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

Future of networking is the future of Big Data, The

2019 Summer.Includes bibliographical references.Scientific domains such as Climate Science, High Energy Particle Physics (HEP), Genomics, Biology, and many others are increasingly moving towards data-oriented workflows where each of these communities generates, stores and uses massive datasets that reach into terabytes and petabytes, and projected soon to reach exabytes. These communities are also increasingly moving towards a global collaborative model where scientists routinely exchange a significant amount of data. The sheer volume of data and associated complexities associated with maintaining, transferring, and using them, continue to push the limits of the current technologies in multiple dimensions - storage, analysis, networking, and security. This thesis tackles the networking aspect of big-data science. Networking is the glue that binds all the components of modern scientific workflows, and these communities are becoming increasingly dependent on high-speed, highly reliable networks. The network, as the common layer across big-science communities, provides an ideal place for implementing common services. Big-science applications also need to work closely with the network to ensure optimal usage of resources, intelligent routing of requests, and data. Finally, as more communities move towards data-intensive, connected workflows - adopting a service model where the network provides some of the common services reduces not only application complexity but also the necessity of duplicate implementations. Named Data Networking (NDN) is a new network architecture whose service model aligns better with the needs of these data-oriented applications. NDN's name based paradigm makes it easier to provide intelligent features at the network layer rather than at the application layer. This thesis shows that NDN can push several standard features to the network. This work is the first attempt to apply NDN in the context of large scientific data; in the process, this thesis touches upon scientific data naming, name discovery, real-world deployment of NDN for scientific data, feasibility studies, and the designs of in-network protocols for big-data science

An Energy-conscious Transport Protocol for Multi-hop Wireless Networks

We present a transport protocol whose goal is to reduce power consumption without compromising delivery requirements of applications. To meet its goal of energy efficiency, our transport protocol (1) contains mechanisms to balance end-to-end vs. local retransmissions; (2) minimizes acknowledgment traffic using receiver regulated rate-based flow control combined with selected acknowledgements and in-network caching of packets; and (3) aggressively seeks to avoid any congestion-based packet loss. Within a recently developed ultra low-power multi-hop wireless network system, extensive simulations and experimental results demonstrate that our transport protocol meets its goal of preserving the energy efficiency of the underlying network.Defense Advanced Research Projects Agency (NBCHC050053

JTP, an energy-aware transport protocol for mobile ad hoc networks (PhD thesis)

Wireless ad-hoc networks are based on a cooperative communication model, where all nodes not only generate traffic but also help to route traffic from other nodes to its final destination. In such an environment where there is no infrastructure support the lifetime of the network is tightly coupled with the lifetime of individual nodes. Most of the devices that form such networks are battery-operated, and thus it becomes important to conserve energy so as to maximize the lifetime of a node. In this thesis, we present JTP, a new energy-aware transport protocol, whose goal is to reduce power consumption without compromising delivery requirements of applications. JTP has been implemented within the JAVeLEN system. JAVeLEN [RKM+08], is a new system architecture for ad hoc networks that has been developed to elevate energy efficiency as a first-class optimization metric at all protocol layers, from physical to transport. Thus, energy gains obtained in one layer would not be offset by incompatibilities and/or inefficiencies in other layers. To meet its goal of energy efficiency, JTP (1) contains mechanisms to balance end-toend vs. local retransmissions; (2) minimizes acknowledgment traffic using receiver regulated rate-based flow control combined with selected acknowledgments and in-network caching of packets; and (3) aggressively seeks to avoid any congestion-based packet loss. Within this ultra low-power multi-hop wireless network system, simulations and experimental results demonstrate that our transport protocol meets its goal of preserving the energy efficiency of the underlying network. JTP has been implemented on the actual JAVeLEN nodes and its benefits have been demonstrated on a real system

JTP, an energy-aware transport protocol for mobile ad hoc networks

Wireless ad-hoc networks are based on a cooperative communication model, where all nodes not only generate traffic but also help to route traffic from other nodes to its final destination. In such an environment where there is no infrastructure support the lifetime of the network is tightly coupled with the lifetime of individual nodes. Most of the devices that form such networks are battery-operated, and thus it becomes important to conserve energy so as to maximize the lifetime of a node. In this thesis, we present JTP, a new energy-aware transport protocol, whose goal is to reduce power consumption without compromising delivery requirements of applications. JTP has been implemented within the JAVeLEN system. JAVeLEN~\cite{javelen08redi}, is a new system architecture for ad hoc networks that has been developed to elevate energy efficiency as a first-class optimization metric at all protocol layers, from physical to transport. Thus, energy gains obtained in one layer would not be offset by incompatibilities and/or inefficiencies in other layers. To meet its goal of energy efficiency, JTP (1) contains mechanisms to balance end-to-end vs. local retransmissions; (2) minimizes acknowledgment traffic using receiver regulated rate-based flow control combined with selected acknowledgments and in-network caching of packets; and (3) aggressively seeks to avoid any congestion-based packet loss. Within this ultra low-power multi-hop wireless network system, simulations and experimental results demonstrate that our transport protocol meets its goal of preserving the energy efficiency of the underlying network. JTP has been implemented on the actual JAVeLEN nodes and its benefits have been demoed on a real system

Effective and Economical Content Delivery and Storage Strategies for Cloud Systems

