bluuebunny/crossref_metadata_2025 huggingface dataset:
Advance care planning: the impact of Ceiling of Treatment plans in patients with Coordinate My Care
Authors: Broadhurst Helen Lucy , Droney Joanne , Callender Tom , Shaw Amanda , Riley Julia
Published: March 2018

Objectives

The aim of this evaluation is to describe the components and results of urgent care planning in Coordinate My Care (CMC), a digital clinical service for patients with life-limiting illness, for use if a patient is unable to make or express choices. Ceiling of treatment (CoT) plans were created detailing where the patient would like to receive their care and how aggressive medical interventions should be.

Methods

A retrospective service evaluation was completed of all CMC records created between December 2015 and September 2016 (n=6854). CMC records were divided into two cohorts: those with a CoT plan and those without. The factors associated with these cohorts were reviewed including age, diagnosis, resuscitation status and preferences for place of death (PPD). Analysis of the non-mandatory free text section was carried out.

Results

Two-thirds of patients had recorded decisions about CoT. Regardless of which CoT option was chosen, for most patients, PPD was home or care home. Patients with a CoT plan were more likely to have a documented resuscitation status. Patients with a CoT were more likely to die in their PPD (82%vs71%, OR 1.79, p<0.0001). A higher proportion of patients with a CoT decision died outside hospital.

Conclusion

This analysis demonstrates that a substantial proportion of patients are willing to engage in urgent care planning. Three facets of urgent care planning identified include PPD, CoT and resuscitation status.
Gender differences in the associations of body mass index, physical fitness and tobacco use with lower extremity musculoskeletal injuries among new US Army soldiers
Authors: Bedno Sheryl A , Nelson D Alan , Kurina Lianne M , Choi Y Sammy
Published: March 2018

Background

Musculoskeletal (MSK) injuries are a leading cause of morbidity among US Army soldiers, especially among women and the newly enlisted. While extremes of body mass index (BMI), low physical fitness and tobacco use have been identified as risk factors, no prior studies have determined the combined effects of these risk factors or tested for gender differences in the associations.

Methods

We conducted a retrospective cohort study utilising the Stanford Military Data Repository to study all enlisted soldier accessions to the US Army between January 2011 and January 2014, with follow-up through December 2014 (n=238 772). We used discrete-time logistic regression to test for associations between demographic, socioeconomic and health behaviour-related predictors and incident lower extremity injuries. We included interaction terms to test for gender differences.

Results

Among men, there were greater adjusted odds of injury among underweight (OR 1.27), overweight (OR 1.14) and obese soldiers (OR 1.54) (all p<0.001) relative to normal-weight soldiers. No statistically significant differences in the odds of injury on the basis of BMI were observed for female soldiers. Physical fitness was a significantly stronger predictor of injury for female as compared with male soldiers, while the association between tobacco use and injury was stronger for men (OR 1.47) than for women (OR 1.30) (p<0.001 for both).

Conclusion

This is the largest known study to simultaneously assess important, modifiable musculoskeletal injury risk factors and to test for gender differences in these associations. These findings provide critical gender-related nuances for clinicians and leaders seeking to reduce the risk of these problems.
Effect of horse sex status on British Eventing competition performance: an observational study between 1998 and 2016
Authors: Hanousek Katherine , Salavati Mazdak , Fouladi‐Nashta Ali
Published: March 2018

To test the hypothesis that gender affects horse scores in eventing competition, data on the scores and points awarded to 681 horses was collected from the British Eventing website. Equal numbers of mares, geldings and stallions were used, all foaled during or after 1994 and aged 4–10 years. The study included five levels of competition (BE90, BE100, Novice, Intermediate and Advanced) and investigated differences in mean phased scores, total scores and rank in competition. Additionally, the mean and median ‘BE points per competition’ of each gender were compared. Significant differences in performance between genders were found at all levels except Advanced. Differences were highlighted in average phased and total scores, rank and median points per competition. There was an overall pattern of stallions and geldings outperforming mares, though this was not found to be true at all levels. The only area in which mares were found to perform significantly better than geldings or stallions was showjumping time penalties at BE90.
Philip Taylor Williamson
Authors: Barry Fiona
Published: March 2018

A practitioner who involved himself in local and national veterinary associations and also served in the army, as a government vet, a media vet, as well as teaching veterinary nurses.
Evaluating Pacific cod migratory behavior and site fidelity in a fjord environment using acoustic telemetry
Authors: Lewandoski Sean A. , Bishop Mary Anne , McKinzie Megan K.
Published: March 2018

