Content Defined Optical Network

Optical interconnection has become one of the key technologies to adapt the needs of large-scale data center networking with the advantages of large capacity, high bandwidth, and high efficiency. Data center optical interconnection has the characteristics of resource and technology heterogeneity. Its networking and control face enormous challenges for the increasing number of users with a high level quality of service requirements. Around different scenarios, there are a series of key networking and control problems in data center optical interconnection, such as multiple layers and stratums resources optimization in inter-data center, and time-aware resource scheduling in intra-data center. To solve these problems and challenges, this chapter mainly researches on content defined optical networking and integrated control for data center. For networking of vertical “multi-layer-carried” and horizontal “heterogeneous-cross-stratum”, the chapter launches research work around application scenarios about inter-data center optical interconnection with optical network, and intra-data center. The model architecture, implementation mechanism and control strategy are analyzed and demonstrated on the experiment and simulation platform of data center optical interconnection. This chapter will provide important references for future diverse applications of data center optical interconnection and software defined networking and control in practice

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Analyzing Methods and Opportunities in Software-Defined (SDN) Networks for Data Traffic Optimizations

Computer networks are dynamic and require constant updating and monitoring of operations to meet the growing volume of data trafficked. This generates a number of cost issues as well as performance management and tuning to deliver granular quality of service (QoS), balancing data load, and controlling the occurrence of bottlenecks. As an alternative, a new programmable network paradigm has been used under the name of Software Defined Networks (SDN). The SDN consists of decoupling the data plane and controlling the network, where a programmable controller is responsible for managing rules for routing the data to various devices. Thus, the hardware that remains in the network data stream simply addresses the routing of the packets quickly according to these rules. In this context, this article conducts a study on different methods and approaches that are being used in the literature to solve problems in the optimization of data traffic in the network through the use of SDN. In particular, this study differs from other reviews of SDN because it focuses on issues such as QoS, load balancing, and congestion control. Finally, in addition to the review of the SDN's state-of-the-art in the areas mentioned, a survey of future challenges and research opportunities in the area is also presented. load balancing and congestion control. Finally, in addition to the review of the SDN's state-of-the-art in the areas mentioned, a survey of future challenges and research opportunities in the area is also presented. load balancing and congestion control. Finally, in addition to the review of the SDN's state-of-the-art in the areas mentioned, a survey of future challenges and research opportunities in the area is also presented

QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts

Large inter-datacenter transfers are crucial for cloud service efficiency and are increasingly used by organizations that have dedicated wide area networks between datacenters. A recent work uses multicast forwarding trees to reduce the bandwidth needs and improve completion times of point-to-multipoint transfers. Using a single forwarding tree per transfer, however, leads to poor performance because the slowest receiver dictates the completion time for all receivers. Using multiple forwarding trees per transfer alleviates this concern--the average receiver could finish early; however, if done naively, bandwidth usage would also increase and it is apriori unclear how best to partition receivers, how to construct the multiple trees and how to determine the rate and schedule of flows on these trees. This paper presents QuickCast, a first solution to these problems. Using simulations on real-world network topologies, we see that QuickCast can speed up the average receiver's completion time by as much as $10\times$ while only using $1.04\times$ more bandwidth; further, the completion time for all receivers also improves by as much as $1.6\times$ faster at high loads.Comment: [Extended Version] Accepted for presentation in IEEE INFOCOM 2018, Honolulu, H

OFLoad: An OpenFlow-based dynamic load balancing strategy for datacenter networks

The latest tremendous growth in the Internet traffic has determined the entry into a new era of mega-datacenters, meant to deal with this explosion of data traffic. However this big data with its dynamically changing traffic patterns and flows might result in degradations of the application performance eventually affecting the network operators’ revenue. In this context there is a need for an intelligent and efficient network management system that makes the best use of the available bisection bandwidth abundance to achieve high utilization and performance. This paper proposes OFLoad, an OpenFlow-based dynamic load balancing strategy for datacenter networks that enables the efficient use of the network resources capacity. A real experimental prototype is built and the proposed solution is compared against other solutions from the literature in terms of load-balancing. The aim of OFLoad is to enable the instant configuration of the network by making the best use of the available resources at the lowest cost and complexity

OFLoad: An OpenFlow-based dynamic load balancing strategy for datacenter networks

The latest tremendous growth in the Internet traffic has determined the entry into a new era of mega-datacenters, meant to deal with this explosion of data traffic. However this big data with its dynamically changing traffic patterns and flows might result in degradations of the application performance eventually affecting the network operators’ revenue. In this context there is a need for an intelligent and efficient network management system that makes the best use of the available bisection bandwidth abundance to achieve high utilization and performance. This paper proposes OFLoad, an OpenFlow-based dynamic load balancing strategy for datacenter networks that enables the efficient use of the network resources capacity. A real experimental prototype is built and the proposed solution is compared against other solutions from the literature in terms of load-balancing. The aim of OFLoad is to enable the instant configuration of the network by making the best use of the available resources at the lowest cost and complexity

Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201