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In this paper, we introduce a new methodology for automatic phase detection and characterization for applications running on the cloud. In contrast to existing approaches, our approach is novel in the fact that it is non-intrusive, more general (supports multiple programming languages), lightweight and can detect phase changes online as the application runs. We evaluate our approach for a number of...
In this work, we introduce and experimentally evaluate a novel approach for real time anomaly detection in smart car parking applications. We attach semantics on top of raw real time parking data collected from sensors of parking lots. We use knowledge from historical data to detect anomalies on real time data. Attaching semantics on top of raw data helps reduce the learning time by a factor of 3...
Thread Level Speculation (TLS) speculatively executes parts of a program in parallel. Statically determined may dependences between store-load pairs prevent the compiler from speculatively executing parts of programs (e.g loop iterations or functions). If a compiler can determine that the probability of a may dependence occurring at runtime is low, then it can use TLS to execute the loop in parallel...
As cloud based platforms become more popular, it becomes an essential task for the cloud administrator to efficiently manage the costly hardware resources in the cloud environment. Prompt action should be taken whenever hardware resources are faulty, or configured and utilized in a way that causes application performance degradation, hence poor quality of service. In this paper, we propose a semantic...
As Cloud platforms are becoming more popular, efficient resource management in these Cloud platforms helps the Cloud provider to deliver better quality of service to its customers. In this paper, we present an online characterization method that can identify potentially failing jobs in a Cloud platform by analyzing the jobs' resource usage profile as the job runs. We show that, by tracking the online...
Performance modeling can be utilized in a number of scenarios, starting from finding performance bugs to the scalability study of applications. Existing dynamic and static approaches for automating the generation of performance models have limitations for precision and overhead. In this work, we explore combination of a number of static and dynamic analyses for life-long performance modeling and investigate...
Traditional means of gathering performance data are tracing, which is limited by the available storage, and profiling, which has limited accuracy. Performance modeling is often used to interpret the tracing data and generate performance predictions. We aim to complement the traditional data collection mechanisms with online performance modeling, a method that generates performance models while the...
Figure 1 shows the performance of three parallel versions (auto-SIMDized, auto-SIMDized+auto-OpenMP by bgxlc r and auto-SIMDized+auto-OpenMP+speculatively parallelized by an automatic speculative parallelization framework developed) of the SPEC2006 and PolyBench/C benchmarks. The speculative loops in lbm have 98% coverage that accounts for the speedup while in bzip2(35%) and dynprog (26%), the poor...
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