Environmentally sustainable food consumption : a review and research agenda from a goal-directed perspective

The challenge of convincing people to change their eating habits toward more environmentally sustainable food consumption (ESFC) patterns is becoming increasingly pressing. Food preferences, choices and eating habits are notoriously hard to change as they are a central aspect of people's lifestyles and their socio-cultural environment. Many people already hold positive attitudes toward sustainable food, but the notable gap between favorable attitudes and actual purchase and consumption of more sustainable food products remains to be bridged. The current work aims to (1) present a comprehensive theoretical framework for future research on ESFC, and (2) highlight behavioral solutions for environmental challenges in the food domain from an interdisciplinary perspective. First, starting from the premise that food consumption is deliberately or unintentionally directed at attaining goals, a goal-directed framework for understanding and influencing ESFC is built. To engage in goal-directed behavior, people typically go through a series of sequential steps. The proposed theoretical framework makes explicit the sequential steps or hurdles that need to be taken for consumers to engage in ESFC. Consumers need to positively value the environment, discern a discrepancy between the desired versus the actual state of the environment, opt for action to reduce the experienced discrepancy, intend to engage in behavior that is expected to bring them closer to the desired end state, and act in accordance with their intention. Second, a critical review of the literature on mechanisms that underlie and explain ESFC (or the lack thereof) in high-income countries is presented and integrated into the goal-directed framework. This contribution thus combines a top-down conceptualization with a bottom-up literature review; it identifies and discusses factors that might hold people back from ESFC and interventions that might promote ESFC; and it reveals knowledge gaps as well as insights on how to encourage both short- and long-term ESFC by confronting extant literature with the theoretical framework. Altogether, the analysis yields a set of 33 future research questions in the interdisciplinary food domain that deserve to be addressed with the aim of fostering ESFC in the short and long term

Vers la fiabilité et la performance des services de Cloud Computing

Cloud computing models are attractive because of various benefits such asscalability, cost and exibility to develop new software applications. However,availability, reliability, performance and security challenges are still not fullyaddressed. Dependability is an important issue for the customers of cloudcomputing who want to have guarantees in terms of reliability and availability.Many studies investigated the dependability and performance of cloud services,ranging from job scheduling to data placement and replication, adaptiveand on-demand fault-tolerance to new fault-tolerance models. However, thead-hoc and overly simplified settings used to evaluate most cloud service fault toleranceand performance improvement solutions pose significant challengesto the analysis and comparison of the effectiveness of these solutions.This thesis precisely addresses this problem and presents a benchmarkingapproach for evaluating the dependability and performance of cloud services.Designing of dependability and performance benchmarks for a cloud serviceis a particular challenge because of high complexity and the large amount ofdata processed by such service. Infrastructure as a Service (IaaS), Platform asa Service (PaaS) and Software as a Service (SaaS) are the three well definedmodels of cloud computing. In this thesis, we will focus on the PaaS modelof cloud computing. PaaS model enables operating systems and middlewareservices to be delivered from a managed source over a network. We introduce ageneric benchmarking architecture which is further used to build dependabilityand performance benchmarks for PaaS model of cloud services.Le Cloud Computing est en plein essor, grace a ses divers avantages, telsl'elasticite, le cout, ou encore son importante exibilite dans le developpementd'applications. Il demeure cependant des problemes en suspens, lies auxperformances, a la disponibilite, la fiabilite, ou encore la securite. De nombreusesetudes se focalisent sur la fiabilite et les performance dans les servicesdu Cloud, qui sont les points critiques pour le client. On retrouve parmicelles-ci plusieurs themes emergents, allant de l'ordonnancement de tachesau placement de donnees et leur replication, en passant par la tolerance auxfautes adaptative ou a la demande, et l'elaboration de nouveaux modeles defautes.Les outils actuels evaluant la fiabilite des services du Cloud se basent surdes parametres simplifies. Ils ne permettent pas d'analyser les performancesou de comparer l'efficacite des solutions proposees. Cette these aborde precisement ce probleme en proposant un modele d'environnement complet detest destine a evaluer la fiabilite et les performances des services de CloudComputing. La creation d'outils de tests destines a l'evaluation de la fiabiliteet des performances des services du Cloud pose de nombreux defis, en raisonde la grande quantite et de la complexite des donnees traitees par ce genrede services. Les trois principaux modeles de Cloud Computing sont respectivement:Infrastructure en tant que Service (IaaS), Plate-forme en tant queService (PaaS) et Logiciel en tant que Service (SaaS).Dans le cadre de cettethese, nous nous concentrons sur le modele PaaS. Il permet aux systemesd'exploitation ou aux intergiciels d'etre accessibles via une connexion internet.Nous introduisons une architecture de test generique, qui sera utiliseepar la suite lors de la creation d'outils de tests, destines a l'evaluation de lafiabilite et de la performance

