Cloud Networks as Platform-based Ecosystems : Detecting Management Implications for Actors in Cloud Networks

Purpose The purpose of this study is to extend theoretical advancements in ecosystem innovation by examining derivatives exchange platforms–a long-established yet increasingly digitalized segment of global financial services. Whereas most prior research has centered on platform-based ecosystems in high-technology industries, derivatives exchanges operate with distinct transaction dynamics, offering a valuable setting to uncover contingencies relevant to B2B marketing and ecosystem management. Design/methodology/approach This study analyzes a sample of 119 derivative exchanges from 1996 to 2013 across 37 countries. Using a zero-inflated Poisson (ZIP) model, the authors examine how product portfolio complexity (PPC) influences new product introduction and removal and how these effects vary with PPC magnitude. Findings The findings indicate that there is a nonlinear S-shaped relationship between PPC and product renewal in the context of platform-based ecosystems. The study reveals that both new product introduction and product removal are essential strategies for managing a complex product portfolio within derivatives exchange platforms, highlighting the cumulative effects of the knowledge base and ecosystem dynamics. Originality/value This research contributes to B2B marketing and innovation research by exploring the unique dynamics of derivatives exchange platforms within platform-based ecosystems. By identifying the S-shaped relationship between PPC and product renewal, this study provides valuable insights for practitioners and scholars seeking to navigate the complexities of ecosystem management and innovation strategies in digitalized B2B markets.

Solving the chicken-or-egg problem and leveraging value contributing actors on the platform is crucial to establish dynamic platform-based ecosystems. A digital platform provider is challenged to manage multilateral platform architecture and governance mechanisms to establish an attractive platform-based ecosystem to foster third-party complementors to join. One of the key issues while establishing a platform-based ecosystem remains the decision about an adequate pricing model. Despite a large number of publications on platform governance, detailed pricing model analyses remain rare. In this explorative paper, we conduct a literature review, studying 62 relevant papers to explore the pricing impact factors to create a foundation for future research of price models in the under-researched setting of the Industrial Internet of Things (IIoT). The most relevant pricing factors and their distinctive characteristics are summed up in a multi-dimensional taxonomy. The developed taxonomy includes 13 impact factors and 38 characteristics of platform pricing. Our findings enable the decomposition and understanding of price models for their future improvement.

The application of big data, new algorithms, and cloud computing is creating a digital platform economy (DPE) built around platform organizations and their platform-based ecosystem. We use the DPE Index to examine Europe’s digital efficiency across countries and explain its global position by analyzing Brexit and the electric vehicle industry. We argue that the United Kingdom left the European Union because E.U. regulations were holding back the U.K.’s strong DPE and that a weak DPE is holding German back from being a leader in the electric vehicle industry. The problem for Europe’s DPE is that the entrepreneurial state is strong and the private sector is weak.

Cloud computing has gained lots of popularity in recent times as it fulfil the dream model “Computing as Utility”, it emerged in the market as huge technological growth in the form of on-demand service for business customers such as software, platform and infrastructure needs and requirements. The model as “pay as you go” help companies in IT growth without much planning as everything is on pay-based model. Despite all the factors and scalable infrastructure of cloud network, there are less concern and cautions about the security issues pertain to cloud computing. Cloud computing can be taken as most significant shift in IT paradigm of recent times but still there are great deal of work is required in the area of security to handle the attack spreading and polluting the cloud infrastructure. In this paper, the security issues by gathering the attak data in the cloud network by desiging, implementing the honeypot as cyber attack mitigation solution is presented. The concept is to design a cloud setup and assess the security issues by putting the honeypot in the cloud enviornment. This artical include the honeypot implementation along with Intrusion detection system using a Ubuntu based OwnCloud utility. The real statistical data collected, analysed and categorized in a automatic way through novel signatures based engines.

In recent days, cloud computing has become an outstanding technique in all areas that provide various computing resources. The specialty of cloud computing is that it provides excellent services in monitoring, communication, software platform, and infrastructure. Basically, cloud network limits its services in a specific location on the particular zone. When the targeted area is near, the specific server completes the request generated from the user. Few servers are busy while some other servers are idle when it comes to the services. In this case, the provided resources are not used efficiently. To overcome this problem, this paper proposes a concept known as flow-based dynamic load balancing algorithm in cloud networking with software defined networks (SDN). The fundamental principle of this proposed algorithm is distributing client requests equally to all the available servers in a specific cloud network. The main aim of the load balancing algorithm is to optimise the resource utilisation by increasing the throughput, reduce flow completion time, decrease the response time and eliminate the overloading of any individual resource. SDN is mainly used in applying various software functionalities into hardware devices such as routers, switches, etc.

