Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling

How to select combination operators for fuzzy expert systems using CRI

A method to select combination operators for fuzzy expert systems using the Compositional Rule of Inference (CRI) is proposed. First, fuzzy inference processes based on CRI are classified into three categories in terms of their inference results: the Expansion Type Inference, the Reduction Type Inference, and Other Type Inferences. Further, implication operators under Sup-T composition are classified as the Expansion Type Operator, the Reduction Type Operator, and the Other Type Operators. Finally, the combination of rules or their consequences is investigated for inference processes based on CRI

Tuning a fuzzy controller using quadratic response surfaces

Response surface methodology, an alternative method to traditional tuning of a fuzzy controller, is described. An example based on a simulated inverted pendulum 'plant' shows that with (only) 15 trial runs, the controller can be calibrated using a quadratic form to approximate the response surface

Optimization Of Fuzzy Logic Controllers With Genetic Algorithm For Two-Part-Type And Re-Entrant Production Systems

Improvement in the performance of production control systems is so important that many of past studies were dedicated to this problem. The applicability of fuzzy logic controllers (FLCs) in production control systems has been shown in the past literature. Furthermore, genetic algorithm (GA) has been used to optimize the FLCs performance. This is addressed as genetic fuzzy logic controller (GFLC). The GFLC methodology is used to develop two production control architectures named “genetic distributed fuzzy” (GDF), and “genetic supervisory fuzzy” (GSF) controllers. These control architectures have been applied to single-part-type production systems. In their new application, the GDF and GSF controllers are developed to control multipart- type and re-entrant production systems. In multi-part-type and re-entrant production systems the priority of production as well as the production rate for each part type is determined by production control systems. A genetic algorithm is developed to tune the membership functions (MFs) of input variables of GDF and GSF controllers. The objective function of the GSF controller is to minimize the overall production cost based on work-in-process (WIP) and backlog cost, while surplus minimization is considered in GDF controller. The GA module is programmed in MATLAB® software. The performance of each GDF or GSF controllers in controlling the production system model is evaluated using Simulink® software. The performance indices are used as chromosomes ranking criteria. The optimized GDF and GSF can be used in real implementations. GDF and GSF controllers are evaluated for two test cases namely “two-part-type production line” and “re-entrant production system”. The results have been compared with two heuristic controllers namely “heuristic distributed fuzzy” (HDF) and “heuristic supervisory fuzzy” (HSF) controllers. The results showed that GDF and GSF controllers can improve the performance of production system. In GSF control architecture, WIP level is 30% decreased rather than HSF controllers. Moreover the overall production cost is reduced in most of the test cases by 30%. GDF controllers show their abilities in reducing the backlog level but generally production cost for GDF controller is greater than GSF controller

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing

A High Performance Fuzzy Logic Architecture for UAV Decision Making

The majority of Unmanned Aerial Vehicles (UAVs) in operation today are not truly autonomous, but are instead reliant on a remote human pilot. A high degree of autonomy can provide many advantages in terms of cost, operational resources and safety. However, one of the challenges involved in achieving autonomy is that of replicating the reasoning and decision making capabilities of a human pilot. One candidate method for providing this decision making capability is fuzzy logic. In this role, the fuzzy system must satisfy real-time constraints, process large quantities of data and relate to large knowledge bases. Consequently, there is a need for a generic, high performance fuzzy computation platform for UAV applications. Based on Lees’ [1] original work, a high performance fuzzy processing architecture, implemented in Field Programmable Gate Arrays (FPGAs), has been developed and is shown to outclass the performance of existing fuzzy processors