Reliable Multipath Transfer Scheduling Algorithm Research and Prototype Implementation

End-to-end data transfer has long been a fundamental service of Internet. With the trendof multi-interface communication terminals and redundant paths between them both in cellularnetworks and in the Internet, efficient protocols of multipath transfer are needed to gain thebenefits of multi-interfaces. In this paper, we propose SAR-RMTP, a Self-adapted ReschedulingReliable Multipath Transfer Protocol for end-to-end file transfer, which can adaptively rescheduledata to different paths according to the current average bandwidth to achieve nearly the sametransferring finish time between different paths, and thus results in effective use of overallbandwidth. We implement the prototype of SAR-RMTP in Linux and compare its performancewith existing scheduling algorithms in experimental environment. The results show that SARRMTPcan notably decrease the difference of transfer finish time of different paths and thusshorten the overall file transfer time and increase the overall bandwidth. The results also show thatcompared with other scheduling algorithms SAR-RMTP can achieve much better performancewhen bandwidth changes more dramatically

Transferring big data across the globe

Transmitting data via the Internet is a routine and common task for users today. The amount of data being transmitted by the average user has dramatically increased over the past few years. Transferring a gigabyte of data in an entire day was normal, however users are now transmitting multiple gigabytes in a single hour. With the influx of big data and massive scientific data sets that are measured in tens of petabytes, a user has the propensity to transfer even larger amounts of data. When transferring data sets of this magnitude on public or shared networks, the performance of all workloads in the system will be impacted. This dissertation addresses the issues and challenges inherent with transferring big data over shared networks. A survey of current transfer techniques is provided and these techniques are evaluated in simulated, experimental and live environments. The main contribution of this dissertation is the development of a new, nice model for big data transfers, which is based on a store-and-forward methodology instead of an end-to-end approach. This nice model ensures that big data transfers only occur when there is idle bandwidth that can be repurposed for these large transfers. The nice model improves overall performance and significantly reduces the transmission time for big data transfers. The model allows for efficient transfers regardless of time zone differences or variations in bandwidth between sender and receiver. Nice is the first model that addresses the challenges of transferring big data across the globe

High-performance and fault-tolerant techniques for massive data distribution in online communities

The amount of digital information produced and consumed is increasing each day. This rapid growth is motivated by the advances in computing power, hardware technologies, and the popularization of user generated content networks. New hardware is able to process larger quantities of data, which permits to obtain finer results, and as a consequence more data is generated. In this respect, scientific applications have evolved benefiting from the new hardware capabilities. This type of application is characterized by requiring large amounts of information as input, generating a significant amount of intermediate data resulting in large files. This increase not only appears in terms of volume, but also in terms of size, we need to provide methods that permit a efficient and reliable data access mechanism. Producing such a method is a challenging task due to the amount of aspects involved. However, we can leverage the knowledge found in social networks to improve the distribution process. In this respect, the advent of the Web 2.0 has popularized the concept of social network, which provides valuable knowledge about the relationships among users, and the users with the data. However, extracting the knowledge and defining ways to actively use it to increase the performance of a system remains an open research direction. Additionally, we must also take into account other existing limitations. In particular, the interconnection between different elements of the system is one of the key aspects. The availability of new technologies such as the mass-production of multicore chips, large storage media, better sensors, etc. contributed to the increase of data being produced. However, the underlying interconnection technologies have not improved with the same speed as the others. This leads to a situation where vast amounts of data can be produced and need to be consumed by a large number of geographically distributed users, but the interconnection between both ends does not match the required needs. In this thesis, we address the problem of efficient and reliable data distribution in a geographically distributed systems. In this respect, we focus on providing a solution that 1) optimizes the use of existing resources, 2) does not requires changes in the underlying interconnection, and 3) provides fault-tolerant capabilities. In order to achieve this objectives, we define a generic data distribution architecture composed of three main components: community detection module, transfer scheduling module, and distribution controller. The community detection module leverages the information found in the social network formed by the users requesting files and produces a set of virtual communities grouping entities with similar interests. The transfer scheduling module permits to produce a plan to efficiently distribute all requested files improving resource utilization. For this purpose, we model the distribution problem using linear programming and offer a method to permit a distributed solving of the problem. Finally, the distribution controller manages the distribution process using the aforementioned schedule, controls the available server infrastructure, and launches new on-demand resources when necessary

Advances in Computer Science and Engineering

The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling