Spark-based Cloud Data Analytics using Multi-Objective Optimization

International audienceData analytics in the cloud has become an integral part of enterprise businesses. Big data analytics systems, however, still lack the ability to take user performance goals and budgetary constraints for a task, collectively referred to as task objectives, and automatically configure an analytic job to achieve these objectives. This paper presents a data analytics optimizer that can automatically determine a cluster configuration with a suitable number of cores as well as other system parameters that best meet the task objectives. At a core of our work is a principled multi-objective optimization (MOO) approach that computes a Pareto optimal set of job configurations to reveal tradeoffs between different user objectives, recommends a new job configuration that best explores such tradeoffs, and employs novel optimizations to enable such recommendations within a few seconds. We present efficient incremental algorithms based on the notion of a Progressive Frontier for realizing our MOO approach and implement them into a Spark-based prototype. Detailed experiments using benchmark workloads show that our MOO techniques provide a 2-50x speedup over existing MOO methods, while offering good coverage of the Pareto frontier. When compared to Ottertune, a state-of-the-art performance tuning system, our approach recommends configurations that yield 26%-49% reduction of running time of the TPCx-BB benchmark while adapting to different application preferences on multiple objectives

Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing

International audienceBig data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary constraints of analytical users. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This paper presents a MaxCompute based integrated system to support multi-objective resource optimization via ne-grained instance-level modeling and optimization. We propose a new architecture that breaks RO into a series of simpler problems, new ne-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level RO decisions well under a second. Evaluation using production workloads shows that our new RO system could reduce 37-72% latency and 43-78% cost at the same time, compared to the current optimizer and scheduler, while running in 0.02-0.23s

Smartphone Uses in Brick-and-mortar Retailing Stores: Gratifications as Antecedents of Consumer’s State Anxiety and Purchase Intention

Grounded in the Uses and Gratifications (U&G) theory, this paper investigates the distinctive smartphone uses and consumers’ expected gratifications during a shopping journey in brick-and-mortar retailing stores, and explores whether in-store consumers should be encouraged to use smartphones. Through a mixed-method research, we followed a micro-ethnography approach along with a survey to examine the relationships between the constructs through Structural Equation Modeling (SEM). The results indicate that smartphone’s utilitarian and hedonic gratifications reduce consumer’s state anxiety while social gratifications do not have any impact. Higher level of state anxiety undermines consumer’s in-store purchase intention

Neural-based Modeling for Performance Tuning of Spark Data Analytics

Cloud data analytics has become an integral part of enterprisebusiness operations for data-driven insight discovery. Performancemodeling of cloud data analytics is crucial for performance tuning andother critical operations in the cloud. Traditional modeling techniquesfail to adapt to the high degree of diversity in workloads and systembehaviors in this domain. In this paper, we bring recent Deep Learningtechniques to bear on the process of automated performance modeling ofcloud data analytics, with a focus on Spark data analytics as representativeworkloads. At the core of our work is the notion of learning workloadembeddings (with a set of desired properties) to represent fundamentalcomputational characteristics of different jobs, which enable performanceprediction when used together with job configurations that control resourceallocation and other system knobs. Our work provides an in-depthstudy of different modeling choices that suit our requirements. Resultsof extensive experiments reveal the strengths and limitations of differentmodeling methods, as well as superior performance of our best performingmethod over a state-of-the-art modeling tool for cloud analytics

UDAO: A Next-Generation Unified Data Analytics Optimizer

International audienceBig data analytics systems today still lack the ability to take user performance goals and budgetary constraints, collectively referred to as "objectives", and automatically configure an analytic job to achieve the objectives. This paper presents UDAO, a unified data analytics optimizer that can automatically determine the parameters of the runtime system, collectively called a job configuration, for general dataflow programs based on user objectives. UDAO embodies key techniques including in-situ modeling, which learns a model for each user objective in the same computing environment as the job is run, and multi-objective optimization, which computes a Pareto optimal set of job configurations to reveal tradeoffs between different objectives. Using benchmarks developed based on industry needs, our demonstration will allow the user to explore (1) learned models to gain insights into how various parameters affect user objectives; (2) Pareto frontiers to understand interesting tradeoffs between different objectives and how a configuration recommended by the optimizer explores these tradeoffs; (3) end-to-end benefits that UDAO can provide over default configurations or those manually tuned by engineers

Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing

International audienceBig data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary constraints of analytical users. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This paper presents a MaxCompute based integrated system to support multi-objective resource optimization via ne-grained instance-level modeling and optimization. We propose a new architecture that breaks RO into a series of simpler problems, new ne-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level RO decisions well under a second. Evaluation using production workloads shows that our new RO system could reduce 37-72% latency and 43-78% cost at the same time, compared to the current optimizer and scheduler, while running in 0.02-0.23s

Leveraging single-cell RNA sequencing to unravel the impact of aging on stroke recovery mechanisms in mice

Aging compromises the repair and regrowth of brain vasculature and white matter during stroke recovery, but the underlying mechanisms remain elusive. To understand how aging jeopardizes brain tissue repair after stroke, we performed single-cell transcriptomic profiling of young adult and aged mouse brains at acute (3 d) and chronic (14 d) stages after ischemic injury, focusing a priori on the expression of angiogenesis- and oligodendrogenesis-related genes. We identified unique subsets of endothelial cells (ECs) and oligodendrocyte (OL) progenitors in proangiogenesis and pro-oligodendrogenesis phenotypic states 3 d after stroke in young mice. However, this early prorepair transcriptomic reprogramming was negligible in aged stroke mice, consistent with the impairment of angiogenesis and oligodendrogenesis observed during the chronic injury stages after ischemia. In the stroke brain, microglia and macrophages (MG/MΦ) may drive angiogenesis and oligodendrogenesis through a paracrine mechanism. However, this reparative cell–cell cross talk between MG/MΦ and ECs or OLs is impeded in aged brains. In support of these findings, permanent depletion of MG/MΦ via antagonism of the colony-stimulating factor 1 receptor resulted in remarkably poor neurological recovery and loss of poststroke angiogenesis and oligodendrogenesis. Finally, transplantation of MG/MΦ from young, but not aged, mouse brains into the cerebral cortices of aged stroke mice partially restored angiogenesis and oligodendrogenesis and rejuvenated sensorimotor function and spatial learning and memory. Together, these data reveal fundamental mechanisms underlying the age-related decay in brain repair and highlight MG/MΦ as effective targets for promoting stroke recovery

Neuroprotection against ischemic stroke requires a specific class of early responder T cells in mice

Immunomodulation holds therapeutic promise against brain injuries, but leveraging this approach requires a precise understanding of mechanisms. We report that CD8+CD122+CD49dlo T regulatory-like cells (CD8+ TRLs) are among the earliest lymphocytes to infiltrate mouse brains after ischemic stroke and temper inflammation; they also confer neuroprotection. TRL depletion worsened stroke outcomes, an effect reversed by CD8+ TRL reconstitution. The CXCR3/CXCL10 axis served as the brain-homing mechanism for CD8+ TRLs. Upon brain entry, CD8+ TRLs were reprogrammed to upregulate leukemia inhibitory factor (LIF) receptor, epidermal growth factor-like transforming growth factor (ETGF), and interleukin 10 (IL-10). LIF/LIF receptor interactions induced ETGF and IL-10 production in CD8+ TRLs. While IL-10 induction was important for the antiinflammatory effects of CD8+ TRLs, ETGF provided direct neuroprotection. Poststroke intravenous transfer of CD8+ TRLs reduced infarction, promoting long-term neurological recovery in young males or aged mice of both sexes. Thus, these unique CD8+ TRLs serve as early responders to rally defenses against stroke, offering fresh perspectives for clinical translation