SLA violations do happen in real world. An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions. Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations. While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking. In this article, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud. We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba). Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers.

Abstract Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.

The reliability of delivering packets through multi-hop intermediate nodes is a significant issue in the mobile ad hoc networks (MANETs). The distributed mobile nodes establish connections to form the MANET, which may include selfish and misbehaving nodes. Recommendation based trust management has been proposed in the literature as a mechanism to filter out the misbehaving nodes while searching for a packet delivery route. However, building a trust model that adopts recommendations by other nodes in the network is a challenging problem due to the risk of dishonest recommendations like bad-mouthing, ballot-stuffing, and collusion. This paper investigates the problems related to attacks posed by misbehaving nodes while propagating recommendations in the existing trust models. We propose a recommendation based trust model with a defence scheme, which utilises clustering technique to dynamically filter out attacks related to dishonest recommendations between certain time based on number of interactions, compatibility of information and closeness between the nodes. The model is empirically tested under several mobile and disconnected topologies in which nodes experience changes in their neighbourhood leading to frequent route changes. The empirical analysis demonstrates robustness and accuracy of the trust model in a dynamic MANET environment.

A key challenge in online communities is that of keeping a community active and alive. All online communities work hard to keep their members through various initiatives, such as personalisation and recommendation technologies. In online communities aimed at supporting behavioural change, that is, in domains such as diet, lifestyle, or the environment, the main reason for participation is not to connect with real-world friends for sharing and communicating, but to meet and gain support from like-minded people in an online environment. Introducing personalisation and recommendation features in these networks is challenging, as traditional approaches leverage the densely populated friendship relations found in typical social networks, and these are not present in these new community types. We address this challenge by looking beyond the articulated friendships of a community for evidence of relationships. In particular, we look at the interactions of members of an online community with other members and resources. In this article, we present a social behaviour model and apply it to two types of recommendation systems, a people recommender and a content recommender system. We evaluate our systems using the interaction logs of an online diet and lifestyle community in which 5,000 Australians participated in a 12-week programme. Our results show that our social behaviour-based recommendation algorithms outperform baselines, friendship-based, and link-prediction algorithms.

Gamification, the idea of inserting game dynamics into portals or social networks, has recently evolved as an approach to encourage active participation in online communities. For an online community to start and proceed on to a sustainable operation, it is important that members are encouraged to contribute positively and frequently. We decided to introduce gamification in an online community that we designed and developed with the Australian Government's Department of Human Services to support welfare recipients transitioning from one payment to another. We first defined a formal model of gamification and a gamification design process. In instantiating our model to the online community, we realised that our context applied a number of constraints on the gamification elements that could be introduced. In this paper, we outline the design and implementation of a gamification model for online communities and its instantiation into our context, with its specific requirements. While we cannot comment on the success of gamification to drive user engagement in our context (for lack of the possibility of a controlled experiment), we found our implementation of badges-based gamification a helpful way to provide a useful abstraction on the life of the community, providing feedback enabling us to monitor and analyze the community. We thus show how feedback provided by such gamification data has a potential to be useful to community providers to better understand the community needs and addressing them appropriately to maintain a level of engagement in the community.

Recommendations to connect like-minded people can result in increased engagement amongst members of online communities, thus playing an important role in their sustainability. We have developed a suite of algorithms for friend recommendations using a social trust model called STrust. In STrust, the

Gamification has recently evolved as an approach to engage and encourage active participation of members in online communities. For an online community to start and proceed on to a sustainable operation, it is important that members are encouraged to contribute positively and frequently. This pa

Engagement serves as an important metrics in judging the success of any online social network. It is thus important to identify how specific mechanisms can help build engagement. In this work, we investigate how rewarding members for their honesty can offer better engagement and raise the level of cooperativeness in the society. We define a write-rate game to simulate activities in an online forum like setting and conduct experiments with it. Our results show that providing right reward for honest actions promotes engagement and cooperation in the society.

This paper presents an application of reputation based trust assessment model in mobile ad hoc network setting. A game between the Sender (originator) and Intermediary (forwarder) nodes in the network is modelled and plugged-in to a trust assessment framework that classifies feedback according to past acquaintances between the nodes. Simulations for the experiments have been carried out in an Iterated Prisoner's dilemma like setting, where each player represents a network node. Through our experiments we show that having trust model decreases the Packet Drop Ratio (PDR) in an ad hoc network setting. Further, the results show that Gain through the utility and savings of energy is better with application of the trust model.