A parallel downloading algorithm for redundant networks

In this paper, we study the downloading mechanism of BitTorrent (or BT), a P2P based popular and convenient parallel downloading software tool, point out some of its limitations, and propose an algorithm to improve its performance. In particular, we address the limitations of BT by using neighbours in P2P networks to resolve the redundant copies problem and to optimise the downloading speed. Our preliminary experiments show that the proposed enhancement algorithm works well

Reliable downloading algorithms for bittorrent-like systems

In this paper we study a reliable downloading algorithm for BitTorrent-like systems, and attest it in mathematics. BitTorrent-like systems have become immensely popular peer-to-peer file distribution tools in the internet in recent years. We analyze them in theory and point out some of their limitations especially in reliability, and propose an algorithm to resolve these problems by using the redundant copies in neighbors in P2P networks and can further optimize the downloading speed in some condition. Our preliminary simulations show that the proposed reliable algorithm works well; the improved BitTorrent-like systems are very stable and reliable.<br /

BitTorrent under a microscope : towards static QoS provision in dynamic peer-to-peer networks

For peer-to-peer (P2P) networks continually to flourish, QoS provision is critical. However, the P2P networks are notoriously dynamic and heterogeneous. As a result, QoS provision in P2P networks is a challenging task with nodes of the varying and intermittent throughput. This raises a fundamental problem: is stable and delicate QoS provision achievable in the highly dynamic and heterogeneous P2P networks? In this work, we investigate BitTorrent (BT) with the particular interest in its QoS performance in the highly dynamic and heterogeneous network. Our contributions are two-fold. First, we develop an analytical model to examine a randomly selected BT node under a microscope. Based on the model, we study the mean and variance of nodal download rate in the dynamic network and the performance of BT in QoS provision under different levels of peer churns. Our analysis unveils that although BT strives to provide nodes with guaranteed throughput, due to the network dynamics, the download rates of the peers oscillate extraordinarily and can hardly converge to the target QoS as proposed in previous literature. Second, to improve the QoS provision, we propose an enhanced protocol incorporating with BT. The proposed protocol enables nodes to quickly and elaborately search their uploaders, and as a result, achieve guaranteed and stable QoS in the dynamic networks. Using both analysis and simulations, we validate the effectiveness of the proposed protocol in comparisons with the original BT

Improving file distribution performance by grouping in peer-to-peer networks

It has been shown that the peer-to-peer paradigm is more efficient than the traditional client-server model for file sharing among a large number of users. Given a group of leechers who wants to download a single file and a group of seeds who possesses the whole file, the minimum time needed for distributing the file to all users can be calculated based on their bandwidth availabilities. A scheduling algorithm has been developed so that every leecher can obtain the file within this minimum time. Unfortunately, this mechanism is not optimal with regard to the average download time among the peers. In this paper, we study how to reduce the average download time without prolonging the time needed for all leechers to obtain the file from a theoretical perspective. Based on the bandwidth capacities, the seeds and leechers are divided into different groups. We identify the necessary conditions for grouping to bring about benefits. We also study the impact on performance when leechers leave the system before the downloading process is complete. To evaluate our mechanism, we conduct extensive simulations and compare the performance with a BitTorrentlike file sharing algorithm. The results show that our grouping protocol successfully reduces the average download time over a wide range of system configurations. © 2009 IEEE.published_or_final_versio

Implementation of Sub-Grid-Federation Model for Performance Improvement in Federated Data Grid

In this work, a new model for federation data grid system called Sub-Grid-Federation was designed to improve access latency by accessing data from the nearest possible sites. The strategy in optimising data access was based on the process of searching into the area identified as ‘Network Core Area’ (NCA). The performance of access latency in Sub-Grid-Federation was tested based on the mathematical proving and simulated using OptorSim simulator. Four case studies were carried out and tested in Optimal Downloading Replication Strategy (ODRS) and the Sub-Grid-Federation. The results show that Sub-Grid-Federation is 20% better in terms of access latency and 21% better in terms of reducing remotes sites access compared to ODRS. The results indicate that the Sub-Grid-Federation is a better alternative for the implementation of collaboration and data sharing in data grid system.                                                                                    Keywords: Data grid, replication, scheduling, access latenc

