Cloud provider capacity augmentation through automated resource bartering

© 2017 Elsevier B.V. Growing interest in Cloud Computing places a heavy workload on cloud providers which is becoming increasingly difficult for them to manage with their primary data centre infrastructures. Resource scarcity can make providers vulnerable to significant reputational damage and it often forces customers to select services from the larger, more established companies, sometimes at a higher price. Funding limitations, however, commonly prevent emerging and even established providers from making a continual investment in hardware speculatively assuming a certain level of growth in demand. As an alternative, they may opt to use the current inter-cloud resource sharing systems which mainly rely on monetary payments and thus put pressure on already stretched cash flows. To address such issues, a new multi-agent based Cloud Resource Bartering System (CRBS) is implemented in this work that fosters the management and bartering of pooled resources without requiring costly financial transactions between IAAS cloud providers. Agents in CRBS collaborate to facilitate bartering among providers which not only strengthens their trading relationships but also enables them to handle surges in demand with their primary setup. Unlike existing systems, CRBS assigns resources by considering resource urgency which comparatively improves customers’ satisfaction and the resource utilization rate by more than 50%. The evaluation results verify that our system assists providers to timely acquire the additional resources and to maintain sustainable service delivery. We conclude that the existence of such a system is economically beneficial for cloud providers and enables them to adapt to fluctuating workloads

Free Riding in the Lab and in the Field

We run a public good experiment in the field and in the lab with (partly) the same subjects. The field experiment is a true natural field experiment as subjects do not know that they are exposed to an experimental variation. We can show that subjects' behavior in the classic lab public good experiment correlates with their behavior in the structurally comparable public good treatment in the field but not with behavior in any of two control treatments we ran in the field. This effect is also economically significant. We conclude that a) the classic lab public good experiment captures important aspects of structurally equivalent real life situations and b) that behavior in lab and field at least in our setting is driven by the same underlying forces

Free Riding in the Lab and in the Field

We run a public good experiment in the field and in the lab with (partly) the same subjects. The field experiment is a true natural field experiment as subjects do not know that they are exposed to an experimental variation. We can show that subjects' behavior in the classic lab public good experiment correlates with their behavior in the structurally comparable public good treatment in the field but not with behavior in any of two control treatments we ran in the field. This effect is also economically significant. We conclude that a) the classic lab public good experiment captures important aspects of structurally equivalent real life situations and b) that behavior in lab and field at least in our setting is driven by the same underlying forces.Field and Lab Experiments; External Validity; Public Goods; Team Production

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

The End of ODA (II): The Birth of Hypercollective Action

The development business has become much more complex in the past decade, with actors proliferating and collaboration fragmenting. This trend is characteristic of the change from collective action to what the authors term hypercollective action. Such a shift brings new energy and resources to international development, but also more difficulty managing global public policy. Severino and Ray use the lessons of the Paris Declaration— the first large-scale effort to coordinate hypercollective action—as a starting point for envisioning a new conceptual framework to manage the complexity of current international collaboration. They offer concrete suggestions to improve the management of global policies, including new ways to share information, align the goals of disparate actors, and create more capable bodies for international collaboration.hypercollective action, collective action, paris declaration, ODA, aid effectiveness

Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review

Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze 11 literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources

Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios