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An overview on smart contracts : Challenges, advances and platforms

In this paper, we introduce methods to differentiate posed expressions from spontaneous ones by capturing global spatial patterns embedded in posed and spontaneous expressions, and by incorporating gender and expression categories as privileged information during spatial pattern modeling. Speciﬁcally, we construct multiple restricted Boltzmann machines (RBMs) with continuous visible units to model spatial patterns from facial geometric features given expression-related factors, i.e., gender and expression categories. During testing, only facial geometric features are provided, and the samples are classiﬁed into posed or spontaneous expressions according to the RBM with the largest likelihood. Furthermore, we propose eﬃcient inference algorithm by extending annealing importance sampling to RBM with continuous visible units for calculating partition function of RBMs. Experimental results on benchmark databases demonstrate the effectiveness of the proposed approach in modelling global spatial patterns as well as its superior posed and spontaneous expression distinction performance over existing approaches.

Computer Vision and Image Understanding

Inter-coflow scheduling improves application-level communication performance in data-parallel clusters. However, existing efficient schedulers require a priori coflow information and ignore cluster dynamics like pipelining, task failures, and speculative executions, which limit their applicability. Schedulers without prior knowledge compromise on performance to avoid head-of-line blocking. In this paper, we present Aalo that strikes a balance and efficiently schedules coflows without prior knowledge. Aalo employs Discretized Coflow-Aware Least-Attained Service (D-CLAS) to separate coflows into a small number of priority queues based on how much they have already sent across the cluster. By performing prioritization across queues and by scheduling coflows in the FIFO order within each queue, Aalo's non-clairvoyant scheduler reduces coflow completion times while guaranteeing starvation freedom. EC2 deployments and trace-driven simulations show that communication stages complete 1.93X faster on average and 3.59X faster at the 95th percentile using Aalo in comparison to per-flow mechanisms. Aalo's performance is comparable to that of solutions using prior knowledge, and Aalo outperforms them in presence of cluster dynamics.

/pdf/efficient-coflow-scheduling-without-prior-knowledge-5d672ar244.pdf

Efficient Coflow Scheduling Without Prior Knowledge

Natural graphs with skewed distributions raise unique challenges to distributed graph computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high-degree vertices (e.g., Pregel and GraphLab) or incur high communication cost and memory consumption even for low-degree vertices (e.g., PowerGraph and GraphX). In this article, we argue that skewed distributions in natural graphs also necessitate differentiated processing on high-degree and low-degree vertices. We then introduce PowerLyra, a new distributed graph processing system that embraces the best of both worlds of existing graph-parallel systems. Specifically, PowerLyra uses centralized computation for low-degree vertices to avoid frequent communications and distributes the computation for high-degree vertices to balance workloads. PowerLyra further provides an efficient hybrid graph partitioning algorithm (i.e., hybrid-cut) that combines edge-cut (for low-degree vertices) and vertex-cut (for high-degree vertices) with heuristics. To improve cache locality of inter-node graph accesses, PowerLyra further provides a locality-conscious data layout optimization. PowerLyra is implemented based on the latest GraphLab and can seamlessly support various graph algorithms running in both synchronous and asynchronous execution modes. A detailed evaluation on three clusters using various graph-analytics and MLDM (Machine Learning and Data Mining) applications shows that PowerLyra outperforms PowerGraph by up to 5.53X (from 1.24X) and 3.26X (from 1.49X) for real-world and synthetic graphs, respectively, and is much faster than other systems like GraphX and Giraph, yet with much less memory consumption. A porting of hybrid-cut to GraphX further confirms the efficiency and generality of PowerLyra.

PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs

In the data flow models of today's data center applications such as MapReduce, Spark and Dryad, multiple flows can comprise a coflow group semantically. Only completing all flows in a coflow is meaningful to an application. To optimize application performance, routing and scheduling must be jointly considered at the level of a coflow rather than individual flows. However, prior solutions have significant limitation: they only consider scheduling, which is insufficient. To this end, we present Rapier, a coflow-aware network optimization framework that seamlessly integrates routing and scheduling for better application performance. Using a small-scale testbed implementation and large-scale simulations, we demonstrate that Rapier significantly reduces the average coflow completion time (CCT) by up to 79.30% compared to the state-of-the-art scheduling-only solution, and it is readily implementable with existing commodity switches.

