LXC Container Migration in Cloudlets under Multipath TCP

Recent data centers provide dense inter-connectivity between each pair of servers through multiple paths. These data centers offer high aggregate bandwidth and robustness by using multiple paths simultaneously. Multipath TCP (MPTCP) protocol is developed for improving throughput, fairly sharing network link capacity and providing robustness during path failure by utilizing multiple paths over multi-homed data center networks. Running MPTCP protocol for latency-sensitive rack-local short flows with many-to-one communication pattern at the access layer of multi-homed data center networks creates MPTCP incast problem. In this paper, Balanced Multipath TCP (BMPTCP) protocol is proposed to mitigate MPTCP incast problem in multi-homed data center networks. BMPTCP is a window-based congestion control protocol that prevents constant growth of each worker’s subflow congestion window size. BMPTCP computes identical congestion window size for all concurrent subflows by considering bottleneck Top of Rack (ToR) switch buffer size and increasing count of concurrently transmitting workers. This helps BMPTCP to avoid timeout events due to full window loss at ToR switch. Based on current congestion situation at ToR switches, BMPTCP adjust transmission rates of each worker’s subflow so that total amount of data transmitted by all concurrent subflows does not overflow bottleneck ToR switch buffer. Simulation results show that BMPTCP effectively alleviates MPTCP incast. It improves goodput, reduces flow completion time as compared to existing MPTCP and EW-MPTCP protocols.

With the growing demand of Internet services, network operators have put significant efforts to improve network error resilience and efficiency. Since there exist different wired/wireless technologies for Internet access such as digital subscriber line (DSL), Ethernet, and worldwide interoperability for microwave access (WiMax), a mobile host can use multiple access networks simultaneously with multipath transmission. Taking the advantage of heterogeneous environment, multipath transmission through the Internet can improve service reliability and network flexibility. Ensuring a reliable end-to-end connection oriented communication with satisfactory quality of service (QoS) and maintaining congestion control and flow control are the main responsibilities of the Transmission Control Protocol (TCP), the dominant transport layer protocol in the Internet. In this paper, we survey the state-of-the-art of multipath transmission techniques for QoS provisioning in wireless Internet access, focusing on the end-to-end transport layer protocols. The main challenges for the design of multipath TCP are reviewed, and the existing transport layer congestion control schemes are categorized. Multipath TCP and stream control transmission protocol (SCTP)-based transport layer protocols are discussed, and their limitations and/or impractical assumptions are addressed. Open research issues on the development of an effective, efficient, and practical multipath TCP protocol are summarized.

Modern computing devices such as smartphones and personal computers provide multiple network interfaces such as WiFi and 3G/LTE cellular network. Multipath TCP (MPTCP) is a commonly proposed method of aggregating the bandwidth of these interfaces. However, how MPTCP affects the quality of experience of existing services, especially to variances in bandwidth and latency among the individual paths over the wireless networks is not well-known. In this work, we explore the quality of service (QoS) and quality of experience (QoE) of adaptive video streaming using MPTCP over wireless network given its vast popularity and significant bandwidth demand. Unlike prior works, we conduct systematic measurements over three mobile network operators, AT&T, Verizon Wireless (VzW), and T-Mobile, along with WiFi. Based on extensive measurements, we show that MPTCP can improve the QoS and QoE of video streaming only if the network interfaces have the roughly similar bandwidth and latency. Our studies also show that MPTCP can perform worse than TCP in case of extreme differences between the network interfaces.

Multipath Transmission Control Protocol (MPTCP) enables multi-homed devices to establish multiple simultaneous routes for data transmission. Congestion Control (CC) is a fundamental mechanism for implementing and designing MPTCP. The Internet of Things (IoT) networks generate a massive volume of heterogeneous traffic with high dimensional states and diverse QoS characteristics. The existing MPTCP CC algorithms are unable to perform efficiently under highly mobile and dynamic IoT environments. We propose a novel model-free SDN-based adaptive actor-critic deep reinforcement learning framework based on a fuzzy normalized neural network to address the issue of CC for MPTCP in the IoT networks. In the proposed method, an agent can learn efficiently and better approximate the state-action value function of the actor and the action function of the critic to adjust the sub-flows congestion windows size adaptively according to the dynamic condition of a network. Simulation results show that the proposed scheme outperforms the state of the art schemes in terms of the goodput under highly-dynamic IoT environments.

