Cloud platforms for remote monitoring system : a comparative case study

Currently, industrial companies are increasingly introducing services to extend their tangible products. Remote monitoring solutions are one of the most implemented services by machine builders to manage their relationship with customers and also improve their business performance in the digital manufacturing era. However, the conventional method of remote monitoring cannot fulfil distributed business environments. Therefore, new solutions are needed to enable remote connection in manufacturing. By reviewing recent literature and proposing new features for software which can be used for remote service and operations, this research paper introduces a remote monitoring system connecting into a central cloud-based system with edge computing network architecture, namely Cloud-based Remote Monitoring (CloudRM). This proposed CloudRM also has been implemented in two different case companies for analysis and evaluation from a value proposition and technical implementation point of view. It shows significant improvement of production management and measurement by using CloudRM.fi=vertaisarvioitu|en=peerReviewed

Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

The resources of information technology and the availability of services on non-cloud network systems are limited. This constitutes problems for companies, especially in the efficient management of information technology. The high investment in infrastructure procurement is an obstacle in building centralized systems, including the adoption of cloud computing through Infrastructure as a Service (IaaS), as an elective solution. This research aims to analyze the performance of cloud servers on IaaS services using the parameters of cloud service availability, resource utilization, and throughput transfer which were implemented in companies engaged in the toll road concession sector. Furthermore, the results are expected to be a reference in supporting company decisions/policies related to cloud system adoption. The methodology involved the Network Development Life Cycle (NDLC), a system constituted by 6 (six) stages of management, namely user, proxy server, database, web service, monitoring service, and Remote Desktop Protocol (RDP). The results of cloud service availability indicate that the cloud system provides service availability (system interface, broad network access, and resource pooling). Furthermore, cloud systems have a significant performance on resource utilization (CPU) and throughput transfer parameters, while non-cloud systems only excel in response time and resource utilization (Memory) parameters. The overall result analysis based on this research scenario showed that the cloud system provides services according to user needs and has a better speed in data transmission, but has shortcomings in response time

Mist Data: Leveraging Mist Computing for Secure and Scalable Architecture for Smart and Connected Health

The smart health paradigms employ Internet-connected wearables for tele-monitoring, diagnosis providing inexpensive healthcare solutions. Mist computing reduces latency and increases throughput by processing data near the edge of the network. In the present paper, we proposed a secure mist Computing architecture that is validated on recently released public geospatial health dataset. Results and discussion support the efficacy of proposed architecture for smart geospatial health applications. The present research paper proposed SoA-Mist i.e. a three-tier secure framework for efficient management of geospatial health data with the use of mist devices. It proposed the security aspects in client layer, mist layer, fog layer and cloud layer. It has defined the prototype development by using win-win spiral model with use case and sequence diagram. Overlay analysis has been performed with the developed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014 in mobile clients as test case. Finally, It concludes with the comparison analysis of cloud based framework and proposed SoA-Mist framework

Data Quality Challenges in Net-Work Automation Systems Case Study of a Multinational Financial Services Corporation

With the emerging trends of IPv6 rollout, Bring Your Own Device, virtualization, cloud computing and the Internet of Things, corporations are continuously facing challenges regarding data collection and analysis processes for multiple purposes. These challenges can also be applied to network monitoring practices: available data is used not only to assess network capacity and latency, but to identify possible security breaches and bottlenecks in network performance. This study will focus on assessing the collected network data from a multinational financial services corporation on its quality and attempts to link the concept of network data quality with process automation of network management and monitoring. Information Technology (IT) can be perceived as the lifeblood within the financial services industry, yet within the discussed case study the corporation strives to cut down operational expenditures on IT by 2,5 to 5 percent. This study combines both theoretical and practical approaches by conducting a literature review followed by a case study of abovementioned financial organization. The literature review focuses on (a) the importance of data quality, (b) IP Address Management (IPAM), and (c) network monitoring practices. The case study discusses the implementation of a network automation solution powered by Infoblox hardware and software, which should be capable of scanning all devices in the network along with DHCP lease history while having the convenience of easy IP address management mapping. Their own defined monitoring maturity levels are also taken into consideration. Twelve data quality issues have been identified using the network data management platform during the timeline of the research which potentially hinder the network management lifecycle of monitoring, configuration, and deployment. While network management systems are not designed to identify, document, and repair data quality issues, representing the network’s performance in terms of capability, latency and behavior is dependent on data quality on the dimensions of completeness, timeliness and accuracy. The conclusion of the research is that the newly implemented network automation system has potential to achieve better decision-making for relevant stakeholders, and to eliminate business silos by centralizing network data to one platform, supporting business strategy on an operational, tactical, and strategic level; however, data quality is one of the biggest hurdles to overcome to achieve process automation and ultimately to achieve a passive network appliance monitoring system.siirretty Doriast

Observing the clouds : a survey and taxonomy of cloud monitoring

This research was supported by a Royal Society Industry Fellowship and an Amazon Web Services (AWS) grant. Date of Acceptance: 10/12/2014Monitoring is an important aspect of designing and maintaining large-scale systems. Cloud computing presents a unique set of challenges to monitoring including: on-demand infrastructure, unprecedented scalability, rapid elasticity and performance uncertainty. There are a wide range of monitoring tools originating from cluster and high-performance computing, grid computing and enterprise computing, as well as a series of newer bespoke tools, which have been designed exclusively for cloud monitoring. These tools express a number of common elements and designs, which address the demands of cloud monitoring to various degrees. This paper performs an exhaustive survey of contemporary monitoring tools from which we derive a taxonomy, which examines how effectively existing tools and designs meet the challenges of cloud monitoring. We conclude by examining the socio-technical aspects of monitoring, and investigate the engineering challenges and practices behind implementing monitoring strategies for cloud computing.Publisher PDFPeer reviewe

Checkpointing as a Service in Heterogeneous Cloud Environments

A non-invasive, cloud-agnostic approach is demonstrated for extending existing cloud platforms to include checkpoint-restart capability. Most cloud platforms currently rely on each application to provide its own fault tolerance. A uniform mechanism within the cloud itself serves two purposes: (a) direct support for long-running jobs, which would otherwise require a custom fault-tolerant mechanism for each application; and (b) the administrative capability to manage an over-subscribed cloud by temporarily swapping out jobs when higher priority jobs arrive. An advantage of this uniform approach is that it also supports parallel and distributed computations, over both TCP and InfiniBand, thus allowing traditional HPC applications to take advantage of an existing cloud infrastructure. Additionally, an integrated health-monitoring mechanism detects when long-running jobs either fail or incur exceptionally low performance, perhaps due to resource starvation, and proactively suspends the job. The cloud-agnostic feature is demonstrated by applying the implementation to two very different cloud platforms: Snooze and OpenStack. The use of a cloud-agnostic architecture also enables, for the first time, migration of applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201