Analysing BitTorrent's seeding strategies

BitTorrent is a typical peer-to-peer (P2P) file distribution application that has gained tremendous popularity in recent years. A considerable amount of research exists regarding BitTorrent’s choking algorithm, which has proved to be effective in preventing freeriders. However, the effect of the seeding strategy on the resistance to freeriders in BitTorrent has been largely overlooked. In addition to this, a category of selfish leechers (termed exploiters), who leave the overlay immediately after completion, has never been taken into account in the previous research. In this paper two popular seeding strategies, the Original Seeding Strategy (OSS) and the Time- based Seeding Strategy (TSS), are chosen and we study via mathematical models and simulation their effects on freeriders and exploiters in BitTorrent networks. The mathematical model is verified and we discover that both freeriders and exploiters impact on system performance, despite the seeding strategy that is employed. However, a selfish-leechers threshold is identified; once the threshold is exceeded, we find that TSS outperforms OSS – that is, TSS reduces the negative impact of selfish lechers more effectively than OSS. Based on these results we discuss the choice of seeding strategy and speculate as to how more effective BitTorrent-based file distribu- tion applications can be built

Parallelisation for data-intensive applications over peer-to-peer networks

In Data Intensive Computing, properties of the data that are the input for an application decide running performance in most cases. Those properties include the size of the data, the relationships inside data, and so forth. There is a class of data intensive applications (BLAST, SETI@home, Folding@Home and so on so forth) whose performances solely depend on the amount of input data. Another important characteristic of those applications is that the input data can be split into units and these units are not related to each other during the runs of the applications. This characteristic helps this class of data intensive applications to be parallelised in the way where the input data is split into units and application runs on different computer nodes for certain portion of the units. SETI@home and Folding@Home have been successfully parallelised over peer-to-peer networks. However, they suffer from the problems of single point of failure and poor scalability. In order to solve these problems, we choose BLAST as our example data intensive applications and parallelise BLAST over a fully distributed peer-to-peer network. BLAST is a popular bioinformatics toolset which can be used to compare two DNA sequences. The major usage of BLAST is searching a query of sequences inside a database for their similarities so as to identify whether they are new. When comparing single pair of sequences, BLAST is efficient. However, due to growing size of the databases, executing BLAST jobs locally produces prohibitively poor performance. Thus, methods for parallelising BLAST are sought. Traditional BLAST parallelisation approaches are all based on clusters. Clusters employ a number of computing nodes and high bandwidth interlinks between nodes. Cluster-based BLAST exhibits higher performance; nevertheless, clusters suffer from limited resources and scalability problems. Clusters are expensive, prohibitively so when the growth of the sequence database are taken into account. It involves high cost and complication when increasing the number of nodes to adapt to the growth of BLAST databases. Hence a Peer-to-Peer-based BLAST service is required. This thesis demonstrates our parallelisation of BLAST over Peer-to-Peer networks (termed ppBLAST), which utilises the free storage and computing resources in the Peer-to-Peer networks to complete BLAST jobs in parallel. In order to achieve the goal, we build three layers in ppBLAST each of which is responsible for particular functions. The bottom layer is a DHT infrastructure with the support of range queries. It provides efficient range-based lookup service and storage for BLAST tasks. The middle layer is the BitTorrent-based database distribution. The upper layer is the core of ppBLAST which schedules and dispatches task to peers. For each layer, we conduct comprehensive research and the achievements are presented in this thesis. For the DHT layer, we design and implement our DAST-DHT. We analyse balancing, maximum number of children and the accuracy of the range query. We also compare the DAST with other range query methodology and state that if the number of children is adjusted to more two, the performance of DAST overcomes others. For the BitTorrent-like database distribution layer, we investigate the relationship between the seeding strategies and the selfish leechers (freeriders and exploiters). We conclude that OSS works better than TSS in a normal situation

Parallelisation for data-intensive applications over peer-to-peer networks

In Data Intensive Computing, properties of the data that are the input for an application decide running performance in most cases. Those properties include the size of the data, the relationships inside data, and so forth. There is a class of data intensive applications (BLAST, SETI@home, Folding@Home and so on so forth) whose performances solely depend on the amount of input data. Another important characteristic of those applications is that the input data can be split into units and these units are not related to each other during the runs of the applications. This characteristic helps this class of data intensive applications to be parallelised in the way where the input data is split into units and application runs on different computer nodes for certain portion of the units. SETI@home and Folding@Home have been successfully parallelised over peer-to-peer networks. However, they suffer from the problems of single point of failure and poor scalability. In order to solve these problems, we choose BLAST as our example data intensive applications and parallelise BLAST over a fully distributed peer-to-peer network. BLAST is a popular bioinformatics toolset which can be used to compare two DNA sequences. The major usage of BLAST is searching a query of sequences inside a database for their similarities so as to identify whether they are new. When comparing single pair of sequences, BLAST is efficient. However, due to growing size of the databases, executing BLAST jobs locally produces prohibitively poor performance. Thus, methods for parallelising BLAST are sought. Traditional BLAST parallelisation approaches are all based on clusters. Clusters employ a number of computing nodes and high bandwidth interlinks between nodes. Cluster-based BLAST exhibits higher performance; nevertheless, clusters suffer from limited resources and scalability problems. Clusters are expensive, prohibitively so when the growth of the sequence database are taken into account. It involves high cost and complication when increasing the number of nodes to adapt to the growth of BLAST databases. Hence a Peer-to-Peer-based BLAST service is required. This thesis demonstrates our parallelisation of BLAST over Peer-to-Peer networks (termed ppBLAST), which utilises the free storage and computing resources in the Peer-to-Peer networks to complete BLAST jobs in parallel. In order to achieve the goal, we build three layers in ppBLAST each of which is responsible for particular functions. The bottom layer is a DHT infrastructure with the support of range queries. It provides efficient range-based lookup service and storage for BLAST tasks. The middle layer is the BitTorrent-based database distribution. The upper layer is the core of ppBLAST which schedules and dispatches task to peers. For each layer, we conduct comprehensive research and the achievements are presented in this thesis. For the DHT layer, we design and implement our DAST-DHT. We analyse balancing, maximum number of children and the accuracy of the range query. We also compare the DAST with other range query methodology and state that if the number of children is adjusted to more two, the performance of DAST overcomes others. For the BitTorrent-like database distribution layer, we investigate the relationship between the seeding strategies and the selfish leechers (freeriders and exploiters). We conclude that OSS works better than TSS in a normal situation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Asiaticoside Mitigates Alzheimer’s Disease Pathology by Attenuating Inflammation and Enhancing Synaptic Function