Network functions virtualization (NFV) offers an alternative way to design, deploy, and manage networking services. This paper presents the implementation and deployment of a virtualized network function to solve MP-TCP’ shared bottlenecks problem. MP-TCP (MultiPath-TCP) is a protocol that allows hosts that have several interfaces to send data through multiple paths in the network. An MP-TCP connection is divided into several TCP connections called subflows that may follow different paths, in this way effective connection rates and resilience can be increased. However, in a scenario where MP-TCP is used with hosts having a single network interface, created subflows follow the same path even if several are available; this generates the problem known as a shared bottleneck. A solution to this problem is implemented as a SDN (Software Defined Networking) module that is integrated into OpenDayLight’s framework which is used as the SDN controller. This solution is then deployed as a NFV over a cloud infrastructure implemented with OpenStack over a high-end physical server. The deployment of the NFV is automated to a certain degree using Ansible, a DevOps tool. Finally, results of the tests of the SDN solution to MP-TCP’s shared bottlenecks and its operation as a NFV using a completely virtualized network are reported.

The spread of Edge Internet of Things (IoT) radically changes our lifestyle. However, the multimedia services in edge IoT are still stuck by inefficiency. The dynamic typologies perplex the transmission of massive real-time data. To solve this problem, multipath transmission control protocol (MPTCP) which has a natural advantage in transmission robustness and bandwidth aggregation is becoming a good choice. In this paper, failure-aware and delay-predicted multipath virtual queue scheduling (FD-MVQS) is proposed to optimize the MPTCP performance in edge IoT. FD-MVQS constructs a two-plane cooperative scheduling system. In the control plane, the transmission failure estimation and chaos theory-based arrival delay prediction methods are introduced to provide the foundation for prescheduling. In the data plane, the multipath virtual queue scheduling is designed to allocate segments to different subflows. Simulation results showed that the proposed FD-MVQS performed better than standard and typical multipath transmission solutions in throughput, delay, and segment disorder.

TCP was originally designed for single session, thus could not utilize resources of multiple paths. MPTCP (Multipath TCP) provides enhanced performance and reliability by using multiple paths simultaneously at the Transport layer. But, LIA based congestion control of basic MPTCP does not show good performance when characteristics of two path are asymmetrical in the aspects of bandwidth in high-speed networks. In this paper, we suggest new MPTCP control scheme by applying TCP CUBIC on the high-speed path and modified LIA on the other low-speed data path. Our proposed scheme can enhance performance of high-speed asymmetric path more than 4 times compared to the basic MPTCP protocol.

We present TeamBoost, a collaborative solution enabling a client device to leverage the cellular links of its nearby devices to enhance its overall connectivity to the cloud. TeamBoost architecture can serve for numerous internet-of-things (IoT) applications, yet in this work it is proposed as an alternative to conventional and costly H/W upgrades aiming to maintain up-to-date vehicle connectivity experience throughout the vehicles extended lifetime. TeamBoost's approach is to build upon a relatively new and yet well established Multi-Path TCP (MPTCP) protocol, exploiting the natural way that MPTCP splits an end-to-end single TCP stream into multiple MPTCP sub-streams that traverse different communication links. TeamBoost's novel implementation provides a non-trivial extension of MPTCP to TeamBoost's collaborative setting, where sub-streams are relayed through multiple independent devices. To support scalability of the system, the implementation of TeamBoost on the devices is restricted to the user-space, allowing it to be installed as any other application supported by its operating system (OS). Experimental results show the benefits of TeamBoost, in terms of both connectivity performance and robustness.

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record

Data transfer is a primary mechanism that can directly affect the overall performance of Big Data analytic systems. This is because most data are generated from several locations. It has been challenging to collect and transfer data among multiple storage regions. Using the traditional single-path transfer approaches is not efficient to serve several requirements of Big Data applications. In this dissertation, we propose an SDN-coordinated multipath transmission steering framework for Big Data transfer application. Multipath TCP protocol (MPTCP) and SDN architecture are mainly considered to design and develop our multipath transmission framework. To provide a practical routing solution, we propose a novel OpenFlow-Stats routing algorithm. In our algorithm, a new topology-pruning technique is applied, and the transmission paths are selected based on switch-port statistics. Our proposed framework is evaluated using the Mininet emulator and ONOS controller. The results show that our routing scheme can reduce the completion time of Big Data transfer up to 90% compared with the traditional routing scheme with disjoint paths and up to 35% compared with the previous work. Moreover, our proposed routing is more scalable than other previous works in that it can provide lower complexity and system overhead. The results show that our routing scheme produces 57% less overhead compared with the previous work.

