Modeling Data Center Co-Tenancy Performance Interference

A multi-core machine allows executing several applications simultaneously. Those jobs are scheduled on different cores and compete for shared resources such as the last level cache and memory bandwidth. Such competitions might cause performance degradation. Data centers often utilize virtualization to provide a certain level of performance isolation. However, some of the shared resources cannot be divided, even in a virtualized system, to ensure complete isolation. If the performance degradation of co-tenancy is not known to the cloud administrator, a data center often has to dedicate a whole machine for a latency-sensitive application to guarantee its quality of service. Co-run scheduling attempts to make good utilization of resources by scheduling compatible jobs into one machine while maintaining their service level agreements. An ideal co-run scheduling scheme requires accurate contention modeling. Recent studies for co-run modeling and scheduling have made steady progress to predict performance for two co-run applications sharing a specific system. This thesis advances co-tenancy modeling in three aspects. First, with an accurate co-run modeling for one system at hand, we propose a regression model to transfer the knowledge and create a model for a new system with different hardware configuration. Second, by examining those programs that yield high prediction errors, we further leverage clustering techniques to create a model for each group of applications that show similar behavior. Clustering helps improve the prediction accuracy of those pathological cases. Third, existing research is typically focused on modeling two application co-run cases. We extend a two-core model to a three- and four-core model by introducing a light-weight micro-kernel that emulates a complicated benchmark through program instrumentation. Our experimental evaluation shows that our cross-architecture model achieves an average prediction error less than 2% for pairwise co-runs across the SPECCPU2006 benchmark suite. For more than two application co-tenancy modeling, we show that our model is more scalable and can achieve an average prediction error of 2-3%

Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models

Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed skepticism that neural ranking models were actually improving ad hoc retrieval effectiveness in limited data scenarios. He provided anecdotal evidence that authors of neural IR papers demonstrate "wins" by comparing against weak baselines. This paper provides a rigorous evaluation of those claims in two ways: First, we conducted a meta-analysis of papers that have reported experimental results on the TREC Robust04 test collection. We do not find evidence of an upward trend in effectiveness over time. In fact, the best reported results are from a decade ago and no recent neural approach comes close. Second, we applied five recent neural models to rerank the strong baselines that Lin used to make his arguments. A significant improvement was observed for one of the models, demonstrating additivity in gains. While there appears to be merit to neural IR approaches, at least some of the gains reported in the literature appear illusory.Comment: Published in the Proceedings of the 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019

Seasonal changes and serum 25-hydroxyvitamin D levels among community-dwelling elders who live in Boston, Massachusetts and Stockholm, Sweden

BACKGROUND: The prevalence of Vitamin D deficiency is roughly 40% in the world and is increasing every year. Populations 65 years and older show a higher prevalence of vitamin D deficiency, because the aging process decreases the capacity of the skin to produce vitamin D. Some studies have reported that the prevalence of vitamin D deficiency is higher in the winter, however the effect of seasonal change on serum vitamin D level remains controversial in some specific populations. Moreover, this association remains uncertain in the elderly population because there is no study that specifically targets individuals over the age of 65. This study investigated the effect of seasonal changes and serum 25-hydroxyvitamin D among individuals 65 years and older residing in the Boston, Massachusetts and Stockholm, Sweden. METHODS: Cross-sectional and longitudinal cohort designs were both adapted to examine an existing data from VIVE2 parent study; the data was collected from 2012 to 2014. Data from the subjects who had finished this 6-month trial were analyzed for this study. Serum 25(OH)D levels, BMI, sex, study sites and age were collected and analyzed by univariate regression analysis and t-test. Serum 25(OH)D and confounders were included in multivariate analysis. Study sites were analyzed by effect modification model. RESULTS: In total, the prevalence of vitamin D deficiency (serum 25(OH)D levels less than 20 ng/ml) was 70%, while the mean serum 25(OH)D level was 20 ng/ml in summer and 16.4 ng/ml in winter. The average of seasonal serum 25(OH)D level changes were 6 ng/ml and 3 ng/ml in Stockholm, Sweden and Boston, MA, respectively. In addition, the prevalence of vitamin D deficiency increased 80% during winter (95CI: 1.1 – 2.9). There was no significant different in serum 25(OH)D levels among elderly populations between low latitude study site Boston, MA and high latitude site Stockholm, Sweden. There was no significant relation found in BMI, age and sex with serum 25(OH)D levels in the study. The seasonal serum 25(OH)D level changes was significantly different in the cross-sectional study design but not in the longitudinal study. CONCLUSION: Serum 25(OH)D levels were higher in the summer than in the winter among the elderly population resided in Boston, MA and Stockholm, Sweden

Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition

Motion representation plays a vital role in human action recognition in videos. In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach. The OFF is derived from the definition of optical flow and is orthogonal to the optical flow. The derivation also provides theoretical support for using the difference between two frames. By directly calculating pixel-wise spatiotemporal gradients of the deep feature maps, the OFF could be embedded in any existing CNN based video action recognition framework with only a slight additional cost. It enables the CNN to extract spatiotemporal information, especially the temporal information between frames simultaneously. This simple but powerful idea is validated by experimental results. The network with OFF fed only by RGB inputs achieves a competitive accuracy of 93.3% on UCF-101, which is comparable with the result obtained by two streams (RGB and optical flow), but is 15 times faster in speed. Experimental results also show that OFF is complementary to other motion modalities such as optical flow. When the proposed method is plugged into the state-of-the-art video action recognition framework, it has 96:0% and 74:2% accuracy on UCF-101 and HMDB-51 respectively. The code for this project is available at https://github.com/kevin-ssy/Optical-Flow-Guided-Feature.Comment: CVPR 2018. code available at https://github.com/kevin-ssy/Optical-Flow-Guided-Featur

Sharp bounds for harmonic numbers

In the paper, we first survey some results on inequalities for bounding harmonic numbers or Euler-Mascheroni constant, and then we establish a new sharp double inequality for bounding harmonic numbers as follows: For $n\in\mathbb{N}$, the double inequality -\frac{1}{12n^2+{2(7-12\gamma)}/{(2\gamma-1)}}\le H(n)-\ln n-\frac1{2n}-\gamma<-\frac{1}{12n^2+6/5} is valid, with equality in the left-hand side only when $n=1$, where the scalars $\frac{2(7-12\gamma)}{2\gamma-1}$ and $\frac65$ are the best possible.Comment: 7 page