Author Archives: Mojgan Afshari

Cloud Networked Manufacturing

Cloud Networked Manufacturing

Cloud Networked Manufacturing

Cloud Computing provides a new way to do business by offering a scalable, flexible service over the Internet. Many organizations such as educational institutions, business enterprises have adopted cloud computing as a means to boost both employee and business productivity. Similarly, manufacturing companies found that they may not survive in the competitive market without the support of Information Technology (IT) and computer-aided capabilities. The advent of new technologies has changed the traditional manufacturing business model. Nowadays, collaboration between dispersed factories, different suppliers and distributed stakeholders, in a quick, real-time and effective manner are significant. Cloud manufacturing, as a new form of networked manufacturing, encourages collaboration in any phase of manufacturing and product management. It provides secure, reliable manufacturing lifecycle and on-demand services at low prices through networked systems.

manufacture

In the literature, there are various definitions of Cloud manufacturing (CM). For example, Li, Zhang and Chai (2010) defined cloud manufacturing as “a service-oriented, knowledge-based smart manufacturing system with high efficiency and low energy consumption”. In addition, Xu (2012) described Cloud Manufacturing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable manufacturing resources (e.g., manufacturing software tools, manufacturing equipment, and manufacturing capabilities) that can be rapidly provisioned and released with minimal management effort or service provider interaction“. According to Tao and his colleagues (2011), one of the key characteristics of cloud manufacturing is service-oriented. Manufacturing resources and abilities can be virtualized and encapsulated into different manufacturing cloud services such as Design as a service (DaaS), Manufacturing as a service (MFGaaS), Experimentation as a service (EaaS), Simulation as a service (SIMaaS), Management as a service (MaaS),  Maintain as a service (MAaaS), Integration as a service (INTaaS). Cloud users can use these services based on their requirements via the wide Internet.

Furthermore, Cloud manufacturing can provide various and dynamic resources, services, and solutions for addressing a manufacturing task. Like Wikipedia, Cloud manufacturing is a group innovation-based manufacturing model. Any person or company can participate in and contribute their manufacturing resources, abilities, and knowledge to a cloud manufacturing service platform. Besides, any company can use these resources, abilities and knowledge to carry out its manufacturing actions. It would seem that within a Cloud manufacturing environment, an enterprise does not need to possess the entire hardware manufacturing environment (such as workshop, equipment, IT infrastructures, and personnel) or the software manufacturing ability (such as design, manufacturing, management, and sales ability). An enterprise can obtain the resources and abilities, and services in Cloud manufacturing platform according to its requirements after payment.

Cloud Manufacturing consists of four kinds of cloud manufacturing service platform which are:

  • Public CM service platform: manufacturing resources and abilities are shared with the general public in a multi-tenant environment.
  • Private CM service platform: manufacturing resources and abilities are shared within one company or its subsidiaries. It is managed by an organization or enterprise to provide greater control over its resource and service.
  • Community CM service platform: manufacturing resources and abilities are controlled and used by a group of organizations with common concerns.
  • Hybrid CM service platform: it is a composition of public and private cloud. Services and information which are not critical are stored in Public CM, while critical information and services are kept within the private CM.

Cloud manufacturing consists of technologies such as networked manufacturing, manufacturing grid (MGrid), virtual manufacturing, agile manufacturing, Internet of things and cloud computing. It can reduce cost of production and improve production efficiency, distribution of integrated resources, and resource efficiency.

(Image Source: Shutterstock)

By Mojgan Afshari

An E-learning Ecosystem Based On Cloud Computing

An E-learning Ecosystem Based On Cloud Computing

An E-learning Ecosystem Based on Cloud Computing

E-learning is learning through the use of technologies. It is widely accepted as an effective training method to develop employees’ skills and knowledge in Small-to-Medium sized Enterprises (SMEs). According to Pollard and Hillage, e-learning is defined as “the delivery and administration of learning opportunities and support via computer, networked and web-based technology to help individual performance and development”. E-learning in SMEs can be used for formal training and vocational training. Formal training is “a training that is organised and packaged to cover a given subject, with clearly defined topics, eventually leading to the delivery of a certification” whereas vocational training relates to “the training costs are supported by the company and the topics are related to the job of the individual”. Despite the numerous advantages of e-learning, many practitioners believe that the cost and complexity of integrating these systems with content and with other business systems are important factors affecting the effective implementation of e-learning in organizations.e-learning

Cowley and her colleagues ( 2002) stated that several contextual elements (e.g., environment, tech skills, subject matter skills, study skills, support, content, learners’ characteristics, instructors’ characteristics, and technology ) should take into consideration to make e-learning more effective and successful and to facilitate learning in complex situations. This idea leads to the emergence of a new generation of e-learning which is called e-learning ecosystem (ELE). This model is comprehensive and consists of three components which are Infrastructure (Learning Management System, Tools, Content Delivery System), Content providers (Brand, Custom, Commodity), and Consultants (Strategy, Compensation, Implementation, Information Technology). All these components must integrate and work harmoniously and there must be a balance in the utilization of each components.

