Concurrent Downloads Research Articles

DoFP+: An HTTP/3-Based Adaptive Bitrate Approach Using Retransmission Techniques

HTTP Adaptive Streaming (HAS) solutions use various adaptive bitrate (ABR) algorithms to select suitable video qualities with the objective of coping with the variations of network connections. HTTP has been evolving with various versions and provides more and more features. Most of the existing ABR algorithms do not significantly benefit from the HTTP development when they are merely supported by the most recent HTTP version. An open research question is “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">How can new features of the recent HTTP versions be used to enhance the performance of HAS</i> ?” To address this question, in this paper, we introduce Days of Future Past+ (DoFP+ for short), a heuristic algorithm that takes advantage of the features of the latest HTTP version, HTTP/3, to provide high Quality of Experience (QoE) to the viewers. DoFP+ leverages HTTP/3 features, including (i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stream multiplexing</i> , (ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stream priority</i> , and (iii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">request cancellation</i> to upgrade low-quality segments in the player buffer while downloading the next segment. The qualities of those segments are selected based on an objective function and throughput constraints. The objective function takes into account two factors, namely the (i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">average bitrate</i> and the (ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">video instability</i> of the considered set of segments. We also examine different strategies of download order for those segments to optimize the QoE in limited resources scenarios. The experimental results show an improvement in QoE by up to 33% while the number of stalls and stall duration for DoFP+ are reduced by 86% and 92%, respectively, compared to state-of-the-art ABR schemes. In addition, DoFP+ saves on average up to 16% downloaded data across all test videos. Also, we find that downloading segments sequentially brings more benefits for retransmissions than concurrent downloads; and lower-quality segments should be upgraded before other segments to gain more QoE improvement. Our source code has been published for reproducibility at https://github.com/cd-athena/ DoFP-Plus.

WARCProcessor: An Integrative Tool for Building and Management of Web Spam Corpora.

In this work we present the design and implementation of WARCProcessor, a novel multiplatform integrative tool aimed to build scientific datasets to facilitate experimentation in web spam research. The developed application allows the user to specify multiple criteria that change the way in which new corpora are generated whilst reducing the number of repetitive and error prone tasks related with existing corpus maintenance. For this goal, WARCProcessor supports up to six commonly used data sources for web spam research, being able to store output corpus in standard WARC format together with complementary metadata files. Additionally, the application facilitates the automatic and concurrent download of web sites from Internet, giving the possibility of configuring the deep of the links to be followed as well as the behaviour when redirected URLs appear. WARCProcessor supports both an interactive GUI interface and a command line utility for being executed in background.

A smart storage optimisation technique on the cloud

This paper is an extension to a recent work on enhancing load balancing and optimising storage consumption on the cloud. Our methodology, called ssCloud (Smart Storage Cloud), is based on methods designed to enhance the load balancing in concurrent file download techniques on the cloud using a collaborative dual direction technique. In this paper, we show how ssCloud operates in both the upload and download phases for providing Data as a Service (DaaS) on the cloud. Our goal is to reduce the amount of storage consumed on the cloud while providing a fast and efficient download time for the cloud clients. This is done by having two separate cloud interfaces, one for download requests (load balancing module) and another for upload requests, which involves partitioning and saving files on the available cloud servers (FileController). We also provide an analysis of ssCloud performance and compare it to other reviewed storage optimisation techniques on the cloud by simulating the cloud environment and comparing service times.

A dual-direction technique for fast file downloads with dynamic load balancing in the Cloud

File downloads make up a large percentage of the Internet traffic to satisfy various clients using distributed environments for their Cloud, Grid and Internet applications. In particular, the Cloud has become a popular data storage provider and users (individuals and corporates) are relying heavily on it to keep their data. Furthermore, most cloud data servers replicate their data storage infrastructures and servers at various sites to meet the overall high demands of their clients and increase availability. However, most of them do not use that replication to enhance the download performance per client. To make use of this redundancy and to enhance the download speed, we introduce a fast and efficient concurrent technique for downloading large files from replicated Cloud data servers and traditional FTP servers as well. The technique, DDFTP utilizes the availability of replicated files on distributed servers to enhance file download times through concurrent downloads of file blocks from opposite directions in the files. DDFTP does not require coordination between the servers and relies on the in-order and reliability features of TCP to provide fast file downloads. In addition, DDFTP offers efficient load balancing among multiple heterogeneous data servers with minimal overhead. As a result, we can maximize network utilization while maintaining efficient load balancing on dynamic environments where resources, current loads and operational properties vary dynamically. We implemented and evaluated DDFTP and experimentally demonstrated considerable performance gains for file downloads compared to other concurrent/parallel file/data download models.

A Queuing Model for Evaluating the Transfer Latency of Peer-to-Peer Systems

This paper presents a queuing model to evaluate the latency associated with file transfers or replications in peer-to-peer (P2P) computer systems. The main contribution of this paper is a modeling framework for the peers that accounts for the file size distribution, the search time, load distribution at peers, and number of concurrent downloads allowed by a peer. We propose a queuing model that models the nodes or peers in such systems as M/G/1/K processor sharing queues. The model is extended to account for peers which alternate between online and offline states. The proposed queuing model for the peers is combined with a single class open queuing network for the routers interconnecting the peers to obtain the overall file transfer latency. We also show that in scenarios with multipart downloads from different peers, a rate proportional allocation strategy minimizes the download times.

Analysis of resource transfers in peer-to-peer file sharing applications using fluid models

This paper proposes a stochastic fluid flow model to compute the transfer time distribution of resources in peer-to-peer file sharing applications. The amount of bytes transferred among peers is represented by a continuous quantity (the fluid level) whose flow rate is modulated by a set of discrete states representing the concurrent upload and download operations on the peers participating to the transfer. A transient solution of the model is then performed to compute the probability that a peer can download a given resource in less than t units of time as a function of several system parameters. In particular, the impact of file popularity, bandwidth characteristics, concurrent downloads and uploads, cooperation level among peers, and user behavior are included in our model specification. We also provide numerical results aiming at proving the potentialities of the approach we adopted as well as to investigate interesting issues related to the effect of incentive mechanisms on the user cooperation.