A common practice in system design is to treat features intended to enhance performance and reliability as low priority tasks by scheduling them during idle periods, with the goal to keep these features transparent to the user. In this paper, we present an algorithmic framework that determines the schedulability of non-preemptable low priority tasks in storage systems. The framework estimates when and for how long idle times can be utilized by low priority background tasks, without violating pre-defined performance targets of user foreground tasks. The estimation is based on monitored system information that includes the histogram of idle times. This histogram captures accurately important statistical characteristics of the complex demands of the foreground activity. The robustness and the effectiveness of the proposed framework is corroborated via extensive trace driven simulations under a wide range of system conditions and background activities, and via experimentation on a Linux kernel 2.6.22 prototype.
Complex system software allows a variety of execution conditions on system configurations and workload properties. This paper explores a principled use of reference executions--those of similar execution conditions from the target--to help identify the symptoms and causes of performance anomalies. First, to identify anomaly symptoms, we construct change profiles that probabilistically characterize expected performance deviations between target and reference executions. By synthesizing several single-parameter change profiles, we can scalably identify anomalous reference-to-target changes in a complex system with multiple execution parameters. Second, to narrow the scope of anomaly root cause analysis, we filter anomaly-related low-level system metrics as those that manifest very differently between target and reference executions. Our anomaly identification approach requires little expert knowledge or detailed models on system internals and consequently it can be easily deployed. Using empirical case studies on the Linux I/O subsystem and a J2EE-based distributed online service, we demonstrate our approach's effectiveness in identifying performance anomalies over a wide range of execution conditions as well as multiple system software versions. In particular, we discovered five previously unknown performance anomaly causes in the Linux 2.6.23 kernel. Additionally, our preliminary results suggest that online anomaly detection and system reconfiguration may help evade performance anomalies in complex online systems.
Most users on social media have intrinsic characteristics, such as interests and political views, that can be exploited to identify and track them, thus raising privacy and identity concerns in online communities. In this article, we investigate the problem of user identity linkage on two behavior datasets collected from different experiments. Specifically, we focus on user linkage based on users' interaction behaviors with respect to content topics. We propose an embedding method to model a topic as a vector in a latent space to interpret its deep semantics. Then a user is modeled as a vector based on his or her interactions with topics. The embedding representations of topics are learned by optimizing the joint-objective: the compatibility between topics with similar semantics, the discriminative abilities of topics to distinguish identities, and the consistency of the same user's characteristics from two datasets. The effectiveness of our method is verified on real-life datasets and the results show that it outperforms related methods. We also analyze failure cases in the application of our identity linkage method. Our analysis shows that factors such as the visibility and variance of user behaviors and users' group psychology can result in mis-linkages. We also analyze the details of the behaviors of some representative users to understand the essential reasons for their identity being mis-linked. We find that these users have high variance level in their behaviors. According to the above experimental results, we introduce a confidence score into identity linkage to provide information about the accuracy of the method results. CCS Concepts: • Human-centered computing → Social media; • Security and privacy → Privacy protections;
Seedlings of three species of Malus were used to study the expression of mitogen-activated protein kinase (MAPK) in response to water stress: Malus hupehensis, a drought-sensitive species; Malus sieversii, a drought-tolerant species; and Malus micromalus, a middle type. Results showed that Malus MAPK (MaMAPK, GenBank accession No. AF435805) was expressed in both roots and leaves of seedlings of the three Malus species treated with 20% polyethylene glycol for different time periods. Expression levels peaked at 1.5 h after treatment with polyethylene glycol, then decreased to their lowest levels. Liquid kinase assays indicated that the dynamic changes of MAPK activity were very similar to those of the relative expression of MaMAPK mRNA. However, the peak of the former occurred slightly behind the latter. It was noticed that, although the kinase activity decreased after the peak, it was still higher than that of the control during the whole time period. These results suggested that MaMAPK was regulated not only by water stress at the transcription level, but also by phosphorylation and dephosphorylation at the protein level. In addition, of these three apple species, the highest MAPK activity and MaMAPK expression level was found in M. sieversii, followed by M. micromalus and M. hupehensis, suggesting that MAPK might be correlated with drought tolerance in these three species. The different expression levels might be one of the molecular mechanisms of the different drought tolerances in Malus.
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