pDCell: an End-to-End Transport Protocol for Mobile Edge Computing Architectures

Pendiente publicación 2019To deal with increasingly demanding services and the rapid growth in number of devices and traffic, 5G and beyond mobile networks need to provide extreme capacity and peak data rates at very low latencies. Consequently, applications and services need to move closer to the users into so-called edge data centers. At the same time, there is a trend to virtualize core and radio access network functionalities and bring them to edge data centers as well. However, as is known from conventional data centers, legacy transport protocols such as TCP are vastly suboptimal in such a setting. In this work, we present pDCell, a transport design for mobile edge computing architectures that extends data center transport approaches to the mobile network domain. Specifically, pDCell ensures that data traffic from application servers arrives at virtual radio functions (i.e., C-RAN Central Units) timely to (i) minimize queuing delays and (ii) to maximize cellular network utilization. We show that pDCell significantly improves flow completion times compared to conventional transport protocols like TCP and data center transport solutions, and is thus an essential component for future mobile networks.This work is partially supported by the European Research Council grant ERC CoG 617721, the Ramon y Cajal grant from the Spanish Ministry of Economy and Competitiveness RYC-2012-10788, by the European Union H2020-ICT grant 644399 (MONROE), by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586) and the Madrid Regional Government through the TIGRE5-CM program (S2013/ICE-2919). Further, the work of Dr. Kogan is partially supported by a grant from the Cisco University Research Program Fund, an advised fund of Silicon Valley Community Foundation.No publicad

End-to-End Simulation of 5G mmWave Networks

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns--3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and highly customizable, making it easy to integrate algorithms or compare Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example. The module is interfaced with the core network of the ns--3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.Comment: 25 pages, 16 figures, submitted to IEEE Communications Surveys and Tutorials (revised Jan. 2018

Measurement-Driven Algorithm and System Design for Wireless and Datacenter Networks

The growing number of mobile devices and data-intensive applications pose unique challenges for wireless access networks as well as datacenter networks that enable modern cloud-based services. With the enormous increase in volume and complexity of traffic from applications such as video streaming and cloud computing, the interconnection networks have become a major performance bottleneck. In this thesis, we study algorithms and architectures spanning several layers of the networking protocol stack that enable and accelerate novel applications and that are easily deployable and scalable. The design of these algorithms and architectures is motivated by measurements and observations in real world or experimental testbeds. In the first part of this thesis, we address the challenge of wireless content delivery in crowded areas. We present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast. AMuSe is based on accurate receiver feedback and incurs a small control overhead. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes which periodically send information about the channel quality to the multicast sender. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe's feedback to optimally tune the physical layer multicast rate. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. We implemented the AMuSe system on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi nodes. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions. Further, our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. As an example application, MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality. Next, we specifically focus on ensuring high Quality of Experience (QoE) for video streaming over WiFi multicast. We formulate the problem of joint adaptation of multicast transmission rate and video rate for ensuring high video QoE as a utility maximization problem and propose an online control algorithm called DYVR which is based on Lyapunov optimization techniques. We evaluated the performance of DYVR through analysis, simulations, and experiments using a testbed composed of Android devices and o the shelf APs. Our evaluation shows that DYVR can ensure high video rates while guaranteeing a low but acceptable number of segment losses, buffer underflows, and video rate switches. We leverage the lessons learnt from AMuSe for WiFi to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance. DyMo employs eMBMS for broadcasting instructions which indicate the reporting rates as a function of the observed Quality of Service (QoS) for each UE. This simple feedback mechanism collects very limited QoS reports which can be used for network optimization. We evaluated the performance of DyMo analytically and via simulations. DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. In the second part of the thesis, we study datacenter networks which are key enablers of the end-user applications such as video streaming and storage. Datacenter applications such as distributed file systems, one-to-many virtual machine migrations, and large-scale data processing involve bulk multicast flows. We propose a hardware and software system for enabling physical layer optical multicast in datacenter networks using passive optical splitters. We built a prototype and developed a simulation environment to evaluate the performance of the system for bulk multicasting. Our evaluation shows that the optical multicast architecture can achieve higher throughput and lower latency than IP multicast and peer-to-peer multicast schemes with lower switching energy consumption. Finally, we study the problem of congestion control in datacenter networks. Quantized Congestion Control (QCN), a switch-supported standard, utilizes direct multi-bit feedback from the network for hardware rate limiting. Although QCN has been shown to be fast-reacting and effective, being a Layer-2 technology limits its adoption in IP-routed Layer 3 datacenters. We address several design challenges to overcome QCN feedback's Layer- 2 limitation and use it to design window-based congestion control (QCN-CC) and load balancing (QCN-LB) schemes. Our extensive simulations, based on real world workloads, demonstrate the advantages of explicit, multi-bit congestion feedback, especially in a typical environment where intra-datacenter traffic with short Round Trip Times (RTT: tens of s) run in conjunction with web-facing traffic with long RTTs (tens of milliseconds)

Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

Fog-supported delay-constrained energy-saving live migration of VMs over multiPath TCP/IP 5G connections

The incoming era of the fifth-generation fog computing-supported radio access networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of virtual machines (VMs) toward nearby fog data centers. For this purpose, the bandwidths of the multiple wireless network interface cards of the wireless devices may be aggregated under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to the fading and mobility-induced phenomena, the energy consumptions of the current state-of-the-art VM migration techniques may still offset their expected benefits. Motivated by these considerations, in this paper, we analytically characterize and implement in software and numerically test the optimal minimum-energy settable-complexity bandwidth manager (SCBM) for the live migration of VMs over 5G FOGRAN MPTCP connections. The key features of the proposed SCBM are that: 1) its implementation complexity is settable on-line on the basis of the target energy consumption versus implementation complexity tradeoff; 2) it minimizes the network energy consumed by the wireless device for sustaining the migration process under hard constraints on the tolerated migration times and downtimes; and 3) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the wireless environment without requiring forecasting. The actual effectiveness of the proposed SCBM is supported by extensive energy versus delay performance comparisons that cover: 1) a number of heterogeneous 3G/4G/WiFi FOGRAN scenarios; 2) synthetic and real-world workloads; and, 3) MPTCP and wireless connections

Resource Management Algorithms for Computing Hardware Design and Operations: From Circuits to Systems

The complexity of computation hardware has increased at an unprecedented rate for the last few decades. On the computer chip level, we have entered the era of multi/many-core processors made of billions of transistors. With transistor budget of this scale, many functions are integrated into a single chip. As such, chips today consist of many heterogeneous cores with intensive interaction among these cores. On the circuit level, with the end of Dennard scaling, continuously shrinking process technology has imposed a grand challenge on power density. The variation of circuit further exacerbated the problem by consuming a substantial time margin. On the system level, the rise of Warehouse Scale Computers and Data Centers have put resource management into new perspective. The ability of dynamically provision computation resource in these gigantic systems is crucial to their performance. In this thesis, three different resource management algorithms are discussed. The first algorithm assigns adaptivity resource to circuit blocks with a constraint on the overhead. The adaptivity improves resilience of the circuit to variation in a cost-effective way. The second algorithm manages the link bandwidth resource in application specific Networks-on-Chip. Quality-of-Service is guaranteed for time-critical traffic in the algorithm with an emphasis on power. The third algorithm manages the computation resource of the data center with precaution on the ill states of the system. Q-learning is employed to meet the dynamic nature of the system and Linear Temporal Logic is leveraged as a tool to describe temporal constraints. All three algorithms are evaluated by various experiments. The experimental results are compared to several previous work and show the advantage of our methods

