Business Objective Oriented Problem Determination and Mitigation

1. Business Objective Oriented Problem Determination and Mitigation Presented by: Opher Etzion – HRL The Team members: Dagan Gilat, Segev Wasserkrug, Natalia Razinkov, Sarel Aiber, Aviad Sela, Ariel landau

2. Business Objective Oriented Problem Prediction and Mitigation • Current goals for managing an IT infrastructure site typically focus on IT measuressuch as ensuring that the site availability is 99.9% • However, what the enterprise really cares about are the business objectives, such as total profit • Therefore, the following is required: • The ability to predict when a problem will significantly impact the business objectives • The ability to mitigate the problem so as to minimize its adverse effect on the business objective

3. What does a solution require? • An economic model of business transactions (gains, explicit penalties, hidden penalties – customer deserting, reputation) • A model of the IT infrastructure • A model of impact analysis – how do problems impact the business objectives…

4. Requirements – drilling down one more level… • We need the ability to optimize business goals based on dynamic knowledge • We need the ability to model the relationships among IT and business model • We need the ability to identify problems that impact the business objectives in order to re-optimize the IT in order to mitigate these problems

5. ARAD architecture

6. AMOBO problem detection and mitigation process

7. Proactive Significant Problem Detection • A significant problem is defined as a problem which will have a significant impact on the business objectives • In order to decide whether a problem is significant, the following is carried out: • Whenever the monitoring tools detect a problem, i.e. server failure, a copy of the simulation model is created to reflect this problem • The business objective results as predicted by the updated model are compared with the business objective results as predicted by the previous model using statistical tests – e.g. the Chi-squared test • If according to the statistical tests the difference between the two is significant – the problem is deemed significant

8. Proactive Significant Problem Mitigation • The mitigation of the problem takes place as follows: • The previous simulation model is replaced with the updated model • Re-optimization is carried out • Alternatively, a list of problems deemed significant may be defined, that would always result in re-optimization

9. Significant Problem Detection and Mitigation – Case Study • Scenario: • eTrading Web site • Customers have two important attributes: • Average spending amount – High, Medium, Low • Average response time SLA – Platinum, Gold, Regular (WSLA) • Business Objective – optimize income generated from customers according to the following rules: • There is a 2% commission on each stock trade • Penalties are paid for each SLA violation • A flat fee is paid by each customer with a SLA

10. Significant Problem Detection and Mitigation – Case Study Results • System was optimized for two servers • Two IT failures occur: • Failure of a CPU, which is not deemed significant • Failure of a disk, which causes one of the servers to fail – significant problem • This failure is recognized and the system is re-optimized (Initiating re-optimization process as a result of significant problem detection) • The business objectives results both before and after the failure were : • Conclusion: Recognizing this problem as significant and re-optimizing the IT policy, results in significantly higher profit than the profit generated by remaining with the original policy

11. Summary • The need for business objective based problem determination and mitigation was introduced • The requirements enabling such problem determination were defined • An architecture, process and algorithms enabling the both problem determination and mitigation were introduced • A case study and demo were shown