Cloud data centers seek to optimize their provision of pooled CPU, bandwidth and storage resources. While over-provision is wasteful, under-provision may lead to violating Service Level Agreements (SLAs) with their consumers; yet the relationship between low-level Key Performance Indicators (KPIs) and SLA violations is not well understood. State-of-the art monitoring systems typically react to service failures after the fact, partly due to unexpected nonlinearities in the aggregated performance data. We seek to provide better modelling of KPIs using predictive algorithms that could be used for the proactive monitoring and adaptation of cloud services. In this paper, we investigate the Boosted Decision Tree (BDT) regression algorithm. We tested the BDT algorithm in a real monitoring framework deployed on a novel Azure cloud test-bed distributed over multiple geolocations, using thousands of robot-user requests to produce huge volumes of KPI data. The BDT algorithm achieved an R-Squared score of 0.9991 at the 0.2 learning rate. This closely predicted the KPI data and outperformed other approaches, such as Ordinary Least Squares and Stochastic Gradient Descent; and is a promising candidate for making short- and long-term predictions for cloud resource allocation.

Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for predicting future resource exhaustion and cloud service failures. Our work uses a realistic test bed in the cloud, which is instrumented to monitor and analyze resource usage. In this article, we employed the optimal Kalman filtering technique to build a predictive and analytic framework for cloud server KPIs, based on historical data. Our$k$-step-ahead predictions on historical data yielded a prediction accuracy of 95.59%. The information generated from the framework can best be used for optimal resources provisioning, admission control and cloud SLA management.

We investigate the logical connection between (spatial) isotropy, homogeneity of space, and homogeneity of time within a general axiomatic framework. We show that isotropy not only entails homogeneity of space, but also, in certain cases, homogeneity of time. In turn, homogeneity of time implies homogeneity of space in general, and the converse also holds true in certain cases. An important innovation in our approach is that formulations of physical properties are simultaneously empirical and axiomatic (in the sense of first-order mathematical logic). In this case, for example, rather than presuppose the existence of spacetime metrics – together with all the continuity and smoothness apparatus that would entail – the basic logical formulas underpinning our work refer instead to the sets of (idealised) experiments that support the properties in question, e.g., isotropy is axiomatised by considering a set of experiments whose outcomes remain unchanged under spatial rotation. Higher-order constructs are not needed.

Graduates of computer science and software engineering degrees are often expected by employers to possess various technical skills as well as competencies in project management, testing, teamwork, and other soft skills. Extant literature has identified that these competencies are often not addressed by traditional teaching approaches such as lectures and labs. In this paper, we present a project-based learning approach to teaching agile software development where students work in multicultural teams to develop software for clients. This approach to teaching software development addresses some of the competencies required by employers, and the feedback from students, clients, and tutors are discussed and analysed critically.

Motivation: Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this article is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest. Results: We provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the 'usability barrier' enabling biologists to have access to this powerful computational technology. Availability and implementation: SMC Predictor tool is available at http://www.smcpredictor.com. Supplementary information: Supplementary data are available at Bioinformatics online.

In this paper we investigate the use of general topological spaces in connection with a generalised variant of membrane systems. We provide an approach which produces a fine grain description of local operations occurring simultaneously in sets of compartments of the system by restricting the interactions between objects. This restriction is given by open sets of a topology and multisets of objects associated with them, which dynamically change during the functioning of the system and which together define a notion of vicinity for the objects taking part in the interactions.

Abstract Although many transducer testing techniques can establish whether a system Imp correctly implements a specification Spec , the notion of 'correctness' used in this context is often rather weak, in that it fails to distinguish correctly between behaviours that are related functionally, but distinct as processes. By appealing to the process-theoretic notion of (strong) simulation, we develop a theory of transducer, process and FSM testing capable of establishing whether Imp is a full simulation of Spec . We show, moreover, that our approach is consistent with top–down integration-testing approaches.

Abstract In recent years, Mazurkiewicz trace theory has become increasingly popular as a model of semantics for non-interleaving concurrency, and the theory is here further extended to allow descriptions of infinite behaviours based on alphabets of arbitrary cardinality. We describe an unconventional but nonetheless natural, ordering on trace space, and show that under this ordering, every nonempty set of traces has a greatest lower bound. Consequently, the ordering is consistently complete, i.e. if a set of traces has an upper bound, it has a least such bound. This parallels the result that traces over finite alphabets form a domain. Kwiatkowska has demonstrated an ultrametric, definable on (standard) trace spaces, under which they become compact, and hence complete, topological spaces. For finite alphabets, thus ultrametric essentially agrees with that of Comyn and Dauchet, but for infinite alphabets they differ in behaviour. We investigate the structure of trace space under various topologies, demonstrate the relationship between order convergence and the various metric convergence regimes, and thereby explain apparent behaviourial anomalies.

Abstract We describe a novel machine model of computation, and prove that this model is capable of performing calculations beyond the capability of the standard Turing machine model. In particular, we demonstrate the ability of our model to solve the Halting problem for Turing machines. We discuss the issues involved in implementing the model as a physical device, and offer some tentative suggestions.