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder, hallmarked by the accumulation of amyloid-β (Aβ) plaques and neurofibrillary tangles. Due to the uncertainty of the pathogenesis of AD, strategies aimed at suppressing neuroinflammation and fostering synaptic repair are eagerly sought. Asiaticoside (AS), a natural triterpenoid derivative derived from Centella asiatica, is known for its anti-inflammatory, antioxidant, and wound-healing properties; however, its neuroprotective function in AD remains unclear. Our current study reveals that AS, when administered (40 mg/kg) in vivo, can mitigate cognitive dysfunction and attenuate neuroinflammation by inhibiting the activation of microglia and proinflammatory factors in Aβ1-42-induced AD mice. Further mechanistic investigation suggests that AS may ameliorate cognitive impairment by inhibiting the activation of the p38 MAPK pathway and promoting synaptic repair. Our findings propose that AS could be a promising candidate for AD treatment, offering neuroinflammation inhibition and enhancement of synaptic function

Hydrocortisone Mitigates Alzheimer’s-Related Cognitive Decline through Modulating Oxidative Stress and Neuroinflammation

Alzheimer’s disease (AD), an age-related degenerative disorder, is characterized by β-amyloid deposition, abnormal phosphorylation of tau proteins, synaptic dysfunction, neuroinflammation, and oxidative stress. Despite extensive research, there are no medications or therapeutic interventions to completely treat and reverse AD. Herein, we explore the potential of hydrocortisone (HC), a natural and endogenous glucocorticoid known to have potent anti-inflammatory properties, in an Aβ1–42-induced AD mouse model. Our investigation highlights the beneficial effects of HC administration on cognitive impairment, synaptic function enhancement, and neuronal protection in Aβ1–42-induced AD mice. Notably, HC treatment effectively suppresses the hyperactivation of microglia and astrocytes, leading to a reduction in proinflammatory factors and alleviation of neuroinflammation. Furthermore, HC intervention demonstrates the capacity to mitigate the generation of ROS and oxidative stress. These compelling findings underscore the potential therapeutic application of HC in AD and present promising opportunities for its utilization in AD prevention and treatment. The implications drawn from our findings indicate that hydrocortisone holds promise as a viable candidate for adjunctive use with other anti-AD drugs for the clinical management of patients presenting with moderate to severe AD

Design and implementation of efficient range query over DHT services

This paper describes the design and implementation of DAST, a Distributed Arbitrary Segment Tree structure that gives support of range query for public Distributed Hash Table (DHT) services. DAST does not modify the underlying DHT infrastructure, instead it utilises the scalability and robustness of DHT while providing simplicity of implementation and deployment for applications. Compared with traditional segment trees, the arbitrary segment tree used by a DAST reduces the number of key-space segments that need to be maintained, which in turn results in fewer query operations and lower overheads. Moreover, considering that range queries often contain redundant entries that the clients do not need, we introduce the concept of Accuracy of Results (AoR) for range queries. We demonstrate that by adjusting AoR, the DHT operational overhead can be improved. DAST is implemented on a well-known public DHT service (OpenDHT) and validation through experimentation and supporting simulation is performed. The results demonstrate the effectiveness of DAST over exiting methods

Distributed arbitrary segment trees : providing efficient range query support over public DHT services

In this paper we define a Distributed Arbitrary Segment Tree (DAST), a distributed tree-like structure that layers the range query processing mechanism over public Distributed Hash Table (DHT) services. Compared with traditional segment trees, the arbitrary segment tree used by a DAST reduces the number of key-space segments that need to be maintained, which in turn results in fewer query operations and lower overheads. Moreover, considering that range queries often contain redundant entries that the clients do not need, we introduce the concept of Accuracy of Results (AoR) for range queries. We demonstrate that by adjusting AoR, the DHT operational overhead can be improved. DAST is implemented on a well-known public DHT service (OpenDHT) and validation through experimentation and supporting simulation is performed. The results demonstrate the effectiveness of DAST over exiting methods