Research on trust model in container-based cloud service

Container virtual technology aims to provide program independence and resource sharing. The container enables flexible cloud service. Compared with traditional virtualization, traditional virtual machines have difficulty in resource and expense requirements. The container technology has the advantages of smaller size, faster migration, lower resource overhead, and higher utilization. Within container-based cloud environment, services can adopt multi-target nodes. This paper reports research results to improve the traditional trust model with consideration of cooperation effects. Cooperation trust means that in a container-based cloud environment, services can be divided into multiple containers for different container nodes. When multiple target nodes work for one service at the same time, these nodes are in a cooperation state. When multi-target nodes cooperate to complete the service, the target nodes evaluate each other. The calculation of cooperation trust evaluation is used to update the degree of comprehensive trust. Experimental simulation results show that the cooperation trust evaluation can help solving the trust problem in the container-based cloud environment and can improve the success rate of following cooperation

Application of Mixed Simulation Method to Modelling Port Traffic

Marine ports are the largest single business complex in the maritime sector impacting the coastal, marine, and atmospheric environment. The environmental effects of port operations mostly originate from the vessel and cargo handling operations, and maintenance. Port operations generate marine pollution in many forms (chemical, biological, solid waste, and sedimentation) and present a challenge to all port operators. Because ports are often located near urban areas, the wider impact of port operations on the environment cannot be ignored as it can potentially affect the economy of these areas as a whole. Air pollution is a significant externality for ports located close to urban areas. Around 4.5% and 6.2% of the total SO2 and NOX respectively, emitted by ships are due to in-port activities such as manoeuvring (approaching harbours) and hoteling (at the dock in port). A vessel consumes around 10% of fuel during slow manoeuvring. Assuming around 4.5% and 6.2% of the total SO2 and NOx emitted by ships are due to in-port activities such as manoeuvring (approaching harbours) and hoteling (at the dock in port), simplifying the traffic model hinders the ability to conduct accurate emission assessment and limits the ability to conduct an environmental assessment as a result of increased port capacity. The research aim is to develop a multi-method simulation model of port systems to simulate port traffic for assessing various port challenges like emission, throughputs, etc. The study will develop a mixed simulation model of port systems comprising of marine traffic and associated processes using the port of Liverpool as a case study. The developed simulation model will be used to estimate emission within the case study port. The study developed a multi-method simulation model representing individual actors and specific processes of the entire port system. The developed simulation method integrates two major modelling approaches: discrete-event simulation and agent-based simulation. Due to the complexity within the port, the study focused on the vessel and cargo handling sector of the port because manoeuvring (approaching harbours) is a significant source of pollution. The developed method adopts an object-oriented approach. Object-oriented modelling is an important aspect of the modelling methodology because it supports the reusability and scalability of the developed model as entities are represented as objects with specific characteristics based on their types. This is significant in representing vessel and cargo terminal types. Each vessel type was encapsulated with internal characteristics e.g. passage plan, speed, etc. A terminal developed to handle bulk cargoes is different from a terminal that handles container cargoes. Therefore, agents were developed to represent various cargo terminal types (such as container terminal, bulk terminal, passenger terminal, etc.), with each terminal type possessing its characteristics specific to itself. The method was applied in the study area. AIS data was collected for the Port of Liverpool over the 12 months of 2016. The data provides information on all marine traffic (fitted with AIS) for the Port of Liverpool outer channel (Liverpool Bay) and the port inbound and outbound lanes along the River Mersey. This data set was used to design and validated the simulation model. A maximum of seven vessels was observed to be transiting through the outer waterway, four at the inner and two in the manoeuvring waterway. Vessel transit times and speed variation are observed to be influenced by the vessel traffic density within each waterway. Vessel waiting and dwell time are seen to be influenced by lock availability and the tidal condition of the port. An increase in tidal duration results in an increase in both waiting and dwell time and vice versa. The validation outcome reveals that the developed model also possesses a relative realistic speed changing behaviour when compared to real-world data. The simulation result also shows a realistic relationship with the travel time distribution from the historical data set. The developed model represents the port as an entire system, however, the study only focussed on the vessel handling process. Previous port modelling has witnessed lots of simplification in vessel traffic models, port process models, and exclusions of external condition models over the years, but the object-oriented programme implemented in this study can help solve these issues. Therefore, the developed methodology would enable better models to be integrated

Primary vertex reconstruction using GPUs for the upgrade of the Inner Tracking System of the ALICE experiment at LHC

L'abstract è presente nell'allegato / the abstract is in the attachmen

The Multi-Agent Transport Simulation MATSim

"The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations.The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains