Summary Predicting resource consumption and run time of computational workloads is crucial for efficient resource allocation, or cost and energy optimization. In this paper, we evaluate various machine learning techniques to predict the execution time of computational jobs. For experiments we use datasets from two application areas: scientific workflow management and data processing in the ALICE experiment at CERN. We apply a two‐stage prediction method and evaluate its performance. Other evaluated aspects include: (1) comparing performance of global (per‐workflow) versus specialized (per‐job) models; (2) impact of prediction granularity in the first stage of the two‐stage method; (3) using various feature sets, feature selection, and feature importance analysis; (4) applying symbolic regression in addition to classical regressors. Our results provide new valuable insights on using machine learning techniques to predict the runtime behavior of computational jobs.

In this paper we present a new c-means clustering algorithm for combined continuous-nominal data. We use spherical representation of nominal data. The impact of specific features is modeled with corresponding weights in metric definition. To solve constrained minimization problem we transfer the me thodology of reformulation functions to the considered context. As a result we obtain a clustering algorithm with adaptation of weights. Series of numerical experiments on real and synthetic data show that the algorithm can successfully cluster raw, non-normalized data.

In this article we propose a new clustering algorithm for combinations of continuous and nominal data. The proposed algorithm is based on embedding of the nominal data into the unit sphere with a quadrance metrics, and adaptation of the general k-means clustering algorithm for the embedding data. It is also shown that the distortion of new embedding with respect to the Hamming metrics is less than that of other considered possibilities. A series of numerical experiments on real and synthetic datasets show that the proposed algorithm provide a comparable alternative to other clustering algorithms for combinations of continuous and nominal data.

Every proof system for (partial) correctness yields an enumeration procedure for correctness assertions. Other researchers have proved results on the existence of (sound and complete) enumeration procedures for assertions about programs from an acceptable programming language where the assertion language is first-order logic. It is shown that some of the assumptions are stronger than necessary, whereas others must not be weakened. Two novel procedures are given that work for more interpretations with a smaller oracle than those known up to now.>

In this paper a generalization of a certain Lipton's theorem (see Lipton [5]) is presented. Namely, we show that for a wide class of programming languages the following holds: the set of all partial correctness assertions true in an expressive interpretation I is uniformly decidable (in I) in the theory of I iff the halting problem is decidable for finite interpretations. In the effect we show that such limitations as effectiveness or Herbrand definability of interpretation (they are relevant in the previous proofs) can be removed in the case of partial correctness.