Multiarmed bandit (MAB) problems, typically modeled as Markov decision processes (MDPs), exemplify the learning versus earning trade-off. An area that has motivated theoretical research in MAB designs is the study of clinical trials, where the application of such designs has the potential to significantly improve patient outcomes. However, for many practical problems of interest, the state space is intractably large, rendering exact approaches to solving MDPs impractical. In particular, settings that require multiple simultaneous allocations lead to an expanded state and action-outcome space, necessitating the use of approximation approaches. We propose a novel approximation approach that combines the strengths of multiple methods: grid-based state discretization, value function approximation methods, and techniques for a computationally efficient implementation. The hallmark of our approach is the accurate approximation of the value function that combines linear interpolation with bounds on interpolated value and the addition of a learning component to the objective function. Computational analysis on relevant datasets shows that our approach outperforms existing heuristics (e.g., greedy and upper confidence bound family of algorithms) and a popular Lagrangian-based approximation method, where we find that the average regret improves by up to 58.3%. A retrospective implementation on a recently conducted phase 3 clinical trial shows that our design could have reduced the number of failures by 17% relative to the randomized control design used in that trial. Our proposed approach makes it practically feasible for trial administrators and regulators to implement Bayesian response-adaptive designs on large clinical trials with potential significant gains.

Dramatic progress has been made in the usage of semantic word embeddings for solving word analogy tasks in recent years. Word embeddings or vector representation of words has been the key to many advances in natural language processing. This paper presents a novel application of Word-Embeddings for Anomaly Classification (WEAC), where we detect whether an event log entry is an anomalous one or not. Additionally, WEAC helps us classify the anomaly by identifying the anomalous feature(s) in the event log. For example, an unusual network activity such as a store transaction server logging into dropbox.com would be automatically flagged as anomalous because of the wrong feature associations for entries in the corresponding event log. WEAC works with two training models: Skip-Gram (SG) and Continuous Bag of Words (CBOW). Negative sampling is used to boost the training. The initial results on wikipedia text8 dataset, as well as investigation on enterprise HTTP logs are promising. The model achieved an average detection rate of 65-100% and classification accuracy of 85-100%. The detection rate was superior to state-of-the-art anomaly detection techniques.

Network throughput is scaling "up" to higher data transfer rates while processors are scaling "out" to multiple cores. As a result, network adapter "offloads" and performance "tuning" have received a good deal of attention lately. However, much of this attention is focused on the "how" and not the "why" of performance efficiency. There are two types of efficiencies that we have found particularly intriguing: First, processor core "affinity," or "binding" is fundamentally the choice of which processor core or cores handle certain tasks in a network- or I/O-heavy application running on a MIMD machine. Second, Ethernet "pause frames" slightly violate the "end-to-end" nature of TCP/IP in order to perform link-to-link flow control. The goal of our research is to delve deeper into why these tuning suggestions and this offload exist, and how they affect the end-to-end performance and efficiency of a single, large TCP flow.

Clinical trials have traditionally followed a fixed randomized design, where patients are typically allocated once, at random, and usually equally to various treatments. Such designs provide a clean way of separating out the effects of alternate treatments. Response-adaptive designs, where assignment to treatments evolves as patient outcomes are observed, are gaining in popularity due to potential for improvements in both cost and efficiency over traditional designs. Such designs can be modeled as a Bayesian adaptive Markov decision process (BAMDP), essentially an MDP with unknown transition probabilities. Given the forward-looking nature of the underlying algorithms which solve BAMDP, the problem size grows as the trial becomes larger or more complex, often exponentially, making it computationally challenging to solve. In this study, we propose grid-based approximation as a way to reduce the problem dimensionality and the associated computationally burden. The proposed methods also open the possibility of implementing adaptive designs to large clinical trials. Further, we use numerical examples to demonstrate the effectiveness of our approach, including the effects of number of patient observations and the grid resolution.

For a given TCP or UDP flow, protocol processing of incoming packets is performed on the core that receives the interrupt, while the user-space application which consumes the data may run on the same or a different core. If the cores are not the same, additional costs due to context switches, cache misses, and the movement of data between the caches of the cores may occur. The magnitude of this cost depends upon the processor affinity of the user-space process relative to the network stack. In this paper we present a prototype implementation of a tool which enables the application processing and protocol processing to occur on cores which share the lowest cache level. The Cache-Aware Affinity Deamon (CAAD) analyzes the topology of the die and the NIC characteristics and conveys information to the sender which allows the entire end-to-end path for each new flow to be be managed and controlled. This is done in a light-weight manner for both uni and bi-directional flows. Measurements show that for bulk data transfers using commodity multicore machines, the use of CAAD improves the overall TCP throughput by as much as 31%, and reduces the cache miss rate as much as 37.5%. GridFTP combined with CAAD improves the download time for big file transfers by up to 18%.

Data centers are being deployed in a wide variety of environments (cloud computing, scientific, financial, defense, etc.). When geographically distributed, these data centers must transmit and receive growing volumes of data. In order to avoid congestion in the public internet, most use high speed dedicated optical networks, which can be thought of as private highways for carrying data. In this work, we examined the impact of such high speed network traffic on a commodity multicore machine, and identified a number of scenarios that cause packet loss and degraded throughput due to an end-system inability to consume incoming data fast enough. We show that high speed single flow traffic nullifies the benefits of multicore systems and multiqueue NICs, and we propose an end-system aware flow bifurcation technique to optimize the data transfer time using rate based protocols. Using introspective end-system modeling, we determine the optimal number of parallel flows required to utilize the available bandwidth, and the optimal rate for each of the flows. We compare our approach with GridFTP, which is a widely used data transfer protocol in computational grids, and show that our approach performs better (particularly when the end-system losses are in the receive ring buffer.).

The transmission capacity of today's high-speed networks is often greater than the capacity of an end-system (such as a server or a remote client) to consume the incoming data. The mismatch between the network and the end-system, which can be exacerbated by high end-system workloads, will result in incoming packets being dropped at different points in the packet receiving process. In particular, a packet may be dropped in the NIC, in the kernel ring buffer, and (for rate based protocols) in the socket buffer. To provide reliable data transfers, these losses require retransmissions, and if the loss rate is high enough result in longer download times. In this paper, we focus on UDP-like rate based transport protocols, and address the question of how best to estimate the rate at which the end-system can consume data which minimizes the overall transfer time of a file.