Recent growth of the Internet in both scale and complexity has imposed a number of difficult challenges on existing measurement techniques and approaches, which are essential for both network management and many ongoing research projects. For any measurement algorithm, achieving both accuracy and scalability is very challenging given hard resource constraints (e.g., bandwidth, delay, physical memory, and CPU speed). My dissertation research tackles this problem by first proposing a novel mechanism called residual sampling, which intentionally introduces a predetermined amount of bias into the measurement process. We show that such biased sampling can be extremely scalable; moreover, we develop residual estimation algorithms that can unbiasedly recover the original information from the sampled data. Utilizing these results, we further develop two versions of the residual sampling mechanism: a continuous version for characterizing the user lifetime distribution in large-scale peer-to-peer networks and a discrete version for monitoring flow statistics (including per-flow counts and the flow size distribution) in high-speed Internet routers. For the former application in P2P networks, this work presents two methods: ResIDual-based Estimator (RIDE), which takes single-point snapshots of the system and assumes systems with stationary arrivals, and Uniform RIDE (U-RIDE), which takes multiple snapshots and adapts to systems with arbitrary (including non-stationary) arrival processes. For the latter application in traffic monitoring, we introduce Discrete RIDE (D-RIDE), which allows one to sample each flow with a geometric random variable. Our numerous simulations and experiments with P2P networks and real Internet traces confirm that these algorithms are able to make accurate estimation about the monitored metrics and simultaneously meet the requirements of hard resource constraints. These results show that residual sampling indeed provides an ideal solution to balancing between accuracy and scalability.

With rapid development of information technologies, especially the Internet technology, people can communicate more flexibly via various media, in which knowledge can be also shared. In gaining knowledge through the Internet, either digital content retrieval or inter-personal interaction, learning activities conducted on the Web are getting popular. This research has two main objectives. One is to develop incentive mechanisms to enhance the quantity and quality of information shared through peer-to-peer (P2P) networks. The other objective is to implement and evaluate the proposed mechanisms for inquiry-based learning on P2P networks. The pricing-like incentive mechanism is embedded on each peer to determine the price to share a document, to issue a question, and respond to a question. Through experiments, this study evaluates the effects on mitigating the free-riding problems and exchanging information through the P2P network. The results show the effectiveness of the incentive mechanisms for inquiry-based learning on P2P networks.

Most of virtual communities are built on the client/server system. There are some limitations on the client/server system such as the maintenance cost and the personal attribute protection. The peer-to-peer system has some strengths to overcome the limitations of client/server system. Therefore, we are willing to export the virtual community on the peer-to-peer system. There are two main team formation approaches in the current virtual community collaboration. Either one of these approaches alone has its limitations. In this study, we adopt the social network concept to design a team formation mechanism in order to overcome the limitations of current approaches. Besides, because of the natural of peer-to-peer system, the exchange of messages is sending and receiving on the network. The mechanism proposed in this research can also reduce the traffic cost of the team formation process. Furthermore, it maintains the fitness of members who are chosen in the same team.

Providing an interoperability infrastructure for Electronic Healthcare Records (EHRs) is on the agenda of many national and regional eHealth initiatives. Two important integration profiles have been specified for this purpose: the &quot / IHE Cross-enterprise Document Sharing (XDS)&quot / and the &quot / IHE Cross Community Access (XCA)&quot / . XDS describes how to share EHRs in a community of healthcare enterprises and XCA describes how EHRs are shared across communities. However, currently no solution addresses some of the important challenges of cross community exchange environments. The first challenge is scalability. If every community joining the network needs to connect to every other community, this solution will not scale. Furthermore, each community may use a different coding vocabulary for the same metadata attribute in which case the target community cannot interpret the query involving such an attribute. Another important challenge is that each community has a different patient identifier domain. Querying for the patient identifiers in another community using patient demographic data may create patient privacy concerns. Yet another challenge in cross community EHR access is the EHR interoperability since the communities may be using different EHR content standards.

Prior researches in P2P file sharing mostly focus on several topics such as the overlay topology, the content searching, the peer discovery, the sharing fairness, incentive mechanisms, except scheduling algorithms for peer-to-peer collaborative file distribution. The scheduling algorithm specifies how file pieces are distributed among peers. When a peer that has the rarest piece leaves, the other peers probably download the incomplete file in the network. Our algorithm is involved in the lifetime of peers in the P2P networks. We first use the distribution of peer¡¦s lifetime and the demand of each peer to decide which peers send which pieces that are rarities, and then also consider the distribution of peer¡¦s lifetime and the demand of each peer to decide which peers receive these pieces. Our goals are to maximize number of peers which have downloaded an entire file before it leaves, to increase the availability of different file pieces, and to minimize the transmission time of the latest completion. Lastly, we show the comparison of the performances of RPF, MDNF, Lifetime-based RPF, and Lifetime-based MDNF algorithms.

