Understanding collaboration in volunteer computing systems

Volunteer computing is a paradigm in which devices participating in a distributed environment share part of their resources to help others perform their activities. The effectiveness of this computing paradigm depends on the collaboration attitude adopted by the participating devices. Unfortunately for software designers it is not clear how to contribute with local resources to the shared environment without compromising resources that could then be required by the contributors. Therefore, many designers adopt a conservative position when defining the collaboration strategy to be embedded in volunteer computing applications. This position produces an underutilization of the devices’ local resources and reduces the effectiveness of these solutions. This article presents a study that helps designers understand the impact of adopting a particular collaboration attitude to contribute with local resources to the distributed shared environment. The study considers five collaboration strategies, which are analyzed in computing environments with both, abundance and scarcity of resources. The obtained results indicate that collaboration strategies based on effort-based incentives work better than those using contribution-based incentives. These results also show that the use of effort-based incentives does not jeopardize the availability of local resources for the local needs.Peer ReviewedPostprint (published version

Credibility-Based Binary Feedback Model for Grid Resource Planning

In commercial grids, Grid Service Providers (GSPs) can improve their profitability by maintaining the lowest possible amount of resources to meet client demand. Their goal is to maximize profits by optimizing resource planning. In order to achieve this goal, they require an estimate of the demand for their service, but collecting demand data is costly and difficult. In this paper we develop an approach to building a proxy for demand, which we call a value profile. To construct a value profile, we use binary feedback from a collection of heterogeneous clients. We show that this can be used as a proxy for a demand function that represents a client’s willingness-to-pay for grid resources. As with all binary feedback systems, clients may require incentives to provide feedback and deterrents to selfish behavior, such as misrepresenting their true preferences to obtain superior services at lower costs. We use credibility mechanisms to detect untruthful feedback and penalize insincere or biased clients. Finally, we use game theory to study how cooperation can emerge in this community of clients and GSPs

Game Theoretic Approaches to Massive Data Processing in Wireless Networks

Wireless communication networks are becoming highly virtualized with two-layer hierarchies, in which controllers at the upper layer with tasks to achieve can ask a large number of agents at the lower layer to help realize computation, storage, and transmission functions. Through offloading data processing to the agents, the controllers can accomplish otherwise prohibitive big data processing. Incentive mechanisms are needed for the agents to perform the controllers' tasks in order to satisfy the corresponding objectives of controllers and agents. In this article, a hierarchical game framework with fast convergence and scalability is proposed to meet the demand for real-time processing for such situations. Possible future research directions in this emerging area are also discussed

Socially Trusted Collaborative Edge Computing in Ultra Dense Networks

Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author

On the Design and Analysis of Incentive Mechanisms in Network Science

With the rapid development of communication, computing and signal processing technologies, the last decade has witnessed a proliferation of emerging networks and systems, examples of which can be found in a wide range of domains from online social networks like Facebook or Twitter to crowdsourcing sites like Amazon Mechanical Turk or Topcoder; to online question and answering (Q&A) sites like Quora or Stack Overflow; all the way to new paradigms of traditional systems like cooperative communication networks and smart grid. Different from tradition networks and systems where uses are mandated by fixed and predetermined rules, users in these emerging networks have the ability to make intelligent decisions and their interactions are self-enforcing. Therefore, to achieve better system-wide performance, it is important to design effective incentive mechanisms to stimulate desired user behaviors. This dissertation contributes to the study of incentive mechanisms by developing game-theoretic frameworks to formally analyze strategic user behaviors in a network and systematically design incentive mechanisms to achieve a wide range of system objectives. In this dissertation, we first consider cooperative communication networks and propose a reputation based incentive mechanism to enforce cooperation among self-interested users. We analyze the proposed mechanism using indirect reciprocity game and theoretically demonstrate the effectiveness of reputation in cooperation stimulation. Second, we propose a contract-based mechanism to incentivize a large group of self-interested electric vehicles that have various preferences to act coordinately to provide ancillary services to the power grid. We derive the optimal contract that maximizes the system designer's profits and propose an online learning algorithm to effectively learn the optimal contract. Third, we study the quality control problem for microtask crowdsourcing from the perspective of incentives. After analyzing two widely adopted incentive mechanisms and showing their limitations, we propose a cost-effective incentive mechanism that can be employed to obtain high quality solutions from self-interested workers and ensure the budget constraint of requesters at the same time. Finally, we consider social computing systems where the value is created by voluntary user contributions and understanding how user participate is of key importance. We develop a game-theoretic framework to formally analyze the sequential decision makings of strategic users under the presence of complex externality. It is shown that our analysis is consistent with observations made from real-word user behavior data and can be applied to guide the design of incentive mechanisms in practice

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference