X-ACT supports IBM Cloud Innovation

In his latest blog post, The Right Way to Ensure Operational Stability while moving to the Cloud, Thomas Rose, IBM Cloud Innovation Advisory Leader Germany, Austria and Switzerland, explained how IBM is using X-ACT® to optimize and improve the operational stability of their cloud services.

X-ACT is the innovation behind IBM’s Cloud Load Simulation solution. The emulation and analytics capabilities of X-ACT allows IBM to identify when and if dynamic complexity will lead to system limits within cloud environments, diagnose the root cause of the limit and determine the best remedial actions by weighing the benefits, complexity and cost of proposed solutions.

Many cloud initiatives fail to meet client expectations due to inadequate risk identification and mitigation early in the outsourcing project’s lifecycle. Due to the complexity of modern business, it is no longer possible to intuitively assess both the benefits and risks posed by moving services to the cloud.

Now with X-ACT, users can quickly model existing systems and evaluate how proposed changes will impact the cost efficiency, scalability and performance of business operations. By effectively identifying any potential risks that could impact the quality of service and verifying that all applications will behave correctly under any operational condition, decisions can be made with confidence in the outcome.

This is a win-win for cloud vendors and their clients, as it provides an insurance mechanism to verify that the cloud envrionment will continuously meet the promises made during the early stages of project definition.

Next we will be working to combine IBM Watson and X-ACT technology to implement cloud production control environments, which dynamically react to market events, and control system loads and resources to operate at an optimum cost level.

Using X-ACT Metrics to Guide Decisions

Learn how to make operational risk decisions with confidence

The X-ACT: Using Metrics to Guide Decisions | How to Guide shows how companies use the advanced analytics and emulation capabilities supported by X-ACT to identify how dynamic complexity leads to system limits, diagnose the root cause of limits and determine the best remedial actions by weighing the benefits, complexity and cost of proposed solutions.

The analytics and emulation capabilities supported by X-ACT® arm business and technology leaders worldwide with the foresights they need to confidently respond to changing system dynamics and clearly understand which (and when) preventive and opportunistic actions should be taken to ensure the continuous efficiency and cost effectiveness of operations.

Using accurate, representative and reproducible models of business processes, applications and infrastructure, X-ACT delivers an end-to-end emulation of a service that accurately represents the behavior of system dynamics. The emulation replaces structures, characteristics and behaviors by perturbations exerted on dynamic equations through multiple order perturbations on dynamic coordinates such as volume, service quality and cost. This is very complex math, but it is handled entirely by X-ACT.

Once a system is transformed into an emulation, it allows users to quickly test and economically explore an unlimited number of scenarios that would otherwise be complex, expensive or even impossible to test on a real system. In comparison to other practices, such as simulation, emulation is superior in its ability to accurately replicate a system, but its biggest advantage is that it allows for the discovery of previously unknown patterns, which cannot be determined using any other method.

Now users can emulate risk because X-ACT can mathematically reproduce unknowns that may happen under certain conditions. Once the emulation process is complete, X-ACT users can change variables—such as volume, architecture and infrastructure or perform sensitivity predictions on changing process dynamics—to observe the outcomes (even when we have no historical record of these events ever happening).

Discovering the cause and effects of dynamic complexity is foundational to our universal risk management approach. Since conventional methods ignore the unknowns, risk often appears as a surprise that may potentially impact operational performance. To predict risk and anticipate the appropriate course of treatment, we must discover these unknowns and determine their current and future influence on system behavior.

The X-ACT: Using Metrics to Guide Decisions | How to Guide shows how companies use the advanced analytics and emulation capabilities supported by X-ACT to identify risk and take remedial actions by weighing the benefits, complexity and cost of available solutions.

How Dynamic Complexity Disrupts Business Operations

Dynamic complexity always produces a negative effect in the form of loss, time elongation or shortage—causing inefficiencies and side effects, which are similar to friction, collision or drag. Dynamic complexity cannot be observed directly, only its effects can be measured. Additionally, dynamic complexity is impossible to predict from historic data—no matter the amount—because the number of states tend to be too large for any given set of samples. Therefore, trend analysis alone cannot sufficiently represent all possible and yet to be discovered system dynamics.

In the early stages, dynamic complexity is like a hidden cancer. Finding a single cancer cell in the human body is like looking for a specific grain of sand in a sandbox. And like cancer, often the disease will only be discovered once unexplained symptoms appear. To proactively treat dynamic complexity before it negatively impacts operations, we need diagnostics that can reliably reveal its future impact. System modeling and mathematical emulation allow us to provoke the existence of dynamic complexity through two hidden exerted system properties: the degree of interdependencies among system components and the multiple perturbations exerted by internal and external influences on both components and the edges connecting them directly or indirectly.

Successful risk determination and mitigation is dependent on how well we understand and account for dynamic complexity, its evolution, and the amount of time before the system will hit the singularity (singularities) through the intensification of stress on the dependencies and intertwined structures forming the system.

Knowing what conditions will cause singularities allows us to understand how the system can be stressed to the point at which it will no longer meet business objectives, and proactively put the risk management practices into place to avoid these unwanted situations.

Below we provide an example of a client case where dynamic complexity played a key role in terms of resource consumption, time to deliver, and volume to deliver. The scenario presented in the graph represents a trading and settlement implementation used by a volume of business that continuously increases. The reaction of the system is shown by the curves.

In the graph above, the production efficiency increases until it hits a plateau after which the business is increasingly impacted by a slowdown in productivity and increase in costs. The amount of loss is proportional to the increase in dynamic complexity, which gradually absorbs the resources (i.e. the cost) to deliver little. The singularity occurs when the two curves (productivity/revenue and cost) join, which in turn translates into loss in margin, over costing and overall instability.

In client cases such as this one, we have successfully used predictive emulation to isolate the evolving impact of dynamic complexity and calculate risk as an impact on system performance, cost, scalability and dependability. This allows us to measure changes in system health, when provoked by a change in dynamic complexity’s behavior under different process operational dynamics and identify the component(s) that cause the problem.

But knowing the how and what isn’t sufficient. We also need to know when, so we measure dynamic complexity itself, which then allows us to monitor its evolution and apply the right mitigation strategy at the right time.

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