Pacific cod (Gadus macrocephalus) inhabiting Prince William Sound (PWS) may constitute a localized population separate from Gulf of Alaska (GOA) populations; however, connectivity between these regions has not been previously explored. To address this knowledge gap, we investigated Pacific cod migratory behavior and site fidelity using passive acoustic telemetry techniques. Acoustic-tagged Pacific cod (n = 111) were monitored by Ocean Tracking Network acoustic arrays located at the straits and passages connecting PWS with the GOA and arrays deployed in two PWS fjords. Few Pacific cod tagged in PWS moved to the PWS–GOA boundary (1.8%), indicating that demographic connectivity with the GOA was low. Furthermore, 77% of tagged cod spent at least 90% of the time they were known to be alive within small (less than 30 km2) fjords. Cod were present at monitored fjords every month of the study, though some cod migrated away from the fjords during the summer and returned the following winter (11% in 2015 and 5% in 2016). Using continuous-time multistate Markov models, we determined that movement behavior was related to fish length. Larger fish tended to emigrate from monitored fjords more often and undergo longer duration migrations.
Radiation forces and the Abraham–Minkowski problem
Authors: Brevik Iver
Published: March 2018

Recent years have witnessed a number of beautiful experiments in radiation optics. Our purpose with this paper is to highlight some developments of radiation pressure physics in general, and thereafter to focus on the importance of the mentioned experiments in regard to the classic Abraham–Minkowski problem. That means, what is the “correct” expression for electromagnetic momentum density in continuous matter. In our opinion, one often sees that authors over-interpret the importance of their experimental findings with respect to the momentum problem. Most of these experiments are actually unable to discriminate between these energy–momentum tensors at all, since they can be easily described in terms of force expressions that are common for Abraham and Minkowski. Moreover, we emphasize the inherent ambiguity in applying the formal conservation principles to the radiation field in a dielectric, the reason being that the electromagnetic field in matter is only a subsystem which has to be supplemented by the mechanical subsystem to be closed. Finally, we make some suggestions regarding the connection between macroscopic electrodynamics and the Casimir effect, suggesting that there is a limit for the magnitudes of the cutoff parameters in QFT related to surface tension in ordinary hydromechanics.
Index theorem for non-supersymmetric fermions coupled to a non-Abelian string and electric charge quantization
Authors: Shifman M. , Yung A.
Published: March 2018

Non-Abelian strings are considered in non-supersymmetric theories with fermions in various appropriate representations of the gauge group U[Formula: see text]. We derive the electric charge quantization conditions and the index theorems counting fermion zero modes in the string background both for the left-handed and right-handed fermions. In both cases we observe a non-trivial [Formula: see text] dependence.
Toward the self-consistent model of cold disk accretion
Authors: Bogovalov S. V. , Tronin I. V.
Published: March 2018

Observations of active galactic nuclei show increasing number of cases when the kinetic luminosity of jets exceeds the bolometric luminosity of disks. We develop a model of so-called cold disk accretion when the majority of the angular momentum is carried out by the wind from the disk rather than by turbulent viscous stresses. In this case the luminosity of the disk can be essentially suppressed and kinetic-to-bolometric luminosity ratio can be consistent with the observations. The method of self-consistent numerical solution of the problem of the outflow and disk accretion is proposed in this work. In this problem the angular momentum carried out by the wind is equal to the angular momentum loss by the Keplerian disk with specified accretion mass rate. Thus, the problem of the wind outflow is consistent with the disk accretion. Example of self-consistent solution of the problem by this method is presented.
Calculation of thermal conductivity coefficients of electrons in magnetized dense matter
Authors: Glushikhina M. V. , Bisnovatyi-Kogan G. S.
Published: March 2018

The solution of Boltzmann equation for plasma in magnetic field, with arbitrarily degenerate electrons and nondegenerate nuclei, is obtained by Chapman–Enskog method. Fully ionized plasma is considered. The tensor of the heat conductivity coefficients in nonquantized magnetic field is calculated. For strongly degenerate plasma the asymptotic analytic formulas are obtained. The Lorentz approximation, with neglecting of electron–electron encounters, is asymptotically exact for strongly degenerate plasma. For strongly degenerate electrons we obtain an asymptotically exact analytical solution for the heat conductivity tensor in presence of a magnetic field. This solution has considerably more complicated dependence on the magnetic field than those in previous publications, and gives several times smaller relative value of a thermal conductivity across the magnetic field at [Formula: see text].
Precursors of short gamma-ray bursts detected by the INTEGRAL observatory
Authors: Minaev Pavel , Pozanenko Alexei , Molkov Sergei
Published: March 2018

We have analyzed the light curves of 527 short gamma-ray bursts (GRBs) registered by the SPI-ACS, SPI and IBIS/ISGRI experiments of INTEGRAL observatory totally to search for precursors. Both the light curves of each 527 individual burst and the averaged light curve of 372 brightest SPI-ACS bursts have been analyzed. In a few cases, we have found and investigated precursor candidates using SPI-ACS, SPI and IBIS/ISGRI of INTEGRAL, GBM and LAT of Fermi data. No convincing evidence for the existence of precursors of short GRBs has been found. A statistical analysis of the averaged light curve for the sample of brightest short bursts has revealed no regular precursor. Upper limits for the relative intensity of precursors have been estimated. We show that the fraction of short GRBs with precursors is less than 0.4% of all short bursts, detected by INTEGRAL.
AGILE results on relativistic outflows above 100MeV
Authors: Pittori Carlotta
Published: March 2018

