The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

https://publications.uni.lu/bitstream/10993/20596/1/survey_on_SDN.pdf

Software-Defined Networking: A Comprehensive Survey

Large-scale Internet services require a computing infrastructure that can beappropriately described as a warehouse-sized computing system. The cost ofbuilding datacenter facilities capable of delivering a given power capacity tosuch a computer can rival the recurring energy consumption costs themselves.Therefore, there are strong economic incentives to operate facilities as closeas possible to maximum capacity, so that the non-recurring facility costs canbe best amortized. That is difficult to achieve in practice because ofuncertainties in equipment power ratings and because power consumption tends tovary significantly with the actual computing activity. Effective powerprovisioning strategies are needed to determine how much computing equipmentcan be safely and efficiently hosted within a given power budget.In this paper we present the aggregate power usage characteristics of largecollections of servers (up to 15 thousand) for different classes ofapplications over a period of approximately six months. Those observationsallow us to evaluate opportunities for maximizing the use of the deployed powercapacity of datacenters, and assess the risks of over-subscribing it. We findthat even in well-tuned applications there is a noticeable gap (7 - 16%)between achieved and theoretical aggregate peak power usage at the clusterlevel (thousands of servers). The gap grows to almost 40% in wholedatacenters. This headroom can be used to deploy additional compute equipmentwithin the same power budget with minimal risk of exceeding it. We use ourmodeling framework to estimate the potential of power management schemes toreduce peak power and energy usage. We find that the opportunities for powerand energy savings are significant, but greater at the cluster-level (thousandsof servers) than at the rack-level (tens). Finally we argue that systems needto be power efficient across the activity range, and not only at peakperformance levels.

/pdf/power-provisioning-for-a-warehouse-sized-computer-3iyo1da2iy.pdf

Power provisioning for a warehouse-sized computer

The idea of programmable networks has recently re-gained considerable momentum due to the emergence of the Software-Defined Networking (SDN) paradigm. SDN, often referred to as a ''radical new idea in networking'', promises to dramatically simplify network management and enable innovation through network programmability. This paper surveys the state-of-the-art in programmable networks with an emphasis on SDN. We provide a historic perspective of programmable networks from early ideas to recent developments. Then we present the SDN architecture and the OpenFlow standard in particular, discuss current alternatives for implementation and testing of SDN-based protocols and services, examine current and future SDN applications, and explore promising research directions based on the SDN paradigm.

/pdf/a-survey-of-software-defined-networking-past-present-and-6tt309ed7r.pdf

A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks

Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

The memory subsystem accounts for a significant cost and power budget of a computer system. Current DRAM-based main memory systems are starting to hit the power and cost limit. An alternative memory technology that uses resistance contrast in phase-change materials is being actively investigated in the circuits community. Phase Change Memory (PCM) devices offer more density relative to DRAM, and can help increase main memory capacity of future systems while remaining within the cost and power constraints.In this paper, we analyze a PCM-based hybrid main memory system using an architecture level model of PCM.We explore the trade-offs for a main memory system consisting of PCMstorage coupled with a small DRAM buffer. Such an architecture has the latency benefits of DRAM and the capacity benefits of PCM. Our evaluations for a baseline system of 16-cores with 8GB DRAM show that, on average, PCM can reduce page faults by 5X and provide a speedup of 3X. As PCM is projected to have limited write endurance, we also propose simple organizational and management solutions of the hybrid memory that reduces the write traffic to PCM, boosting its lifetime from 3 years to 9.7 years.

http://people.cs.uchicago.edu/~ftchong/290N-W10/pcm.pdf

Scalable high performance main memory system using phase-change memory technology

Cloud computing makes extensive use of virtual machines because they permit workloads to be isolated from one another and for the resource usage to be somewhat controlled. In this paper, we explore the performance of traditional virtual machine (VM) deployments, and contrast them with the use of Linux containers. We use KVM as a representative hypervisor and Docker as a container manager. Our results show that containers result in equal or better performance than VMs in almost all cases. Both VMs and containers require tuning to support I/Ointensive applications. We also discuss the implications of our performance results for future cloud architectures.

/pdf/an-updated-performance-comparison-of-virtual-machines-and-51dkcxz9au.pdf

An updated performance comparison of virtual machines and Linux containers

Servers: high-end, multiprocessor systems running commercial workloads, have typically included extensive cooling systems and resided in custom-built rooms for high-power delivery. Recently, as transistor density and demand for computing resources have rapidly increased, even these high-end systems face energy-use constraints. Commercial-server energy management now focuses on conserving power in the memory and microprocessor subsystems. Because their workloads are typically structured as multiple application programs, system-wide approaches are more applicable to multiprocessor environments in commercial servers than techniques that primarily apply to single-application environments, such as those based on compiler optimizations.

Energy management for commercial servers

The combination of increasing component power consumption, a desire for denser systems, and the required performance growth in the face of technology-scaling issues are posing enormous challenges for powering and cooling of server systems. The challenges are directly linked to the peak power consumption of servers.Our solution, Power Shifting, reduces the peak power consumption of servers minimizing the impact on performance. We reduce peak power consumption by using workload-guided dynamic allocation of power among components incorporating real-time performance feedback, activity-related power estimation techniques, and performance-sensitive activity-regulation mechanisms to enforce power budgets.We apply our techniques to a computer system with a single processor and memory. Power shifting adds a system power manager with a dynamic, global view of the system's power consumption to continuously re-budget the available power amongst the two components. Our contributions include:• Demonstration of the greater effectiveness of dynamic power allocation over static budgeting,• Evaluation of different power shifting policies,• Analysis of system and workload factors critical to successful power shifting, and• Proposal of performance-sensitive power budget enforcement mechanisms that ensure system reliability.

/pdf/a-performance-conserving-approach-for-reducing-peak-power-3korzdba85.pdf

A performance-conserving approach for reducing peak power consumption in server systems

Datacenter networks deal with a variety of workloads, ranging from latency-sensitive small flows to bandwidth-hungry large flows. Load balancing schemes based on flow hashing, e.g., ECMP, cause congestion when hash collisions occur and can perform poorly in asymmetric topologies. Recent proposals to load balance the network require centralized traffic engineering, multipath-aware transport, or expensive specialized hardware. We propose a mechanism that avoids these limitations by (i) pushing load-balancing functionality into the soft network edge (e.g., virtual switches) such that no changes are required in the transport layer, customer VMs, or networking hardware, and (ii) load balancing on fine-grained, near-uniform units of data (flowcells) that fit within end-host segment offload optimizations used to support fast networking speeds. We design and implement such a soft-edge load balancing scheme, called Presto, and evaluate it on a 10 Gbps physical testbed. We demonstrate the computational impact of packet reordering on receivers and propose a mechanism to handle reordering in the TCP receive offload functionality. Presto's performance closely tracks that of a single, non-blocking switch over many workloads and is adaptive to failures and topology asymmetry.

/pdf/presto-edge-based-load-balancing-for-fast-datacenter-4bf7h8gu42.pdf

Presto: Edge-based Load Balancing for Fast Datacenter Networks

Software-defined networking introduces the possibility of building self-tuning networks that constantly monitor network conditions and react rapidly to important events such as congestion. Unfortunately, state-of-the-art monitoring mechanisms for conventional networks require hundreds of milliseconds to seconds to extract global network state, like link utilization or the identity of "elephant" flows. Such latencies are adequate for responding to persistent issues, e.g., link failures or long-lasting congestion, but are inadequate for responding to transient problems, e.g., congestion induced by bursty workloads sharing a link. In this paper, we present Planck, a novel network measurement architecture that employs oversubscribed port mirroring to extract network information at 280 µs--7 ms timescales on a 1 Gbps commodity switch and 275 µs--4 ms timescales on a 10 Gbps commodity switch,over 11x and 18x faster than recent approaches, respectively (and up to 291x if switch firmware allowed buffering to be disabled on some ports). To demonstrate the value of Planck's speed and accuracy, we use it to drive a traffic engineering application that can reroute congested flows in milliseconds. On a 10 Gbps commodity switch, Planck-driven traffic engineering achieves aggregate throughput within 1--4% of optimal for most workloads we evaluated, even with flows as small as 50 MiB, an improvement of up to 53% over previous schemes.

/pdf/planck-millisecond-scale-monitoring-and-control-for-3feapffen1.pdf

Wesley M. Felter

Papers

An updated performance comparison of virtual machines and Linux containers

Energy management for commercial servers

A performance-conserving approach for reducing peak power consumption in server systems

Presto: Edge-based Load Balancing for Fast Datacenter Networks

Planck: millisecond-scale monitoring and control for commodity networks