Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems - A Bibliometric Analysis

The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on machine behavior models, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the bibliometric analysis method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems

SERCON-BASED TIMESTAMPED VIRTUAL MACHINE MIGRATION SCHEME FOR CLOUD

With the advent of cloud computing, the need for deploying multiple virtual machines (VMs) on multiple hosts to address the ever-increasing user demands for services has raised concerns regarding energy consumption. Considerable energy is consumed while keeping the data centers with a large number of servers active. However, in data centers, there are cases where these servers may not get utilized efficiently. There can be servers that consume sufficient energy while running resources for a small task (demanding fewer resources), but there can also be servers that receive user requests so frequently that resources may be exhausted, and the server becomes unable to fulfill requests. In such a scenario, there is an urgent need to conserve energy and resources which is addressed by performing server consolidation. Server consolidation aims to reduce the total number of active servers in the cloud such that performance does not get compromised as well as energy is conserved in an attempt to make each server run to its maximum. This is done by reducing the number of active servers in a data center by transferring the workload of one or more VM(s) from one server to another, referred to as VM Migration (VMM). During VMM, time is supposed as a major constraint for effective and user-transparent migration. Thus, this paper proposes a novel VM migration strategy considering time sensitivity as a primary constraint. The aim of the proposed Time Sensitive Virtual Machine Migration (TS-VMM) is to reduce the number of migrations to a minimum with effective cost optimization and maximum server utilization

Energy Efficient Virtual Machine Services Placement in Cloud-Fog Architecture

The proliferation in data volume and processing requests calls for a new breed of on-demand computing. Fog computing is proposed to address the limitations of cloud computing by extending processing and storage resources to the edge of the network. Cloud and fog computing employ virtual machines (VMs) for efficient resource utilization. In order to optimize the virtual environment, VMs can be migrated or replicated over geo-distributed physical machines for load balancing and energy efficiency. In this work, we investigate the offloading of VM services from the cloud to the fog considering the British Telecom (BT) network topology. The analysis addresses the impact of different factors including the VM workload and the proximity of fog nodes to users considering the data rate of state-of-the-art applications. The results show that the optimum placement of VMs significantly decreases the total power consumption by up to 75% compared to a single cloud placement

Керування ресурсами хмарних центрів обробки даних на основі евристичного пошуку

Проаналізовано особливості хмарного центру обробки даних (ЦОД) з точки зору керування ресурсами. Для вирішення задачі керування ресурсами хмарного центру обробки даних запропоновано і досліджено двостадійний метод консолідації віртуальних машин на базі використання локального променевого пошуку. В статті проаналізовано роботу евристики першої та другої стадій запропонованого методу, розроблений алгоритм променевого пошуку для вирішення задачі керування ресурсами. Для аналізу роботи методу використані дані про надходження завдань в кластер Google. Запропонований метод дозволяє переключити в режим зниженого енергоспоживання в середньому 56 відсотків фізичних серверів, що потенційно визначені для переключення в режим сну за допомогою верхньої оцінки необхідної ємності ресурсів. Перерозподіл віртуальних машин виконується з урахуванням обмеження допустимої кількості міграцій на один фізичний сервер.Проанализированы особенности облачного центра обработки данных с точки зрения управления ресурсами. Для решения задачи управления ресурсами облачного центра обработки данных предложено и исследовано двухэтапный метод консолидации виртуальных машин на основе использования локального лучевого поиска. В статье проанализирована работа эвристики первой и второй стадий предложенного метода, разработан алгоритм лучевого поиска для решения задачи управления ресурсами. Для анализа работы метода использованы данные о поступлении задач в кластер Google. Предложенный метод позволяет переключить в режим пониженного энергопотребления в среднем 56 процентов физических серверов, потенциально определенных для переключения в режим сна на основе верхней оценки необходимой емкости ресурсов. Перераспределение виртуальных машин выполняется с учетом ограничения допустимого количества миграций на один физический сервер.The features of the cloud data center are analyzed from the point of view of resource management. The two-stage method for consolidating virtual machines based on the use of local beam search algorithm is proposed and investigated with aim to solve the problem of managing the resources of a cloud data center. In this paper, the work of heuristics of the first and second stages of the proposed method is analyzed. The beam search algorithm was developed for solving the data center resource management problem. The data about tasks and physical machines from the Google cluster-usage traces are used to evaluate the proposed method. The proposed method allows to switch to a low-power mode on average 56 percent of physical servers potentially identified for switching to sleep mode based on an upper estimate of the required capacity of resources. Virtual machine consolidation is performed taking into account the limitation of the permissible number of migrations per physical server