José Manuel Velasco

The Autonomic Cloud

The cloud case study within ASCENS explores the vision of an autonomic cloud, which is a cloud providing a platform-as-a-service computing infrastructure which, contrary to the usual practice, does not consist of a well-maintained set of reliable high-performance computers, but instead is formed by a loose collection of voluntarily provided heterogeneous nodes which are connected in a peer-to-peer manner. Such an infrastructure must deal with network resilience, data redundancy, and failover mechanisms for executing applications. As such, the autonomic cloud thus requires a certain degree of self-awareness, monitoring, and self-adaptation to reach its goals, which has been achieved with the integration of ASCENS methods and techniques.

Dynamic management of nursery space organization in generational collection

The use of automatic memory management in object-oriented languages like Java is becoming eagerly accepted due to its software engineering benefits, its reduction in programming time and safety aspects. Nevertheless, the complexity of garbage collection results in an important overhead for the virtual machine job. Until now, the strategies in garbage collection have focused in defining and fixing regions in the heap based in different approached and algorithms. Each of these strategies can beat the others depending on the data behavior of a specific application, but they fail to take advantage of the available resources for other cases. There is not a static solution to this problem. In this paper, we present and evaluate two dynamic strategies based in data lifetime that reallocate at run time the reserved space in the nursery of generational Appel collectors. The dynamic tuning of the reserved space produces a drastic reduction in the number of collections and the total collection time and has a clear effect in the final execution time.

Adaptive tuning of reserved space in an appel collector

The use of automatic memory management in object-oriented languages like Java is becoming widely accepted because of its software engineering benefits, its reduction of programming time and its safety aspects. Nevertheless, the complexity of garbage collection results in an important time cost for the virtual machine’s job. Until now, garbage collection strategies have focused on analyzing and adjusting regions in the heap based on different approaches and algorithms. Each strategy has its own distinct advantages over the others depending on the data behavior of a specific application, but none succeeds in taking advantage of all available resources for all application behaviors. In this paper, we present and evaluate two adaptive strategies based on data lifetime that reallocate at run time the reserved space in the nursery of generational Appel collectors. The adaptive tuning of reserved space produces a drastic reduction in the number of collections and the total collection time, and has a clear effect on the final execution time.

Memory power optimization of Java-based embedded systems exploiting garbage collection information

Nowadays, Java is used in all types of embedded devices. For these memory-constrained systems, the automatic dynamic memory manager (Garbage Collector or GC) has been always a key factor in terms of the Java Virtual Machine (JVM) performance. Moreover, in current embedded platforms, power consumption is becoming as important as performance. Thus, in this paper we present an exploration, from an energy viewpoint, of the different possibilities of memory hierarchies for high-performance embedded systems when used by state-of-the-art GCs. This is a starting point for a better understanding of the interactions between the Java applications, the memory hierarchy and the GC. Hence, we subsequently present two techniques to reduce energy consumption on Java-based embedded systems, based on exploiting GC information. The first technique uses GC execution behavior to reduce leakage energy consumption taking advantage of the low-power mode of actual multi-banked SDRAM memories and it is intended for generational collectors. This technique can achieve a reduction up to 50% of SDRAM memory leakage. The second technique involves the inclusion of a software-controlled (scratch-pad) memory that stores GC instructions under the JVM control to reduce the active energy consumption and also improve the performance of the target embedded system and it is aimed at all kind of garbage collectors. For this last technique we have experimented with two different approaches for selecting the GC code to be stored in the scratchpad memory: one static and one dynamic. Our experimental results show that the proposed dynamic scratchpad management approach for GCs enables up to 63% energy consumption reduction and 25% performance improvement during the collector phase, which means, in terms of JVM execution, a global reduction of 29% and 17% for energy and cycles, respectively. Overall, this work outlines that the key for an efficient low-power implementation of Java Virtual Machines for high-performance embedded systems is the synergy between the GC choice, the memory architecture tuning, and the inclusion of power management schemes controlled by the JVM, exploiting knowledge of the GC behavior.

Exploration of memory hierarchy configurations for efficient garbage collection on high-performance embedded systems

Modern embedded devices (e.g., PDAs, mobile phones) are now incorporating Java as a very popular implementation language in their designs. These new embedded systems include multiple applications that are dynamically launched by the user, which can produce very energy-hungry systems if the interactions between the applications and the garbage collectors (GCs) are not properly understood. In this paper we present a complete exploration, from an energy viewpoint, of the different possibilities of memory hierarchies for high-performance embedded systems when used by state-of-the-art GCs. Moreover, we explore the potential peformance improvement and energy reductions of using a scratchpad memory directed by the virtual machine to store critical code and data structures of the GCs; thus, enabling up to 40% performance improvements and 41% leakage reduction with respect to classical cache-based memory architectures. Our experimental results show that the key for an efficient low-power implementation of Java Virtual Machines (JVM) for high-performance embedded systems is the synergy between the GC choice, the memory architecture tuning, and the inclusion of power management schemes controlled by the JVM, exploiting knowledge of the used GC.

