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Container clusters play an increasingly important role in cloud computing for processing dynamic computing tasks. The resource manager (i.e., orchestrater) of the cluster automates the scheduling of the dynamic requests, effectively manages the resources’ utilization across distributing infrastructure resources. For many applications, the requests to the cluster are often with restricted deadlines. The scheduling of container clusters is often tricky, especially when the cluster’s size is large and the load of the requests is dynamically changing. Machine learningbased approaches such as reinforcement learning have attracted lots of research attention during the past years; However, those approaches suffer from low robustness when the requests in an operational environment are changing and different from the training data sets. This paper investigates this problem by quantifying the robustness and proposing meta-gradient reinforcement learning to improve the robustness of classical reinforcement learning-based approaches. The proposed approach can lead to better deadline guarantees and faster adaptation for timecritical task scheduling under dynamic environments. We then empirically test the benefits of our method using both real-world and synthetic data sets. The evaluation results show that the proposed method outperforms the compared RL methods in scheduling performance and robustness.

Item does not contain fulltext On the Move to Meaningful Internet Systems. OTM 2018 Conferences.Confederated International Conferences: CoopIS, C&TC, and ODBASE 2018, Valletta, Malta, October 22-26, 2018

Item does not contain fulltext 605 p.

Smart contracts gain rapid exposure since the inception of blockchain technology. Yet there is no unified ontology for smart contracts. Being categorized as coded contracts or substitutes of conventional legal contracts, there is a need to reduce the conceptual ambiguity of smart contracts. We applied enterprise ontology and model driven architectures to abstract smart contracts at the essential, infological and datalogical level to explain the system behind computation and platform independent smart contracts rather than its functional behavior. This conceptual paper introduces commitment-based smart contracts, in which a contract is viewed as a business exchange consisting of a set of reciprocal commitments. A smart contract ensures the automated execution of most of these commitments.

Distributing software processes by software producing organizations (SPOs) is emerging increasingly due to benefits that global software engineering (GSE) brings in terms of cost reduction, leveraging competencies, and market expansion. However, these organizations are facing communication and project control issues that can slow down the overall organization performance. Therefore, SPOs should be able to manage inter-dependencies among the tasks distributed to the globally dispersed teams. We analyze existing works and product software companies’ best practices in coordinating tasks in GSE. This paper specifically focuses on constructing methodological support for task coordination that can be influenced by the situational factors at the companies. The support comprises a framework and a method developed by using a method engineering approach. We introduce the framework that depicts the aspects that should be examined by companies and the method that elaborates the practices to guide companies to coordinate tasks in GSE projects. The validation results show that the framework and the method are accepted by experts regarding completeness and applicability to help SPOs in managing coordination among globally distributed teams.

The competition in globalized markets forces organizations to provide mass-customized integrated solutions for customers. Mass-customization of integrated solutions by business network requires adaptive interactions between parties to address emerging requirements of customers. These adaptive interactions need to be enabled by dynamic networked business processes (DNBP) that are supported by high quality information. However, the dynamic collaboration between parties can result in information quality (IQ) issues such as information syntactic and semantic misalignment, information leakage, and unclear information ownership. To counter negative consequences of poor IQ on the performance, the orchestrator of business network needs to clearly recognize these IQ issues. In this paper, we develop and evaluate a framework to address potential IQ issues related to DNBP. The development of the framework is based on a three step methodology that includes the characterization of dynamism of networked business processes, the characterization of IQ dimensions, and the exploration of IQ issues. To evaluate the practical significance of the explored IQ issues, we conduct a case study in a service ecosystem that is shaped by a car leasing organization to provide integrated mobility solutions for customers.

The separation design and fabrication process in the semiconductor industry leads to potential threats such as trojan side-channels (TSCs). In this paper we design a new family of TSCs from physical unclonable functions (PUFs). In particular, a dedicated attack on the PRESENT block cipher is described by using our PUF-based TSCs. Finally we analyze the performance of our PUF-based TSCs and discuss other potential applications.

While certification is widely recognized as a means to increase system trustworthiness and reduce uncertainty in decision making, it faces severe challenges preventing a wider adoption thereof. Certification is not adequately planned and integrated within the development process, leading to suboptimal scenarios where certification introduces the need to further modify the developed system with high costs. We propose a methodology that bridges the gap between software development and certification processes. Our methodology automatically produces the certification requirements driving all steps of the development process, and maximizes the strength of certificates while taking costs under control. We formalize the above problem as a multi-objective mathematical program and solve it through a genetic algorithm. The proposed approach is tested in a real-world, cloud-based financial scenario at CaixaBank and its performance and quality is evaluated in a simulated scenario.

High-Performance Computing (HPC) is one of the strategic priorities for research and innovation worldwide due to its relevance for industrial and scientific applications. We envision HPC as composed of three pillars: infrastructures, applications, and key technologies and tools. While infrastructures are by construction centralized in large-scale HPC centers, and applications are generally within the purview of domain-specific organizations, key technologies fall in an intermediate case where coordination is needed, but design and development are often decentralized. A large group of Italian researchers has started a dedicated laboratory within the National Interuniversity Consortium for Informatics (CINI) to address this challenge. The laboratory, albeit young, has managed to succeed in its first attempts to propose a coordinated approach to HPC research within the EuroHPC Joint Undertaking, participating in the calls 2019 - 20 to five successful proposals for an aggregate total cost of 95M€. In this paper, we outline the working group's scope and goals and provide an overview of the five funded projects, which become fully operational in March 2021, and cover a selection of key technologies provided by the working group partners, highlighting their usage development within the projects.

Social Sciences identify similarity and mutual trust as main criteria to consider in group formation processes. On this basis, we present a group formation technique which exploits measures of both similarity and trust, in order to improve the compactness of groups in Online Social Networks. Similarity and trust have been jointly exploited to design two algorithms designed to match groups and users, in order to capture the gain of a user who desires to join with a group and the benefit of the group itself. Experimental results show that trust is more valuable than similarity in forming groups and that the two proposed algorithms are capable to deal with large networks.