in terms of growth and performance, but also this level of isolation limits impact of any disruption (planned or not) to only one shard. Volume = Architecture 3Finally, all three dimensions can be combined in various routing priorities to achieve near-infinite scaling. In my example, I route to Shards of contexts scoped by a “marketing channel' which are further routed to Functionally Segmented Components that are load balanced across cloned components. This partitioning and segmentation creates the opportunity to use “shared nothing architecture” for virtually unlimited scale, extreme high-availability and low-risk continuous delivery.
Figure 7 Architecture Cubed - Routing to Shards with Routing to Components that are Load Balanced ClonesIn this example, instances of the functional decomposition (Y-axis) are grouped within the shard context (Z-axis) based on an obvious data affinity around a “marketing channel” to imply computational grouping with the data (move compute to the data). However, this does not have to be the only model. Depending up on your specific needs, moving compute to data may be the right answer, however, different routing orders could be applied to route to functions that then route to retrieve data at shards when a “move data to compute” is the right answer such as solving problems of global directories for a global authentication model or a global look-up coordinating a distributed search.
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ConclusionThe Scale Cube helps us keep the critical dimensions of system scale in mind as we search for solutions and make decisions. As with many architectural metaphors and pattern languages, the Scale Cube provides a great framework to work through options, but you must find the specific answer for your specific business problem.These concepts couple very well with modern infrastructure platforms such as public or private clouds that open new opportunities with highly elastic computational resource management (right-sizing based on granular and even cyclical needs).
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Using the Scale Cube in concert with these platforms opens the door to cost effective scaling capabilities without sacrificing our other critical system qualities in a way that is not only economically responsible for our business, but game changing.