Comparative Study of Security Algorithms in Cloud Computing

Artificial Intelligence (AI) techniques provide many intelligent methods for security solutions in various domains such as finance, networking, cloud computing, health records and individual's identity. AI achieves security mechanisms like antivirus, firewalls, intrusion detection system (IDS) and cryptography by using machine learning methods and data analysis techniques. As the modern AI techniques help improving security systems, criminal activities are also becoming updated simultaneously. Machine learning methods along with data analysis tools have become popular to prevent security systems from threats and hacking activities. This work contributes to secure cloud networks and help them prevent malicious attacks. In this paper, Bidirectional long short-term memory (BLSTM) is used to detect incidents over unified threat management (UTM) platform operated on cloud network. Results are compared with K-nearest neighbor which is a baseline technique. Time series input samples recorded over UTM platform are used for training and testing purposes. We obtain accuracy score of 98.47% with 0.0186 mean squared error (MSE) using KNN while BLSTM provides 98.6% accuracy score with 0.002 loss, which is better than the KNN.

Internet, since the beginning of its work, has undergone many changes one of the latest changes is how the internet cloud computing. New technologies cloud computing offers because of features all kinds of facilities to the users as a service. Each evolution, change and novel concept in the world of technologies has its own problems and complications. Accordingly, benefiting from cloud computing is no exception to this rule and it has challenged researchers and proponents in this research domain. Indeed, some major challenges in cloud computing are: load balancing, safety, reliability, ownership, data backup, data portability and supporting several platforms. One challenge for such matters in the field of cloud computing is load balancing optimization in the cloud. The so-called cloud computing, including virtualization, distributed computing, networking, software and Web services. With respect to the ever increasing significance of load balancing in cloud computing the researchers in this paper intended to improve load balancing by using a novel method. The related studies were reviewed, evaluated and compared with each other. The efficiency of the proposed method was analyzed and compared with those of other studies. The results of the present study revealed that the proposed method is better than other dynamic virtual machine (VM) consolidation algorithms in terms of reducing SLA (service levels agreement) violation and the amount of transmitted data volume transmission has to present a better performance than other methods.

This paper explores the emergence and adoption of cloud computing by small and medium‐sized enterprises (SMEs) and points towards its implications for developing countries in sub‐Saharan Africa. Several studies have shown the importance of technologies to SMEs and the potentials of SMEs for economic growth. Using qualitative techniques we obtained and analysed data from ten SMEs that have adopted cloud computing as an IT strategy. These SMEs span across various sectors including finance, information and communication technology (ICT), and manufacturing in Nigeria, a developing country in sub‐Saharan Africa. We found that, contrary to the literature on cloud computing adoption in the global north, these SMEs are less concerned with challenges like security, privacy and data loss rather; they continue to show optimism in using the potential opportunities that cloud computing presents to them. We envisage that as cloud computing evolves, more SMEs in sub‐Saharan Africa will adopt it as an IT Strategy. This could positively contribute to the successes of these SMEs and consequently contribute to the economic growth desired by these developing countries.

Innovations are essential to ride the inevitable tide of revolutions. Most of enterprises are striving to reduce their computing cost through the means of virtualization. This demand of reducing the computing cost has led to the innovation of cloud computing. With the increasing number of companies resorting to employ resources in the cloud, the protection of the users' data is becoming a significant issue of concern. This report tackles this concern for enterprises in terms of security with intrusion detection while adopting cloud computing. The main aim of this research is to understand the security threats and identify a security technique used to mitigate them in cloud computing. The intrusion detection will be undertaken on the basis of anomaly-detection on the data generated from the transactions captured within the cloud network. The data captured will undergo intense data mining through clustering and then performing classification using BOAT algorithm to detect the presence of intruders on the cloud. The mining approach will consider various attributes of the data to scrutinize the user behaviour.

