A Robotic Cloud Advisory Service Deepak Poola, Jigar Kapasi, and Sreekrishnan Venkateswaran  IBM Corporation, India

Background Currently, enterprise cloud architects develop solutions based on prior experience, reading source, and consulting SMEs There are multiple solution layers Storage Network Compute CMP Security Performance Etc. Each layer is complex with multiple options/ solutions/ products Additionally, these layers are interlinked with one layer affecting the other. Talk about solution layers – target cloud, n/w, storage, compute, CMP, security, performance, DR, etc Each layer is represented as a complex decision tree Interdependency on layers… therefore, decision tree of decision trees (master decisio tree)

Introduction To assist architect, we propose a methodology that brings in a rapid, automated and standardized process of creating a feasible and best-available-fit end-to-end solution. As the first step, the content in every solution area is translated into decision trees. Navigating this master decision tree manually is not feasible due to its sheer size and complexity. We propose a two-step approach to tackle this computationally intensive problem. In Step 1, we propose a heuristic that will automatically reduce the master data tree into a smaller tree composed of viable solution options that satisfy the given requirement constraints. In Step 2, we propose a cognitive conversational interface with an architect’s participation, to progressively shrink the decision tree from Step 1, to the most effective solution. Talk about solution layers – target cloud, n/w, storage, compute, CMP, security, performance, DR, etc Each layer is represented as a complex decision tree Interdependency on layers… therefore, decision tree of decision trees (master decisio tree)

Introduction

Related Works Among classification models, decision trees are widely used as they have reasonably higher accuracy and are computationally inexpensive. Decision-tree induction comprises of mainly three stages: creation of complete tree, pruning the tree and post-processing the pruned tree to achieve the required results. Pruning is performed on tree-based models to judicious remove unnecessary and redundant paths of the model, and thus develop a simpler model with higher predictive accuracy. The objective of pruning is to minimize error, loss minimization, probability estimation, and predictive accuracy. Existing pruning methods include :   rule simplification technique using fuzzy rule, minimum description length principle, cost-complexity, penalty pruning, error-based pruning. We use a heuristic based pruning methodology that is suited for expert systems and is not computationally intense.

Definitions Node: A node in the decision tree is a decision point that maps to one or more requirements. Solution Layer and Master data tree: A solution layer is a design layer of the hybrid cloud solution being built. Solution Layer examples include security, performance, availability, networking, compute, and storage. The master data tree is a decision tree combining the decision trees created for each solution layer. Solution: A solution is an implementable choice in a solution layer made through a sequence of decision points. Thus, n solution layers will have n solutions. Requirement priority: A requirement is a superset of functional and non-functional requirements. Requirement priority is a client provided value indicating the importance of a requirement to the client's solution needs.

Definitions (contd…) Node weight: Each node corresponds to a set of requirements that can be satisfied. Node weight is a vector indicating the extent to which the node fulfills the list of requirements. Path: A path is route from the root node to a leaf node of the master decision tree, which is a set of decision points including solutions from different solution layers. The solutions inherent in a path combine to create an end-to-end hybrid cloud solution. Combined Solution Estimate (CSE): CSE is the value computed for a given individual path derived based on Equation given below. It signifies the extent to which a given path meets the requirements of a client.

Proposed Approach

Step 1: Heuristic to Auto-Prune Q – Node to path mapping matrix S – Node weightage matrix T – Parent Child Association Matrix U – Client Priorities Vector

Proposed Approach - Illustration Here, The number of customer requirements are n = 3 , The number of nodes in the decision tree are m = 5 , And the number of exhaustive paths in the decision tree are p = 3 . Exhaustive PATHS for the tree are:

Proposed Approach – Illustration (Contd…) Here, we illustrate by calculating the weightage of PATH2:  The node to paths mapping for the Matrix Qp,m  will be as follows

Proposed Approach – Illustration (Contd…) Similarly, The values for Node weightage Matrix Sm,n  are given as shown Additionally, Client Priorities Vector U is also

Proposed Approach – Illustration (Contd…) Now we have for SA,n (1*(0*0.8+0.2*0+0.3*0.3)*1) The parent child association are provided in the following Table: Matrix S Vector U Matrix Q Matrix TA,C

Proposed Approach – Illustration (Contd…) Combined Solution Estimate of PATH2 according to the Equation is: = (1*(0*0.8+0.2*0+0.3*0.3)*1)+ ( 0*(0*0.8+1*0+0.3*0.3)*1)+ (1*(0.5*0.8+0*0+0*0.3)*1)+ ( 1* 0.5*0.8+0.4*0+0.1*0.3)*1)+ (0*(0.2*0.8+0*0+0.6*0.3)*1) = 0.9 + 0 + 0.4 + 0.3 + 0  Therefore, CSE of PATH2  is 1.6

Step 2: Cognitive Conversational Engine This narrowed solution space now lends itself to a conversational exchange, that we propose implementing as an interactive robotic architect, leveraging cognitive and natural language processing (NLP) abilities embedded in tools such as IBM Watson. Using this conversational interface, an architect can iteratively explore alternatives and decisions leading to solutions that meet customer requirements. As part of the human interaction, we propose using cognitive services such as NLP to help better understand and analyze user responses.  We built a tool named the Robotic Cloud Advisor (RCA), and hosted it on IBM Bluemix Cloud

Step 2: Cognitive Conversational Engine Content: The pruned decision tree is derived from the heuristic in Step 1. Data Model: The knowledge from the content above is represented in the form of a decision tree stored using the dialog capability available the IBM's Watson Conversation service. Consumption Model: We have built a custom user interface and integrated it with Watson's conversation engine to provide a solution portal to the cloud architect. Cognitive Model: The implementation uses Watson's Natural Language Understanding capabilities to process requirements. Our implementation uses architect feedback as training data to improve the design and navigation of the decision tree.

Working of RCA Bluemix RCA Conversation Cloudant User Insights, instincts, business context, organizational considerations, practical contratits, delivery constraints RCA Conversation Cloudant User

Conclusion We have proposed a methodology that involves a heuristic and a framework that aid solutions architects to devise complex hybrid solution. Our proposed heuristic, traverses through a large graph to find viable solutions that best address the client requirements. The framework utilizes these viable solutions to translate them into conversation like service, that a solution architect can use to navigate across to derive the effective solution for a given set of requirements.