(Image Source: Shutterstock)

Nowadays, majority of companies employ e-learning ecosystem which is integrated with cloud computing. In fact, e-learning ecosystem has some problems in “optimizing resource allocations, dealing with dynamic demands on getting information and knowledge anywhere and anytime, handling rapid storage growth requirements, cost controlling and greater flexibility”. Moreover, e-learning ecosystems’ infrastructure which provides computation and storage resources as services, needs improvement. Cloud computing technologies can run applications as services over the Internet on a flexible infrastructure. Cloud computing provides a low cost solution to academic institutions for their researchers, faculty and students. Dong et al. (2009) reported that contribution of cloud computing to an e-learning ecosystem offers many benefits to organizations. They are as follows:

  • Cloud provides QoS-guaranteed infrastructures, e.g., time, cost, reliability, and hardware performance like CPU bandwidth and memory size, and sustains SLA-oriented resource allocation.
  • Cloud provides the support for variety of applications, making it convenient and rapid to get the required computation and storage resources.
  • Cloud provides real-time configuration information and resource utilization information, allocates resources on demand, and improves the usage rate of resources.
  • Through the automatic resource management, emergencies can be solved rapidly, and labour-intensive jobs can be achieved. Therefore, the cost is cut down.

As discussed above, an e-learning ecosystem based on cloud computing can transform education and guarantee the teaching and learning activities. Teachers and students can access information at anytime, anywhere, from any devices. E-learning ecosystem based on cloud computing enables students across the globe to acquire the 21st century skills and training they need to succeed in global information society.

By Mojgan Afshari

Computing Security – Network And Application Levels

Computing Security – Network And Application Levels

Computing Security

The intention to adopt cloud computing has increased rapidly in many organizations. Cloud computing offers many potential benefits to small and medium enterprises such as fast deployment, pay-for-use, lower costs, scalability, rapid provisioning, rapid elasticity, ubiquitous network access, greater resiliency, and on-demand security controls. Despite these extraordinary benefits of cloud computing, studies indicate that organizations are slow in adopting it due to security issues and challenges associated with it. In other words, security is one of the major issues which reduces the cloud computing adoption. Hence, cloud service providers should address privacy and security issues as an urgent priority and develop efficient and effective solutions.

Cloud computing utilizes three delivery models (SaaS, PaaS, and IaaS) to provide infrastructure resources, application platform and software as services to the consumer. These service models need different level of security in the cloud environment. According to Takabi et al. (2010), cloud service providers and customers are responsible for security and privacy in cloud computing environments but their level of responsibility will differ for different delivery models. Infrastructure as a Service (IaaS) serves as the foundation layer for the other delivery models, and a lack of security in this layer affects the other delivery models. In IaaS, although customers are responsible for protecting operating systems, applications, and content, the security of customer data is a significant responsibility for cloud providers. In Platform as a service (PaaS), users are responsible for protecting the applications that developers build and run on the platforms, while providers are responsible for taking care of the users’ applications and workspaces from one another. In SaaS, cloud providers, particularly public cloud providers, have more responsibility than clients for enhancing the security of applications and achieving a successful data migration. In the SaaS model, data breaches, application vulnerabilities and availability are important issues that can lead to financial and legal liabilities.

cloud-stack-images

(Image Source: via Brightpattern.com)

Bhadauria and his colleagues (2011) conducted a study on cloud computing security and found that security should be provided at different levels such as network level, host level, application level, and data level.

Network Level Security: All data on the network need to be secured. Strong network traffic encryption techniques such as Secure Socket Layer (SSL) and the Transport Layer Security (TLS) can be used to prevent leakage of sensitive information. Several key security elements such as data security, data integrity, authentication and authorization, data confidentiality, web application security, virtualization vulnerability, availability, backup, and data breaches should be carefully considered to keep the cloud up and running continuously.

