Antonio M. Campos

A QoS Control Mechanism to Provide Service Differentiation and Overload Protection to Internet Scalable Servers

Nowadays, enterprises providing services through Internet often require online services supplied by other enterprises. This entails the cooperation of enterprise servers using Web services technology. The service exchange between enterprises must be carried out with a determined level of quality, which is usually established in a service level agreement (SLA). However, the fulfilment of SLAs is not an easy task and requires equipping the servers with special control mechanisms which control the quality of the services supplied. The first contribution of this research work is the analysis and definition of the main requirements that these control mechanisms must fulfil. The second contribution is the design of a control mechanism which fulfils these requirements and overcomes numerous deficiencies posed by previous mechanisms. The designed mechanism provides differentiation between distinct categories of service consumers as well as protection against server overloads. Furthermore, it scales in a cluster and does not require any modification to the system software of the host server, or to its application logic.

Pessimism in the stochastic analysis of real-time systems: concept and applications

The exact stochastic analysis of most real-time systems is becoming unaffordable in current practice. On one side, the exact calculation of the response time distribution of the tasks is not possible except for simple periodic and independent task sets. On the other side, in practice, tasks introduce complexities like release jitter, blocking in shared resources, stochastic dependencies, etc, which can not be handled by the periodic and independent task set model. This paper introduces the concept of pessimism in the stochastic analysis of real-time systems in the following sense: the exact probability of missing any deadline is always lower than that derived from the pessimistic analysis. Therefore, if real-time constraints are expressed as probabilities of missing deadlines, the pessimistic stochastic analysis provides safe results. Some applications of the pessimism concept are presented. Firstly, the practical problems that arise in the stochastic analysis of periodic and independent task sets are addressed. Secondly, we extend to the stochastic case some well known techniques of the deterministic analysis, such as the blocking in shared resources, and the task priority assignment.

Autonomic scheduling of deadline-constrained bag of tasks in hybrid clouds

The use of Hybrid Cloud technologies in large scale applications allows organizations to complement on-premises infrastructure with hired infrastructure from Public Cloud providers. The efficient use of the hired resources to provide the expected quality of service while dealing with the heterogeneity and uncertainty of Public Clouds is the main difficulty. A scheduler able to deal with deadline-constrained bag of tasks in Hybrid Clouds is presented in this work. The main contribution of this scheduler is that task runtime estimations are not necessary as inputs. The scheduler includes a runtime estimator based on sampled data to generate the estimation autonomously. A discrete event simulator was developed in order to validate the proposed scheduler in different scenarios. Results show that an estimator based on the Chebyshevs inequality obtains very good results in terms of deadlines met and cost.

A real-time expert system architecture based on a novel dynamic task scheduling technique

Computational characteristics of real-time expert systems have limited its integration in real-time control and monitoring environments. The computation time required to complete inferences carried out by expert systems present high variability, which usually leads to severe under-utilization of resources when the schedule of inferences is based on their worst computation times. Moreover, the event-based aperiodic activation of inferences increases the risk of transient overloads, as during critical conditions of the controlled or monitored environment the arrival rate of events increases. The dynamic scheduling technique presented in this article obtains statistical bounds of the time required to complete inferences on-line, and uses these bounds to schedule inferences achieving highly effective utilization of resources. In addition, this technique handles transient overloads using a robust approach. During overloads this technique completes nearly as many inferences as other dynamic scheduling techniques, but shows significantly better effective utilization of resources. The specific real-time architecture presented in this work, based on component object model (COM) technology, completes our approach to the problem of building efficient real-time rule-based systems suitable for controlling or monitoring complex industrial processes.

Real-time rule-based control of the thermal crown of work rolls installed in hot strip mills

The extreme thermal conditions suffered by the work rolls of hot strip mills provoke the appearance of nonuniform thermal expansion or thermal crown. The rolling of strips under bad thermal crown conditions generally produces products of unacceptable quality. An adequate cooling system can reduce thermal crown and improve production quality but, due to the complexity of the rolling process itself, and to the impossibility of measuring the thermal crown of the work rolls on-line, the coolant fluxes are rarely modified by the mill operators, thereby reducing cooling effectiveness. This paper presents a novel real-time control system for the thermal crown. It uses a thermal model to estimate the thermal crown of the work rolls and a set of rules that, using the estimated thermal crown and information about the rolling program and the fluxes of coolant, generates adequate set-points for a cooling system.

Online scheduling of deadline-constrained bag-of-task workloads on hybrid clouds

The advent of hybrid cloud technologies and public Infrastructures as a Service (IaaS) allows service developers to offer services to their customers with little upfront investment and to adapt services to different workload sizes. The problem of minimizing the costs of the hired public infrastructure while providing the quality of service needed by the final customer arises when using hybrid clouds. Several scheduling strategies have been proposed to solve this problem for services dealing with deadline‐constrained bag‐of‐tasks workloads. Most of these solutions do not consider the variable performance of the clouds, the provisioning delay of virtual machine instances that affects the elasticity, and the impracticality of having good processing time estimations in real systems. We propose a scheduler algorithm that overcomes previous limitations and can minimize the cost of the infrastructure while maximizing the number of deadlines met by the service. Our solution can work autonomously by using sampled observations of the processing times and considers the heterogeneity and the provisioning time of the virtual machine instances. An evaluation was conducted by simulating different scenarios and workload types. Simulation results show that our solution obtains better or similar results than previous techniques in most scenarios in terms of deadlines met.

A Dynamic Scheduling Algorithm for Real-Time Expert Systems

Computational characteristics of real-time expert systems have been the subject of research for more than a decade. The computation time required to complete inferences carried out by expert systems present high variability, which usually leads to severe under-utilization of resources when the design of the schedule of inferences is based on their worst computation times. Moreover, the event-based aperiodic activation of inferences increases the risk of transient overloads, as during critical conditions of the controlled or monitored environment the arrival rate of events increases. The dynamic scheduling algorithm presented in this article obtains statistical bounds of the time required to complete inferences on-line, and uses these bounds to schedule inferences achieving highly effective utilization of resources. In addition, this algorithm handles transient overloads using a robust approach. During overloads our algorithm completes nearly as many inferences as other dynamic scheduling algorithms, but shows significantly better effective utilization of resources.

Automatic auditing of the quality of environmental simulation tests in the aerospace industry

Abstract In the aerospace industry, the verification of the behaviour of the components of aircrafts under extreme environmental conditions is essential. When a component passes an environmental simulation test, the quality manager of the certification laboratory must guarantee that the component has been tested with the plan required by the component manufacturer. In other words, the quality of all tests must be audited. This work presents a layered architecture approach to build generic auditing systems. An easily customizable, distributed computer system is used to automatically audit all the test chambers and all tests carried out in the laboratory. To verify the effectiveness of this approach, a customized version of the generic auditing system was installed in the laboratory of Drager Hispania (Aerospace Division).

The application of fieldbus technology to the automatic control of quality and costs in environmental simulation test laboratories

When a modern factory plans to automate an industrial process, it must solve a common problem: to centralize all the signals from/to the installed sensors/actuators in only one control computer if they are distributed by the factory plant. This is especially critical for environmental simulation test laboratories. In these laboratories, pieces and critical units of a device suffer environmental simulated conditions into a set of test chambers distributed by the laboratory plant. The automatic real-time tracking of all the conditions that those units suffer is a pressing need in order to supply a laboratorys customers with detailed information about the tests and to guarantee their quality. The dispersion of the data sources (test chambers) makes the traditional data acquisition techniques useless. The solution for supervising and controlling distributed processes comes up with a day by day emerging technology: the fieldbus.