We give an overview of the AGILE [Formula: see text]-ray satellite scientific highlights. AGILE is an Italian Space Agency (ASI) mission devoted to observations in the 30[Formula: see text]MeV–50[Formula: see text]GeV [Formula: see text]-ray energy range, with simultaneous X-ray imaging in the 18–60[Formula: see text]keV band. Launched in April 2007, the AGILE satellite has completed its tenth year of operations in orbit, and it is substantially contributing to improve our knowledge of the high-energy sky. Emission from cosmic sources at energies above 100[Formula: see text]MeV is intrinsically nonthermal, and the study of the wide variety of observed Galactic and extragalactic [Formula: see text]-ray sources provides a unique opportunity to test theories of particle acceleration and radiation processes in extreme conditions.
EVALUATING ENTREPRENEURSHIP PERFORMANCE IN VIETNAM THROUGH THE GLOBAL ENTREPRENEURSHIP DEVELOPMENT INDEX APPROACH
Authors: THU HA NGUYEN THI , HOA LAM BA
Published: March 2018

This study mainly aims to measure entrepreneurship performance in Vietnam at the national level through a new approach — the Global Entrepreneurship Development Index (GEDI). The GEDI consists of three sub-indices, including Entrepreneurial Attitudes, Entrepreneurial Abilities and Entrepreneurial Aspiration, which are divided into fourteen pillars and can be further subdivided into 28 variables. By analyzing these pillars and variables in comparison to two Southeast Asian developing economies — Thailand and Indonesia — which have similar cultural, economic and social characteristics, the study identifies the best and worst performing variables of the GEDI. The research results indicate that in Vietnam, nine bottlenecks of fourteen pillars are poorly performing with very low scores, in which the highest policy priority is given for four pillars, including risk acceptance, opportunity perception, internationalization and technology absorption. Finally, the Penalty for Bottleneck (PFB) methodology, which is considered as the policy application of the GEDI methodology and a simulation of “optimal” policy allocation are suggested to alleviate the weakest performing pillars, aiming to achieve the greatest improvement of entrepreneurship performance as well as reach the desired five-point increase in Vietnam's GEDI.
INFLUENCE OF CULTURAL PRACTICES ON SOCIAL SUPPORT OF FEMALE ENTREPRENEURS' STARTUPS
Authors: QIU TIANJIAO
Published: March 2018

Social support of female entrepreneurs' startups is critical for the sustainable development of female entrepreneurship in a country. This study empirically tests how nine cultural practices, including performance orientation, uncertainty avoidance, in-group collectivism, power distance, gender egalitarianism, humane orientation, institutional collectivism, future orientation and assertiveness, impact social support of female entrepreneurs' startups across different countries. For the period between 2009 and 2012, sixty-two countries were analyzed using longitudinal data with hierarchical linear modeling techniques. The empirical findings demonstrate three cultural practices (power distance, uncertainty avoidance and future orientation) play major roles in explaining the variation of social support of female entrepreneurs. In contrast, the effects of cultural practices of human orientation, institutional collectivism, in-group collectivism, assertiveness, gender egalitarianism and performance orientation are negligible. Further moderation tests show that a country's macroeconomic environment significantly moderates the relationship between the cultural practice of uncertainty avoidance and social support of female entrepreneurs' startups. The findings provide practical guidance to policymakers on how to develop robust ecosystems with strong cultural practices that enhance social support of female entrepreneurs.
Hadoop on Named Data Networking
Authors: Gibbens Mathias , Gniady Chris , Ye Lei , Zhang Beichuan
Published: March 2018

The Named Data Networking (NDN) architecture retrieves content by names rather than connecting to specific hosts. It provides benefits such as highly efficient and resilient content distribution, which fit well to data-intensive distributed computing. This paper presents and discusses our experience in modifying Apache Hadoop, a popular MapReduce framework, to operate on an NDN network. Through this first-of-its-kind implementation process, we demonstrate the feasibility of running an existing, large, and complex piece of distributed software commonly seen in data centers over NDN. We show advantages such as simplified network code and reduced network traffic which are beneficial in a data center environment. There are also challenges faced by NDN, that are being addressed by the community, which can be magnified under data center traffic. Through detailed evaluation, we show a reduction of 16% for overall data transmission between Hadoop nodes while writing data with default replication settings. Preliminary results also show promise for in-network caching of repeated reads in distributed applications. We also show that overall performance is currently slower under NDN, and we identify challenges and opportunities for further NDN improvements.
Portfolio-driven Resource Management for Transient Cloud Servers
Authors: Sharma Prateek , Irwin David , Shenoy Prashant
Published: March 2018

