Carlos Juiz

An up-to-date survey in web load balancing

This survey is an up-to-date state-of-the-art in Web load balancing mechanisms that includes all the possible classifications and focuses on the advantages of using load balancing solutions to increase the performance of the Web system. A general description of the Web load balancing solutions is included and organised by differentiating the OSI protocol stack layer the load balancing is based on. The most important request distributing polices that are proposed in the literature are also included. This article summarises all previous surveys on the Web load balancing subject and updates the state-of-the-art with the most recent load balancing proposals.

Performance-related ontologies and semantic web applications for on-line performance assessment intelligent systems

Several techniques and applications have been proposed to aid the decision taking process in the system performance domain. Most of these techniques have depicted the performance model of systems through annotations of performance measurements coming from specific software descriptive syntactical languages. However, the semantic representation of performance information provides the possibility of its ulterior machine-processable logical interpretation and therefore the applicability of inference rules about a particular domain. Moreover, ontologies ease the interchange and reuse of knowledge of particular domains, e.g. system performance. In this work, we propose a performance ontology together with the system performance analysis technique as an example of framework building for intelligent applications based on semantic web. The paper also shows the construction of performance rules through OWL to automatically infer new performance constraints and QoS knowledge about the system on execution.

Adaptive admission control algorithm in a QoS-aware Web system

Internet traffic tends to show significant growth of demand at certain times of the day, or in response to special events. The consequence of these traffic peaks is that Web systems that are responding to user demands are congested due to their inability to serve a large volume of requests. The case for admission control in these situations is even stronger when Quality of Service (QoS) is considered as a primary objective in the Web system. In this work, we address two issues: on one hand, we consider and compare five throughput predictors to be used in a Web system in order to track its performance and, on the other hand, we propose a QoS-aware admission control and load balancing algorithm that prevents the Web system from sudden overload. The admission control algorithm is based on a resource allocation scheme that includes a throughput predictor. In order to obtain a low overhead, the monitoring of traffic arriving at the Web system is performed following an adaptive time slot scheduling based on the burstiness factor that we defined in previous work. Results show the benefits of our adaptive time slot scheduling compared to a fixed time scheduling. A discussion of the results of the five throughput predictors and the admission control algorithm is provided. We also compare the performance of our algorithm with Intelligent Queue-based Request Dispatcher (IQRD). The algorithm is designed to be included in a Web system composed by a set of Web servers distributed locally, which can also form part of a wider geographically distributed load balancing architecture.

Performance assessment on ambient intelligent applications through ontologies

This paper brings together the performance assessment of ambient intelligence architectures systems with ontology engineering. Thus, firstly appropriate description methods for distributed intelligent applications are summarized. Derived from the system characterization, typical software performance engineering techniques are based on the augmented description of the model regarding performance annotations. However, these annotations are only related with the syntactical view of the software architecture. In the next generation of performance assessment tools for ambient intelligent systems, the description of the system would be capable of reasoning and acquiring knowledge about performance. Having an appropriate architectural description including performance aspects, any possible design options for intelligent distributed applications can be evaluated according to their performance impact. Therefore, we propose the use of an ontology with performance-related information not only to possible evaluate the architecture through the common off-line procedure but also the first step to build a broker that assesses the performance of the system during its execution.

To govern IT, or not to govern IT?

Business leaders may bemoan the burdens of governing IT, but the alternative could be much worse.

An approach to ontology-aided performance engineering through NFR framework

In this article we intend to make an attempt to formalize the Software Performance Engineering Body of Knowledge (SPEBoK) by means of the formal semantics of an ontology written in OWL. We do not claim that our SPEBoK is complete nor the information contained correct. Rather we propose the structure of an ontological database to contain it. This structure allows the Performance Engineering issues to be related among themselves and even to other non-functional requirements with which they may interact. Our work uses the NFR Framework.

Comparison of predictive techniques in cluster-based network servers with resource allocation

Predictive algorithms play a crucial role in system management by alerting it to future events that may degrade the global performance of the system. Our paper proposes a predictive algorithm for a Web switch that manages a cluster of Web servers. Several throughput predictive techniques have been studied to balance the workload dynamically according to a predetermined quality of service for different types of users, and finally we have obtained the best that fits in our algorithm. In order to reduce the checking time, estimations are only computed in a variable slot scheduling.

Web Performance and Behavior Ontology

We present a Web system architecture using ontologies to improve the behavior of the system from the performance viewpoint. Since Web system performance indexes depend on state and parameter values on runtime period, the proposed system configuration will change during this period. In order to perform this change, the Web system is monitorized and gathered information stored into a knowledge base. We also model the performance of the different Web system elements intervening in the configuration using the knowledge base expressed by means of ontologies. An example of the use of this ontology in cache tier is also presented. We propose the use of performance reasoners to change the configuration during runtime period based on the information supplied from the knowledge base.

Scalable QoS Content-Aware Load Balancing Algorithm for a Web Switch Based on Classical Policies

This paper presents a load balancing solution for a cluster of Web servers that considers QoS in order to differentiate the service provided to the clients when the Web system is receiving a high volume of traffic. Adaptability is a needed feature to suit the balancing parameters to the fluctuations of the Web traffic. We monitor the incoming workload to the system and tune the parameters more frequently when the bursty arrivals trend increases. Throughput prediction enables to estimate the maximum utilization allowed in the servers for each service, and then to set a maximum number of accepted requests to set a limit in the utilization of the servers and to avoid a possible congestion situation, while guaranteeing the QoS. Classical dispatching policies are introduced and compared to analyse the behaviour of the algorithm. HTTP static and dynamic requests are considered in the workload.

Performance assessment of intelligent distributed systems through software performance ontology engineering (SPOE)

In the computer science community there is a growing interest in the field of Ambient Intelligent Systems. This systems surround their human users with computing and networking technology unobtrusively embedded in their environment. This technology is aimed to provide the users with useful information and to take action to make the environment more convenient for them. As the number of users increases the resources that make Ambient Intelligence possible can be easily saturated making the system unstable and projecting an image of poor QoS to the users. The main goal of this paper is to provide the means for the Ambient Intelligent Systems to monitor themselves and take corrective action automatically if performance starts to drop. Our approach uses a Performance Ontology that structures the knowledge about Software Performance Engineering, and a reasoning engine that acts like an expert system with the Performance Ontology as its foundation. The case study at the end shows the applicability of the developed techniques.