Despite many years of improvements to it, TCP still suffers from an unsatisfactory performance. For services dominated by short flows (e.g., web search and e-commerce), TCP suffers from the flow ...startup problem and cannot fully utilize the available bandwidth in the modern Internet: TCP starts from a conservative and static initial window ( IW , 2-4 or 10), while most of the web flows are too short to converge to the best sending rate before the session ends. For services dominated by long flows (e.g., video streaming and file downloading), the congestion control ( CC ) scheme manually and statically configured might not offer the best performance for the latest network conditions. To address these two challenges, we propose TCP-RL , which uses reinforcement learning ( RL ) techniques to dynamically configure IW and CC in order to improve the performance of TCP flow transmission. Basing on the latest network conditions observed at the server side of a web service, TCP-RL dynamically configures a suitable IW for short flows through group-based RL , and dynamically configures a suitable CC scheme for long flows through deep RL . Our extensive experiments show that for short flows, TCP-RL can reduce the average transmission time by about 23%; and for long flows, compared with the performance of 14 CC schemes, TCP-RL 's performance ranks top 5 for about 85% of the 288 given static network conditions, whereas for about 90% of conditions, its performance drops by less than 12% compared with that of the best-performing CC schemes for the same network conditions.
Objective
This meta-analysis was conducted to examine the possible association between serum zinc concentration and cervical cancer risk.
Methods
PubMed, WanFang, China National Knowledge ...Infrastructure, and SinoMed databases were searched for relevant articles published between January 1980 and September 2017. Results were combined using a random-effects model, and pooled standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated to compare serum zinc levels in patients with cervical cancer versus controls. Publication bias was evaluated using Begg’s funnel plot and Egger’s regression asymmetry test.
Results
Twelve articles regarding serum zinc levels and cervical cancer were included in this meta-analysis. Combined results showed that serum zinc levels in cervical cancer cases were significantly lower than in controls without cervical cancer (summary SMD –1.379, 95% CI –1.527, –1.231), with high heterogeneity (I2 = 98.8%). Analysis of data stratified by geographic location showed a significant association between serum zinc levels and cervical cancer risk in Asian populations (summary SMD –1.391, 95% CI –1.543, –1.239).
Conclusions
Higher serum zinc levels may be a protective factor for cervical cancer in Asian women.
Additive key performance indicators (KPIs) (such as page view (PV), revenue, and error count) with multi-dimensional attributes (such as ISP, Province, and DataCenter) are common and important in ...monitoring metrics in Internet companies. When an anomaly happens to an overall KPI, it is critical but challenging to localize the root cause, which is one (or more) combination of attribute values in multiple dimensions. For example, is the total PV decrease caused by the PV decrease from "Beijing" or "China Mobile in Beijing", or "Beijing and Shanghai"? However, this task is very challenging for two major reasons. First, the PVs of different combinations are interdependent; thus, the PV anomalies at the root cause can cause the changes of many other PVs at different aggregation levels. Second, there could be tens of thousands of combinations to investigate in multi-dimensional attribute space. It is a difficulty to find the root cause from a huge search space. To address the first challenge, our approach HotSpot uses a novel potential score based on the ripple effect for anomaly propagation that we reveal. To address the second challenge, HotSpot adopts the Monte Carlo Tree Search algorithm and a hierarchical pruning strategy. Using the real-world data from a top global search engine, we show that HotSpot achieves a great improvement on effectiveness and robustness, i.e., 95% of all types of root cause cases using HotSpot (compared with only 15% using existing approaches) achieves an F-score over 90%. Operational experiences show that HotSpot can reduce the localization time from more than 1 h in manual efforts to less than 20 s.
