Nedeljko Vasic

Energy-aware traffic engineering

Energy consumption of the Internet is already substantial and it is likely to increase as operators deploy faster equipment to handle popular bandwidth-intensive services, such as streaming and video-on-demand. Existing work on energy saving considers local adaptation relying primarily on hardware-based techniques, such as sleeping and rate adaptation. We argue that a complete solution requires a network-wide approach that works in conjunction with local measures. However, traditional traffic engineering objectives do not include energy. This paper presents Energy-Aware Traffic engineering (EATe), a technique that takes energy consumption into account while achieving the same traffic rates as the energy-oblivious approaches. EATe uses a scalable, online technique to spread the load among multiple paths so as to increase energy savings. Our extensive ns-2 simulations over realistic topologies show that EATe succeeds in moving 21% of the links to the sleep state, while keeping the same sending rates and being close to the optimal energy-aware solution. Further, we demonstrate that EATe successfully handles changes in traffic load and quickly restores a low overall energy state. Alternatively, EATe can move links to lower energy levels, resulting in energy savings of 8%. Finally, EATe can succeed in making 16% of active routers sleep.

Identifying and using energy-critical paths

The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energy-reduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately, computing the minimum network subset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimality-scalability trade-off. The insight in REsPoNse is to identify a few energy-critical paths off-line, install them into network elements, and use a simple online element to redirect the traffic in a way that enables large parts of the network to enter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savings as the existing approaches, with marginal impact on network scalability and application performance.

Automatic failure recovery for software-defined networks

Tolerating and recovering from link and switch failures are fundamental requirements of most networks, including Software-Defined Networks (SDNs). However, instead of traditional behaviors such as network-wide routing re-convergence, failure recovery in an SDN is determined by the specific software logic running at the controller. While this admits more freedom to respond to a failure event, it ultimately means that each controller application must include its own recovery logic, which makes the code more difficult to write and potentially more error-prone. In this paper, we propose a runtime system that automates failure recovery and enables network developers to write simpler, failure-agnostic code. To this end, upon detecting a failure, our approach first spawns a new controller instance that runs in an emulated environment consisting of the network topology excluding the failed elements. Then, it quickly replays inputs observed by the controller before the failure occurred, leading the emulated network into the forwarding state that accounts for the failed elements. Finally, it recovers the network by installing the difference ruleset between emulated and current forwarding states.

OF.CPP: consistent packet processing for openflow

This paper demonstrates a new class of bugs that is likely to occur in enterprise OpenFlow deployments. In particular, step-by-step, reactive establishment of paths can cause network-wide inconsistencies or performance- and space-related inefficiencies. The cause for this behavior is inconsistent packet processing: as the packets travel through the network they do not encounter consistent state at the OpenFlow controller. To mitigate this problem, we propose to use transactional semantics at the controller to achieve consistent packet processing. We detail the challenges in achieving this goal (including the inability to directly apply database techniques), as well as a potentially promising approach. In particular, we envision the use of multi-commit transactions that could provide the necessary serialization and isolation properties without excessively reducing network performance.

Thermal-aware workload scheduling for energy efficient data centers

Increasing heat dissipation density is becoming a limiting factor in air-cooled data centers. The main control objective in data center thermal management is to keep the temperature of all the data processing equipment below a certain threshold and at the same time maximize the energy efficiency of the system. Existing work in this field does not take into account unexpected changes in the workload and neglects the cost of control actions taken by the cooling infrastructure. To address this problem, we derive a thermodynamic model of a data center and propose a novel model-based temperature control strategy that combines air flow control and thermal-aware scheduling. The air flow controller is responsible for the long-term decisions by switching between multiple operating points, whereas the scheduler accounts for short-term fluctuations in the workload that are not predictable. Simulations with synthetic and real workload traces show that we can control the temperatures at the racks in an efficient and stable manner with this approach.

One bit is enough: A framework for deploying explicit feedback congestion control protocols

In this paper we describe UNO, a framework for fine-grain explicit feedback congestion control protocols that uses only 1 or 2 existing ECN bits, thus making algorithms that use more than 2 bits for encoding the load factor and the RTT immediately deployable. UNO accomplishes this task by changing the way load and RTT information is encoded in packets in a way that is similar to some existing schemes for encoding bottleneck link load. UNO leverages the values present in the IP identification field and trades-off a small amount of time (to send several packets) for space to emulate the existence of several extra bits within the IP header. The results from extensive ns2 simulations over various bandwidth and delay scenarios are encouraging. By using only one ECN bit we achieve substantially lower convergence times and better link utilization than the existing deployable protocols, with similar low queue size and negligible packet loss. With 2 ECN bits, we achieve very good fairness for flows with different RTTs, while keeping all the good characteristics of the 1-bit protocol and providing functionality that did not previously exist.

Bandwidth adaptation in streaming overlays

A major challenge for real-time streaming overlays is to distribute high bit-rate streams with uninterrupted playback. Hosts usually have sufficient inbound bandwidth to support streaming, but due to the prevalence of asymmetric links in broadband networks, the bottleneck is the aggregate, overlay-wide outbound bandwidth. If this bandwidth is less than what is required to forward the stream to the overlay members, then a large number of users potentially experience poor playback. We argue that for successful streaming in bandwidth constrained situations overlays need to be able to adapt to the aggregate available bandwidth. We present four bandwidth adaptation policies for tree-based streaming overlays and evaluate their efficiency using a large-scale emulation testbed with realistic broadband link characteristics.