Zypr is a Simulator & Optimizer
Zypr is a Simulator-as-a-Service that enables enterprises to quickly and more precisely execute optimal mix and consumption rates of their data center hardware, software, space, and power resources that reduces total spend 10-20% below expected future costs.
Infrastructure planners, engineers and analysts can directly execute simulations by interacting with the Zypr API using Swagger UI, cURL, or other client software. Zypr can also be integrated programmatically with third-party SAM, ITAM, TBM, or IaaS applications or cloud-based services.
How Zypr Reduces Costs
Zypr enables enterprises to simulate the future evolution of their infrastructure environments using two different hardware lifespan policies:
- Server turnover based on a predetermined, fixed lifespan (e.g., refresh servers every four years). Lifespan is therefore an input to a simulation.
- Server turnover is variable and dynamically determined using Zypr’s specialized optimization engine. Lifespans are an output of a simulation.
Determining which policy is appropriate corresponds to a target environment's cost structure. A cost structure is defined by the ratio of software, power, and space resource costs, whose consumption rate is dependent upon hardware efficiency and/or performance (HEP), to the cost of hardware. This cost ratio is referred to as an environment’s Kratio, and when combined as a coefficient to an improvement in HEP (e.g., from a "better" server), indicates the level of cost avoidance or displacement better HEP offers with respect to the cost to acquire the "better" hardware. Capturing these savings is referred to as monetizing HEP. Zypr minimizes the total cost of an environment by identifying how to optimally evolve its future resource composition.
To illustrate HEP monetization, assume Pool A has a Kratio of 4.0, Pool B's is 2.0, and a new server’s performance improvement, p, is 20% relative to existing pool inventory. The new server’s value to pool A and B can be be expressed by the terms 4p and 2p, respectively. Pool A's potential cost savings is 80% (4 x 20%) of a server's cost, with a payback of 1.25 years, whereas Pool B's is 40%, with a payback of 2.5 years. For pool A to actually monetize this higher value, its refresh rate would need to be relatively faster than pool B. Alternatively, if pool A’s Kratio was 0.8, then p would need to be 156% for the same 1.25 payback time. Low Kratios invite other determinants to hardware lifespan.
For example, major cloud providers typically have very low Kratios, own their software stacks, and charge for its use. Their lifespan policies are informed by direct revenue for that stack. Enterprises have very different infrastructure economics. Their Kratios typically range from 2-20 across the many environments they manage, and therefore greatly benefit from hardware lifespan policies that are more agile and dynamically determined.
Although conceptually easy to appreciate, implementing a more agile lifespan policy requires employing a better inventory model and solving some nasty math — all beyond the capabilities of a spreadsheet model. So Zypr abstracts away these complexities, enabling enterprises to quickly and precisely predict and maintain a more competitive cost structure for their digital services.
To learn more about inventory policies and how Zypr's optimization engine works, you can read more here.
How It Works
Zypr enables enterprises to describe the unique behavior of infrastructure resources and identify optimal inventory use and consumption policies for resource pools, with respect to each pool's unique characteristics.
A JSON-based model is used to define a pool's initial resource composition, service demand, future events, rules, and constraints that govern how resource composition evolves for a desired, future time period. The model is validated, and if everything is okay, Zypr's simulation engine is invoked.
Zypr's simulator is a purpose-built dynamical system that can ingest discrete events — called jump events, which provides the necessary capability to more precisely describe discontinuous resource behavior (e.g., a jump in server performance, a vendor price increase, etc.). Zypr provides nine jump event types to enable a wide range of use cases to describe real-world behaviors.
Zypr usually takes 30-300 seconds to find an optimal solution as it considers a nearly infinite solution space — the many feasible combinations and sequences of server additions and removals from a pool and the corresponding software, power and space resource quantities. At completion, a collection of resource forecasts is returned. Alternatively, Zypr can generate a solution in 5-10 seconds for a model based on a fixed lifespan input.
Operating data center infrastructure requires entering into longer-term commitments for non-hardware resources - irrespective of whether that is considered capital or operating expense by finance. Yet, these resources all depend upon a "point-of-view" about future hardware inventory and their characteristics. Zypr brings a much higher degree of precision when constructing those future estimates, and the necessary clarity when determining how much is required for:
- physical space
- power handling and distribution equipment
- power availability
- software licenses
In addition to determining appropriate inventory policies, Zypr also supports:
- quantifying the impact of network and storage upgrades on existing server pools
- evaluating proposed new technologies, architectures, configurations, or infrastructure services
- comparing on-premise, hybrid, or cloud hosting options