/pdf/rapier-integrating-routing-and-scheduling-for-coflow-aware-245qovgi50.pdf

Rapier: Integrating routing and scheduling for coflow-aware data center networks

Cloud computing has completely changed the economics of IT industry. Recently, the new form of "shared global economy" requires cloud services to be collaboratively provisioned by different cloud providers in a Geo-distributed manner, which brings severe challenges in service performance and cost. To address this problem, in this paper we propose JointCloud, a cross-cloud cooperation architecture for integrated Internet service customization. JointCloud borrows the idea from airline alliances and aims at empowering the cooperation among multiple clouds to provide efficient cross-cloud services. JointCloud focuses not only on the vertical integration of cloud resources but also on the horizontal cooperation among different cloud vendors. This paper describes the concept and architecture of JointCloud, as well as the initial design of the key components, namely, communication, storage, and computation.

JointCloud: A Cross-Cloud Cooperation Architecture for Integrated Internet Service Customization

Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems. In this survey, we systematically categorize the deep RL algorithms and applications, and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods, model-free methods, and advanced RL methods. We thoroughly analyze the advances including exploration, inverse RL, and transfer RL. Finally, we outline the current representative applications, and analyze four open problems for future research.

Deep reinforcement learning: a survey

Distributed systems have become the backbone of modern clouds. Users often expect high scalability and performance isolation from distributed systems. Unfortunately, a type of poor software design, which we refer to as performance cascading bugs (PCbugs), can often cause the slowdown of non-scalable code in one job to propagate, causing global performance degradation and even threatening system availability. This paper presents a tool, PCatch, that can automatically predict PCbugs by analyzing system execution under small-scale workloads. PCatch contains three key components in predicting PCbugs. It uses program analysis to identify code regions whose execution time can potentially increase dramatically with the workload size; it adapts the traditional happens-before model to reason about software resource contention and performance dependency relationship; it uses dynamic tracking to identify whether the slowdown propagation is contained in one job or not. Our evaluation using representative distributed systems, Cassandra, Hadoop MapReduce, HBase, and HDFS, shows that PCatch can accurately predict PCbugs based on small-scale workload execution.

https://dl.acm.org/doi/pdf/10.1145/3190508.3190552

Pcatch: automatically detecting performance cascading bugs in cloud systems

Flow completion times (FCTs) are critical for many cloud applications. To minimize the average FCT, recent transport designs, such as pFabric, PASE, and PIAS, approximate the Shortest Remaining Time First (SRTF) scheduling. A common, implicit assumption of these solutions is that the remaining time is only determined by the remaining flow size. However, this assumption does not hold in many real-world scenarios where applications generate data at diverse rates that are smaller than the network capacity. In this paper, we look into this issue from system perspective and find that the operating system (OS) kernel can be exploited to better estimate the remaining time of a flow. In particular, we use the rate of copying data from user space to kernel space to measure the data generation rate. We design RAX, a rate aware flow scheduling method, that calculates the remaining time of a flow more accurately, based on not only the flow size but also the data generation rate. We have implemented a RAX prototype in Linux kernel and evaluated it through testbed experiments and ns-2 simulations. Our testbed results show that RAX reduces FCT by up to 14.9%/41.8% and 7.8%/22.9% over DCTCP and PIAS for all/medium flows respectively.

Yiming Zhang

Papers

Rapier: Integrating routing and scheduling for coflow-aware data center networks

JointCloud: A Cross-Cloud Cooperation Architecture for Integrated Internet Service Customization

Deep reinforcement learning: a survey

Pcatch: automatically detecting performance cascading bugs in cloud systems

Rate-aware flow scheduling for commodity data center networks