Cloud computing has proved to be an effective infrastructure to host various applications and provide reliable and stable services. Content delivery and storage are two main services provided by the cloud. A high-performance cloud can reduce the cost of both cloud providers and customers, while providing high application performance to cloud clients. Thus, the performance of such cloud-based services is closely related to three issues. First, when delivering contents from the cloud to users or transferring contents between cloud datacenters, it is important to reduce the payment costs and transmission time. Second, when transferring contents between cloud datacenters, it is important to reduce the payment costs to the internet service providers (ISPs). Third, when storing contents in the datacenters, it is crucial to reduce the file read latency and power consumption of the datacenters. In this dissertation, we study how to effectively deliver and store contents on the cloud, with a focus on cloud gaming and video streaming services. In particular, we aim to address three problems. i) Cost-efficient cloud computing system to support thin-client Massively Multiplayer Online Game (MMOG): how to achieve high Quality of Service (QoS) in cloud gaming and reduce the cloud bandwidth consumption; ii) Cost-efficient inter-datacenter video scheduling: how to reduce the bandwidth payment cost by fully utilizing link bandwidth when cloud providers transfer videos between datacenters; iii) Energy-efficient adaptive file replication: how to adapt to time-varying file popularities to achieve a good tradeoff between data availability and efficiency, as well as reduce the power consumption of the datacenters. In this dissertation, we propose methods to solve each of aforementioned challenges on the cloud. As a result, we build a cloud system that has a cost-efficient system to support cloud clients, an inter-datacenter video scheduling algorithm for video transmission on the cloud and an adaptive file replication algorithm for cloud storage system. As a result, the cloud system not only benefits the cloud providers in reducing the cloud cost, but also benefits the cloud customers in reducing their payment cost and improving high cloud application performance (i.e., user experience). Finally, we conducted extensive experiments on many testbeds, including PeerSim, PlanetLab, EC2 and a real-world cluster, which demonstrate the efficiency and effectiveness of our proposed methods. In our future work, we will further study how to further improve user experience in receiving contents and reduce the cost due to content transfer

Data transfer scheduling with advance reservation and provisioning

Over the years, scientific applications have become more complex and more data intensive. Although through the use of distributed resources the institutions and organizations gain access to the resources needed for their large-scale applications, complex middleware is required to orchestrate the use of these storage and network resources between collaborating parties, and to manage the end-to-end processing of data. We present a new data scheduling paradigm with advance reservation and provisioning. Our methodology provides a basis for provisioning end-to-end high performance data transfers which require integration between system, storage and network resources, and coordination between reservation managers and data transfer nodes. This allows researchers/users and higher level meta-schedulers to use data placement as a service where they can plan ahead and reserve time and resources for their data movement operations. We present a novel approach for evaluating time-dependent structures with bandwidth guaranteed paths. We present a practical online scheduling model using advance reservation in dynamic network with time constraints. In addition, we report a new polynomial algorithm presenting possible reservation options and alternatives for earliest completion and shortest transfer duration. We enhance the advance network reservation system by extending the underlying mechanism to provide a new service in which users submit their constraints and the system suggests possible reservation requests satisfying users\u27 requirements. We have studied scheduling data transfer operation with resource and time conflicts. We have developed a new scheduling methodology considering resource allocation in client sites and bandwidth allocation on network link connecting resources. Some other major contributions of our study include enhanced reliability, adaptability, and performance optimization of distributed data placement tasks. While designing this new data scheduling architecture, we also developed other important methodologies such as early error detection, failure awareness, job aggregation, and dynamic adaptation of distributed data placement tasks. The adaptive tuning includes dynamically setting data transfer parameters and controlling utilization of available network capacity. Our research aims to provide a middleware to improve the data bottleneck in high performance computing systems

Data Management and Wireless Transport for Large Scale Sensor Networks

Today many large scale sensor networks have emerged, which span many different sensing applications. Each of these sensor networks often consists of millions of sensors collecting data and supports thousands of users with diverse data needs. Between users and wireless sensors there are often a group of powerful servers that collect and process data from sensors and answer users\u27 requests. To build such a large scale sensor network, we have to answer two fundamental research problems: i) what data to transmit from sensors to servers? ii) how to transmit the data over wireless links? Wireless sensors often can not transmit all collected data due to energy and bandwidth constraints. Therefore sensors need to decide what data to transmit to best satisfy users\u27 data requests. Sensor network users can often tolerate some data errors, thus sensors may transmit data in lower fidelity but still satisfy users\u27 requests. There are generally two types of requests-raw data requests and meta-data requests. To answer users\u27 raw data requests, we propose a model-driven data collection approach, PRESTO. PRESTO splits intelligence between sensors and servers, i.e., resource-rich servers perform expensive model training and resource-poor sensors perform simple model evaluation. PRESTO can significantly reduce data to be transmitted without sacrificing service quality. To answer users\u27 meta-data request, we propose a utility-driven multi-user data sharing approach, MUDS. MUDS uses utility function to unify diverse meta-data metrics. Sensors estimate utility value of each data packet and sends packets with highest utility first to improve overall system utility. After deciding what data to transmit from sensors to servers, the next question is how to transmit these data over wireless links. Wireless transport often suffers low bandwidth and unstable connectivity. In order to improve wireless transport, I propose a clean-slate re-design of wireless transport, Hop. Hop uses reliable perhop block transfer as a building block and builds all other components including hidden-terminal avoidance, congestion avoidance, and end-to-end reliability on top of it. Hop is built based on three key ideas: a) hop-by-hop transfer adapts to the lossy and highly variable nature of wireless channel significantly better than end-to-end transfer, b) the use of blocks as the unit of control is more efficient over wireless links than the use of packets, and c) the duplicated functionality in different layers in the network stack should be removed to simplify the protocol and avoid complex interaction