Nazareno Andrade

OurGrid: An Approach to Easily Assemble Grids with Equitable Resource Sharing

Available grid technologies like the Globus Toolkit make possible for one to run a parallel application on resources distributed across several administrative domains. Most grid computing users, however, don’t have access to more than a handful of resources onto which they can use this technologies. This happens mainly because gaining access to resources still depends on personal negotiations between the user and each resource owner of resources. To address this problem, we are developing the OurGrid resources sharing system, a peer-to-peer network of sites that share resources equitably in order to form a grid to which they all have access. The resources are shared accordingly to a network of favors model, in which each peer prioritizes those who have credit in their past history of bilateral interactions. The emergent behavior in the system is that peers that contribute more to the community are prioritized when they request resources. We expect, with OurGrid, to solve the access gaining problem for users of bag-of-tasks applications (those parallel applications whose tasks are independent).

Labs of the World, Unite!!!

AbstracteScience is rapidly changing the way we do research. As a result, many research labs now need non-trivial computational power. Grid and voluntary computing are well-established solutions for this need. However, not all labs can effectively benefit from these technologies. In particular, small and medium research labs (which are the majority of the labs in the world) have a hard time using these technologies as they demand high visibility projects and/or high-qualified computer personnel. This paper describes OurGrid, a system designed to fill this gap. OurGrid is an open, free-to-join, cooperative Grid in which labs donate their idle computational resources in exchange for accessing other labs’ idle resources when needed. It relies on an incentive mechanism that makes it in the best interest of participants to collaborate with the system, employs a novel application scheduling technique that demands very little information, and uses virtual machines to isolate applications and thus provide security. The vision is that OurGrid enables labs to combine their resources in a massive worldwide computing platform. OurGrid is in production since December 2004. Any lab can join it by downloading its software from http://www.ourgrid.org.

Discouraging free riding in a peer-to-peer CPU-sharing grid

Grid computing has excited many with the promise of access to huge amounts of resources distributed across the globe. However, there are no largely adopted solutions for automatically assembling grids, and this limits the scale of todays grids. Some argue that this is due to the overwhelming complexity of the proposed economy-based solutions. Peer-to-peer grids Iwve emerged as a less complex alternative. We are currently deploying OurGrid, one such peer-to-peer grid. OurGrid is a CPU-sharing grid that targets bag-of-tasks applications (i.e. parallel applications whose tasks are independent). In order to ease system deployment, OurGrid is based on a very lightweight autonomous reputation scheme. Free riding is an important issue for any peer-to-peer system. The aim is to show that OurGrids reputation system successfully discourages free riding, making it in each peer s own interest to collaborate with the peer-to-peer community. We show this in two steps. First, we analyze the conditions under which a reputation scheme can discourage free riding in a CPU-sharing grid. Second, we show that OurGrids reputation scheme satisfies these conditions, even in the presence of malicious peers. Unlike other distributed mechanisms for discouraging free riding, OurGrids reputation scheme achieves this without requiring a shared cryptographic infrastructure or specialized storage.

Automatic grid assembly by promoting collaboration in peer-to-peer grids

Currently, most computational grids (systems allowing transparent sharing of computing resources across organizational boundaries) are assembled using human negotiation. This procedure does not scale well, and is too inflexible to allow for large open grids. Peer-to-peer (P2P) grids present an alternative way to build grids with many sites. However, to actually assemble a large grid, peers must have an incentive to provide resources to the system. In this paper we present an incentive mechanism called the Network of Favors, which makes it in the interest of each participating peer to contribute its spare resources. We show through simulations with up to 10,000 peers and experiments with software implementing the mechanism in a deployed system that the Network of Favors promotes collaboration in a simple, robust and scalable fashion. We also discuss experiences of using OurGrid, a grid based on this mechanism.

