Load Index Metrics for an Optimized Management of Web Services: A Systematic Evaluation

The lack of precision to predict service performance through load indices may lead to wrong decisions regarding the use of web services, compromising service performance and raising platform cost unnecessarily. This paper presents experimental studies to qualify the behaviour of load indices in the web service context. The experiments consider three services that generate controlled and significant server demands, four levels of workload for each service and six distinct execution scenarios. The evaluation considers three relevant perspectives: the capability for representing recent workloads, the capability for predicting near-future performance and finally stability. Eight different load indices were analysed, including the JMX Average Time index (proposed in this paper) specifically designed to address the limitations of the other indices. A systematic approach is applied to evaluate the different load indices, considering a multiple linear regression model based on the stepwise-AIC method. The results show that the load indices studied represent the workload to some extent; however, in contrast to expectations, most of them do not exhibit a coherent correlation with service performance and this can result in stability problems. The JMX Average Time index is an exception, showing a stable behaviour which is tightly-coupled to the service runtime for all executions. Load indices are used to predict the service runtime and therefore their inappropriate use can lead to decisions that will impact negatively on both service performance and execution cost

An intelligent and generic approach for detecting human emotions: A case study with facial expressions

Several studies in the field of human-computer interaction have focused on the importance of emotional factors related to the interaction of humans with computer systems. According to the knowledge of the users' emotions, intelligent software can be developed for interacting and even influencing users. However, such a scenario is still a challenge in the field of human-computer interaction. This article endeavors to enhance intelligence in such types of systems by adopting an ensemble-based model that is able to identify and classify emotions. We developed a system (music player) that can be used as a mechanism to interact and/or persuade someone to "change" his/her current emotional state. In order to do this, we also designed a generic model that accepts any kind of interaction or persuasion mechanism (e.g., preferred YouTube channel videos, games, etc.) to be deployed at runtime based on the needs of each user. We showed that the approach based on a genetic algorithm for the weight assignment of the ensemble achieved an accuracy average of 80%. Moreover, the results showed a 60% increase in the level of user's satisfaction regarding the interaction with users' emotions

Relation of the service runtime versus eight load indices, considering the sixth scenario (not-overloaded platform).

<p>The left-hand scale of the y-axis always represents the service runtimes in seconds (s). The right-hand scale of the y-axis, where necessary, represents the metric used by the load index. The x-axis represents the samples collected every 10 s by Ganglia and JMX while the benchmarks were requesting services for the server.</p

Relation of the service runtime versus eight load indices, considering the first scenario (CPU-bound service).

<p>The left-hand scale of the y-axis always represents the service runtimes in seconds (s). The right-hand scale of the y-axis, where necessary, represents the metric used by the load index. The x-axis represents the samples collected every 10 s by Ganglia and JMX while the benchmarks were requesting services for the server.</p

Significance of the indices to explain the variability in the service runtimes evaluated through the stepwise-AIC method.

<p>Significance of the indices to explain the variability in the service runtimes evaluated through the stepwise-AIC method.</p

Graphs showing the relation between the predicted runtimes based on multiple linear models (used in the six scenarios) and the actual runtimes.

<p>The x-axis represents the samples collected every 10 s by Ganglia and JMX while the benchmarks were requesting services for the server.</p

Relation of the service runtime versus eight load indices, considering the fourth scenario (all services).

<p>The left-hand scale of the y-axis always represents the service runtimes in seconds (s). The right-hand scale of the y-axis, where necessary, represents the metric used by the load index. The x-axis represents the samples collected every 10 s by Ganglia and JMX while the benchmarks were requesting services for the server.</p

Relation of the service runtime versus eight load indices, considering the fifth scenario (overloaded platform).

<p>The left-hand scale of the y-axis always represents the service runtimes in seconds (s). The right-hand scale of the y-axis, where necessary, represents the metric used by the load index. The x-axis represents the samples collected every 10 s by Ganglia and JMX while the benchmarks were requesting services for the server.</p

Graphs showing the relation between actual runtimes and the predicted ones.

<p>The x-axis represents the samples collected every 10 s by Ganglia and JMX while the benchmarks were requesting services for the server.</p

Sliding windows used to evaluate JMX Average Time.

<p>Sliding windows used to evaluate JMX Average Time.</p