Abstract: Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources.

Abstract: Being the largest blockchain with the capability of running smart contracts, Ethereum has attracted wide attention and its market capitalization has reached 20 billion USD. Ethereum not only supports its cryptocurrency named Ether but also provides a decentralized platform to execute smart contracts in the Ethereum virtual machine. Although Ether's price is approaching 200 USD and nearly 600K smart contracts have been deployed to Ethereum, little is known about the characteristics of its users, smart contracts, and the relationships among them. To fill in the gap, in this paper, we conduct the first systematic study on Ethereum by leveraging graph analysis to characterize three major activities on Ethereum, namely money transfer, smart contract creation, and smart contract invocation. We design a new approach to collect all transaction data, construct three graphs from the data to characterize major activities, and discover new observations and insights from these graphs. Moreover, we propose new approaches based on cross-graph analysis to address two security issues in Ethereum. The evaluation through real cases demonstrates the effectiveness of our new approaches.

Abstract: Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time applications, such as instantaneous object recognition and safety assistance in intelligent transportation systems, that require very low latency. One key issue that comes with proximity is how to ensure that users always receive good performance as they move across different locations. Migrating services between MECs is seen as the means to achieve this. This article presents a layered framework for migrating active service applications that are encapsulated either in virtual machines (VMs) or containers. This layering approach allows a substantial reduction in service downtime. The framework is easy to implement using readily available technologies, and one of its key advantages is that it supports containers, which is a promising emerging technology that offers tangible benefits over VMs. The migration performance of various real applications is evaluated by experiments under the presented framework. Insights drawn from the experimentation results are discussed.

Abstract: When users flood in cloud data centers, how to efficiently manage hardware resources and virtual machines (VMs) in a data center to both lower economical cost and ensure a high service quality becomes an inevitable work for cloud providers. VM migration is a cornerstone technology for the majority of cloud management tasks. It frees a VM from the underlying hardware. This feature brings a plenty of benefits to cloud providers and users. Many researchers are focusing on pushing its cutting edge. In this paper, we first give an overview of VM migration and discuss both its benefits and challenges. VM migration schemes are classified from three perspectives: 1) manner; 2) distance; and 3) granularity. The studies on non-live migration are simply reviewed, and then those on live migration are comprehensively surveyed based on the three main challenges it faces: 1) memory data migration; 2) storage data migration; and 3) network connection continuity. The works on quantitative analysis of VM migration performance are also elaborated. With the development and evolution of cloud computing, user mobility becomes an important motivation for live VM migration in some scenarios (e.g., fog computing). Thus, the studies regarding linking VM migration to user mobility are summarized as well. At last, we list the open issues which are waiting for solutions or further optimizations on live VM migration.

Abstract: Many organizations are developing autonomous driving systems, which are expected to be deployed at a large scale in the near future. Despite this, there is a lack of agreement on appropriate methods to test, debug, and certify the performance of these systems. One of the main challenges is that many autonomous driving systems have machine learning (ML) components, such as deep neural networks, for which formal properties are difficult to characterize. We present a testing framework that is compatible with test case generation and automatic falsification methods, which are used to evaluate cyber-physical systems. We demonstrate how the framework can be used to evaluate closed-loop properties of an autonomous driving system model that includes the ML components, all within a virtual environment. We demonstrate how to use test case generation methods, such as covering arrays, as well as requirement falsification methods to automatically identify problematic test scenarios. The resulting framework can be used to increase the reliability of autonomous driving systems.

Abstract: Traditional virtual reality (VR) mainly focuses on visual feedback, which is not accessible for people with visual impairments. We created Canetroller, a haptic cane controller that simulates white cane interactions, enabling people with visual impairments to navigate a virtual environment by transferring their cane skills into the virtual world. Canetroller provides three types of feedback: (1) physical resistance generated by a wearable programmable brake mechanism that physically impedes the controller when the virtual cane comes in contact with a virtual object; (2) vibrotactile feedback that simulates the vibrations when a cane hits an object or touches and drags across various surfaces; and (3) spatial 3D auditory feedback simulating the sound of real-world cane interactions. We designed indoor and outdoor VR scenes to evaluate the effectiveness of our controller. Our study showed that Canetroller was a promising tool that enabled visually impaired participants to navigate different virtual spaces. We discuss potential applications supported by Canetroller ranging from entertainment to mobility training.