Cloud providers have begun to offer their surplus capacity in the form of low-cost transient servers, which can be revoked unilaterally at any time. While the low cost of transient servers makes them attractive for a wide range of applications, such as data processing and scientific computing, failures due to server revocation can severely degrade application performance. Since different transient server types offer different cost and availability tradeoffs, we present the notion of server portfolios that is based on financial portfolio modeling. Server portfolios enable construction of an "optimal" mix of severs to meet an application's sensitivity to cost and revocation risk. We implement model-driven portfolios in a system called ExoSphere, and show how diverse applications can use portfolios and application-specific policies to gracefully handle transient servers. We show that ExoSphere enables widely-used parallel applications such as Spark, MPI, and BOINC to be made transiency-aware with modest effort. Our experiments show that allowing the applications to use suitable transiency-aware policies, ExoSphere is able to achieve 80% cost savings when compared to on-demand servers and greatly reduces revocation risk compared to existing approaches.
Accelerating Performance Inference over Closed Systems by Asymptotic Methods
Authors: Casale Giuliano
Published: March 2018

Recent years have seen a rapid growth of interest in exploiting monitoring data collected from enterprise applications for automated management and performance analysis. In spite of this trend, even simple performance inference problems involving queueing theoretic formulas often incur computational bottlenecks, for example upon computing likelihoods in models of batch systems. Motivated by this issue, we revisit the solution of multiclass closed queueing networks, which are popular models used to describe batch and distributed applications with parallelism constraints. We first prove that the normalizing constant of the equilibrium state probabilities of a closed model can be reformulated exactly as a multidimensional integral over the unit simplex. This gives as a by-product novel explicit expressions for the multiclass normalizing constant. We then derive a method based on cubature rules to efficiently evaluate the proposed integral form in small and medium-sized models. For large models, we propose novel asymptotic expansions and Monte Carlo sampling methods to efficiently and accurately approximate normalizing constants and likelihoods. We illustrate the resulting accuracy gains in problems involving optimization-based inference.
Hieroglyph
Authors: Ju Xiaoen , Jamjoom Hani , Shin Kang G.
Published: March 2018

Despite their widespread adoption, large-scale graph processing systems do not fully decouple computation and communication, often yielding suboptimal performance. Locally-sufficient computation-computation that relies only on the graph state local to a computing host-can mitigate the effects of this coupling. In this paper, we present Compute-Sync-Merge (CSM), a new programming abstraction that achieves efficient locally-sufficient computation. CSM enforces local sufficiency at the programming abstraction level and enables the activation of vertex-centric computation on all vertex replicas, thus supporting vertex-cut partitioning. We demonstrate the simplicity of expressing several fundamental graph algorithms in CSM. Hieroglyph-our implementation of a graph processing system with CSM support-outperforms state of the art by up to 53x, with a median speedup of 3.5x and an average speedup of 6x across a wide range of datasets.
Understanding Reduced-Voltage Operation in Modern DRAM Devices
Authors: Chang Kevin K. , Yağlıkçı A. Giray , Ghose Saugata , Agrawal Aditya , Chatterjee Niladrish , Kashyap Abhijith , Lee Donghyuk , O'Connor Mike , Hassan Hasan , Mutlu Onur
Published: March 2018

The energy consumption of DRAM is a critical concern in modern computing systems. Improvements in manufacturing process technology have allowed DRAM vendors to lower the DRAM supply voltage conservatively, which reduces some of the DRAM energy consumption. We would like to reduce the DRAM supply voltage more aggressively, to further reduce energy. Aggressive supply voltage reduction requires a thorough understanding of the effect voltage scaling has on DRAM access latency and DRAM reliability.

In this paper, we take a comprehensive approach to understanding and exploiting the latency and reliability characteristics of modern DRAM when the supply voltage is lowered below the nominal voltage level specified by DRAM standards. Using an FPGA-based testing platform, we perform an experimental study of 124 real DDR3L (low-voltage) DRAM chips manufactured recently by three major DRAM vendors. We find that reducing the supply voltage below a certain point introduces bit errors in the data, and we comprehensively characterize the behavior of these errors. We discover that these errors can be avoided by increasing the latency of three major DRAM operations (activation, restoration, and precharge). We perform detailed DRAM circuit simulations to validate and explain our experimental findings. We also characterize the various relationships between reduced supply voltage and error locations, stored data patterns, DRAM temperature, and data retention.

