Abstract. Performance evaluation in multi-cluster processor co-allocation -like in many other parallel job scheduling problems-is mostly done by computing the average metric value for the entire job stream. This does not give a comprehensive understanding of the relative performance of the different jobs grouped by their characteristics. It is however the characteristics that affect how easy/hard jobs are to schedule. We, therefore, do not get to understand scheduler performance at job type level. In this paper, we study the performance of multi-cluster processor co-allocation for different job groups grouped by their size, components and widest component. We study their relative performance, sensitivity to parameters and how their performance is affected by the heuristics used to break them up into components. We show that the widest component us characteristic that most affects job schedulability. We also show that to get better performance, jobs should be broken up in such a way that the width of the widest component is minimized.

Polymorphic malware has evolved as a major threat in Computer Systems. Their creation technology is constantly evolving using sophisticated tactics to create multiple instances of the existing ones. Current solutions are not yet able to sufficiently address this problem. They are mostly signature based; however, a changing malware means a changing signature. They, therefore, easily evade detection. Classifying them into their respective families is also hard, thus making elimination harder. In this paper, we propose a new feature engineering (NFE) approach for a better classification of polymorphic malware based on a hybrid of structural and behavioural features. We use accuracy, recall, precision, and F score to evaluate our approach. We achieve an improvement of 12% on accuracy between raw features and NFE features. We also demonstrated the robustness of NFE on feature selection as compared to other feature selection techniques.

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Parallel job schedulers are mostly evaluated using performance metrics. Deductions however can be misleading due to selective job starvation (unfairness). To choose a better scheduler, therefore, there is a need to compare schedulers for fairness as well. Performance and fairness, however, have mostly been studied independently. We examine characteristics of three approaches to fairness evaluation in parallel job scheduling. We examine how they represent job starvation and other aspects of discrimination. We show that the implied unfairness is not always starvation/discrimination in practice. We use simultaneous consideration of performance and fairness and compare deductions with scheduler effectiveness derived from group-wise performance evaluation. We observe that due to possible misrepresentation of starvation by fairness metrics, schedulers shown as superior may not be so in practice.

Fairness is an important aspect in queuing systems. Several fairness measures have been proposed in queuing systems in general and parallel job scheduling in particular. Generally, a scheduler is considered unfair if some jobs are discriminated whereas others are favored. Some of the metrics used to measure fairness for parallel job schedulers can imply unfairness where there is no discrimination (and vice versa). This makes them inappropriate. In this paper, we show how the existing approach misrepresents fairness in practice. We then propose a new approach for measuring fairness for parallel job schedulers. Our approach is based on two principles: (i) as jobs have different resource requirements and find different queue/system states, they need not have the same performance for the scheduler to be fair and (ii) to compare two schedulers for fairness, we make comparisons of how the schedulers favor/discriminate individual jobs. We use performance and discrimination trends to validate our approach. We observe that our approach can deduce discrimination more accurately. This is true even in cases where the most discriminated jobs are not the worst performing jobs.Performance metrics, in some cases, may not accurately represent the user's needs. They may misrepresent them in specific circumstances leading to users drawing wrong deductions. AJSD, for example, may exaggerate poor performance in short jobs. A job stream with many short jobs will have a misleading poor performance if the AJSD metric is used. The implication of the performance metrics also depends on the system setup. Differences in system setups may call for differences in deductions got from the metric values. For example, in space-slicing systems, AWT and ART can be interchangeably used. This is because ART = AWT + ( = mean execution time and is independent of the scheduler). However, in time-slicing systems, the two metrics do not lead to related conclusions. This is because job response time cannot be deduced from the time it starts processing. Therefore, a lot of care has to be taken when choosing a performance metric [2].Even when an appropriate performance metric is used, the average metric value can give misleading results. This is because it gives a global view of performance but does not show internal discrimination/favoritism among the jobs. A scheduler may have an impressive (average) metric value yet some jobs perform well at the expense of others. Such a scheduler is unfair. Unfair schedulers may have impressive performance metric values that hide the underlying discrimination leading to user dissatisfaction [3]. There are many performance metrics [1] and specific metrics are appropriate in specific scenarios. Fairness metrics [4-6] also exist. However, they misrepresent fairness (discrimination/favoritism) in some cases. This may lead to counter intuitive deductions.In this paper, we study how fairness/discrimination is represented in three common approaches used to evaluate fairness for parallel job schedulers. The approaches considered...

The study integrates ensemble learning into a task of classifying if a news article is on food insecurity or not. Similarity algorithms were exploited to imitate human cognition, an innovation to enhance performance. Four out of six classifiers generated performance improvement with the innovation. Articles on food insecurity identified with best classifier were generated into trends which were comparable with official trends. This paper provides information useful to stake holders in taking appropriate action depending on prevailing conditions of food insecurity. Two suggestions are put forth to promote performance: (1) using articles aggregated from several news media and (2) blending more classifiers in an ensemble.

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