Collaborative Cloud Research Articles

Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments

Cloud computing has been studied and used extensively in many scenarios for its nearly unlimited resources and X as a service model. To reduce the latency for accessing the remote cloud data centers, small data centers or cloudlets are deployed near end-users, which is also called edge computing. In this paper, we mainly focus on the efficient scheduling of distributed simulation tasks in collaborative cloud and edge environments. Since simulation tasks are usually tightly coupled with each other by sending many messages and the status of tasks and hosts may also change frequently, it is essentially a dynamic bin-packing problem. Unfortunately, popular methods, such as meta-heuristics, and accurate algorithms are time-consuming and cannot deal with the dynamic changes of tasks and hosts efficiently. In this paper, we present Pool, an incremental flow-based scheduler, to minimize the overall communication cost of all tasks in a reasonable time span with the consideration of migration cost of task. After formulating such a scheduling problem as a min-cost max-flow (MCMF) problem, incremental MCMF algorithms are adopted to accelerate the procedure of calculating an optimal flow and heuristic scheduling algorithm, with the awareness of task migration cost, designed to assign tasks. Simulation experiments on Alibaba cluster trace show that Pool can schedule all of the tasks efficiently and is almost 5.8 times faster than the baseline method when few tasks and hosts change in the small problem scale.

OFP-TM: an online VM failure prediction and tolerance model towards high availability of cloud computing environments.

The indispensable collaboration of cloud computing in every digital service has raised its resource usage exponentially. The ever-growing demand of cloud resources evades service availability leading to critical challenges such as cloud outages, SLA violation, and excessive power consumption. Previous approaches have addressed this problem by utilizing multiple cloud platforms or running multiple replicas of a Virtual Machine (VM) resulting into high operational cost. This paper has addressed this alarming problem from a different perspective by proposing a novel mathbb {O}nline virtual machine mathbb {F}ailure mathbb {P}rediction and mathbb {T}olerance mathbb {M}odel (OFP-TM) with high availability awareness embedded in physical machines as well as virtual machines. The failure-prone VMs are estimated in real-time based on their future resource usage by developing an ensemble approach-based resource predictor. These VMs are assigned to a failure tolerance unit comprising of a resource provision matrix and Selection Box (S-Box) mechanism which triggers the migration of failure-prone VMs and handle any outage beforehand while maintaining the desired level of availability for cloud users. The proposed model is evaluated and compared against existing related approaches by simulating cloud environment and executing several experiments using a real-world workload Google Cluster dataset. Consequently, it has been concluded that OFP-TM improves availability and scales down the number of live VM migrations up to 33.5% and 83.3%, respectively, over without OFP-TM.

Managing Digital Integration Routines in Engineering Firms: Cases of Disruptive BIM Cloud Collaboration Protocols

The challenge for engineering firms to align with an unprecedented variation of digital integration agendas across projects is a recent phenomenon. The literature has offered only a partial understanding of this challenge due to the episodic focus on the digital delivery of individual projects or the initial adoption of digital practice in firms. Through a longitudinal ethnographic study of an engineering firm, this paper explored how practitioners act upon the surge of custom digital integration agendas across multiple projects. By looking at and beyond a large portfolio of projects that demanded building information modeling (BIM) cloud collaboration, this study found that disparate digital agendas across projects disrupt digital integration routines, and they have knock-on effects that can overreach project boundaries and impact projects portfolios and firm operations. With this evidence, this research spotlights the shifting loci of information integration in practice, wherein custom agendas pressure practitioners to shift the focus of integration from their permanent practice at firms to temporary workflows in projects. By revealing organizational responses to this phenomenon, this paper introduces the concept of genericity–complexity trade-off in digital integration routines to explain how firms generate routines that address integration either with ad hoc responses in projects or with systemized integrative efforts across all constellations of practice.

Flexible Task Scheduling Based on Edge Computing and Cloud Collaboration

With the rapid development and popularization of 5G and the Internet of Things, a number of new applications have emerged, such as driverless cars. Most of these applications are time-delay sensitive, and some deficiencies were found during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved at present. In 5 g environments, edge computing can better meet the needs of low delay and wide connection applications, and support the fast request of terminal users. However, edge computing only has the edge layer computing advantage, and it is difficult to achieve global resource scheduling and configuration, which may lead to the problems of low resource utilization rate, long task processing delay and unbalanced system load, so as to lead to affect the service quality of users. To solve this problem, this paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes a genetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve task scheduling and resource allocation, and designs a series of experiments to verify the effectiveness of the GSA-EDGE algorithm. The experimental results show that the proposed method can reduce the time delay of task processing compared with the local task processing method and the task average allocation method.

