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In their quest toward Exascale, High Performance Computing (HPC) systems are rapidly becoming larger and more complex, together with the issues concerning their maintenance. Luckily, many current HPC systems are endowed with data monitoring infrastructures that characterize the system state, and whose data can be used to train Deep Learning (DL) anomaly detection models, a very popular research area. However, the lack of labels describing the state of the system is a wide-spread issue, as annotating data is a costly task, generally falling on human system administrators and thus does not scale toward exascale. In this article we investigate the possibility to extract labels from a service monitoring tool (Nagios) currently used by HPC system administrators to flag the nodes which undergo maintenance operations. This allows to automatically annotate data collected by a fine-grained monitoring infrastructure; this labelled data is then used to train and validate a DL model for anomaly detection. We conduct the experimental evaluation on a tier-0 production supercomputer hosted at CINECA, Bologna, Italy. The results reveal that the DL model can accurately detect the real failures, and, moreover, it can predict the insurgency of anomalies, by systematically anticipating the actual labels (i.e., the moment when system administrators realize when an anomalous event happened); the average advance time computed on historical traces is around 45 minutes. The proposed technology can be easily scaled toward exascale systems to easy their maintenance. [ABSTRACT FROM AUTHOR]

This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art. [ABSTRACT FROM PUBLISHER]

Scheduling and dispatching tools for high-performance computing (HPC) machines have the key role of mapping jobs to the available resources, trying to maximize performance and quality-of-service (QoS). Allocation and Scheduling in the general case are well-known NP-hard problems, forcing commercial schedulers to adopt greedy approaches to improve performance and QoS. Search-based approaches featuring the exploration of the solution space have seldom been employed in this setting, but mostly applied in off-line scenarios. In this paper, we present the first search-based approach to job allocation and scheduling for HPC machines, working in a production environment. The scheduler is based on Constraint Programming, an effective programming technique for optimization problems. The resulting scheduler is flexible, as it can be easily customized for dealing with heterogeneous resources, user-defined constraints and different metrics. We evaluate our solution both on virtual machines using synthetic workloads, and on the Eurora HPC with production workloads. Tests on a wide range of operating conditions show significant improvements in waitings and QoS in mid-tier HPC machines w.r.t state-of-the-art commercial rule-based dispatchers. Furthermore, we analyze the conditions under which our approach outperforms commercial approaches, to create a portfolio of scheduling algorithms that ensures robustness, flexibility and scalability. [ABSTRACT FROM AUTHOR]

Effective multicore computing requires to make efficient usage of the computational resources on a chip. Offline mapping and scheduling can be applied to improve the performance, but classical approaches require considerable a priori knowledge of the target application. In a practical setting, precise information is often unavailable; one can then resort to approximate time and resource usage figures, but this usually requires to make conservative assumptions. The issue is further stressed if real-time guarantees must be provided. We tackle predictable and efficient nonpreemptive scheduling of multitask applications in the presence of duration uncertainty. Hard real-time guarantees are provided with limited idle time insertion, by exploiting a hybrid offline/online technique known as Precedence Constraint Posting (PCP). Our approach does not require probability distributions to be specified, relying instead on simple and cheaper-to-obtain information (bounds, average values). The method has been tested on synthetic applications/platforms and compared with an offline optimized Fixed Priority Scheduling (FPS) approach and a pure online FIFO scheduler; the results are very promising, as the PCP schedules exhibit good stability and improved average execution time (14 percent on average, up to 30 percent versus FPS and up to 40 percent versus the FIFO scheduler). [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to solve the allocation and scheduling problems for variable voltage/frequency multiprocessor systems-on-chip, which minimizes overall system energy dissipation. The optimality of derived system configurations is guaranteed, while the computation efficiency of the optimizer allows for solving problem instances that were traditionally considered beyond reach for exact solvers (optimality gap). Furthermore, this paper illustrates the development- and run-time software infrastructures that assist the user in developing applications and implementing optimizer solutions. The proposed approach guarantees a high level of power, performance, and constraint satisfaction predictability as from validation on the target platform, thus bridging the abstraction gap. [ABSTRACT FROM AUTHOR]

Discusses the process of planning for security management in a computing environment. Advantages of using artificial intelligence planning; Responsibilities of a planner for security management; Background on the constraint-based partial-order planning. INSETS: Intrusion Detection Systems.;Constraint-Based Partial-Order Planning..

An introduction to the journal is presented in which the guest editors discuss on the topics including computational sustainability, monitoring of environmental phenomena and a decision support system (DSS) for the waste water management.