Virtual economy | EPFL Graph Search

A virtual economy (or sometimes synthetic economy) is an emergent economy existing in a virtual world, usually exchanging virtual goods in the context of an online game, particularly in massively multiplayer online games (MMOs). People enter these virtual economies for recreation and entertainment rather than necessity, which means that virtual economies lack the aspects of a real economy that are not considered to be "fun" (for instance, avatars in a virtual economy often do not need to buy food in order to survive, and usually do not have any biological needs at all). However, some people do interact with virtual economies for "real" economic benefit. Despite primarily dealing with in-game currencies, this term also encompasses the selling of virtual currency for real money, in what is sometimes called "open centralised marketplaces". Overview Virtual economies are observed in MUDs and massively multiplayer online role-playing games (MMORPGs). The largest virtual econom

Linear Scalability of Distributed Applications

Nicolas Bonvin

The explosion of social applications such as Facebook, LinkedIn and Twitter, of electronic commerce with companies like Amazon.com and Ebay.com, and of Internet search has created the need for new technologies and appropriate systems to manage effectively a considerable amount of data and users. These applications must run continuously every day of the year and must be capable of surviving sudden and abrupt load increases as well as all kinds of software, hardware, human and organizational failures. Increasing (or decreasing) the allocated resources of a distributed application in an elastic and scalable manner, while satisfying requirements on availability and performance in a cost-effective way, is essential for the commercial viability but it poses great challenges in today's infrastructures. Indeed, Cloud Computing can provide resources on demand: it now becomes easy to start dozens of servers in parallel (computational resources) or to store a huge amount of data (storage resources), even for a very limited period, paying only for the resources consumed. However, these complex infrastructures consisting of heterogeneous and low-cost resources are failure-prone. Also, although cloud resources are deemed to be virtually unlimited, only adequate resource management and demand multiplexing can meet customer requirements and avoid performance deteriorations. In this thesis, we deal with adaptive management of cloud resources under specific application requirements. First, in the intra-cloud environment, we address the problem of cloud storage resource management with availability guarantees and find the optimal resource allocation in a decentralized way by means of a virtual economy. Data replicas migrate, replicate or delete themselves according to their economic fitness. Our approach responds effectively to sudden load increases or failures and makes best use of the geographical distance between nodes to improve application-specific data availability. We then propose a decentralized approach for adaptive management of computational resources for applications requiring high availability and performance guarantees under load spikes, sudden failures or cloud resource updates. Our approach involves a virtual economy among service components (similar to the one among data replicas) and an innovative cascading scheme for setting up the performance goals of individual components so as to meet the overall application requirements. Our approach manages to meet application requirements with the minimum resources, by allocating new ones or releasing redundant ones. Finally, as cloud storage vendors offer online services at different rates, which can vary widely due to second-degree price discrimination, we present an inter-cloud storage resource allocation method to aggregate resources from different storage vendors and provide to the user a system which guarantees the best rate to host and serve its data, while satisfying the user requirements on availability, durability, latency, etc. Our system continuously optimizes the placement of data according to its type and usage pattern, and minimizes migration costs from one provider to another, thereby avoiding vendor lock-in.

EPFL2012

Fuzzy Trust Aggregation And Personalized Trust Inference In Virtual Social Networks

Mohsen Lesani

Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of time. As it is important to do business with trustful individuals and companies, there is a need to survive the reputation concept in virtual markets. Auction sites generally employ reputation systems based on feedbacks that provide a global view to a cyber dealer. In contrast to global trust, people usually infer their personal trust about someone whose reputation is completely or partially unknown by asking their trusted friends. Personal reputation is what makes a person trusted for some people and untrusted for others. There should be a facility for users in a virtual market to specify how much they trust a friend and also a mechanism that infers the trust of a user to another user who is not directly a friend of her. There are two main issues that should be addressed in trust inference. First, the trust modeling and aggregation problem needs to be challenged. Second, algorithms should be introduced to find and select the best paths among the existing trust paths from a source to a sink. First, as trust to a person can be stated more naturally using linguistic expressions, this work suggests employing linguistic terms for trust specification. To this end, corresponding fuzzy sets are defined for trust linguistic terms and a fuzzy trust aggregation method is also proposed. Comparing the fuzzy aggregation method to the existing aggregation methods shows superiority of fuzzy approach especially at aggregating contradictory information. Second, this paper proposes an incremental trust inference algorithm. The results show improvement in preciseness of inference for the proposed inference algorithm over the existing and recently proposed algorithm named TidalTrust.

2009

Dynamic Cost-Efficient Replication in Data Clouds

Karl Aberer, Nicolas Bonvin

Hardware failures in current data centers are common partly due to the higher data scales supported. Data replication is the common approach for improving availability. However, mostly static replication approaches have been proposed, i.e. the number of replicas and their locations are fixed. Moreover, the geographical diversity of data locations has not explicitly been considered. In this paper, we propose a cost-efficient replication scheme across data centers that dynamically adapts the number of replicas employed per partition to the query load, while maintaining availability guarantees in case of failures. Our approach employs a virtual economy that is experimentally proved in a simulated environment to achieve load balancing among data servers at the minimum cost.

2009

Linear Scalability of Distributed Applications

Nicolas Bonvin

EPFL2012

Fuzzy Trust Aggregation And Personalized Trust Inference In Virtual Social Networks

Mohsen Lesani

2009