Self-* overload control for distributed web systems

Unexpected increases in demand and most of all flash crowds are considered the bane of every web application as they may cause intolerable delays or even service unavailability. Proper quality of service policies must guarantee rapid reactivity and responsiveness even in such critical situations. Previous solutions fail to meet common performance requirements when the system has to face sudden and unpredictable surges of traffic. Indeed they often rely on a proper setting of key parameters which requires laborious manual tuning, preventing a fast adaptation of the control policies. We contribute an original Self-* Overload Control (SOC) policy. This allows the system to self-configure a dynamic constraint on the rate of admitted sessions in order to respect service level agreements and maximize the resource utilization at the same time. Our policy does not require any prior information on the incoming traffic or manual configuration of key parameters. We ran extensive simulations under a wide range of operating conditions, showing that SOC rapidly adapts to time varying traffic and self-optimizes the resource utilization. It admits as many new sessions as possible in observance of the agreements, even under intense workload variations. We compared our algorithm to previously proposed approaches highlighting a more stable behavior and a better performance.Comment: The full version of this paper, titled "Self-* through self-learning: overload control for distributed web systems", has been published on Computer Networks, Elsevier. The simulator used for the evaluation of the proposed algorithm is available for download at the address: http://www.dsi.uniroma1.it/~novella/qos_web

Integrating Adaptation Mechanisms Using Control Theory Centric Architecture Models: A Case Study

International audienceControl theory provides solid foundations for developing reliable and scalable feedback control for software systems. Although, feedback controllers have been acknowledged to efficiently solve common classes of problems, their adoption by state-of-the-art approaches for designing self-adaptation in legacy software systems remains limited and at best consists in ad hoc integrations, which are usually engineered manually. In this paper, we revisit the Znn.com case study and we present an alternative implementation based on classical feedback controllers. We show how these controllers can be easily integrated into software systems through control theory centric architecture models and domain-specific modeling support. We also provide an assessment of the resulting properties, quality attributes and limitations

Self-Optimization of Internet Services with Dynamic Resource Provisioning

Self-optimization through dynamic resource provisioning is an appealing approach to tackle load variation in Internet services. It allows to assign or release resources to/from Internet services according to the varying load. However, dynamic resource provisioning raises several challenges among which: (i) How to plan a good capacity of an Internet service, i.e.~a necessary and sufficient amount of resource to handle the Internet service workload, (ii) How to manage both gradual load variation and load peaks in Internet services, (iii) How to prevent system oscillations in presence of potentially concurrent dynamic resource provisioning, and (iv) How to provide generic self-optimization that applies to different Internet services such as e-mail services, streaming servers or e-commerce web systems. This paper precisely answers these questions. It presents the design principles and implementation details of a self-optimization autonomic manager. It describes the results of an experimental evaluation of the self-optimization manager with a realistic e-commerce multi-tier web application running in a Linux cluster of computers. The experimental results show the usefulness of self-optimization in terms of end-user's perceived performance and system's operational costs, with a negligible overhead

Self-Regulation in a Web-Based Course: A Case Study

Little is known about how successful students in Web-based courses self-regulate their learning. This descriptive case study used a social cognitive model of self-regulated learning (SRL) to investigate how six graduate students used and adapted traditional SRL strategies to complete tasks and cope with challenges in a Web-based technology course; it also explored motivational and environmental influences on strategy use. Primary data sources were three transcribed interviews with each of the students over the course of the semester, a transcribed interview with the course instructor, and the students’ reflective journals. Archived course documents, including transcripts of threaded discussions and student Web pages, were secondary data sources. Content analysis of the data indicated that these students used many traditional SRL strategies, but they also adapted planning, organization, environmental structuring, help seeking, monitoring, record keeping, and self-reflection strategies in ways that were unique to the Web-based learning environment. The data also suggested that important motivational influences on SRL strategy use—self-efficacy, goal orientation, interest, and attributions—were shaped largely by student successes in managing the technical and social environment of the course. Important environmental influences on SRL strategy use included instructor support, peer support, and course design. Implications for online course instructors and designers, and suggestions for future research are offered

Self-configured Elastic Database with Deep Q-Learning Approach

Elastic databases have grown in popularity over conventional databases in recent years due to their ability to be allocated with sufficient capacity for peak load. Especially with the support of the cloud platform, which provides flexible resources and low cost, elastic databases on the cloud show their excellent potential in scalability, flexibility, and accessibility. However, the interaction between the cloud layers of virtual machines (VMs) and databases further complicates the issue of cloud configuration to adapt to dynamic workloads. In this paper, I explore a framework for a self-configured elastic database that can optimize the cloud configuration and adaptively allocate resources under the constraints of databases\u27 Service Level Agreement (SLA). At the core of the framework is a Deep Q learning approach, which combines the advantages of Reinforcement Learning (RL) and Deep Learning (DL). The framework is built on Amazon Web Service (AWS)\u27s cloud environment and uses MySQL database for its high availability replication mechanism. Experimental results on the TPC-W benchmark demonstrate that with the implementation of Deep Q learning, the elastic database reduces SLA violation by more than 90\%, in the response to the steep slope of workload change

Self-regulation and computer based learning

In recent years, interest in self-regulated learning has risen considerably. While self-regulatory activities are controlled cognitively, they encompass more than the monitoring of cognitive activities. Motivational and emotional processes are also important in learning and they too need to be regulated. At the same time, multimedia computer programs and theInternet have come to play un important role in present day 's learning environments. The question therefore arises as to what extent these new technologies facilitate the acquisition and improvement of self-regulated learning strategies. In the present article, we first explore the field of self-regulated learning and then try to come up with un answer to the question posed.En los últimos años, el interés por el aprendizaje autorregulado se ha desarrollado considerablemente. Aunque las actividades autorreguladas son controladas cognitivamente, abarcan más que el control de las actividades cognitivas. Los procesos motivacionales y emocionales también son importantes en el aprendizaje y también requieren ser controlados. Al mismo tiempo los programas multimedia e Internet han logrado unpapel importante en los entornos de aprendizaje y se presenta la pregunta de si las nuevas tecnologías facilitan la adquisición y el perfeccionamiento de las estrategias autorregulativas. En este articulo exploramos primero el campo de aprendizaje autorregulado y después tratamos de dar una respuesta a la pregunta planteada

Self-sovereign identity: a primer and call for research in information systems

In this research-in-progress paper, we encourage information systems (IS) researchers to consider the self-sovereign identity (SSI) approach to identity management. We highlight several issues with current data practices, then provide an overview of SSI by discussing the technology and actors involved. Finally, we call for more IS research on SSI to ultimately increase its adoption

Self-Organising in Blockchain Infrastructures: Generativity through Shifting Objectives and Forking

Given the ubiquity of digital technologies, and increased use of autonomous algorithms, it is likely that many of today’s social and organizational processes will one day include autonomous elements. The Bitcoin blockchain is likely the first case of an increasingly generative and autonomous way of organizing, and the specific properties of blockchain infrastructures—distribution of control, openness to manipulation, and generativity of the underlying source code—make it an ideal case to study patterns of self-organizing. This paper investigates the phenomenon of self-organizing through a study of forking in the Bitcoin blockchain infrastructure between 2010 and 2016. It adds to the emerging body of research on digital infrastructures, and particularly blockchain infrastructures, by conceptualizing forking as a pattern of self-organizing in blockchain infrastructures that specifically involves the underlying infrastructure, the scale of code changes, individual objectives, and collective adoption, whether specific or general. Thus, this paper demonstrates how forking in blockchain infrastructures mediates between divergent organizing objectives and existing capabilities, on the one hand, and generates self-organizing on the other hand. In this paper, we further contextualize our findings in extant work on digital infrastructures, offer a guide for designers of blockchain infrastructures, and propose the concept of “generative mirroring” as a pattern through which blockchain infrastructures and organizing adaptively coevolve