Abstract: In this article we propose a new paradigm of resource-efficient edge computing for the emerging intelligent IoT applications such as flying ad hoc networks for precision agriculture, e-health, and smart homes We devise a resource-efficient edge computing scheme such that an intelligent IoT device user can well support its computationally intensive task by proper task offloading across the local device, nearby helper device, and the edge cloud in proximity Different from existing studies for mobile computation offloading, we explore the novel perspective of resource efficiency and devise an efficient computation offloading mechanism consisting of a delay-aware task graph partition algorithm and an optimal virtual machine selection method in order to minimize an intelligent IoT device's edge resource occupancy and meanwhile satisfy its QoS requirement Performance evaluation corroborates the effectiveness and superior performance of the proposed resource-efficient edge computing scheme

Abstract: Serverless computing provides a small runtime container to execute lines of codes without infrastructure management which is similar to Platform as a Service (PaaS) but a functional level Amazon started the event-driven compute named Lambda functions in 2014 with a 25 concurrent limitation, but it now supports at least a thousand of concurrent invocation to process event messages generated by resources like databases, storage and system logs Other providers, ie, Google, Microsoft, and IBM offer a dynamic scaling manager to handle parallel requests of stateless functions in which additional containers are provisioning on new compute nodes for distribution However, while functions are often developed for microservices and lightweight workload, they are associated with distributed data processing using the concurrent invocations We claim that the current serverless computing environments can support dynamic applications in parallel when a partitioned task is executable on a small function instance We present results of throughput, network bandwidth, a file I/O and compute performance regarding the concurrent invocations We deployed a series of functions for distributed data processing to address the elasticity and then demonstrated the differences between serverless computing and virtual machines for cost efficiency and resource utilization

Abstract: In this paper, we introduce an infrastructure in edge computing environment, KubeEdge, to extend cloud capabilities to the edge. In the new form of cloud architecture, Cloud consists of computing resources both at centralized data centers and at distributed edges. KubeEdge infrastructure connects and coordinates two computing environments for applications leveraging both computing resources to achieve better performance and user experience. Technically, KubeEdge provides the network protocol infrastructure and the same runtime environment on the edge as in the cloud, which allows the seamless communication of applications with components running on edge nodes as well as cloud servers. It also allows the existing cloud services and cloud development model to be adopted at edge. Based on Kubernetes [1], KubeEdge architecture includes a network protocol stack called KubeBus, a distributed metadata store and synchronization service, and a lightweight agent (EdgeCore) for the edge. KubeBus is designed to have its own implementation of OSI network protocol layers, which connects servers at edge and VMs in the cloud as one virtual network. KubeBus provides a unified multitenant communication infrastructure with fault tolerance and high availability. The distributed metadata store and sync service is designed to support the offline scenario when edge nodes are not connected to the cloud. EdgeController component in KubeEdge architecture is a controller plugin for Kubernetes [1] to manage remote edge nodes and cloud VMs as one logical cluster, which enables KubeEdge to schedule, deploy and manage container applications across edge and cloud with the same API.

Abstract: Mobile devices have several restrictions due to design choices that guarantee their mobility. A way of surpassing such limitations is to utilize cloud servers called cloudlets on the edge of the network through Mobile Edge Computing. However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limit and quality threshold. This can be achieved by deploying and activating more cloudlets, but this solution is expensive due to the cost of the physical servers. The best choice is to optimize the resources of the cloudlets through an intelligent choice of configuration that lowers delay and raises scalability. Thus, in this paper we propose an algorithm that utilizes Virtual Machine Migration and Transmission Power Control, together with a mathematical model of delay in Mobile Edge Computing and a heuristic algorithm called Particle Swarm Optimization, to balance the workload between cloudlets and consequently maximize cost-effectiveness. Our proposal is the first to consider simultaneously communication, computation, and migration in our assumed scale and, due to that, manages to outperform other conventional methods in terms of number of serviced users.