Algorithms for the NJIT turbonet parallel computer

Element selection for arrays, array merging, and sorting are very frequent operations in many of today\u27s important applications. These operations are of interest to scientific, as well as other applications where high-speed database search, merge, and sort operations are necessary and frequent. Therefore, their efficient implementation on parallel computers should be a worthwhile objective. Parallel algorithms are presented in this thesis for the implementation of these operations on the NET TurboNet system, an in-house built experimental parallel computer with TMS320C40 Digital Signal Processors interconnected in a 3-D hypercube structure. The first algorithm considered is selection. It involves finding the k-th smallest element in an unsorted sequence of n elements, where 1≤k≤n. The second algorithm involves the merging of two sequences sorted in nondecreasing order to form a third sequence, also sorted in nondecreasing order. The third parallel algorithm is sorting. For a given unsorted sequence S of size n, we want to sort the sequence such that st\u27≤i+1\u27 for all n elements. Performance results show that the robust structure of TurboNet results in significant speedups

Combining behavioural types with security analysis

Today's software systems are highly distributed and interconnected, and they increasingly rely on communication to achieve their goals; due to their societal importance, security and trustworthiness are crucial aspects for the correctness of these systems. Behavioural types, which extend data types by describing also the structured behaviour of programs, are a widely studied approach to the enforcement of correctness properties in communicating systems. This paper offers a unified overview of proposals based on behavioural types which are aimed at the analysis of security properties

Energy-optimal collaborative file distribution in wired networks

The impact of the ICT sector in worldwide power consumption is an increasing concern, motivating the research community to devote an important effort to define novel energy efficient networking solutions. Despite file distribution is responsible for a major portion of the current Internet traffic, little effort has been dedicated to address the issue of its energy efficiency so far. Most of the previous literature focuses on optimizing the download time of file distribution schemes (e.g. centralized server-based or distributed peer-to-peer solutions) while it is yet unclear how to optimize file distribution schemes from the point of view of energy consumed. In this paper, we present a general modelling framework to analyze the energy consumption of file distribution systems. First, we show that the general problem of minimizing energy consumption in file distribution is NP-hard. Then, for restricted versions of the problem, we establish theoretical bounds to minimal energy consumption. Furthermore, we define a set of optimal algorithms for a variety of system settings, which exploit the service capabilities of hosts in a P2P fashion. We show that our schemes are capable of reducing at least 50 % of the energy consumed by traditional (yet largely used) centralized distribution schemes even when considering effects such as network congestion and heterogeneous access speed across nodes.Supported in part by Ministerio de Economia y Competitividad grant TEC2014- 55713-R, the DRONEXT project (TEC2014-58964-C2-1-R), Regional Government of Madrid (CM) grant Cloud4BigData (S2013/ICE-2894, co- funded by FSE & FEDER), and BRADE Project (P2013/ICE-2958), NSF of China grant 61520106005, and European Commission H2020 grants ReCred and NOTRE

Federated Learning Based Proactive Content Caching in Edge Computing

This is the author accepted manuscript. the final version is available from IEEE via the DOI in this recordContent caching is a promising approach in edge computing to cope with the explosive growth of mobile data on 5G networks, where contents are typically placed on local caches for fast and repetitive data access. Due to the capacity limit of caches, it is essential to predict the popularity of files and cache those popular ones. However, the fluctuated popularity of files makes the prediction a highly challenging task. To tackle this challenge, many recent works propose learning based approaches which gather the users' data centrally for training, but they bring a significant issue: users may not trust the central server and thus hesitate to upload their private data. In order to address this issue, we propose a Federated learning based Proactive Content Caching (FPCC) scheme, which does not require to gather users' data centrally for training. The FPCC is based on a hierarchical architecture in which the server aggregates the users' updates using federated averaging, and each user performs training on its local data using hybrid filtering on stacked autoencoders. The experimental results demonstrate that, without gathering user's private data, our scheme still outperforms other learning-based caching algorithms such as m-epsilon-greedy and Thompson sampling in terms of cache efficiency.Engineering and Physical Sciences Research Council (EPSRC)National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaEuropean Union Seventh Framework Programm