Women have been underrepresented in administrative roles in education, due to a wide variety of barriers. In the province of Ontario, women comprise approximately seventy one percent of teachers, yet occupy only sixty percent of school principal and vice principal roles. More recently, there has been an increase in the ranks of women working as principals and vice-principals; this study examines one Ontario School Board using quantitative and qualitative data. It finds that the percentage of women working as administrators has moved closer to a reflection of the provincial percentage of women in teaching. Interviews with active school administrators reveal the barriers and supports that are important. Their contributions reveal three distinct themes: the importance of mentorship and peer networks, the challenges of maintaining a work / family balance, and the continued importance of gender in educational administration.

Women have been underrepresented in administrative roles in education, due to a wide variety of barriers. In the province of Ontario, women comprise approximately seventy one percent of teachers, yet occupy only sixty percent of school principal and vice principal roles. More recently, there has been an increase in the ranks of women working as principals and vice-principals; this study examines one Ontario School Board using quantitative and qualitative data. It finds that the percentage of women working as administrators has moved closer to a reflection of the provincial percentage of women in teaching. Interviews with active school administrators reveal the barriers and supports that are important. Their contributions reveal three distinct themes: the importance of mentorship and peer networks, the challenges of maintaining a work / family balance, and the continued importance of gender in educational administration.

This dissertation presents algorithms for achieving communication in the presence of adversarial attacks in large, decentralized, resource-constrained networks. We consider abstract single-hop communication settings where a set of senders 𝙎 wishes to directly communicate with a set of receivers 𝙍. These results are then extended to provide resource-efficient, multi-hop communication in wireless sensor networks (WSNs), where energy is critically scarce, and peer-to-peer (P2P) networks, where bandwidth and computational power are limited. Our algorithms are provably correct in the face of attacks by a computationally bounded adversary who seeks to disrupt communication between correct participants. The first major result in this dissertation addresses a general scenario involving single-hop communication in a time-slotted network where a single sender in 𝙎 wishes to transmit a message 𝘮 to a single receiver in 𝙍. The two players share a communication channel; however, there exists an adversary who aims to prevent the transmission of 𝘮 by periodically blocking this channel. There are costs to send, receive or block 𝘮 on the channel, and we ask: How much do the two players need to spend relative to the adversary in order to guarantee transmission of the message? This problem abstracts many types of conflict in information networks, and the associated costs represent an expenditure of network resources. We show that it is significantly more costly for the adversary to block 𝘮 than for the two players to achieve communication. Specifically, if the cost to send, receive and block 𝘮 in a slot are fixed constants, and the adversary spends a total of 𝘉 slots to try to block the message, then both the sender and receiver must be active in only O(𝘉ᵠ⁻¹ + 1) slots in expectation to transmit 𝘮, where φ = (1+ √5)/2 is the golden ratio. Surprisingly, this result holds even if (1) the value of 𝘉 is unknown to either player; (2) the adversary knows the algorithms of both players, but not their random bits; and (3) the adversary is able to launch attacks using total knowledge of past actions of both players. Finally, these results are applied to two concrete problems. First, we consider jamming attacks in WSNs and address the fundamental task of propagating 𝘮 from a single device to all others in a WSN in the presence of faults; this is the problem of reliable broadcast. Second, we examine how our algorithms can mitigate application-level distributed denial-of-service attacks in wired client-server scenarios. The second major result deals with a single-hop communication problem where now 𝙎 consists of multiple senders and there is still a single receiver who wishes to obtain a message 𝘮. However, many of the senders (strictly less than half) can be faulty, failing to send 𝘮 or sending incorrect messages. While the majority of the senders possess 𝘮, rather than listening to all of 𝙎 and majority filtering on the received data, we desire an algorithm that allows the single receiver to decide on 𝘮 in a more efficient manner. To investigate this scenario, we define and devise algorithms for a new data streaming problem called the Bad Santa problem which models the selection dilemma faced by the receiver. With our results for the Bad Santa problem, we consider the problem of energy-efficient reliable broadcast. All previous results on reliable broadcast require devices to spend significant time in the energy-expensive receiving state which is a critical problem in WSNs where devices are typically battery powered. In a popular WSN model, we give a reliable broadcast protocol that achieves optimal fault tolerance (i.e., tolerates the maximum number of faults in this WSN model) and improves over previous results by achieving an expected quadratic decrease in the cost to each device. For the case where the number of faults is within a (1-∊)-factor of the optimal fault tolerance, for any constant ∊>0, we give a reliable broadcast protocol that improves further by achieving an expected (roughly) exponential decrease in the cost to each device. The third and final major result of this dissertation addresses single-hop communication where 𝙎 and 𝙍 both consist of multiple peers that need to communicate in an attack-resistant P2P network. There are several analytical results on P2P networks that can tolerate an adversary who controls a large number of peers and uses them to disrupt network functionality. Unfortunately, in such systems, operations such as data retrieval and message sending incur significant communication costs. Here, we employ cryptographic techniques to define two protocols both of which are more efficient than existing solutions. For a network of 𝘯 peers, our first protocol is deterministic with O(log²𝘯) message complexity and our second protocol is randomized with expected O(log 𝘯) message complexity; both improve over all previous results. The hidden constants and setup costs for our protocols are small and no trusted third party is required. Finally, we present an analysis showing that our protocols are practical for deployment under significant churn and adversarial behaviour.