Experience with benchmarking dependability and performance of MapReduce systems

International audienceMapReduce provides a convenient means for distributed data processing and automatic parallel execution on clusters of machines. It has various applications and is used by several services featuring fault tolerance and scalability. Many studies investigated the dependability and performance of MapReduce, ranging from job scheduling to data placement and replication, adaptive and on-demand fault tolerance to new fault tolerance models. However, the ad-hoc and overly simplified setting used to evaluate most MapReduce fault tolerance and performance improvement solutions poses significant challenges to the analysis and comparison of the effectiveness of these solutions. The paper precisely addresses this issue and presents MRBS, a comprehensive benchmark suite for evaluating the dependability and performance of MapReduce systems. MRBS includes five benchmarks covering several application domains and a wide range of execution scenarios such as data-intensive vs. compute-intensive applications, or batch applications vs. online interactive applications. MRBS allows to inject various workloads, dataloads and faultloads, and produces extensive reliability, availability and performance statistics. We implemented the MRBS benchmark suite for Hadoop MapReduce, and we illustrate its use with various case studies running on Amazon EC2 and on a private cloud

Experience with benchmarking dependability and performance of MapReduce systems

International audienceMapReduce provides a convenient means for distributed data processing and automatic parallel execution on clusters of machines. It has various applications and is used by several services featuring fault tolerance and scalability. Many studies investigated the dependability and performance of MapReduce, ranging from job scheduling to data placement and replication, adaptive and on-demand fault tolerance to new fault tolerance models. However, the ad-hoc and overly simplified setting used to evaluate most MapReduce fault tolerance and performance improvement solutions poses significant challenges to the analysis and comparison of the effectiveness of these solutions. The paper precisely addresses this issue and presents MRBS, a comprehensive benchmark suite for evaluating the dependability and performance of MapReduce systems. MRBS includes five benchmarks covering several application domains and a wide range of execution scenarios such as data-intensive vs. compute-intensive applications, or batch applications vs. online interactive applications. MRBS allows to inject various workloads, dataloads and faultloads, and produces extensive reliability, availability and performance statistics. We implemented the MRBS benchmark suite for Hadoop MapReduce, and we illustrate its use with various case studies running on Amazon EC2 and on a private cloud

Does gamified interaction build a strong consumer-brand connection? A study of mobile applications

In recent times gamification has increasingly been used by brands through smartphones to interact effectively with their consumers. The core assumption for creating gamified environment is that it will develop engagement with the consumer and motivate them to use their product or services. However, beyond this assumption, there is dearth empirical evidence regarding how much effective these gamified features are in engaging consumers. Therefore, this research analyses the relationship between flow, brand engagement, self-brand connection and brand usage intent among consumers (N = 360)of two gamified mobile applications. The findings show that the multidimensional construct flow formed by five dimensions i.e. challenge, feedback, autonomy, immersion, and interaction positively associates with cognitive brand engagement and emotional brand engagement. Additionally, both these forms of brand engagement further strengthen consumers’ brand connection and motivate them for further use. These results imply that gamified environment can augment consumer engagement with brand and further increase usage intention. Therefore, gamification can be an effective technique in brand management and brand managers can use it to strengthen relationship with consumers and increasing possibility of using their brands

Benchmarking Dependability of MapReduce Systems

International audienceMapReduce is a popular programming model for distributed data processing. Extensive research has been con- ducted on the reliability of MapReduce, ranging from adaptive and on-demand fault-tolerance to new fault-tolerance models. However, realistic benchmarks are still missing to analyze and compare the effectiveness of these proposals. To date, most MapReduce fault-tolerance solutions have been evaluated using microbenchmarks in an ad-hoc and overly simplified setting, which may not be representative of real-world applications. This paper presents MRBS, a comprehensive benchmark suite for evaluating the dependability of MapReduce systems. MRBS includes five benchmarks covering several application domains and a wide range of execution scenarios such as data-intensive vs. compute-intensive applications, or batch applications vs. online interactive applications. MRBS allows to inject various types of faults at different rates. It also considers different application workloads and dataloads, and produces extensive reliability, availability and performance statistics. We illustrate the use of MRBS with Hadoop clusters running on Amazon EC2, and on a private cloud