Abstract: Digital twins open up new possibilities in terms of monitoring, simulating, optimizing and predicting the state of cyber-physical systems (CPSs). Furthermore, we argue that a fully functional, virtual replica of a CPS can also play an important role in securing the system. In this work, we present a framework that allows users to create and execute digital twins, closely matching their physical counterparts. We focus on a novel approach to automatically generate the virtual environment from specification, taking advantage of engineering data exchange formats. From a security perspective, an identical (in terms of the system's specification), simulated environment can be freely explored and tested by security professionals, without risking negative impacts on live systems. Going a step further, security modules on top of the framework support security analysts in monitoring the current state of CPSs. We demonstrate the viability of the framework in a proof of concept, including the automated generation of digital twins and the monitoring of security and safety rules.

Abstract: With the development of cloud computing, virtual machine migration is emerging as a promising technique to save energy, enhance resource utilizations, and guarantee Quality of Service (QoS) in cloud datacenters. Most of existing studies on the virtual machine migration, however are based on a single virtual machine migration. Although there are some researches on multiple virtual machines migration, the author usually does not consider the correlation among these virtual machines. In practice, in order to save energy and maintain system performance, cloud providers usually need to migrate multiple correlated virtual machines or migrate the entire virtual datacenter (VDC) request. In this paper, we focus on the efficient online live migration of multiple correlated VMs in VDC requests, for optimizing the migration performance. To solve this problem, we propose an efficient VDC migration algorithm (VDC-M). We use the US-wide US National Science Foundation (NSF) network as substrate network to conduct extensive simulation experiments. Simulation results show that the performance of the proposed algorithm is promising in terms of the total VDC remapping cost, the blocking ratio, the average migration time and the average downtime.

Abstract: Cloud computing has become an attractive computing paradigm in both academia and industry. Through virtualization technology, Cloud Service Providers (CSPs) that own data centers can structure physical servers into Virtual Machines (VMs) to provide services, resources, and infrastructures to users. Profit-driven CSPs charge users for service access and VM rental, and reduce power consumption and electric bills so as to increase profit margin. The key challenge faced by CSPs is data center energy cost minimization. Prior works proposed various algorithms to reduce energy cost through Resource Provisioning (RP) and/or Task Scheduling (TS). However, they have scalability issues or do not consider TS with task dependencies, which is a crucial factor that ensures correct parallel execution of tasks. This paper presents DRL-Cloud, a novel Deep Reinforcement Learning (DRL)-based RP and TS system, to minimize energy cost for large-scale CSPs with very large number of servers that receive enormous numbers of user requests per day. A deep Q-learning-based two-stage RP-TS processor is designed to automatically generate the best long-term decisions by learning from the changing environment such as user request patterns and realistic electric price. With training techniques such as target network, experience replay, and exploration and exploitation, the proposed DRL-Cloud achieves remarkably high energy cost efficiency, low reject rate as well as low runtime with fast convergence. Compared with one of the state-of-the-art energy efficient algorithms, the proposed DRL-Cloud achieves up to 320% energy cost efficiency improvement while maintaining lower reject rate on average. For an example CSP setup with 5,000 servers and 200,000 tasks, compared to a fast round-robin baseline, the proposed DRL-Cloud achieves up to 144% runtime reduction.

Abstract: Recent technological developments have finally brought virtual reality (VR) out of the laboratory and into the hands of developers and consumers. However, a number of challenges remain. Virtual travel is one of the most common and universal tasks performed inside virtual environments, yet enabling users to navigate virtual environments is not a trivial challenge—especially if the user is walking. In this article, we initially provide an overview of the numerous virtual travel techniques that have been proposed prior to the commercialization of VR. Then we turn to the mode of travel that is the most difficult to facilitate, that is, walking. The challenge of providing users with natural walking experiences in VR can be divided into two separate, albeit related, challenges: (1) enabling unconstrained walking in virtual worlds that are larger than the tracked physical space and (2) providing users with appropriate multisensory stimuli in response to their interaction with the virtual environment. In regard to the first challenge, we present walking techniques falling into three general categories: repositioning systems, locomotion based on proxy gestures, and redirected walking. With respect to multimodal stimuli, we focus on how to provide three types of information: external sensory information (visual, auditory, and cutaneous), internal sensory information (vestibular and kinesthetic/proprioceptive), and efferent information. Finally, we discuss how the different categories of walking techniques compare and discuss the challenges still facing the research community.