Many important cloud services require replicating massive data from one datacenter (DC) to multiple DCs. While the performance of pair-wise inter-DC data transfers has been much improved, prior ...solutions are insufficient to optimize bulk-data multicast, as they fail to explore the rich inter-DC overlay paths that exist in geo-distributed DCs, as well as the remaining bandwidth reserved for online traffic under fixed bandwidth separation scheme. To take advantage of these opportunities, we present BDS+ , a near-optimal network system for large-scale inter-DC data replication. BDS+ is an application-level multicast overlay network with a fully centralized architecture, allowing a central controller to maintain an up-to-date global view of data delivery status of intermediate servers, in order to fully utilize the available overlay paths. Furthermore, in each overlay path, it leverages dynamic bandwidth separation to make use of the remaining available bandwidth reserved for online traffic. By constantly estimating online traffic demand and rescheduling bulk-data transfers accordingly, BDS+ can further speed up the massive data multicast. Through a pilot deployment in one of the largest online service providers and large-scale real-trace simulations, we show that BDS+ can achieve 3-<inline-formula> <tex-math notation="LaTeX">5\times </tex-math></inline-formula> speedup over the provider's existing system and several well-known overlay routing baselines of static bandwidth separation. Moreover, dynamic bandwidth separation can further reduce the completion time of bulk data transfers by 1.2 to 1.3 times.
The failures of software service directly affect user experiences and service revenue. Thus operators monitor both service-level KPIs (e.g., response time) and machine-level KPIs (e.g., CPU usage) on ...each machine underlying the service. When a service fails, the operators must localize the root cause machines, and mitigate the failure as quickly as possible. Existing approaches have limited application due to the difficulty to obtain the required additional measurement data. As a result, failure localization is largely manual and very time-consuming. This paper presents FluxRank, a widely-deployable framework that can automatically and accurately localize the root cause machines, so that some actions can be triggered to mitigate the service failure. Our evaluation using historical cases from five real services (with tens of thousands of machines) of a top search company shows that the root cause machines are ranked top 1 (top 3) for 55 (66) cases out of 70 cases. Comparing to existing approaches, FluxRank cuts the localization time by more than 80% on average. FluxRank has been deployed online at one Internet service and six banking services for three months, and correctly localized the root cause machines as the top 1 for 55 cases out of 59 cases.
Microservice architecture is applied by an increasing number of systems because of its benefits on delivery, scalability, and autonomy. It is essential but challenging to localize root-cause ...microservices promptly when a fault occurs. Traces are helpful for root-cause microservice localization, and thus many recent approaches utilize them. However, these approaches are less practical due to relying on supervision or other unrealistic assumptions. To overcome their limitations, we propose a more practical root-cause microservice localization approach named TraceRCA. The key insight of TraceRCA is that a microservice with more abnormal and less normal traces passing through it is more likely to be the root cause. Based on it, TraceRCA is composed of trace anomaly detection, suspicious microservice set mining and microservice ranking. We conducted experiments on hundreds of injected faults in a widely-used open-source microservice benchmark and a production system. The results show that TraceRCA is effective in various situations. The top-1 accuracy of TraceRCA outperforms the state-of-the-art unsupervised approaches by 44.8%. Besides, TraceRCA is applied in a large commercial bank, and it helps operators localize root causes for real-world faults accurately and efficiently. We also share some lessons learned from our real-world deployment.
Using large-scale multi-dimensional data for root cause analysis (MDRCA) is vitally important for online software services. It helps operators narrow down the scope of anomalies and failures quickly ...and localize the root cause to a finer granularity. However, most existing MDRCA algorithms can only solve low-dimensional problems. When dealing with high-dimensional data, the complexity of these algorithms would significantly increase, and even some algorithms would no longer work. Intuitively, passing only a subset of attributes rather than full attributes can improve the performance of these MDRCA algorithms. However, it is challenging due to data imbalance and novel root cause attributes. To better understand the problem of root-cause-oriented attribute selection (RCOAS), we conduct a preliminary study based on real-world data. We find that there exist several straightforward rules to filter out some attributes. In addition, we reveal that existing approaches do not fit the requirements of RCOAS. Motivated by the study, we propose an RCOAS approach, RC-LIR, to select a subset of attributes for downstream algorithms. RC-LIR first performs rule-based selection. Then it improves a feature selection algorithm by two strategies, i.e., scaling up imbalanced data and considering the redundant cost. Experiments on 1000 real-world fault cases demonstrate that RC-LIR can achieve an F1-score of 0.88, outper-forming the baseline approaches by at least 0.15. Furthermore, our experiments with four widely adopted MDRCA algorithms show that integrating RC-LIR can lead to more effective and efficient MDRCA.