Application level security

Studies indicate that most websites are secured at the network level while there may be security loopholes at the application level which may allow information access to unauthorized users. Software and hardware resources can be used to provide security to applications. In this way, attackers will not be able to get control over these applications and change them. XSS attacks, Cookie Poisoning, Hidden field manipulation, SQL injection attacks, DoS attacks, and Google Hacking are some examples of threats to application level security which resulting from the unauthorized usage of the applications.

Data Security

Majority of cloud service providers store customers’ data on large data centres. Although cloud service providers say that data stored is secure and safe in the cloud, customers’ data may be damaged during transition operations from or to the cloud storage provider. In fact, when multiple clients use cloud storage or when multiple devices are synchronized by one user, data corruption may happen. Cachin and his colleagues (2009) proposed a solution, Byzantine Protocols, to avoid data corruption. In cloud computing, any faults in software or hardware that usually relate to inappropriate behavior and intrusion tolerance are called Byzantine fault tolerance (BFT). Scholars use BFT replication to store data on several cloud servers, so if one of the cloud providers is damaged, they are still able to retrieve data correctly. In addition, different encryption techniques like public and private key encryption for data security can be used to control access to data. Service availability is also an important issue in cloud services. Some cloud providers such as Amazon mentions in their licensing agreement that it is possible that their service is not available from time to time. Backups or use of multiple providers can help companies to protect services from such failure and ensure data integrity in cloud storage.

By Mojgan Afshari

Big Data Analytics Adoption

Big Data Analytics Adoption

Big Data Analytics Adoption

Big Data is an emerging phenomenon. Nowadays, many organizations have adopted information technology (IT) and information systems (IS) in business to handle huge amounts of data and gain better insights into their business.

Many scholars believe that Business Intelligence (BI), solutions with Analytics capabilities, offer benefits to companies to achieve competitive advantage towards their competitors. According to Adelman et al. (2002), “Business Intelligence is a term that encompasses a broad range of analytical software and solutions for gathering, consolidating, analyzing and providing access to information in a way that is supposed to let an enterprise’s users make better business decisions”.

A report by Gartner (2010) lists data analytics as one of the top 10 strategic technologies. In fact, “Analytics facilitates realization of business objectives through reporting of data to analyze trends, creating predictive models for forecasting, and optimizing business processes for enhanced performance.”

The Institute for Operations Research and Management Science ( INFORMS) identifies three main categories of analytics: Descriptive Analytics (The representation of a set of data is used to understand and analyze business performance); Predictive Analytics (extensive use of data and statistical techniques to explain and predict models of business performance); and Prescriptive Analytics (It comprises a set of mathematical techniques that can be used to generate high-value decisions for actions to improve business performance). Selecting the most appropriate statistical techniques depends on research objectives and nature of data.

Executive Study

Big-data-infographic-steps

MIT and IBM (2011) conducted a study on 3000 executives, managers and analysts across 30 different industries and 100 countries to identify the relationship between the company’s performance and its Analytics ability. They found that Analytics-leading organizations are three times more successful than Analytics-starter organizations. Top performing organizations prefer to use analytics five times more than lower performers. It is clear that the adoption of advanced analytic approaches guide organization’s future strategies and help them to make smarter decisions.

The IBM study, “Big data, Analytics and the Path from Insights to Value” has categorized organizations based on their level of analytic adoption in three levels: Aspirational (Aspirational organizations have few people, processes or tools to collect, understand, incorporate or act on analytic insights);Experienced (Experienced organizations develop better ways to collect, incorporate and act on analytics effectively so they can begin to optimize their organizations); and Transformed (These organizations have substantial experience using analytics across a broad range of functions) . Therefore, identifying the level of organizations’ analytics capability helps them to be better prepared to turn challenges into opportunities.

Data Scientists

In addition, there are several factors that directly affect adoption of analytics approaches. A primary obstacle is data quality. Accurate data should be collected and structured in order to extract useful insights out of those data. Lack of understanding of how to use analytics to improve the business is another obstacle in analytics adoption. According to Davenport and Patil (2012), the shortage of data scientists is becoming a serious constraint in many companies. In fact, “data scientist” is considered as a high-ranking professional. “Data analysts are creative; have analytical skills, ability to bring structure to large quantities of formless data; and make analysis possible”. Furthermore, they are able to advise managers on the implications of the data for products, process, and decisions. They also have a solid foundation in math, statistics, probability, computer science, and business. Furthermore, studies indicated that managerial and cultural factors are important in the adoption of analytics. Unfortunately, some organizations do not encourage sharing information and do not intend to apply analytics. They make decisions based on intuition and personal experience instead of fact-based. Therefore, managers, decision makers should realize the importance of big data analytics in improving their business. Changes should be created in organizations for successful adoption of business analytics.

