Increasing Performances of TCP Data Transfers Through Multiple Parallel Connections

Although Transmission Control Protocol (TCP) is a widely deployed and successful protocol, it shows some limitations in present-day environments. In particular, it is unable to exploit multiple (physical or logical) paths between two hosts. This paper presents PATTHEL, a session-layer solution designed for parallelizing stream data transfers. Parallelization is achieved by striping the data flow among multiple TCP channels. This solution does not require invasive changes to the networking stack and can be implemented entirely in user space. Moreover, it is flexible enough to suit several scenarios - e.g. it can be used to split a data transfer among multiple relays within a peer-to-peer overlay networ

Stream Control Transmission Protocol (SCTP): Robust and Efficient for Data Centre Applications

Due to rapid advancement in modern technology, as one of the major concerns is the stability of business. The organizations depend on their systems to provide robust and faster processing of information for their operations. Efficient data centers are key sources to handle these operations. If the organizational system is not fully functional, the performance of organization may be impaired or clogged completely. With the developments of real-time applications into data centers for data communications, there is a need to use an alternative of the standard TCP protocol to provide reliable data transfer. Stream Control Transmission Protocol (SCTP) consists of several well built-in characteristics that make it capable to work efficiently with real-time applications. In this paper, we evaluate an optimized version of STCP. The optimized version of SCTP is tested against a non optimized version of STCP and TCP in a data center environment. Simulations of the protocols are carried using NS2 simulator.http://arxiv.org/abs/1312.062

Increasing Performances of TCP Data Transfers Through Multiple Parallel Connections

Although Transmission Control Protocol (TCP) is a widely deployed and successful protocol, it shows some limitations in present-day environments. In particular, it is unable to exploit multiple (physical or logical) paths between two hosts. This paper presents PATTHEL, a session-layer solution designed for parallelizing stream data transfers. Parallelization is achieved by striping the data flow among multiple TCP channels. This solution does not require invasive changes to the networking stack and can be implemented entirely in user space. Moreover, it is flexible enough to suit several scenarios - e.g. it can be used to split a data transfer among multiple relays within a peer-to-peer overlay network

Deployment of Stream Control Transmission Protocol (SCTP) to Maintain the Applications of Data Centers

With developments of real-time applications into data centers, the need for alternatives of the standard TCP protocol has been prime demand in several applications of data centers. The several alternatives of TCP protocol has been proposed but SCTP has edge due to its several well-built characteristics that make it capable to work efficiently. In this paper, we examine the features of SCTP into data centers like Multi-streaming and Multi-Homing over the features of TCP protocol. In this paper, our objective is to introduce internal problems of data centers. Robust transport protocol reduces the problems with some extend. Focusing the problems of data centers, we also examine weakness of highly deployed standard TCP, and evaluate the performance of SCTP in context of faster communication for data centers. We also discover some weaknesses and shortcomings of SCTP into data centers and try to propose some ways to avoid them by maintaining SCTP native features. To validate strength and weakness of TCP and SCTP, we use ns2 for simulation in context of data center. On basis of findings, we highlight major strength of SCTP. At the end, we Implement finer grain TCP locking mechanisms for larger messages.http://arxiv.org/abs/1311.263

Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

Experience-driven Control For Networking And Computing

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design philosophy, we present the design, implementation, and evaluation of multiple DRL-based control frameworks on two fundamental networking problems, Traffic Engineering (TE) and Multi-Path TCP (MPTCP) congestion control, as well as one cutting-edge application, resource co-scheduling for Deep Neural Network (DNN) models on mobile and edge devices with heterogeneous hardware. We first propose DRL-TE, a DRL-based framework that enables experience-driven networking for TE. DRL-TE maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful DNNs. We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. Furthermore, we propose an Actor-Critic-based Transfer learning framework for TE, ACT-TE, which solves a practical problem in experience-driven networking: when network configurations are changed, how to train a new DRL agent to effectively and quickly adapt to the new environment. In the new network environment, ACT-TE leverages policy distillation to rapidly learn a new control policy from both old knowledge (i.e., distilled from the existing agent) and new experience (i.e., newly collected samples). In addition, we propose DRL-CC to enable experience-driven congestion control for MPTCP. DRL-CC utilizes a single (instead of multiple independent) DRL agent to dynamically and jointly perform congestion control for all active MPTCP flows on an end host with the objective of maximizing the overall utility. The novelty of our design is to utilize a flexible recurrent neural network, LSTM, under a DRL framework for learning a representation for all active flows and dealing with their dynamics. Moreover, we integrate the above LSTM-based representation network into an actor-critic framework for continuous congestion control, which applies the deterministic policy gradient method to train actor, critic, and LSTM networks in an end-to-end manner. With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing DNN models to empower mobile and edge devices with intelligence such that they can support attractive AI applications on the edge in a real-time or near real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc) to effectively and efficiently support concurrent inference of multiple DNN models on a mobile or edge device, in the last part of this thesis, we propose a novel experience-driven control framework for resource co-scheduling, which we call COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages DRL to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To validate and evaluate the proposed frameworks, we conduct extensive experiments on packet-level simulation (for TE), testbed with modified Linux kernel (for MPTCP), and off-the-shelf Android devices (for resource co-scheduling). The results well justify the effectiveness of these frameworks, as well as their superiority over several baseline methods

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Real-time social multimedia level threat monitoring is becoming harder, due to higher and rapidly increasing data induction. Data induction through electric smart devices is greater compared to information processing capacity. Nowadays, data becomes humongous even coming from the single source. Therefore, when data emanates from all heterogeneous sources distributed over the globe makes data magnitude harder to process up to a needed scale. Big data and Deep learning have become standard in providing well-known solutions built-up using algorithms and techniques in resolving data matching issues. Now, with the involvement of sensors and automation in generating data obscures everything, predicting results to overcome a current era of ever enhancing demands and getting real-time visualization brings the need of feature like human behavior mode extraction to overcome any future threats. Big data analytics can bring the opportunity of predicting any misfortune even before they happen. Map reduce feature of big data supports massive data oriented process execution using distributed processing. Real-time human feature identification and detection can occur through sensors and internet sources. A behavioral prediction can further classify the information collected for introducing enhanced security extents. Real-time sensor devices are producing 24/7-hour data for further processing recording each event. IoT-based sensors can support in behavioral analysis model of a human. Real-time human behavioral monitoring based on image processing and IoT using big data analytics

Improved transmission control protocol congestion control technique for high bandwidth long distance networks

Transmission Control Protocol (TCP) is responsible for reliable communication of data in high bandwidth long distance networks. TCP is reliable because of its congestion control technique. Many TCP congestion control techniques for different operating systems have been developed previously. TCP Compound and TCP CUBIC are current congestion control techniques being used in Microsoft Windows and Linux operating systems respectively. TCP Reno is Standard TCP congestion control technique. TCP CUBIC does not perform well in high bandwidth long distance networks due to its exponential growth and less reduction in congestion window size. This leads to burst packet losses, unfair allocation of unused link bandwidth, long convergence time, and poor TCP friendliness among competing flows. The aim of this research work is to develop an improved congestion control technique based on TCP CUBIC for high bandwidth long distance networks. This improved technique is based on three components which are Congestion Control Technique for Slow Start (CCT-SS), Congestion Control Technique for Loss Occurrence (CCT-LO), and Enhanced Response Function of TCP CUBIC (ERFC). CCT-SS is proposed which increases the lower boundary limit of congestion window, which in turn, decreases the packet loss rate. CCT-LO is proposed which introduces a new congestion window reduction parameter in order to achieve fairer and quicker allocation of link bandwidth among the competing flows. ERFC is proposed which reduces the average congestion window size of TCP CUBIC in order to improve the TCP friendliness. As a conjunctive result of this research work, an improved congestion control technique is developed by combining the CCT-SS, CCT-LO and ERFC components. Network Simulator 2 is used to evaluate the performance of the proposed congestion control technique and to compare it with the current and other congestion control techniques. Results show that the performance of the proposed congestion control technique outperforms by 8.4% as compared to current congestion control technique