RepFlow: Minimizing Flow Completion Times with Replicated Flows in Data Centers

Short TCP flows that are critical for many interactive applications in data centers are plagued by large flows and head-of-line blocking in switches. Hash-based load balancing schemes such as ECMP aggravate the matter and result in long-tailed flow completion times (FCT). Previous work on reducing FCT usually requires custom switch hardware and/or protocol changes. We propose RepFlow, a simple yet practically effective approach that replicates each short flow to reduce the completion times, without any change to switches or host kernels. With ECMP the original and replicated flows traverse distinct paths with different congestion levels, thereby reducing the probability of having long queueing delay. We develop a simple analytical model to demonstrate the potential improvement of RepFlow. Extensive NS-3 simulations and Mininet implementation show that RepFlow provides 50%--70% speedup in both mean and 99-th percentile FCT for all loads, and offers near-optimal FCT when used with DCTCP.Comment: To appear in IEEE INFOCOM 201

Causes and classification of EMD mode mixing

At present, the lack of insight into the problem of mode mixing in Empirical Mode Decomposition (EMD) hinders the development of solutions to the problem. Starting with the phenomenon that the EMD decomposition cannot be accomplished when the number of signal extrema is abnormal, the causes of mode mixing were investigated and the conclusion was reached that there are only two basic types of mode mixing. In light of this finding, the mechanisms of the three typical mode mixing solutions and their limitations were analyzed. It was found from the analysis process and results that the findings of this study regarding the causes and types of mode mixing were correct

Study of modal acoustic emission to monitor the impact damage in a composite plate

Based on the theory of modal acoustic emission, this paper describes a new method to determine whether a plate made of a composite material has been damaged following external impact. Using modal acoustic emission theory, the relationship between the Lamb-wave mode of acoustic emission and the force direction in the plate was analyzed. This relationship was then verified using a lead-break experiment. The Lamb-wave mode of the acoustic emission signals produced by the damage in the plate was investigated, and the criterion for assessing the impact damage was proposed. During the impact experiments, the proposed criterion was employed to monitor the impact damage of the plate. The results indicate that it is feasible to use acoustic emission signals to directly determine whether an impact had caused damage to the plate

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Drag reduction in turbulent channel flow using bidirectional wavy Lorentz force

Turbulent control and drag reduction in a channel flow via a bidirectional traveling wave induced by spanwise oscillating Lorentz force have been investigated in the paper. The results based on the direct numerical simulation (DNS) indicate that the bidirectional wavy Lorentz force with appropriate control parameters can result in a regular decline of near-wall streaks and vortex structures with respect to the flow direction, leading to the effective suppression of turbulence generation and significant reduction in skin-friction drag. In addition, experiments are carried out in a water tunnel via electro-magnetic (EM) actuators designed to produce the bidirectional traveling wave excitation as described in calculations. As a result, the actual substantial drag reduction is realized successfully in these experiments

Cost efficient datacenter selection for cloud services

Abstract—Many cloud services nowadays are running on top of geographically distributed infrastructures for better reliability and performance. They need an effective way to direct the user requests to a suitable datacenter, in a cost efficient manner. Previous work focused mostly on the electricity cost of datacenters. The approaches favor datacenters at locations with cheaper electricity prices. In this paper, we augment the picture by considering another significant cost contributor: network bandwidth. We propose to utilize statistical multiplexing to strategically bundle demands at different locations. The anti-correlation between demands effectively smooths out the aggregated bandwidth usage, thereby saving the bandwidth cost calculated by burstable billing methods that charge the peak bandwidth usage. We present an optimization framework that models the realistic environment and practical constraints a cloud faces. We develop an efficient distributed algorithm based on dual decomposition and the subgradient method, and evaluate its effectiveness and practicality using realworld traffic traces and electricity costs. I