With the extremely rapid development of the Internet in recent years, emerging peer-to-peer network overlays are meeting the requirements of a more sophisticated communications environment, providing a useful substrate for applications such as scalable file sharing, data storage, large-scale multicast, web-cache, and publish-subscribe services. Due to its design flexibility, peer-to-peer networks can offer features including self-organization, fault-tolerance, scalability, load-balancing, locality and anonymity. As the Internet grows, there is an urgent requirement to understand real-time network performance degradation. Measurement tools currently used are ping, traceroute and variations of these. SNMP (Simple Network Management Protocol) is also used by network administrators to monitor local networks. However, ping and traceroute can only be used temporarily, SNMP can only be deployed at certain points in networks and these tools are incapable of sharing network measurements among end-users. Due to the distributed nature of networking performance data, peer-to-peer overlay networks present an attractive platform to distribute this information among Internet users. This thesis aims at investigating the desirable locality property of peer-to-peer overlays to create an application to share Internet measurement performance. When measurement data are distributed amongst users, it needs to be localized in the network allowing users to retrieve it when external Internet links fail. Thus, network locality and robustness are the most desirable properties. Although some unstructured overlays also integrate locality in design, they fail to reach rarely located data items. Consequently, structured overlays are chosen because they can locate a rare data item deterministically and they can perform well during network failures. In structured peer-to-peer overlays, Tapestry, Pastry and Chord with proximity neighbour selection, were studied due to their explicit notion of locality. To differentiate the level of locality and resiliency in these protocols, P2Psim simulations were performed. The results show that Tapestry is the more suitable peer-to-peer substrate to build such an application due to its superior localizing data performance. Furthermore, due to the routing similarity between Tapestry and Pastry, an implementation that shares network measurement information was developed on freepastry, verifying the application feasibility. This project also contributes to the extension of P2Psim to integrate with GT-ITM and link failures.

O ataque Sybil consiste na criação indiscriminada de identidades forjadas por um usuário malicioso (atacante). Uma abordagem promissora para mitigar esse ataque consiste em conceder novas identidades mediante a resolução de desafios computacionais. Apesar de suas potencialidades, as soluções baseadas em tal abordagem não distinguem solicitações de usuários corretos das de atacantes, fazendo com que ambos paguem o mesmo preço por identidade solicitada. Por conta disso, essas soluções podem não ser efetivas quando os recursos computacionais dos atacantes são muito superiores aos que os usuários legítimos dispõem. Assumindo desafios de uma determinada dificuldade, atacantes com hardware de maior capacidade conseguiriam resolver um conjunto muito superior de desafios e, com isso, obter um número elevado de identidades. Aumentar uniformemente a dificuldade dos desafios poderia, no outro extremo, tornar proibitivo o ingresso de pares a rede. Para lidar com esse problema, nesta dissertação propi5e-se o use de desafios adaptativos como limitante a disseminação de Sybils. Estima-se um grau de confiança da fonte de onde partem as solicitações de identidade em relação as demais. Quanto maior a frequência de solicitação de identidades, menor o grau de confiança e, consequentemente, maior a complexidade do desafio a ser resolvido pelo(s) usuário(s) associado(s) Aquela fonte. Resultados obtidos por meio de experimentação mostram a capacidade da solução de atribuir desafios mais complexos a potenciais atacantes, penalizando minimamente usuários legítimos. / The Sybil attack consists on the indiscriminate creation of counterfeit identities by a malicious user (attacker). An effective approach to tackle such attack consists of establishing computational puzzles to be solved prior to granting new identities. Despite its potentialities, solutions based on such approach do not distinguish between identity requests from correct users and attackers, and thus require both to afford the same cost per identity requested. Therefore, those approaches may not be effective when the attacker's computational resources are superior than those used by correct users. Assuming any choice of puzzle hardness, attackers that have access to high-performance computing resources will be able to solve puzzles several order of magnitude faster than legitimate users and thus obtain a large amount of identities. On the other way, raising the cost to solve the puzzles could restrict legitimate users too much. To tackle this problem, in this paper we propose the use of adaptive computational puzzles to limit the spread of Sybils. We estimate a trust score of the source of identity requests in regard to the behavior of others. The higher the frequency a source requests identities, the lower its trust score and, consequently, the higher the complexity of the puzzle to be solved by the user(s) associated to that source. Results achieved by means of an experimental evaluation evidence our solution's ability to establish more complex puzzles to potential attackers, while minimally penalizing legitimate users.