Reconfigurable Optically Interconnected Systems

With the immense growth of data consumption in today's data centers and high-performance computing systems driven by the constant influx of new applications, the network infrastructure supporting this demand is under increasing pressure to enable higher bandwidth, latency, and flexibility requirements. Optical interconnects, able to support high bandwidth wavelength division multiplexed signals with extreme energy efficiency, have become the basis for long-haul and metro-scale networks around the world, while photonic components are being rapidly integrated within rack and chip-scale systems. However, optical and photonic interconnects are not a direct replacement for electronic-based components. Rather, the integration of optical interconnects with electronic peripherals allows for unique functionalities that can improve the capacity, compute performance and flexibility of current state-of-the-art computing systems. This requires physical layer methodologies for their integration with electronic components, as well as system level control planes that incorporates the optical layer characteristics. This thesis explores various network architectures and the associated control plane, hardware infrastructure, and other supporting software modules needed to integrate silicon photonics and MEMS based optical switching into conventional datacom network systems ranging from intra-data center and high-performance computing systems to the metro-scale layer networks between data centers. In each of these systems, we demonstrate dynamic bandwidth steering and compute resource allocation capabilities to enable significant performance improvements. The key accomplishments of this thesis are as follows. In Part 1, we present high-performance computing network architectures that integrate silicon photonic switches for optical bandwidth steering, enabling multiple reconfigurable topologies that results in significant system performance improvements. As high-performance systems rely on increased parallelism by scaling up to greater numbers of processor nodes, communication between these nodes grows rapidly and the interconnection network becomes a bottleneck to the overall performance of the system. It has been observed that many scientific applications operating on high-performance computing systems cause highly skewed traffic over the network, congesting only a small percentage of the total available links while other links are underutilized. This mismatch of the traffic and the bandwidth allocation of the physical layer network presents the opportunity to optimize the bandwidth resource utilization of the system by using silicon photonic switches to perform bandwidth steering. This allows the individual processors to perform at their maximum compute potential and thereby improving the overall system performance. We show various testbeds that integrates both microring resonator and Mach-Zehnder based silicon photonic switches within Dragonfly and Fat-Tree topology networks built with conventional equipment, and demonstrate 30-60% reduction in execution time of real high-performance benchmark applications. Part 2 presents a flexible network architecture and control plane that enables autonomous bandwidth steering and IT resource provisioning capabilities between metro-scale geographically distributed data centers. It uses a software-defined control plane to autonomously provision both network and IT resources to support different quality of service requirements and optimizes resource utilization under dynamically changing load variations. By actively monitoring both the bandwidth utilization of the network and CPU or memory resources of the end hosts, the control plane autonomously provisions background or dynamic connections with different levels of quality of service using optical MEMS switching, as well as initializing live migrations of virtual machines to consolidate or distribute workload. Together these functionalities provide flexibility and maximize efficiency in processing and transferring data, and enables energy and cost savings by scaling down the system when resources are not needed. An experimental testbed of three data center nodes was built to demonstrate the feasibility of these capabilities. Part 3 presents Lightbridge, a communications platform specifically designed to provide a more seamless integration between processor nodes and an optically switched network. It addresses some of the crucial issues faced by the works presented in the previous chapters related to optical switching. When optical switches perform switching operations, they change the physical topology of the network, and they lack the capability to buffer packets, resulting in certain optical circuits being unavailable. This prompts the question of whether it is safe to transmit packets by end hosts at any given time. Lightbridge was developed to coordinate switching and routing of optical circuits across the network, by having the processors gain information about the current state of the optical network before transmitting packets, and being able to buffer packets when the optical circuit is not available. This part describes details of Lightbridge which is constituted by a loadable Linux kernel module along with other supporting modifications to the Linux kernel in order to achieve the necessary functionalities

Fairness in a data center

Existing data centers utilize several networking technologies in order to handle the performance requirements of different workloads. Maintaining diverse networking technologies increases complexity and is not cost effective. This results in the current trend to converge all traffic into a single networking fabric. Ethernet is both cost-effective and ubiquitous, and as such it has been chosen as the technology of choice for the converged fabric. However, traditional Ethernet does not satisfy the needs of all traffic workloads, for the most part, due to its lossy nature and, therefore, has to be enhanced to allow for full convergence. The resulting technology, Data Center Bridging (DCB), is a new set of standards defined by the IEEE to make Ethernet lossless even in the presence of congestion. As with any new networking technology, it is critical to analyze how the different protocols within DCB interact with each other as well as how each protocol interacts with existing technologies in other layers of the protocol stack. This dissertation presents two novel schemes that address critical issues in DCB networks: fairness with respect to packet lengths and fairness with respect to flow control and bandwidth utilization. The Deficit Round Robin with Adaptive Weight Control (DRR-AWC) algorithm actively monitors the incoming streams and adjusts the scheduling weights of the outbound port. The algorithm was implemented on a real DCB switch and shown to increase fairness for traffic consisting of mixed-length packets. Targeted Priority-based Flow Control (TPFC) provides a hop-by-hop flow control mechanism that restricts the flow of aggressor streams while allowing victim streams to continue unimpeded. Two variants of the targeting mechanism within TPFC are presented and their performance evaluated through simulation