Abstract: Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3—43.6 percent less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2 with 0.17 percent SLA violation rate as the performance penalty.

Abstract: The resource reservation is one of the key techniques to ensure the quality of service (QoS) of a multimedia application. In mobile cloud computing (MCC), the resource reservation and allocation (RRA) in advance can significantly reduce the total provisioning cost of cloud service providers. However, the uncertain features of mobile users’ demands for resources make RRA challengeable. In MCC, the QoS of a mobile application, such as voice IP or video, is determined by both of the radio resource (RR) and the cloud virtual machine resource (VMR) allocated to the mobile application, so we should jointly allocate these two types of resources. In this paper, RRA with uncertain demands of mobile users is formulated as a robust optimization model. Logarithmic utility functions are defined to capture the mobile users’ satisfaction, which show how to match the allocations between RRs and VMRs according to the resource demands of the mobile applications. Then, a robust joint resource reservation and allocation algorithm in MCC (JRRA-MCC) is proposed to realize the optimal provisioning of RRs and VMRs. Simulation results show that the proposed JRRA-MCC can minimize the total resource provisioning cost of cloud service providers and enhance the resource utilization efficiently.

Abstract: All motile organisms use spatially distributed chemical features of their surroundings to guide their behaviors, but the neural mechanisms underlying such behaviors in mammals have been difficult to study, largely due to the technical challenges of controlling chemical concentrations in space and time during behavioral experiments. To overcome these challenges, we introduce a system to control and maintain an olfactory virtual landscape. This system uses rapid flow controllers and an online predictive algorithm to deliver precise odorant distributions to head-fixed mice as they explore a virtual environment. We establish an odor-guided virtual navigation behavior that engages hippocampal CA1 "place cells" that exhibit similar properties to those previously reported for real and visual virtual environments, demonstrating that navigation based on different sensory modalities recruits a similar cognitive map. This method opens new possibilities for studying the neural mechanisms of olfactory-driven behaviors, multisensory integration, innate valence, and low-dimensional sensory-spatial processing.

Abstract: Visually-induced motion sickness (VIMS), also known as cyber-sickness, is a major challenge for wide-spread Virtual Reality (VR) adoption. VIMS can be reduced in different ways, for example by using high-quality tracking systems and reducing the user's field of view. However, there are no universal solutions for all situations, and a wide variety of techniques are needed in order for developers to choose the most appropriate options depending on their needs. One way to reduce VIMS is through the use of rest frames-portions of the virtual environment that remain fixed in relation to the real world and do not move as the user virtually moves. We report the results of two multi-day within-subjects studies with 44 subjects who used virtual travel to navigate the environment. In the first study, we investigated the influence of static rest frames with fixed opacity on user comfort. For the second study, we present an enhanced version of rest frames that we call dynamic rest frames, where the opacity of the rest frame changes in response to visually perceived motion as users virtually traversed the virtual environment. Results show that a virtual environment with a static or dynamic rest frame allowed users to travel through more waypoints before stopping due to discomfort compared to a virtual environment without a rest frame. Further, a virtual environment with a static rest frame was also found to result in more real-time reported comfort than when there was no rest frame.