A Reciprocation-Based Economy for Multiple Services in Peer-to-Peer Grids

In this paper we study reciprocation-based mechanisms to encourage donation in peer-to-peer grids in which multiple services, such as processing power and data transfers, are shared explicitly. We have modeled such a system and established how peers should assess whether it is profitable to exchange services with another peer, an issue that is not present in the single service case. Unfortunately, this assessment relies on information provided by untrustworthy peers. As an alternative, we have extended, to the case of multiple services, a reciprocation-based mechanism which uses only reliable information gathered locally. We have assessed this mechanism by simulating scenarios in which services are exchanged that are combinations of two different basic services. In the explored scenarios the mechanism performs very well, and can marginalize free riders even when the cost to peers of donating a service is nearly as large as the utility gained by receiving it

Contributor profiles, their dynamics, and their importance in five q&a sites

Q&A sites currently enable large numbers of contributors to collectively build valuable knowledge bases. Naturally, these sites are the product of contributors acting in different ways - creating questions, answers or comments and voting in these - contributing in diverse amounts, and creating content of varying quality. This paper advances present knowledge about Q&A sites using a multifaceted view of contributors that accounts for diversity of behavior, motivation and expertise to characterize their profiles in five sites. This characterization resulted in the definition of ten behavioral profiles that group users according to the quality and quantity of their contributions. Using these profiles, we find that the five sites have remarkably similar distributions of contributor profiles. We also conduct a longitudinal study of contributor profiles in one of the sites, identifying common profile transitions, and finding that although users change profiles with some frequency, the site composition is mostly stable over time.

Scheduling in Bag-of-Task grids: the PAUA case

In this paper we discuss the difficulties involved in the scheduling of applications on computational grids. We highlight two main sources of difficulties: 1) the size of the grid rules out the possibility of using a centralized scheduler; 2) since resources are managed by different parties, the scheduler must consider several different policies. Thus, we argue that scheduling applications on a grid require the orchestration of several schedulers, with possibly conflicting goals. We discuss how we have addressed this issue in the context of PAUA, a grid for Bag-of-Tasks applications (i.e. parallel applications whose tasks are independent) that we are currently deploying throughout Brazil.

Improving Accuracy and Coverage in an Internet-Deployed Reputation Mechanism

P2P systems can benefit from reputation mechanisms to promote cooperation and help peers to identify good service providers. However, in spite of a large number of proposed reputation mechanisms, few have been investigated in real situations. BarterCast is a distributed reputation mechanism used by our Internet-deployed Bittorent-based file-sharing client Tribler. In BarterCast, each peer uses messages received from other peers to build a weighted, directed subjective graph that represents the upload and download activity in the system. A peer calculates the reputations of other peers by applying the maxflow algorithm to its subjective graph. For efficiency reasons, only paths of at most two hops are considered in this calculation. In this paper, we identify and assess three potential modifications to BarterCast for improving its accuracy and coverage (fraction of peers for which a reputation value can be computed). First, a peer executes maxflow from the perspective of the node with the highest betweenness centrality in its subjective graph instead of itself. Second, we assume a gossiping protocol that gives each peer complete information about upload and download activities in the system, and third, we lift the path length restriction in the maxflow algorithm. To assess these modifications, we crawl the Tribler network and collect the upload and download actions of the peers for three months. We apply BarterCast with and without the modifications on the collected data and measure accuracy and coverage.

When can an autonomous reputation scheme discourage free-riding in a peer-to-peer system?

We investigate the circumstances under which it is possible to discourage free-riding in a peer-to-peer system for resource-sharing by prioritizing resource allocation to peers with higher reputation. We use a model to predict conditions necessary for any reputation scheme to succeed in discouraging free-riding by this method. We show with simulations that for representative cases, a very simple autonomous reputation scheme works nearly as well at discouraging free-riding as an ideal reputation scheme. Finally, we investigate the expected dynamic behavior of the system.

Personalizing EigenTrust in the Face of Communities and Centrality Attack

EigenTrust (ET) is a renowned algorithm for reputation management in adversarial P2P systems. It incorporates the opinions of all peers in the network to compute a global trust score for each peer based on its past behavior, and relies on a set of pre-trusted nodes to guarantee that malicious nodes cannot subvert the system. In this paper, we show that ET is vulnerable to community structure and a novel targeted attack based on eigenvector centrality, since ET ranks nodes close to the pre-trusted ones higher than those further away. To address these shortcomings, we propose Personalized EigenTrust (PET) which (i) enables each user to choose her trusted peers from the social network of peers, thereby eliminating the need of pre-trusted nodes and making the system autonomous, (ii) is effective in networks operating under various transaction models based on distributions such as random, community-like and power-law, and (iii) is robust to many types of attacks including the targeted one based on eigenvector centrality. Our simulation results reveal that PET outperforms ET under diverse transaction models and attack strategies.