(Image Source: IBM)

By Mojgan Afshari

Pinup: Alpine Data Labs

Pinup: Alpine Data Labs

Pinup: Alpine Data Labsalpine-data-labs

The amounts of generated structured and multi-structured data are expanding at an extreme pace. Big data which is defined by data volume, variety of data, and velocity of data generation, is considered as an asset for every organization. This data needs to be processed and condensed into more connected forms in order to be useful for decision making. In other words, big data analytic which uses advanced analytic technologies should be utilized to analyse massive amounts of detailed information in order to improve business performance. Advanced analytics is a collection of related analytical techniques and technologies. It helps data analysts to extract knowledge, discover new insights and make predictions. Organizations can use advanced analytics to adjust their plans and strategies to become more competitive, minimize potential risk and make the best decisions in real time.

Nowadays, there are a lot of companies that provide these services. Alpine Data Labs, founded in 2011, is a perfect example of a provider of advanced analytics software on big data and Hadoop. The company’s products are accessible, easy to use, and built for big data. It offers a collaborative analytics solution that is code-free, requires no download and can be accessed by using any browser.

startup-bigdata

The more data you have, the more difficult it can be for data analysts to derive insight from that data. Chorus (a new cloud application which is enabled by Amazon Web Services, Inc) can provide data analysis tools that convert data formats to a common MAP Reduce format for processing large data sets with parallel and distributed algorithms on the cloud. It simplifies the process of building predictive models for big data.

Alpine as a provider an analytic productivity platform, uses Chorus 3 to provide data analysts with the ability to securely store, analyse and share data. Analytic productivity platform help analytics teams to collaborate, explore data, import data, organize workflows, create workspaces and, share knowledge and insights. Hence, it increases the analytics agility of data science team.

Alpine uses Apache Hadoop, an open-source framework, to process large data sets in a distributed environment. In fact, Hadoop’s scalability for big data volumes is impressive. Although it has the ability to manage a very broad range of data types, it usually takes a long time on Hadoop due to the fact that it is natively a batch-based process. Spark, a new technology which sits on top of Hadoop Distributed File System, can solve problems of Hadoop MapReduce. It can outperform Hadoop by 10x in iterative machine learning workloads and allow an efficient, general-purpose programming language to be used interactively to process large datasets. Hence, Alpine adopted this technology to increases the speed of big data analysis.

By Mojgan Afshari

Big Data – Productivity, Innovation And Competitiveness

Big Data – Productivity, Innovation And Competitiveness

Big Data – Productivity, Innovation And Competitiveness

Big Data Analytics

Big data refers to datasets that are so large, diverse, and fast-changing which need advanced and unique storage, management, analysis, and visualization technologies.  According to McKinsey, Big Data is “the next frontier for innovation, competition and productivity”.  The right use of Big Data can increase productivity, innovation, and competitiveness for organizations. Inhi Suh, IBM vice president of big data, stated that businesses should place a greater emphasis on analytics projects. In fact, big data analytic is an important step to extract knowledge from a huge amount of data. It is a competitive advantage for most companies.

big-data-1

According to Gupta and Jyoti (2014), “Big data analytics is the process of analysing big data to find hidden patterns, unknown correlations and other useful information that can be extracted to make better decisions”.Agrawal et al. (2011) described the multiple phases in the big data analysis which are Data Acquisition and Recording; Information Extraction and Cleaning; Data Integration, Aggregation, and Representation; Data Modeling and Analysis; and Interpretation. All these phases are crucial and high accuracy in each of these steps will lead to effective big data analytic. In this way, the promised benefits of big data will be achieved.

A wide variety of analytical techniques and technologies can be used to extract useful information from large collections of data. Such information helps companies to gain valuable insights to predict customer behaviour, effective marketing, increased revenue and so on. Maltby (2011) reviewed several literatures on big data analytics and introduced several techniques, such as Machine learning, Data mining, Text analytics, Crowdsourcing, Cluster analysis, Time series analysis, Network analysis, Predictive modelling, Association rule, and Regression, that can be used to extract information from a data set and transform it into an understandable structure for further use . In fact, using data analytic techniques depends on the research objectives/ questions, nature of the data, and the available technologies.