Based on our observations, we propose a new DRAM energy reduction mechanism, called Voltron. The key idea of Voltron is to use a performance model to determine by how much we can reduce the supply voltage without introducing errors and without exceeding a user-specified threshold for performance loss. Our evaluations show that Voltron reduces the average DRAM and system energy consumption by 10.5% and 7.3%, respectively, while limiting the average system performance loss to only 1.8%, for a variety of memory-intensive quad-core workloads. We also show that Voltron significantly outperforms prior dynamic voltage and frequency scaling mechanisms for DRAM.
Using Burstable Instances in the Public Cloud
Authors: Wang Cheng , Urgaonkar Bhuvan , Nasiriani Neda , Kesidis George
Published: March 2018

Amazon EC2 and Google Compute Engine (GCE) have recently introduced a new class of virtual machines called "burstable" instances that are cheaper than even the smallest traditional/regular instances. These lower prices come with reduced average capacity and increased variance. Using measurements from both EC2 and GCE, we identify key idiosyncrasies of resource capacity dynamism for burstable instances that set them apart from other instance types. Most importantly, certain resources for these instances appear to be regulated by deterministic token bucket like mechanisms. We find widely different types of disclosures by providers of the parameters governing these regulation mechanisms: full disclosure (e.g., CPU capacity for EC2 t2 instances), partial disclosure (e.g., CPU capacity and remote disk IO bandwidth for GCE shared-core instances), or no disclosure (network bandwidth for EC2 t2 instances). A tenant modeling these variations as random phenomena (as some recent work suggests) might make sub-optimal procurement and operation decisions. We present modeling techniques for a tenant to infer the properties of these regulation mechanisms via simple offline measurements. We also present two case studies of how certain memcached workloads might benefit from our modeling when operating on EC2 by: (i) augmenting cheap but low availability in-memory storage offered by spot instances with backup of popular content on burstable instances, and (ii) temporal multiplexing of multiple burstable instances to achieve the CPU or network bandwidth (and thereby throughput) equivalent of a more expensive regular EC2 instance.
Security Game with Non-additive Utilities and Multiple Attacker Resources
Authors: Wang Sinong , Shroff Ness
Published: March 2018

There has been significant interest in studying security games for modeling the interplay of attacks and defenses on various systems involving critical infrastructure, financial system security, political campaigns, and civil safeguarding. However, existing security game models typically either assume additive utility functions, or that the attacker can attack only one target. Such assumptions lead to tractable analysis, but miss key inherent dependencies that exist among different targets in current complex networks. In this paper, we generalize the classical security game models to allow for non-additive utility functions. We also allow attackers to be able to attack multiple targets. We examine such a general security game from a theoretical perspective and provide a unified view. In particular, we show that each security game is equivalent to a combinatorial optimization problem over a set system ε, which consists of defender's pure strategy space. The key technique we use is based on the transformation, projection of a polytope, and the ellipsoid method. This work settles several open questions in security game domain and significantly extends the state-of-the-art of both the polynomial solvable and NP-hard class of the security game.
Quality and Cost of Deterministic Network Calculus
Authors: Bondorf Steffen , Nikolaus Paul , Schmitt Jens B.
Published: March 2018

Networks are integral parts of modern safety-critical systems and certification demands the provision of guarantees for data transmissions. Deterministic Network Calculus (DNC) can compute a worst-case bound on a data flow's end-to-end delay. Accuracy of DNC results has been improved steadily, resulting in two DNC branches: the classical algebraic analysis and the more recent optimization-based analysis. The optimization-based branch provides a theoretical solution for tight bounds. Its computational cost grows, however, (possibly super-)exponentially with the network size. Consequently, a heuristic optimization formulation trading accuracy against computational costs was proposed. In this article, we challenge optimization-based DNC with a new algebraic DNC algorithm. We show that:

no current optimization formulation scales well with the network size and

algebraic DNC can be considerably improved in both aspects, accuracy and computational cost.

To that end, we contribute a novel DNC algorithm that transfers the optimization's search for best attainable delay bounds to algebraic DNC. It achieves a high degree of accuracy and our novel efficiency improvements reduce the cost of the analysis dramatically. In extensive numerical experiments, we observe that our delay bounds deviate from the optimization-based ones by only 1.142% on average while computation times simultaneously decrease by several orders of magnitude.
Expected Values Estimated via Mean-Field Approximation are 1/N-Accurate
Authors: Gast Nicolas
Published: March 2018

Mean-field approximation is a powerful tool to study large-scale stochastic systems such as data-centers -- one example being the famous power of two-choice paradigm. It is shown in the literature that under quite general conditions, the empirical measure of a system of
N
interacting objects converges at rate
O
(1√
N
) to a deterministic dynamical system, called its mean-field approximation.