Cloud computing is formally known as an Internet-centered computing technique used for computing purposes in the cloud network. It must compute on a system where an application may simultaneously run on many connected computers. Cloud computing uses computing resources to achieve the efficiency of data centres using the virtualization concept in the cloud. The load balancers consistently allocate the workloads to all the virtual machines in the cloud to avoid an overload situation. The virtualization process implements the instances from the physical state machines to fully utilize servers. Then the dynamic data centres encompass a stochastic modelling approach for resource optimization for high performance in a cloud computing environment. This paper defines the virtualization process for obtaining energy productivity in cloud data centres. The algorithm proposed involves a stochastic modelling approach in cloud data centres for resource optimization. The load balancing method is applied in the cloud data centres to obtain the appropriate efficiency.

Online data storage technology over the cloud network has become an option for many organizations, even for personal use. The benefits of cloud computing enable many organizations, including the public sector, to use this technology to provide the best service experience. However, there is an issue with the implementation of cloud-based applications when their usage is less than the number of applications offered. Therefore, a study on the acceptance of cloud computing in the public sector should be conducted. This paper aims to evaluate the validity and reliability of the instrument for cloud computing acceptance in Malaysian public sectors. The developed instruments are analyzed through validity and reliability phases. The validity analysis phase involves two stages of face validity and expert validity. The Content Validity Index (CVI) is used, and the feedback of the panel is considered in improving the items used. The reliability phase was conducted by performing an analysis to evaluate Cronbach `alpha for each item and also testing using Exploratory Factor Analysis (EFA). The final instrument contained 71 items of 5-point Likert scale multiple-choice options, classified under 15 variables. As a result, this instrument is successfully validated and are reliable to be used in the actual data collection.

Before the invention of computers, humans tend to findthe calculation, remembrance of information difficult. Even though thecomputer reduced the burden of humans, the invent of distributed computing helped the computers to distribute the workload to other computers within the network. To optimize and evade the disadvantages of Distributed Computing, Cloud Computing was developed. Cloud computing tends to provide services like IaaS, PaaS through internet. To improve the scalability and dynamic feature of Cloud, the Software Defined Network can be embedded to form SDN Cloud. For the purpose of better understanding and representation a Self-Organizing Map is used for reducing the higher level representation data to lower level representation data. One type of Self Organizing Maps is known as Recursive Self Organizing Maps in which the time is representedusing the feedback. DDoS attacks, Data Breach and Data Loss are some of the several security flaws that are used by unauthorized users for illegal access into the cloud. This paper presents an extensive survey of the Recursive Self Organizing Maps and DDoS mitigation in SDN and cloud network.

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We'll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

<p>The Thai government has included health promotion in its national policies and strategies to directly address the health of the elderly. Multiple government organisations at various levels are involved in this health promotion policy and its related efforts. With an emphasis on ensuring that the elderly in the community benefit from national health promotion policies, and have access to health promotion services, the policies directed government organisations to work together as a network to implement the health promotion policy for the elderly at the local level. The Local Administrative Organisations (LAOs), decentralised government organisations, acted as the centre of the networks in each sub-district across the country. Networks play a role as an essential mechanism in the health promotion policy implementation for the elderly and in reaching out to the smallest unit of the community: individual older people. However, there are known gaps in the functioning of the decentralised governance arrangements and in coordination between organisations to implement the health promotion policy. Policy implementation can be improved to ensure that key goals and objectives are met. The objective of this research was to analyse the ways in which the LAOs and other government organisations together implement the health promotion policy for the elderly at the local level in Thailand. Using a network perspective, the governance structure and governance characteristics, including relationships and the functioning of the policy implementation network, are identified and analysed. How the observed network characteristics affect network collaboration, policy outcomes, and actors’ capacity in policy implementation are then explored. Within an interpretivist perspective, the research employed multiple network analysis approaches and mixed methods data collection such as network mapping, non-participant observation, interviews, and questionnaire surveys, across two case study sites. A combination of thematic analysis and constant comparative methods were employed to analyse the data. The networks in this study were found to have a hybrid governance form, being a combination of lead organisation-governed and shared governance. However, it is not possible to predict the likelihood of achieving good policy outcomes based on the form of network governance alone; other networks characteristics must also be studied. At the network level, influential factors indicative of policy outcomes were found to be the exchanges of political and cultural capitals between network actors, with the latter differentiating the policy outcomes across the two cases. To improve the network actors’ capacity in policy implementation, learning and resource exchanges between actors were found to be important. Based on the study findings, an intervention to improve policy outcomes should be encouraged through financial capital exchanges between network actors as this is when administrative authority is most dominant. The research provides an empirical review to inform policymakers and practitioners that the most influential factors should be embedded during the funding process so that the policy implementation can better support health for the elderly and the aged society that Thailand is entering.</p>