Edge–Cloud Collaboration Detection Approach for Small-Sample Imbalanced Faults in Power Lines

Fault detection in power lines is crucial to the reliable operation of power systems. Recent progress in the development of the artificial intelligence-based (AI-based) fault detection for power lines has been receiving an ever-growing attention. Existing AI-based fault detection models mostly rely on the assumption that the power line faults collected in a substation are adequate and balanced. However, obtaining massive and balanced line fault samples is difficult because of the complexity of practical environments and the inherent nature of faults. As a result, the performance of the models gradually deteriorates when the small-sample imbalanced degree of the power line fault set increases. This study proposes a novel edge–cloud collaboration detection method based on transfer and federated learning to address the above issue. Specifically, a fault detection model is established on the basis of a convolutional neural network and pretrained by introducing a new fault set with a sufficient number of labelled samples. The pretrained model is then deployed to each of the substations and fine-tuned in a federated learning manner. By performing fine-tuning in substations and global aggregation in a cloud platform, the proposed method updates the model on the basis of the small-sample imbalanced faults of power lines collected in substations. Case studies verify that after training by using the proposed edge-cloud collaboration detection method, the model can accurately detect faults in power lines even if the fault training set has a small number of samples and is imbalanced.

Retrieval Files in Business Objects using Cloud Collaborative E-business

Abstract: Everything stored on the cloud could potentially be a knowledge source used for e-business. Given learners' profiles, desires and feedback on what they have already learned, a new form of personalized e-business emerges, namely Cloud collaborative E-business (CeL). CeL should be able to choose from structured to totally unstructured business material but needs to make them useful for each individual. Existing metadata standards cannot facilitate composition of personalized business paths as a series of business objects. In this paper, we present the structure of CeL Business Objects (CeLLOs), which include an additional set of metadata suitable for each phase of CeL development. Keywords: Cloud collaborative E-business, computing for Education, Electronic Business

Traditional Trajectory of Nature Conservation Environment Based on Cloud Collaboration

Traditional Trajectory of Nature Conservation Environment Based on Cloud Collaboration

Quality of Service Estimation Enabled With Trust-Based Resource Allocation in Collaborative Cloud Using Improved Grey Wolf Optimization

Abstract Collaborative cloud computing utilizes information technology to successfully provide service over the network and serve the end users with tremendously stronger computational capability and enormous memory space at lower costs. Moreover, providing highly trustworthy service is the most fundamental task even on this platform. So far, only some contributions are there that meet the requirements of trust computing in this scenario. This proposal estimates the Quality of Service and trust by analyzing the system behavior using a new trust computing model. This is handled using Neural Network model. Further, a parallel resource matching framework is introduced using the concept of MapReduce concept, thereby the resource allocation is performed without any conflicts. Particularly, the resource allocation is performed precisely by optimization logic, where an Improved Grey Wolf Optimizer is introduced to do the same. In fact, the proposed algorithm is the enhanced version of traditional Grey Wolf Optimizer. Finally, the performance of the projected model is compared over other state-of-the-art models concerning different performance measures.

PIOT‐Hub ‐ A collaborative cloud tool for generation of physical input–output tables using mechanistic engineering models

AbstractMapping material flows in an economy is crucial to identifying strategies for resource management toward lowering the waste and environmental impacts of society, a key objective of research in industrial ecology. However, constructing models for mapping material flows at a sectoral level, such as in physical input–output tables (PIOTs) at highly disaggregated levels, is tedious and relies on a large amount of empirical data. To overcome this challenge, a novel collaborative cloud platform PIOT‐Hub is developed in this work. This platform utilizes a Python‐based simulation system for extracting material flow data from mechanistic models, thus semi‐automating the generation of PIOTs. The simulation system implements a bottom‐up approach of utilizing scaled engineering models to generate physical supply tables (PSTs) and physical use tables (PUTs) which are converted to PIOTs (described in (Vunnava & Singh, 2021)). Mechanistic models can be uploaded by users for sectors on PIOT‐Hub to develop PIOTs for any region. Both models and resulting PST/PUT/PIOTs can be shared with other users utilizing the collaborative platform. The automation and sharing features provided by PIOT‐Hub will help to significantly reduce the time required to develop PIOT and improve the reproducibility/continuity of PIOT generation, thus allowing the study of the changing nature of material flows in regional economy. In this paper, we describe the simulation system MFDES and PIOT‐Hub architecture/functionality through a demo example for creating PIOT in agro‐based sectors for Illinois. Future work includes scaling up the cloud infrastructure for large scale PIOT generation and enhancing the tool compatibility for different sectors in economy.

Relationship between employees' career maturity and career planning of edge computing and cloud collaboration from the perspective of organizational behavior.