Abstract: Cloud computing is one of today's most popular and important IT trends. Currently, most organizations use cloud computing services (public or private) as part of their computer infrastructure. Virtualization technology is at the core of cloud computing, and virtual resources, such as virtual servers, are commonly used to provide services to the entire organization. Due to their importance and prevalence, virtual servers in an organizational cloud are constantly targeted by cyber-attackers who try to inject malicious code or malware into the server (e.g., ransomware). Many times, server administrators are not aware that the server has been compromised, despite the presence of detection solutions on the server (e.g., antivirus engine). In other cases, the breach is detected after a long period of time when significant damage has already occurred. Thus, detecting that a virtual server has been compromised is extremely important for organizational security. Existing security solutions that are installed on the server (e.g., antivirus) are considered untrusted, since malware (particularly sophisticated ones) can evade them. Moreover, these tools are largely incapable of detecting new unknown malware. Machine learning (ML) methods have been shown to be effective at detecting malware in various domains. In this paper, we present a novel methodology for trusted detection of ransomware in virtual servers on an organization's private cloud. We conducted trusted analysis of volatile memory dumps taken from a virtual machine (memory forensics), using the Volatility framework, and created general descriptive meta-features. We leveraged these meta-features, using machine learning algorithms, for the detection of unknown ransomware in virtual servers. We evaluated our methodology extensively in five comprehensive experiments of increasing difficulty, on two different popular servers (IIS server and an email server). We used a collection of real-world, professional, and notorious ransomware and a collection of legitimate programs. The results show that our methodology is able to detect anomalous states of a virtual machine, as well as the presence of both known and unknown ransomware, obtaining the following results: TPR = 1, FPR = 0.052, F-measure = 0.976, and AUC = 0.966, using the Random Forest classifier. Finally, we showed that our proposed methodology is also capable of detecting an additional type of malware known as a remote access Trojan (RAT), which is used to attack organizational VMs.

Abstract: FPGA accelerators are being applied in various types of systems ranging from embedded systems to cloud computing for their high performance and energy efficiency. Given the scale of deployment, there is a need for efficient application development, resource management, and scalable systems, which make FPGA virtualization extremely important. Consequently, FPGA virtualization methods and hardware infrastructures have frequently been proposed in both academia and industry for addressing multi-tenancy execution, multi-FPGA acceleration, flexibility, resource management and security. In this survey, we identify and classify the various techniques and approaches into three main categories: 1)Resource level, 2)Node level, and 3)Multi-node level. In addition, we identify current trends and developments and highlight important future directions for FPGA virtualization which require further work.

Abstract: Container technique is gaining increasing attention in recent years and has become an alternative to traditional virtual machines. Some of the primary motivations for the enterprise to adopt the container technology include its conveniency to encapsulate and deploy applications, lightweight operations, as well as efficiency and flexibility in resources sharing. However, there still lacks an in-depth and systematic comparison study on how big data applications, such as Spark jobs, perform between a container environment and a virtual machine environment. In this paper, by running various Spark applications with different configurations, we evaluate the two environments from many interesting aspects, such as how convenient the execution environment can be set up, what are makespans of different workloads running in each setup, how efficient the hardware resources, such as CPU and memory, are utilized, and how well each environment can scale. The results show that compared with virtual machines, containers provide a more easy-to-deploy and scalable environment for big data workloads. The research work in this paper can help practitioners and researchers to make more informed decisions on tuning their cloud environment and configuring the big data applications, so as to achieve better performance and higher resources utilization

Abstract: High service availability is crucial for cloud systems. A typical cloud system uses a large number of physical hard disk drives. Disk errors are one of the most important reasons that lead to service unavailability. Disk error (such as sector error and latency error) can be seen as a form of gray failure, which are fairly subtle failures that are hard to be detected, even when applications are afflicted by them. In this paper, we propose to predict disk errors proactively before they cause more severe damage to the cloud system. The ability to predict faulty disks enables the live migration of existing virtual machines and allocation of new virtual machines to the healthy disks, therefore improving service availability. To build an accurate online prediction model, we utilize both disk-level sensor (SMART) data as well as system level signals. We develop a cost-sensitive ranking-based machine learning model that can learn the characteristics of faulty disks in the past and rank the disks based on their error-proneness in the near future. We evaluate our approach using real-world data collected from a production cloud system. The results confirm that the proposed approach is effective and outperforms related methods. Furthermore, we have successfully applied the proposed approach to improve service availability of Microsoft Azure.

Abstract: Knowledge is essential and experience teaches. Therefore, new learning trends in schools are based on experience, making use of technologies such as Virtual Reality (VR) that allow to experience situations that are very close to reality. However, unlike computers, this technology does not present the same level of integration in schools. This article presents a systematic literature review of research conducted into virtual reality and the learning process in order to identify the important characteristics of virtual reality technology and its effect on the learning process. A total of 30 articles published between 1999 and 2017 were selected. The results show that the characteristics of interaction and immersion are the most important to consider in a virtual reality technology and its implementation would support the learning process.