In this paper, we revisit the accuracy of mean-field approximation by focusing on expected values. We show that, under almost the same general conditions, the expectation of any performance functional converges at rate O(1/
N
) to its mean-field approximation. Our result applies for finite and infinite-dimensional mean-field models. We also develop a new perturbation theory argument that shows that the result holds for the stationary regime if the dynamical system is asymptotically exponentially stable. We provide numerical experiments that demonstrate that this rate of convergence is tight and that illustrate the necessity of our conditions. As an example, we apply our result to the classical two-choice model. By combining our theory with numerical experiments, we claim that, as the load rho goes to 1, the average queue length of a two-choice system with
N
servers is log
2
1/(1--ρ) + 1/(2
N
(1-ρ) +
O
(1/
N
2
).
Outward Influence and Cascade Size Estimation in Billion-scale Networks
Authors: Nguyen Hung T. , Nguyen Tri P. , Vu Tam N. , Dinh Thang N.
Published: March 2018

Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed
outward influence
(OI), defined as the (expected) number of nodes that a subset of nodes
S
will activate,
excluding the nodes in S
. Thus, OI equals, the de facto standard measure,
influence spread
of
S
minus |
S
|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods.

Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence
at scale and with rigorous theoretical guarantees
. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log

4
n

) times faster in theory and up to
several orders of magnitude faster
in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the "ground truth" for influence spread.
Optimal Posted Prices for Online Cloud Resource Allocation
Authors: Zhang Zijun , Li Zongpeng , Wu Chuan
Published: March 2018

We study online resource allocation in a cloud computing platform through posted pricing: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios in social welfare. In the basic case of a single-type, non-recycled resource (allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that it achieves the smallest competitive ratio among all possible pricing functions. Insights obtained from the basic case are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned to the cloud resource pool.
Analysis of a Stochastic Model of Replication in Large Distributed Storage Systems
Authors: Sun Wen , Simon Veronique , Monnet Sebastien , Robert Philippe , Sens Pierre
Published: March 2018

Distributed storage systems such as Hadoop File System or Google File System (GFS) ensure data availability and durability using replication. Persistence is achieved by replicating the same data block on several nodes, and ensuring that a minimum number of copies are available on the system at any time. Whenever the contents of a node are lost, for instance due to a hard disk crash, the system regenerates the data blocks stored before the failure by transferring them from the remaining replicas. This paper is focused on the analysis of the efficiency of replication mechanism that determines the location of the copies of a given file at some server. The variability of the loads of the nodes of the network is investigated for several policies. Three replication mechanisms are tested against simulations in the context of a real implementation of a such a system: Random, Least Loaded and Power of Choice.

The simulations show that some of these policies may lead to quite unbalanced situations: if β is the average number of copies per node it turns out that, at equilibrium, the load of the nodes may exhibit a high variability. It is shown in this paper that a simple variant of a power of choice type algorithm has a striking effect on the loads of the nodes: at equilibrium, the distribution of the load of a node has a bounded support, most of nodes have a load less than 2β which is an interesting property for the design of the storage space of these systems. Stochastic models are introduced and investigated to explain this interesting phenomenon.
Optimal Service Elasticity in Large-Scale Distributed Systems
Authors: Mukherjee Debankur , Dhara Souvik , Borst Sem C. , van Leeuwaarden Johan S.H.
Published: March 2018

A fundamental challenge in large-scale cloud networks and data centers is to achieve highly efficient server utilization and limit energy consumption, while providing excellent user-perceived performance in the presence of uncertain and time-varying demand patterns. Auto-scaling provides a popular paradigm for automatically adjusting service capacity in response to demand while meeting performance targets, and queue-driven auto-scaling techniques have been widely investigated in the literature. In typical data center architectures and cloud environments however, no centralized queue is maintained, and load balancing algorithms immediately distribute incoming tasks among parallel queues. In these distributed settings with vast numbers of servers, centralized queue-driven auto-scaling techniques involve a substantial communication overhead and major implementation burden, or may not even be viable at all.

Motivated by the above issues, we propose a joint auto-scaling and load balancing scheme which does not require any global queue length information or explicit knowledge of system parameters, and yet provides provably near-optimal service elasticity. We establish the fluid-level dynamics for the proposed scheme in a regime where the total traffic volume and nominal service capacity grow large in proportion. The fluid-limit results show that the proposed scheme achieves asymptotic optimality in terms of user-perceived delay performance as well as energy consumption. Specifically, we prove that both the waiting time of tasks and the relative energy portion consumed by idle servers vanish in the limit. At the same time, the proposed scheme operates in a distributed fashion and involves only constant communication overhead per task, thus ensuring scalability in massive data center operations. Extensive simulation experiments corroborate the fluid-limit results, and demonstrate that the proposed scheme can match the user performance and energy consumption of state-of-the-art approaches that do take full advantage of a centralized queue.
On Roundoff Error Growth in Elliptic Problems
Authors: Babuška Ivo , Söderlind Gustaf
Published: March 2018

Large-scale linear systems arise in finite-difference and finite-element discretizations of elliptic problems. With increasing computer performance, ever larger systems are solved using direct methods. How large can such systems be without roundoff compromising accuracy?

Here we model roundoff dynamics in standard
LU
and
LDL
T
decompositions with respect to problem size
N
. For the one-dimensional (1D) Poisson equation with Dirichlet boundary conditions on an equidistant grid, we show that the relative error in the factorized matrix grows like O(ε √
N
) if roundoffs are modeled as independent, expectation zero random variables. With bias, the growth rate changes to O(ε
N
).

Subsequent back substitution results in typical error growths of O(ε
N
√
N
) and O(ε
N
2
), respectively. Error growth is governed by the dynamics of the computational process and by the structure of the boundary conditions rather than by the condition number. Computational results are demonstrated in several examples, including a few fourth-order 1D problems and second-order 2D problems, showing that error accumulation depends strongly on the solution method. Thus, the same
LU
solver may exhibit different growth rates for the same 2D Poisson problem, depending on whether the five-point or nine-point FDM operator is used.
Safe Randomized Load-Balanced Switching By Diffusing Extra Loads
Authors: Yang Sen , Lin Bill , Xu Jun
Published: March 2018

Load-balanced switch architectures are known to be scalable in both size and speed, which is of interest due to the continued exponential growth in Internet traffic. However, the main drawback of load-balanced switches is that packets can depart out of order from the switch. Randomized load-balancing of application flows by means of hashing on the packet header is a well-known simple solution to this packet reordering problem in which all packets belonging to the same application flow are routed through the same intermediate port and hence the same path through the switch. Unfortunately, this method of load-balancing can lead to instability, depending on the mix of flow sizes and durations in the group of flows that gets randomly assigned to route through the same intermediate port. In this paper, we show that the randomized load-balancing of application flows can be enhanced to provably guarantee both stability and packet ordering by extending the approach with
safety
mechanisms that can
uniformly diffuse
packets across the switch whenever there is a build-up of packets waiting to route through some intermediate port. Although simple and intuitive, our experimental results show that our extended randomized load-balancing approach outperforms existing load-balanced switch architectures.
Predictive Impact Analysis for Designing a Resilient Cellular Backhaul Network
Authors: Yang Sen , He Yan , Ge Zihui , Wang Dongmei , Xu Jun
Published: March 2018

Backhaul transport network design and optimization for cellular service providers involve a unique challenge stemming from the fact that an end-user's equipment (UE) is within the radio reach of multiple cellular towers: It is hard to evaluate the impact of the failure of the UE's primary serving tower on the UE, because the UE may simply switch to get service from other nearby cellular towers. To overcome this challenge, one needs to quantify the cellular service redundancy among the cellular towers riding on that transport circuit and their nearby cellular towers, which in turn requires a comprehensive understanding of the radio signal profile in the area of the impacted towers, the spatial distribution of UEs therein, and their expected workload (e.g., calls, data throughput). In this work, we develop a novel methodology for assessing the service impact of any hypothetical cellular tower outage scenario, and implement it in an operational system named Tower Outage Impact Predictor (TOIP). Our evaluations, using both synthetic data and historical real tower outages in a large operational cellular network, show conclusively that TOIP gives an accurate assessment of various tower outage scenarios, and can provide critical input data towards designing a reliable cellular backhaul transport network.
Online Learning of Optimally Diverse Rankings
Authors: Magureanu Stefan , Proutiere Alexandre , Isaksson Marcus , Zhang Boxun
Published: March 2018

Search engines answer users' queries by listing relevant items (e.g. documents, songs, products, web pages, ...). These engines rely on algorithms that learn to rank items so as to present an ordered list maximizing the probability that it contains relevant item. The main challenge in the design of learning-to-rank algorithms stems from the fact that queries often have different meanings for different users. In absence of any contextual information about the query, one often has to adhere to the {\it diversity} principle, i.e., to return a list covering the various possible topics or meanings of the query. To formalize this learning-to-rank problem, we propose a natural model where (i) items are categorized into topics, (ii) users find items relevant only if they match the topic of their query, and (iii) the engine is not aware of the topic of an arriving query, nor of the frequency at which queries related to various topics arrive, nor of the topic-dependent click-through-rates of the items. For this problem, we devise LDR (Learning Diverse Rankings), an algorithm that efficiently learns the optimal list based on users' feedback only. We show that after $T$ queries, the regret of LDR scales as $O((N-L)\log(T))$ where $N$ is the number of all items. We further establish that this scaling cannot be improved, i.e., LDR is order optimal. Finally, using numerical experiments on both artificial and real-world data, we illustrate the superiority of LDR compared to existing learning-to-rank algorithms.
Towards Fast-Convergence, Low-Delay and Low-Complexity Network Optimization
Authors: Wang Sinong , Shroff Ness
Published: March 2018