A new IoT (Internet of Things) analysis platform is designed based on edge computing and cloud collaboration from the perspective of organizational behavior, to fundamentally understand the relationship between enterprise career maturity and career planning, and meet the actual needs of enterprises. The performance of the proposed model is further determined according to the characteristic of the edge near data sources, with the help of factor analysis, and through the study and analysis of relevant enterprise data. The model is finally used to analyze the relationship between enterprise career maturity and career planning through simulation experiments. The research results prove that career maturity positively affects career planning, and vocational delay of gratification plays a mediating role in career maturity and career planning. Besides, the content of career choice in career maturity is influenced by mental acuity, result acuity and loyalty. The experimental results indicate that when the load at both ends of the edge and cloud exceeds 80%, the edge delay of the IoT analysis platform based on edge computing and cloud collaboration is 10s faster than that of other models. Meanwhile, the system slowdown is reduced by 36% while the stability is increased when the IoT analysis platform analyzes data. The results of the edge-cloud collaboration scheduling scheme are similar to all scheduling to the edge end, which saves 19% of the time compared with cloud computing to the cloud end. In Optical Character Recognition and Aeneas, compared with the single edge-cloud coordination mode, the model with the Nesterov Accelerated Gradient algorithm achieves the optimal performance. Specifically, the communication delay is reduced by about 25% on average, and the communication time decreased by 61% compared with cloud computing to the edge end. This work has significant reference value for analyzing the relationship between enterprise psychology, behavior, and career planning.

Medical Internet of Things to Realize Elderly Stroke Prevention and Nursing Management.

Stroke is a major disease that seriously endangers the lives and health of middle-aged and elderly people in our country, but its implementation of secondary prevention needs to be improved urgently. The application of IoT technology in home health monitoring and telemedicine, as well as the popularization of cloud computing, contributes to the early identification of ischemic stroke and provides intelligent, humanized, and preventive medical and health services for patients at high risk of stroke. This article clarifies the networking structure and networking objects of the rehabilitation system Internet of Things, clarifies the functions of each part, and establishes an overall system architecture based on smart medical care; the design and optimization of the mechanical part of the stroke rehabilitation robot are carried out, as well as kinematics and dynamic analysis. According to the functions of different types of stroke rehabilitation robots, strategies are given for the use of lower limb rehabilitation robots; standardized codes are used to identify system objects, and RFID technology is used to automatically identify users and devices. Combined with the use of the Internet and GSM mobile communication network, construct a network database of system networking objects and, on this basis, establish information management software based on a smart medical rehabilitation system that takes care of both doctors and patients to realize the system's Internet of Things architecture. In addition, this article also gives the recovery strategy generation in the system with the design method of resource scheduling method and the theoretical algorithm of rehabilitation strategy generation is given and verified. This research summarizes the application background, advantages, and past practice of the Internet of Things in stroke medical care, develops and applies a medical collaborative cloud computing system for systematic intervention of stroke, and realizes the module functions such as information sharing, regional monitoring, and collaborative consultation within the base.

A mobile edge–cloud collaboration outlier detection framework in wireless sensor networks

A mobile edge–cloud collaboration outlier detection framework in wireless sensor networks

Discussion on Industrial Internet Platform Construction Based on Power Internet of Things

At present, the development of industrial Internet platform is in a period of large-scale expansion. Since 2017, China has issued a series of documents, such as the Guidance on Deepening “Internet + Advanced Manufacturing” to Develop Industrial Internet, the Action Plan for Industrial Internet Development (2018-2020), and the Industrial Internet App Cultivation Project (2018-2020), which constantly points out the direction for the development of the industrial Internet industry. The power Internet of Things connects the equipment, customers and data in all links of energy production, transmission and consumption in real time, fully bearing the production and operation of power grid, enterprise management and new business development, etc. It has the characteristics of ubiquitous terminal access, open platform sharing, computing cloud collaboration, convenient and flexible application, etc. This paper aims to discuss how the power Internet of Things can help the construction of the industrial Internet, provide strong data resource support and service for the safe and economic operation of the power grid, improve business performance, improve service quality, cultivate and develop strategic emerging industries, and open up a new road for management innovation, business innovation and value creation.