In addition, there are a wide variety of software products and technologies to facilitate big data analytics. EDWs, Visualization products, NoSQL databases, MapReduce & Hadoop, and cloud computing are examples of the more common technologies used in big data analytics. All these techniques and technologies cannot be used for every project or organization. Needs and potential of each organization should be evaluated in order to choosing the appropriate tools for big data analytic.

Studies indicates that data analysis is considerably more challenging than simply locating, identifying, understanding, and citing data. Many researchers believe that the most of the challenges and concerns with data is related to volume and velocity. However, a recent survey conducted by the creator of open source computational database management system on more than 100 data scientist indicates that variety of data sources (not just data volume & velocity) is the main challenge in analysing data. Furthermore, results of this study indicated that Hadoop cannot be a viable solution for some cases that require complex analytics.  It would seem that data analysis is a clear bottleneck in many applications. In line with this idea, Agrawal and his colleagues (2011) reported common challenges in big data analysis: Heterogeneity and Incompleteness of data, Scale, Timeliness, Privacy, error-handling, lack of structure, and visualization. It is recommended that the highlighted challenges should be addressed for effective data analysis.

By Mojgan Afshari

Wearable Technology In Education

Wearable Technology In Education

Wearable Technology In Education

New wearable technology innovations have transformed the learning and teaching process in which students deal with knowledge in an active, self-directed and constructive way. As an educational tool, wearable technology can help children exercise their creativity and innovation and interact with their surrounding in an easier and a more natural way. It provides opportunities for students to learn more quickly and access information with less effort or mental input. It is important to keep in mind that using wearable technologies in teaching and learning process is very different from the traditional learning experience where students come to class at a fixed time and location. Teachers should learn how to manage effectively the new learners and how to use effectively wearable technologies in an educational setting.

Some examples of wearable technology that can be used in education are Autographers, KeyglovesMuse (Brain-sensing headband), VR, Smart Watches,  GoPro, and Google Glass. These technologies can be used in education to develop student’s skills for cooperation, communication, problem solving and lifelong learning.Autographer-Wearable-Camera

The Autographer helps students to capture photos of the teacher’s direct notes. So, they will always have exact information from their teacher. Keygloves are wireless open-source input glove that can provide flexibility and convenience for gaming, design, art, music, data entry, device control, 3D object. This device can also facilitate singlehanded tasks and is perfect for handicapped or disabled users. Muse can display students’ brain’s activity directly onto a smartphone or tablet. When students are working on a project or studying for an important test, for instance, Muse can be used to measure their brainwaves and detect what activities they need to be active in and can help their mind stay focused and less stressed out.

(Image Source: Autographer / Wearable camera)

Virtual Reality (VR) gives students an opportunity to get hands-on experiences and increases their knowledge. It can present complex data in an accessible way to students which is both fun and easy to learn. Students can interact with each other as well as they can interact with the objects in that environment in order to discover more about them. Furthermore, Smartwatches are able to provide information and remote applications like camera, fitness applications, games and tools applications for measurements and calculations for students. All of the facilities afforded by smartphones are squeezed into Smartwatches. Chiu and Liu (2014) conducted a study on utilization of smartwatches in education and found that this technology can enhance learning outcomes and allow students to access education flexibly, calmly and seamlessly.

In addition, iPod technologies offer great opportunities for flexible learning. Dale and Pymm (2009) found that the iPod as an effective learning tool can empower students to think more creatively about their subject matter and encourage the development of collaborative learning. Hence, it gives a sense of self-empowerment and autonomy to the individual. Moreover, GoPro is an interesting and unique camera that has the ability to capture students and teachers’ view of events, to record instruction, and to explore novel possibilities. The GoPro camera helps teachers to examine their students’ behaviours and to make more informed pedagogical decisions.

Another pretty awesome innovation is Google Glass. “Google Glass is a web-connected wearable computer with an optical head-mounted display (OHMD). In other words, Google Glass integrates eyeglasses with a wearable and connected microcomputer. Users can interact with Google Glass with their voice and information is shown on the display screen. Teachers and students can share information in various modes of interaction by using this technology. Google Glass can help educators and students to search, take a picture, record a video, answer questions and translate their voice to foreign languages. Wu and her colleagues (2014) found that Google Glass can be successfully integrated into simulation-based training exercises without disrupting the learners’ experience. It can increase learners’ experiences and their attention on a current task and the people with whom they are interacting. In addition, Google Glass can revolutionize graduate medical education. It allows medical students watch different medical procedures in real time. Hence, this new technology can improve education and patient outcomes.

