Low latency and high availability of an app or a web service are key, amongst other factors, to the overall user experience (which in turn directly impacts the bottomline). Exogenic and/or endogenic factors often give rise to breakouts in cloud data which makes maintaining high availability and delivering high performance very challenging. Although there exists a large body of prior research in breakout detection, existing techniques are not suitable for detecting breakouts in cloud data owing to being not robust in the presence of anomalies.To this end, we developed a novel statistical technique to automatically detect breakouts in cloud data. In particular, the technique employs Energy Statistics to detect breakouts in both application as well as system metrics. Further, the technique uses robust statistical metrics, viz., median, and estimates the statistical significance of a breakout through a permutation test. To the best of our knowledge, this is the first work which addresses breakout detection in the presence of anomalies.We demonstrate the efficacy of the proposed technique using production data and report Precision, Recall and Fmeasure measure. The proposed technique is 3.5× faster than a state-of-the-art technique for breakout detection and is being currently used on a daily basis at Twitter.T 73% of mobile internet users say that they have encountered a website that was too slow to load. T 38% of mobile internet users say that they have encountered a website that was not available. T A 1 second delay in page response can result in a 7% reduction in conversions.

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Parallel loops account for the greatest percentage of program parallelism. The degree to which parallelism can be exploited and the amount of overhead involved during parallel execution of a nested loop directly depend on partitioning, i.e., the way the different iterations of a parallel loop are distributed across different processors. Thus, partitioning of parallel loops is of key importance for high performance and efficient use of multiprocessor systems. Although a significant amount of work has been done in partitioning and scheduling of rectangular iteration spaces, the problem of partitioning of non-rectangular iteration spaces-e.g. triangular, trapezoidal iteration spaces-has not been given enough attention so far. In this paper, we present a geometric approach for partitioning N-dimensional non-rectangular iteration spaces for optimizing performance on parallel processor systems. Speedup measurements for kernels (loop nests) of linear algebra packages are presented.

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Multi-cores such as the Intel R 1 Core TM 2 Duo processor, facilitate efficient thread-level parallel execution of ordinary programs, wherein the different threads-of-execution are map-ped onto different physical processors. In this context, several techniques have been proposed for auto-parallelization of programs. Recently, thread-level speculation (TLS) has been proposed as a means to parallelize difficult-to-analyze serial codes. In general, more than one technique can be employed for parallelizing a given program. The overlapping nature of the applicability of the various techniques makes it hard to assess the intrinsic performance potential of each. In this paper, we present a tight analysis of the (unique) performance potential of both: (a) TLS in general and (b) specific types of thread-level speculation, viz., control speculation, data dependence speculation and data value speculation, for the SPEC 2 CPU2006 benchmark suite in light of the various limiting factors such as the threading overhead and misspeculation penalty. To the best of our knowledge, this is the first evaluation of TLS based on SPEC CPU2006 and accounts for the aforementioned real-life constraints. Our analysis shows that, at the innermost loop level, the upper bound on the speedup uniquely achievable via TLS with the state-of-the-art thread implementations for both SPEC CINT2006 and CFP2006 is of the order of 1%.