Experimental Analysis on Autonomic Strategies for Cloud Elasticity

International audienceIn spite of the indubitable advantages of elasticity in Cloud infrastructures, some technical and conceptual limitations are still to be considered. For instance , resource start up time is generally too long to react to unexpected workload spikes. Also, the billing cycles' granularity of existing pricing models may incur consumers to suffer from partial usage waste. We advocate that the software layer can take part in the elasticity process as the overhead of software reconfigurations can be usually considered negligible if compared to infrastructure one. Thanks to this extra level of elasticity, we are able to define cloud reconfigurations that enact elasticity in both software and infrastructure layers so as to meet demand changes while tackling those limitations. This paper presents an autonomic approach to manage cloud elasticity in a cross-layered manner. First, we enhance cloud elasticity with the software elasticity model. Then, we describe how our au-tonomic cloud elasticity model relies on dynamic selection of elasticity tactics. We present an experimental analysis of a subset of those elasticity tactics under different scenarios in order to provide insights on strategies that could drive the autonomic selection of the proper tactics to be applied

Architectural Tactics for Energy Efficiency: Review of the Literature and Research Roadmap

The energy consequences of software are rapidly growing: at the high-end, server farms consume enormous amounts of energy; at the low-end there is ever-increasing emphasis on battery-powered mobile and Internet-of-Things (IoT) devices with equally increasing complex usage scenarios. Conversely, there has been little attention to how software architectures can be designed for energy efficiency. While other software qualities—--think of performance or availability--—have been extensively studied, there is little research on how to reason about energy-consumption as a first-class citizen. We provide a basis for reasoning about design decisions for energy efficiency by deriving a kit of reusable architectural tactics derived from literature. We use the well-known open-search and snowballing methodologies to attain primary studies, and subsequently used thematic coding of such studies to identify recurrences and commonalities among the design strategies presented. The result of this process is a set of 10 architectural tactics for energy efficiency. These tactics provide a rational basis for architectural design and analysis for energy efficiency

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research

A framework for the characterization and analysis of software systems scalability

The term scalability appears frequently in computing literature, but it is a term that is poorly defined and poorly understood. It is an important attribute of computer systems that is frequently asserted but rarely validated in any meaningful, systematic way. The lack of a consistent, uniform and systematic treatment of scalability makes it difficult to identify and avoid scalability problems, clearly and objectively describe the scalability of software systems, evaluate claims of scalability, and compare claims from different sources. This thesis provides a definition of scalability and describes a systematic framework for the characterization and analysis of software systems scalability. The framework is comprised of a goal-oriented approach for describing, modeling and reasoning about scalability requirements, and an analysis technique that captures the dependency relationships that underlie typical notions of scalability. The framework is validated against a real-world data analysis system and is used to recast a number of examples taken from the computing literature and from industry in order to demonstrate its use across different application domains and system designs