Abstract: In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically contains a large number of computing nodes. In reality, nodes may fail and affect service availability. In this paper, we propose a failure prediction technique, which can predict the failure-proneness of a node in a cloud service system based on historical data, before node failure actually happens. The ability to predict faulty nodes enables the allocation and migration of virtual machines to the healthy nodes, therefore improving service availability. Predicting node failure in cloud service systems is challenging, because a node failure could be caused by a variety of reasons and reflected by many temporal and spatial signals. Furthermore, the failure data is highly imbalanced. To tackle these challenges, we propose MING, a novel technique that combines: 1) a LSTM model to incorporate the temporal data, 2) a Random Forest model to incorporate spatial data; 3) a ranking model that embeds the intermediate results of the two models as feature inputs and ranks the nodes by their failure-proneness, 4) a cost-sensitive function to identify the optimal threshold for selecting the faulty nodes. We evaluate our approach using real-world data collected from a cloud service system. The results confirm the effectiveness of the proposed approach. We have also successfully applied the proposed approach in real industrial practice.

Abstract: Network Function Virtualization (NFV) has revolutionized service provisioning in cloud datacenter networks. It enables the complete decoupling of Network Functions (NFs) from the physical hardware middle boxes that network operators deploy for implementing service-specific and strictly ordered NF chains. Precisely, NFV allows for dispatching NFs as instances of plain software called virtual network functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. Nevertheless, NF softwarization introduces processing vulnerability ( e.g. , failures caused by hardware or software, and so on). Since any failure of VNFs could break down an entire service chain, thus interrupting the service, the functionality of an NFV-enabled network will require a higher reliability compared with traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF chain placement and flow routing optimization. In order to guarantee the required reliability, an incremental approach is proposed to determine the number of required VNF backups. Through illustration, it is shown herein that the formulated single path routing model can be easily extended to support resource sharing between adjacent backup VNF instances. This paper advocates the absolute existence of a share-resource-based VNF assignment strategy that is capable of trading off all of the reliability, bandwidth, and computing resources consumption of a given service chain. A heuristic is proposed to work around the complexity of the presently formulated integer linear programming (ILP). Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of this proposed heuristic reflecting its ability to achieve very close results to those obtained through the resolution of the complex ILP within a negligible amount of time. Above and beyond, the proposed resource-sharing-based VNF placement scheme outperforms existing resource-sharing agnostic schemes by 15.6% and 14.7% in terms of bandwidth and CPU utilization respectively.

Abstract: This paper introduces a framework to assess the performance of manufacturing systems using hybrid simulation in real time. Continuous and discrete variables of different machines are monitored to analyze performance using a virtual environment running synchronous to plant floor equipment as a reference. Data are extracted from machines using industrial Internet of Things solutions. Productivity and reliability of a physical system are compared in real time with data from a hybrid simulation. The simulation uses discrete-event systems to estimate performance metrics at a system level, and continuous dynamics at a machine level to monitor input and output variables. Simulation outputs are used as a reference to detect abnormal conditions based on deviations of real outputs in different stages of the process. This monitoring method is implemented in a fully automated manufacturing system testbed with robots and CNC machines. Machines are integrated on an Ethernet/IP control network using a programmable logic controller to coordinate actions and transfer data. Results demonstrated the capacity to perform real-time monitoring and capture performance errors within confidence intervals. Note to Practitioners —Estimating expected performance of a manufacturing system processing different parts across multiple machines is a complex problem due to the lack of closed-form equations. Existing solutions focus on monitoring stochastic variables such as production or failure rate, or machine dynamics in separate environments often running asynchronous to the real system. This paper addresses the problem of monitoring and assessing the performance of complex manufacturing systems in real time. The proposed framework uses a real-time hybrid simulation of manufacturing at a machine and system level. The hybrid approach is based on a discrete and continuous model of manufacturing equipment integrated to run synchronously with the real plant floor operation. Data from both the virtual and real environments are merged to assess performance. Deviations from expected values represent an error that can trigger a warning signal to production, maintenance, and/or manufacturing personnel at the plant regarding health and productivity of plant operations.