Distributed network optimization has been studied for well over a decade. However, we still do not have a good idea of how to design schemes that can simultaneously provide good performance across the dimensions of utility optimality, convergence speed, and delay. To address these challenges, in this paper, we propose a new algorithmic framework with all these metrics approaching optimality. The salient features of our new algorithm are three-fold: (i) fast convergence: it converges with only
O(\log(1/\epsilon))
iterations that is the fastest speed among all the existing algorithms; (ii) low delay: it guarantees optimal utility with finite queue length; (iii) simple implementation: the control variables of this algorithm are based on virtual queues that do not require maintaining per-flow information. The new technique builds on a kind of inexact Uzawa method in the Alternating Directional Method of Multiplier, and provides a new theoretical path to prove global and linear convergence rate of such a method without requiring the full rank assumption of the constraint matrix.
On the Convergence Rate of Distributed Gradient Methods for Finite-Sum Optimization under Communication Delays
Authors: Doan Thinh T. , Beck Carolyn L. , Srikant R.
Published: March 2018

Motivated by applications in machine learning and statistics, we study distributed optimization problems over a network of processors, where the goal is to optimize a global objective composed of a sum of local functions. In these problems, due to the large scale of the data sets, the data and computation must be distributed over processors resulting in the need for distributed algorithms. In this paper, we consider a popular distributed gradient-based consensus algorithm, which only requires local computation and communication. An important problem in this area is to analyze the convergence rate of such algorithms in the presence of communication delays that are inevitable in distributed systems. We prove the convergence of the gradient-based consensus algorithm in the presence of uniform, but possibly arbitrarily large, communication delays between the processors. Moreover, we obtain an upper bound on the rate of convergence of the algorithm as a function of the network size, topology, and the inter-processor communication delays.
The PDE Method for the Analysis of Randomized Load Balancing Networks
Authors: Aghajani Reza , Li Xingjie , Ramanan Kavita
Published: March 2018

We introduce a new framework for the analysis of large-scale load balancing networks with general service time distributions, motivated by applications in server farms, distributed memory machines, cloud computing and communication systems. For a parallel server network using the so-called $SQ(d)$ load balancing routing policy, we use a novel representation for the state of the system and identify its fluid limit, when the number of servers goes to infinity and the arrival rate per server tends to a constant. The fluid limit is characterized as the unique solution to a countable system of coupled partial differential equations (PDE), which serve to approximate transient Quality of Service parameters such as the expected virtual waiting time and queue length distribution. In the special case when the service time distribution is exponential, our method recovers the well-known ordinary differential equation characterization of the fluid limit.

Furthermore, we develop a numerical scheme to solve the PDE, and demonstrate the efficacy of the PDE approximation by comparing it with Monte Carlo simulations. We also illustrate how the PDE can be used to gain insight into the performance of large networks in practical scenarios by analyzing relaxation times in a backlogged network. In particular, our numerical approximation of the PDE uncovers two interesting properties of relaxation times under the SQ(2) algorithm. Firstly, when the service time distribution is Pareto with unit mean, the relaxation time decreases as the tail becomes heavier. This is
a priori
counterintuitive given that for the Pareto distribution, heavier tails have been shown to lead to worse tail behavior in equilibrium. Secondly, for unit mean light-tailed service distributions such as the Weibull and lognormal, the relaxation time decreases as the variance increases. This is in contrast to the behavior observed under random routing, where the relaxation time increases with increase in variance.
Designing Low-Complexity Heavy-Traffic Delay-Optimal Load Balancing Schemes
Authors: Zhou Xingyu , Wu Fei , Tan Jian , Sun Yin , Shroff Ness
Published: March 2018

In this paper, we establish a unified analytical framework for designing load balancing algorithms that can simultaneously achieve low latency, low complexity, and low communication overhead. We first propose a general class
\Pi
of load balancing policies and prove that they are throughput optimal and heavy-traffic delay optimal. This class
\Pi
includes popular policies such as join-shortest-queue (JSQ) and power-of-
d
as special cases, but not the recently proposed join-idle-queue (JIQ) policy. In fact, we show that JIQ is not heavy-traffic delay optimal even for homogeneous servers. By exploiting the flexibility offered by the class
\Pi
, we design a new load balancing policy called join-below-threshold (JBT-d), in which the arrival jobs are preferably assigned to queues that are no greater than a threshold, and the threshold is updated infrequently. JBT-d has several benefits: (i) JBT-d belongs to the class
\Pi
and hence is throughput optimal and heavy-traffic delay optimal. (ii) JBT-d has zero dispatching delay, like JIQ and other pull-based policies, and low message overhead due to infrequent threshold update. (iii) Extensive simulations show that JBT-d has excellent delay performance, comparable to the JSQ policy in various system settings.