The Internet of Things (IoT) has become an integral part of our daily lives, growing exponentially from a facility to a necessity. IoT has been utilized extensively through cloud computing and has proven an excellent technology for deploying in various fields. The data generated by the IoT devices gets transmitted to the cloud for processing and storage. However, with this approach, there are specific issues like latency, energy, computation resources availability, bandwidth, heterogeneity, storage, and network failure. To overcome these obstacles, fog computing is utilized as a middle tier. Fog computing gathers and processes the generated data closer to the user end before transmitting it to the cloud. This paper aims to conduct a structured review of the current state of fog computing and its architectures deployed across multiple industries. This paper also focuses on the implementation and critical parameters for introducing fog computing in IoT-cloud architecture. A detailed comparative analysis has been carried out for 5 different architectures considering various crucial parameters to identify how the quality of service and quality of experience for end users can be optimized. Finally, this paper looks at the multiple challenges that fog computing faces in a structured six-level approach. These challenges will also lead the way for future research in resource management, green computing, and security.

Effective live migration of virtual machines using partitioning and affinity aware-scheduling

Cloud computing is an emerging area where all the network services are provided in the form of pay per use. Utilizing the available resources efficiently is one of the challenge in the data center network. Cloud resources are given as virtual machine to the end users. Sometimes, Virtual Machines (VMs) are overloaded or underloaded. The overloaded VMs degrade the performance of applications and underloaded VMs lead to inefficient resource utilization. To effectively utilize the available resources, we have proposed Dynamic Agent-Based’Load Balancing (DA-LB) on Software-Defined Networking (SDN) using Back Propagation Artificial Neural Network (BPANN). This load balancing algorithm uses the global visibility of SDN to efficiently migrate virtual machines in the data center network. The results show that our SDN load balancing approach improves the overall network efficiency and works well on data migration.The proposed algorithm helps to increase the processing speed and predict the loaded VM in heavy load conditions and optimizes the resource utilization. The migration process results are compared with Multi-Path TCP (MPTCP) and Heuristic algorithm (HA) and it is seen that our proposed algorithm takes less migration time compared to MPTCP and HA.

With the development of the Internet of Things (IoT) and 5G, there are ubiquitous smart devices and network functions providing emerging network services efficiently and optimally through building many network connections based on WiFi, LTE/5G, Ethernet, and etc. The Multipath TCP (MPTCP) protocol that enables these devices to establish multiple paths for simultaneous data transmission, has been a widely used extension of standard TCP in smart devices and network functions. On the other hand, more heavy and time-varying traffic loads are generated in an MPTCP network, so that an efficient congestion control mechanism that schedules the traffic between multiple subflows and avoids congestion is highly required. In this paper, we propose a decentralized learning approach, DeepCC, to adapt to the volatile environments and realize the efficient congestion control. The Multi-Agent Deep Reinforcement Learning (MADRL) is used to learn a policy of congestion control for each subflow according to the real-time network states. To deal with the problem of the fixed state space and slow convergence, we adopt two self-attention mechanisms to receive the states and train the policy, respectively. Due to the asynchronous design of DeepCC, the learning process will not introduce extra delay and overhead on the decision-making process. Experiment results show that DeepCC consistently outperforms the well-known heuristic method and DRL-based MPTCP congestion control method in terms of goodput and jitter. Besides, DeepCC with the attention mechanism reduces convergence time by about 50% and increase goodput by about 80% compared with the commonly used structures of neural networks.

In the past decade there is a significant trend of implementing IoT technologies and standards in different industries. This trend brings cost reductions to the companies and other benefits as well. One of the main benefits is real-time and uniform data collection. The data are transferred using diverse communication protocols, from the sensor nodes to the centralized application. So far, current approaches in developing applications are not proved itself to be efficient enough in scenarios when a significant amount of data needs to be stored and analyzed. The focus of this paper is on development of software architecture suitable for usage in Internet of Things (IoT) systems where the larger amount of data can be processed in real-time. The software architecture is developed in order to support the sensor network for monitoring the small data center and it is based on microservices. Besides the system and its architecture, this paper presents the method of analysis of system performances in real-time environment. The proposal for lightweight microservice architecture, presented in this paper, is developed with .NET Core and RabbitMQ, with the utilization of MongoDB and SQLite databases systems for storing data collected with IoT devices. In this paper, the system evaluation and research results in different stress scenarios are also presented. Because of its complexity, only the most significant segments of architecture will be presented in this paper. The proposed solution showed that proposed lightweight architecture based on microservices could deal with the larger amount of sensor data in the case of using MongoDB. On the other hand, the usage of SQLite database is not recommended due to the lower performances and test results.