Auxiliary Diagnosis of COVID-19 Based on 5G-Enabled Federated Learning

The development of 5G and artificial intelligence technologies have brought novel ideas to the prevention, control, and diagnosis of disease. Due to the limitation of the privacy protection of medical big data, releasing the data of patients is not allowed. However, as COVID-19 spreads globally, it is urgent to develop a robust diagnostic model to serve as many institutions as possible. Therefore, we propose a 5G-enabled architecture of auxiliary diagnosis based on federated learning for multiple institutions and central cloud collaboration to realize the sharing of diagnosis models with high generalization performance. In order to exchange model and parameters between the central and distributed nodes, a framework of diagnosis model cognition is constructed for sharing and updating the model adaptively. The severity classification experiments of COVID-19 were carried out on the central cloud and three edge cloud servers to verify the effectiveness of the proposed architecture and model cognition strategy. At the same time, the aggregated model shows good performance with accuracy rates of 95.3, 79.4, and 97.7 percent on distributed nodes, and the recognition results can assist doctors in executing auxiliary diagnosis. Finally, the open issues of model fusion of multimodal data in the 5G network architecture are discussed.

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

Fast tag identification for mobile RFID robots in manufacturing environments

Purpose In manufacturing environments, mobile radio frequency identification (RFID) robots need to quickly identify and collect various types of passive tag and active tag sensor data. The purpose of this paper is to design a robot system compatible with ultra high frequency (UHF) band passive and active RFID applications and to propose a new anti-collision protocol to improve identification efficiency for active tag data collection. Design/methodology/approach A new UHF RFID robot system based on a cloud platform is designed and verified. For the active RFID system, a grouping reservation–based anti-collision algorithm is proposed in which an inventory round is divided into reservation period and polling period. The reservation period is divided into multiple sub-slots. Grouped tags complete sub-slot by randomly transmitting a short reservation frame. Then, in the polling period, the reader accesses each tag by polling. When tags’ reply collision occurs, the reader tries to re-query collided tags once, and the pre-reply tags avoid collisions through random back-off and channel activity detection. Findings The proposed algorithm achieves a maximum theoretical system throughput of about 0.94, and very few tag data frame transmissions overhead. The capture effect and channel activity detection in physical layer can effectively improve system throughput and reduce tag data transmission. Originality/value In this paper, the authors design and verify the UHF band passive and active hybrid RFID robot architecture based on cloud collaboration. And, the proposed anti-collision algorithm would improve active tag data collection speed and reduce tag transmission overhead in complex manufacturing environments.

A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks

The fifth-generation (5G) wireless communication networks are expected to play an essential role in the transformation of vertical industries. Among many exciting applications to be enabled by 5G, logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything (V2X) communications. In this paper, a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks. The proposed framework includes three layers: a perception and execution layer, a logistics layer, and a configuration layer. In addition to the collaboration among these three layers, this study addresses the collaboration among devices, edge servers, and cloud services. For effective logistics in industrial parks, task collaboration is achieved through four functions: environmental perception and map construction, task allocation, path planning, and vehicle movement. To dynamically coordinate these functions, device–edge–cloud collaboration, which is supported by 5G slices and V2X communication technology, is applied. Then, the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks. Finally, a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

Technical study and experimental research on the cloud collaboration model of Soften-standardized

This paper discusses and analyzes the technical ideas of the soften-standardized cloud collaborative manufacturing model of bearing components with seat, including soften-standardized processing, intelligent fitting algorithm, intelligent matching algorithm and product data flow. Because it has obvious advantages compared with the traditional group assembly method, it can be expected that this technology will become a competitive choice for low cost processing and high quality production of large-scale precision assembly components manufacturing enterprises. In order to verify the feasibility of this technology in mass production, a set of experimental methods are designed and verified step by step. The experimental results show that the technology is not only feasible, but also can change the existing factors of production. The failure rate of bearing and bearing can approach zero theoretically and the assembly quality of bearing assembly is higher.

Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.

Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making. Using thematic analysis, we describe challenges, considerations, and solutions for algorithm design decisions in a collaboration between health services researchers, clinicians, and data scientists. We use the design process of an RL algorithm for a mobile health study "DIAMANTE" for increasing physical activity in underserved patients with diabetes and depression. Over the 1.5-year project, we kept track of the research process using collaborative cloud Google Documents, Whatsapp messenger, and video teleconferencing. We discussed, categorized, and coded critical challenges. We grouped challenges to create thematic topic process domains. Nine challenges emerged, which we divided into 3 major themes: 1. Choosing the model for decision-making, including appropriate contextual and reward variables; 2. Data handling/collection, such as how to deal with missing or incorrect data in real-time; 3. Weighing the algorithm performance vs effectiveness/implementation in real-world settings. The creation of effective behavioral health interventions does not depend only on final algorithm performance. Many decisions in the real world are necessary to formulate the design of problem parameters to which an algorithm is applied. Researchers must document and evaulate these considerations and decisions before and during the intervention period, to increase transparency, accountability, and reproducibility. clinicaltrials.gov, NCT03490253.

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks.

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.