A scalable architecture for real-time monitoring of large information systems

Data centers supporting cloud-based services are characterized by a huge number of hardware and software resources often cooperating in complex and unpredictable ways. Understanding the state of these systems for reasons of management and service level agreement requires scalable monitoring architectures that should gather and evaluate continuosly large flows in almost real-time periods. We propose a novel monitoring architecture that, by combining a hierarchical approach with decentralized monitors, addresses these challenges. In this context, fully centralized systems do not scale to the required number of flows, while pure peer-to-peer architectures cannot provide a global view of the system state. We evaluate the monitoring architecture for computational units of gathering and evaluation in real contexts that demonstrate the scalability potential of the proposed system

Real-time adaptive algorithm for resource monitoring

In large scale systems, real-time monitoring of hardware and software resources is a crucial means for any management purpose. In architectures consisting of thousands of servers and hundreds of thousands of component resources, the amount of data monitored at high sampling frequencies represents an overhead on system performance and communication, while reducing sampling may cause quality degradation. We present a real-time adaptive algorithm for scalable data monitoring that is able to adapt the frequency of sampling and data updating for a twofold goal: to minimize computational and communication costs, to guarantee that reduced samples do not affect the accuracy of information about resources. Experiments carried out on heterogeneous data traces referring to synthetic and real environments confirm that the proposed adaptive approach reduces utilization and communication overhead without penalizing the quality of data with respect to existing monitoring algorithms

Adaptive, scalable and reliable monitoring of big data on clouds

Real-time monitoring of cloud resources is crucial for a variety of tasks such as performance analysis, workload management, capacity planning and fault detection. Applications producing big data make the monitoring task very difficult at high sampling frequencies because of high computational and communication overheads in collecting, storing, and managing information. We present an adaptive algorithm for monitoring big data applications that adapts the intervals of sampling and frequency of updates to data characteristics and administrator needs. Adaptivity allows us to limit computational and communication costs and to guarantee high reliability in capturing relevant load changes. Experimental evaluations performed on a large testbed show the ability of the proposed adaptive algorithm to reduce resource utilization and communication overhead of big data monitoring without penalizing the quality of data, and demonstrate our improvements to the state of the art.Real-time monitoring of cloud resources is crucial for a variety of tasks such as performance analysis, workload management, capacity planning and fault detection. Applications producing big data make the monitoring task very difficult at high sampling frequencies because of high computational and communication overheads in collecting, storing, and managing information. We present an adaptive algorithm for monitoring big data applications that adapts the intervals of sampling and frequency of updates to data characteristics and administrator needs. Adaptivity allows us to limit computational and communication costs and to guarantee high reliability in capturing relevant load changes. Experimental evaluations performed on a large testbed show the ability of the proposed adaptive algorithm to reduce resource utilization and communication overhead of big data monitoring without penalizing the quality of data, and demonstrate our improvements to the state of the art

Monitoring large cloud-based systems

Large scale cloud-based services are built upon a multitude of hardware and software resources, disseminated in one or multiple data centers. Controlling and managing these resources requires the integration of several pieces of software that may yield a representative view of the data center status. Today’s both closed and open-source monitoring solutions fail in different ways, including the lack of scalability, scarce representativity of global state conditions, inability in guaranteeing persistence in service delivery, and the impossibility of monitoring multi-tenant applications. In this paper, we present a novel monitoring architecture that addresses the aforementioned issues. It integrates a hierarchical scheme to monitor the resources in a cluster with a distributed hash table (DHT) to broadcast system state information among different monitors. This architecture strives to obtain high scalability, effectiveness and resilience, as well as the possibility of monitoring services spanning across different clusters or even different data centers of the cloud provider. We evaluate the scalability of the proposed architecture through a bottleneck analysis achieved by experimental results

Campagna di scavi ISCAB-FTL e USI alla grotta 11Q di Qumran, marzo 2017

The paper presents the report about the excavation campaign at Qumran cave 11Q, carried out from 9th to 19th March 2017 by the Istituto di cultura e archeologia delle terre bibliche of the Facoltà di Teologia di Lugano (ISCAB-FTL) and by the Università della Svizzera Italiana (USI). Previous archaeological excavations, undertaken by Roland de Vaux (1956) and Joseph Patrick (1988 and 1991), were published in brief preliminary reports only. The 2017 archaeological campaign, complemented by speleological and geological investigation, enriched the dossier for the forthcoming final report. Among the main results are: the discovery of an upper chamber; the opening of a sounding in an area barely interested by previous excavations; the analysis of the entrance area and the natural factors which may concur to the closure of the cave; the documentation of 11Q morphology employing modern technology (3D scan)

Coagulopathy in liver diseases: complication or therapy?

Coagulopathy in cirrhosis is a composite condition where liver synthetic deficit rebalances coagulation to a parallel reduction of both pro- and anticoagulant factors. Cirrhosis is therefore no longer considered a hypocoagulable state but rather a more unstable hemostatic balance with a lower threshold for tipping toward thrombosis or bleeding. Tendency to bleeding in cirrhosis is due to the reduction in the synthesis of procoagulants and a low platelet count as well as hyperfibrinolysis. Variceal hemorrhage is a frequent bleeding complication in decompensated cirrhosis. However, the possible contribution of coagulopathy as a precipitant or an aggravating factor is poorly documented and further data are required to clarify its real contributing role. Moreover, apart from the gastrointestinal tract, the occurrence of spontaneous and procedure-related bleeding elsewhere in the body, whilst not uncommon, is less than would be expected. By contrast, a large-scale population-based study has shown the propensity towards venous thrombosis in patients with liver diseases. Portal vein thrombosis (PVT) is a critical but frequent event occurring in up to 40% of patients with liver cirrhosis. PVT causes deterioration of the clinical course, the complications of portal hypertension and an increase in post-transplant mortality. The pathogenesis of PVT includes both local alterations, like blood flow reduction and endothelial activation, and systemic derangement. Systemic prohemostatic alterations include high von Willebrand factor, low ADAMTS-13, low levels of anticoagulants (antithrombin, proteins C and S) and increases in procoagulants like factor VIII. Low-molecular-weight heparin such as enoxaparin has proven to be safe and effective in both the treatment and prevention of PVT. In addition, patients in prophylaxis with enoxaparin showed a lower rate of decompensation and a better survival without bleeding complications. In such patients, circulating bacterial DNA, endotoxemia and markers of inflammation were attenuated compared to controls. These results therefore suggest a possible connection between enoxaparin, decrease of endotoxemia and reduction of portal hypertension. The approach to the coagulopathy in patients with liver diseases is changing: while the main goal for clinicians so far has been to reduce the risk of bleeding, the results of these new studies highlight the importance of preventing or treating thrombophilic disorders like PVT to avoid microcirculatory damage and eventually liver decompensation

Towards Coordinating Machines and Operators in Industry 5.0 through the Web of Things

This paper proposes a groundbreaking architecture that reimagines Industry 5.0, emphasizing human-centric technological integration via the Web of Things (WoT) standard. Our approach innovatively digitizes human operators and machinery, creating a responsive industrial ecosystem attentive to real-time human conditions. Central to this is the Operator Thing (OT), a digital replica representing the human operator's status and needs. This system not only recognizes operator stress and discomfort but intelligently adjusts, ensuring optimal human-machine synergy. Our methodology extends to redefining operational parameters and tasks in response to human states, balancing well-being with production efficiency. The ultimate goal is a transformative, adaptive, and empathetic Industry 5.0 environment, validated through rigorous interdisciplinary evaluation

Notes from the 2017 Excavation of Cave 11Q

Notes from the 2017 Excavation of Cave 11