Enhancing Grammatical Evolution Through Data Augmentation: Application to Blood Glucose Forecasting

Currently, Diabetes Mellitus Type 1 patients are waiting hopefully for the arrival of the Artificial Pancreas (AP) in a near future. AP systems will control the blood glucose of people that suffer the disease, improving their lives and reducing the risks they face everyday. At the core of the AP, an algorithm will forecast future glucose levels and estimate insulin bolus sizes. Grammatical Evolution (GE) has been proved as a suitable algorithm for predicting glucose levels. Nevertheless, one the main obstacles that researches have found for training the GE models is the lack of significant amounts of data. As in many other fields in medicine, the collection of data from real patients is very complex. In this paper, we propose a data augmentation algorithm that generates synthetic glucose time series from real data. The synthetic time series can be used to train a unique GE model or to produce several GE models that work together in a combining system. Our experimental results show that, in a scarce data context, Grammatical Evolution models can get more accurate and robust predictions using data augmentation.

Energy characterization of garbage collectors for dynamic applications on embedded systems

Modern embedded devices (e.g. PDAs, mobile phones) are now incorporating Java as a very popular implementation language in their designs. These new embedded systems include multiple complex applications (e.g. 3D rendering applications) that are dynamically launched by the user, which can produce very energy-hungry systems if they are not properly designed. Therefore, it is crucial for new embedded devices a better understanding of the interactions between the applications and the garbage collectors to reduce their energy consumption and to extend their battery life. In this paper we present a complete study, from an energy viewpoint, of the different state-of-the-art garbage collectors mechanisms (e.g. mark-and-sweep, generational garbage collectors) for embedded systems. Our results show that traditional solutions of garbage collectors for Java-based systems do not seem to produce the lowest energy consumption solutions.

Garbage collector refinement for new dynamic multimedia applications on embedded systems

Consumer embedded devices must execute concurrently multiple services (e.g. multimedia applications) that are dynamically triggered by the user. For these new embedded multimedia applications, the dynamic memory subsystem is currently one of the main sources of power consumption and its inattentive management can severely affect the performance and power consumption and its attentive management can severely affect the performance and power consumption of the whole system. Therefore, the use of suitable automatic mechanisms to reuse the dynamic computer storage (i.e. garbage collector mechanisms) taking into account the underlying embedded devices would allow the designers to more efficiently design these systems. However, methodologies to explore and implement convenient garbage collector mechanisms for embedded devices have not been developed yet. In this paper we propose a system-level method to define and explore the vast design space of possible garbage collector mechanisms, which enables to define custom garbage collector implementations for the final embedded devices.

Forecasting glucose levels in patients with diabetes mellitus using semantic grammatical evolution and symbolic aggregate approximation

Type 1 Diabetes Mellitus can only be treated injecting insulin and glucagon into the blood stream. This research is motivated by the challenge to accurately predict future blood glucose levels of a diabetic patient so that an automatic system could decide when is necessary the injection of a bolus of insulin to keep blood sugar in the healthy range. In this paper, we have studied different evolutionary strategies based on geometric semantic genetic programming and grammatical evolution. The main contribution of this paper is the use of the symbolic aggregate approximation representation of the glucose time series that allow us to define easily semantic operators. We have developed a new strategy that combines grammatical evolution with the geometric semantic approach and that, thanks to the use of the symbolic representation, improves the previous models of glucose time series. We also present a variation of this technique that employs a univariate marginal distribution algorithm to tune the parameters of the symbolic representation. The experimental results are compared against classical grammatical evolution and geometric semantic hill climbing genetic programming. The baseline is provided by the conventional ARIMA model. Our experimental results show that the symbolic representation improves the performance of the geometric semantic strategy and reduces the number of mistakes that, if in an automatic system, would put patients health at risk.

Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data

Diabetes Mellitus Type 1 patients are waiting for the arrival of the Artificial Pancreas. Artificial Pancreas systems will control the blood glucose of patients, improving their quality of life and reducing the risks they face daily. At the core of the Artificial Pancreas, an algorithm will forecast future glucose levels and estimate insulin bolus sizes. Grammatical Evolution has been proved as a suitable algorithm for predicting glucose levels. Nevertheless, one of the main obstacles that researches have found for training the Grammatical Evolution models is the lack of significant amounts of data. As in many other fields in medicine, the collection of data from real patients is very complex along with the fact that the patients response can vary in a high degree due to a lot of personal factors which can be seen as different scenarios. In this paper, we propose both a classification system for scenario selection and a data augmentation algorithm that generates synthetic glucose time series from real data. Our experimental results show that, in a scarce data context, Grammatical Evolution models can get more accurate and robust predictions using scenario selection and data augmentation.