By Mojgan Afshari

Cloud Computing Adoption In Developing Countries

Cloud Computing Adoption In Developing Countries

Cloud Computing Adoption

n technology. The ability of cloud computing in processing, transmitting, and storing data makes it increasingly significant in the delivery of public and private services. Cloud computing as a disruptive technology with major implications for markets, economies and societies is becoming increasingly important for countries at all levels of development. However, the level of cloud adoption for organizations in developing countries looks very different from those in developed countries.

According to Kshetri’s report (2010), “the market for the cloud in developing countries is small but expanding rapidly”. Developing countries are attractive markets for cloud services and this technology has applications in a wide range of areas, including E-education, E-health, E-commerce, E-business, and supply chain. In reality, there are very few companies in developing countries which are actually using cloud computing. It would seem that cloud computing in developing countries is in its early stages.

research-dev-countries

(Image Source: IEEE Computer Society)

Many scholars believe that cloud computing has the potential to offer users in developing countries access to unique resources of computing power and storage. It is clear that the adoption of cloud computing is of great significance for businesses and organizations. Cost savings in hardware, software and personnel, as one of the main advantages of cloud computing, are most frequently cited in the literature. Cloud service customers can pay for the use of data storage capacity and application software rather than buying the hardware and software. Furthermore, software on the cloud can be easily installed, maintained, and updated. Although companies that use of cloud facilities extensively need competent personnel to manage IT functions, obtain cloud services, and manage their relationship with cloud providers; cloud computing generally leads to lower IT staff costs.cost-savings-cloud

Flexible access to processing and storage capacity is another benefit of cloud computing. In addition, a global study on 400 government executives in 10 developed and developing countries indicated that cost savings (See image), the nature of government activity, administrative advantages (e.g. easier software access, rapid deployment and reduced system administration) are the most significant advantages of cloud adoption.

Recent studies in developing countries indicate that cloud adoption in these countries is low and majority of developing countries encounter significant obstacles to participate effectively in the cloud economy. Barriers affecting cloud adoption in developing countries differ significantly depending on a country’s level of development and business and communications environments. These barriers can be divided into two main categories: internal and external barriers. Attitudes towards cloud computing, concerns and anxieties among managers about data privacy and security in the cloud, the location of data and reliability of services, concerns related to the non-availability of suitable terminal devices, concerns related to the migration of data and upgradability, lack of knowledge and skills to manage cloud resources, and finally lack of awareness of what cloud computing actually involves and its implications are main internal barriers. External barriers to adoption include inadequate infrastructure (lack of reliable power and broadband connectivity), lack of adequate legal and regulatory frameworks for electronic commerce and cybersecurity, and lack of skills to make effective use of ICTs.

Like other new innovations, successful implementation of cloud computing in developing countries needs organizational change which takes time and requires new investment. Policy makers in developing countries should research and analyse the level of cloud readiness and potential implications of cloud adoption. In addition, policies should recognize the diversity of business models and services within the cloud, the diversity of customers of cloud services and the complexity of the cloud economy ecosystem. Then, an effective cloud strategy (which address the areas such as infrastructure, legal and regulatory issues, the supply side of the cloud economy ecosystem, human resources, government cloud use and financial implications) should be designed for a successful implementation of cloud computing.

By Mojgan Afshari

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Digital Twin And The End Of The Dreaded Product Recall

Digital Twin And The End Of The Dreaded Product Recall

The Digital Twin  How smart factories and connected assets in the emerging Industrial IoT era along with the automation of machine learning and advancement of artificial intelligence can dramatically change the manufacturing process and put an end to the dreaded product recalls in the future. In recent news, Samsung Electronics Co. has initiated a global…

Three Challenges of Network Deployment in Hyperconverged Infrastructure for Private Cloud

Three Challenges of Network Deployment in Hyperconverged Infrastructure for Private Cloud

Hyperconverged Infrastructure In this article, we’ll explore three challenges that are associated with network deployment in a hyperconverged private cloud environment, and then we’ll consider several methods to overcome those challenges. The Main Challenge: Bring Your Own (Physical) Network Some of the main challenges of deploying a hyperconverged infrastructure software solution in a data center are the diverse physical…