Incidents in online service systems could incur poor user experience and tremendous economic loss. To reduce the influence of incidents and guarantee service reliability, it is critical to identify ...root-cause metrics for engineers with clues to assist incident diagnosis. However, it is a challenging task due to the complicated dependencies and huge volume of various metrics in large-scale systems. Existing approaches are based on either anomaly detection or correlation analysis, performing not well in terms of accuracy or efficiency. To better understand the problem of root-cause metric identification, we conduct a preliminary study based on real-world data analysis and interactions with engineers. The key observation is that root-cause metrics should satisfy two requirements. One is that the metric is expected to behave abnormally during the incident; the other is that the anomaly pattern should meet physical meaning and engineers' demand. Motivated by the findings obtained from the study, we propose an effective approach named PatternMatcher to identifying root-cause metrics accurately. Specifically, PatternMatcher contains three steps, where coarse-grained anomaly detection aiming to filter out normal metrics, anomaly pattern classification aiming to filter out unimportant anomaly patterns, and root-cause metric ranking. An extensive study on four real-world datasets including 113 incident cases from a large commercial bank demonstrates that PatternMatcher outperforms all baseline approaches, achieving top-3 average accuracy of 0.91. Moreover, we have deployed PatternMatcher in practice and shared some successful cases from real deployment.
Alert is a kind of key data source in monitoring system for online service systems, which is used to record the anomalies in service components and report to engineers. In general, the occurrence of ...a service failure tends to be along with a large number of alerts, which is called alert storm. However, alert storm brings great challenges to diagnose the failure, because it is time-consuming and tedious for engineers to investigate such an overwhelming number of alerts manually. To help understand alert storm in practice, we conduct the first empirical study of alert storm based on large-scale real-world alert data and gain some valuable insights. Based on the findings obtained from the study, we propose a novel approach to handling alert storm. Specifically, this approach includes alert storm detection which aims to identify alert storm accurately, and alert storm summary which aims to recommend a small set of representative alerts to engineers for failure diagnosis. Our experimental study on real-world dataset demonstrates that our alert storm detection can achieve high F1-score (larger than 0.9). Besides, our alert storm summary can reduce the number of alerts that need to be examined by more than 98% and discover representative alerts accurately. We have successfully applied our approach to the service maintenance of a large commercial bank (China EverBright Bank), and we also share our success stories and lessons learned in industry.
The aim of this study is to research the lesion outline and temperature field in different ways in atrial radiofrequency ablation by using finite element method.
This study used the method which ...considered the thermal dosage to determine the boundary between viable and dead tissue, and compared to the 50 °C isotherm results in analyzing lesion outline. Besides, we used Hyperbolic equation which considered the relaxation time to calculate the temperature field and contrasted it with Pennes' bioheat transfer equation.
As the result of the comparison of the lesion outline, when the ablation time was 120 s, the isotherm of the thermal dosage was larger than the 50 °C isotherm and with the increasing of the voltage the gap increased. When the ablation voltage was 30 V, the 50 °C isotherm was larger than the thermal dosage isotherm when the ablation time was less than 160 s. The isotherms overlapped when the time was 160 s. And when the ablation time was more than 160 s, the 50 °C isotherm was less than the thermal dosage isotherm. As to the temperature field, when the ablation voltage was 30 V with the ablation time 120 s the highest temperature decided by Hyperbolic was 0.761 °C higher. The highest temperature changed with relaxation time. In most cases, the highest temperature of the Hyperbolic was higher otherwise the relaxation time was 30-40 s.
It is better to use CEM43 °C to estimate the lesion outline when the ablative time within 160 s. For temperature distribution, the Hyperbolic reflects the influence of heat transmission speed, so the result is more close to the actual situation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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