The United Nations and the Human Rights Issue

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.QC 20140707</p

Foreword: The Nature of Discretion

Power consumption has become a critical issue in large scale clusters. Existing solutions for addressing the servers’ energy consumption suggest “shrinking” the set of active machines, at least until the more power-proport-ional hardware devices become available. This paper demonstrates that leveraging the sleeping state, however, may lead to unacceptably poor performance and low data availability if the distributed services are not aware of the power management’s actions. Therefore, we present an architecture for cluster services in which the deployed services overcome this problem by actively participating in any action taken by the power management. We propose, implement, and evaluate modifications for the Hadoop Distributed File System and the MapReduce clone that make them capable of operating efficiently under limited power budgets.QC 20140707</p

Consistent Packet Processing - Because Consistent Updates Are Not Enough

In this poster and demo, we showcase a new class of problems that can occur due to inconsistent packet processing at an OpenFlow controller. We proceed to outline a potential solution

Energy-Proportional and Scalable Networks

The power consumption of the Internet and datacenter networks is already significant due to a large degree of redundancy and high idle power consumption of the network elements. Therefore, dynamically matching network resources to the actual load is highly desirable. Existing approaches in this domain advocate recomputing network configurations with each substantial change in demand. Unfortunately, computing the minimum network subset is a computationally hard and time-consuming problem, which prevents these approaches from scaling up to large or even medium-sized networks. Thus, the network operates with diminished performance during the periods of energy-aware routing recomputation. In this dissertation, I propose REsPoNse, a design for achieving both energy-proportionality and scalability by taking a fundamentally different hybrid approach. REsPoNse uses additional off-line computation and memory to effectively overcome the optimality-scalability trade-off, leveraging the traffic predictability to: 1) precompute offline as much routing information as it can and install it into a small number of routing tables (called always-on, on-demand, and failover), and 2) utilize a simple, scalable online traffic engineering (EATe) mechanism to deactivate and activate network elements on demand. I then make a significant step towards deployment of REsPoNse by proposing a framework (UNO) that can encode all information about the traffic congestion on the computed paths into an existing IP header. Further, I thoroughly evaluate REsPoNse by: i) replaying traffic demands collected over real topologies, ii) running ns-2 simulations over ISP and data center networks, iii) implementing and experimenting with a Click testbed, and iv) running video-on-demand and web applications live in a network emulator. My findings demonstrate that REsPoNse achieves the same or better energy proportionality as the existing approaches, with little or no impact on network responsiveness, regardless of the network size. Specific energy savings amount to about 30-40% for varying power models of network elements. Finally, the two representative applications experience marginal impact on their application-level throughput and latency when compared to running over an energy-oblivious network

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 optimizing for low link utilization and high end-host sending rates. 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.QC 20140704</p

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 optimizing for low link utilization and high end-host sending rates. 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

Making Cluster Applications Energy-Aware

Power consumption has become a critical issue in large scale clusters. Existing solutions for addressing the servers’ energy consumption suggest “shrinking” the set of active machines, at least until the more power-proport-ional hardware devices become available. This paper demonstrates that leveraging the sleeping state, however, may lead to unacceptably poor performance and low data availability if the distributed services are not aware of the power management’s actions. Therefore, we present an architecture for cluster services in which the deployed services overcome this problem by actively participating in any action taken by the power management. We propose, implement, and evaluate modifications for the Hadoop Distributed File System and the MapReduce clone that make them capable of operating efficiently under limited power budgets.QC 20140707</p

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.QC 20140707</p

DejaVu : Accelerating Resource Allocation in Virtualized Environments

Effective resource management of virtualized environments is a challenging task. State-of-the-art management systems either rely on analytical models or evaluate resource allocations by running actual experiments. However, both approaches incur a significant overhead once the workload changes. The former needs to recalibrate and re-validate models, whereas the latter has to run a new set of experiments to select a new resource allocation. During the adaptation period, the system may run with an inefficient configuration.Qc 20140707</p