Exploring connections within the technology-based entrepreneurial ecosystem (EE) in emerging economies: understanding the entrepreneurship struggle in the Indonesian EE

Purpose Tech startup is the new hope for sustaining economic growth and job creation in a knowledge-based economy. However, research on the entrepreneurial ecosystem (EE) is always constructed upon macro-level analysis and is still very limited to the developed economies. This study aims to tackle those issues by exploring the connections within an EE in an emerging economies context with a micro and meso-level social network approach to unravel the pattern of networks and interactions between each actor in the EE. Design/methodology/approach This research used multi-layered social network analysis, exploring actors in the ecosystem and their interactions. The authors conducted interviews with startups, support organizations and government agencies. The authors used Atlas.ti software to visualize the network structures. Findings The authors found that the content of interaction within the EE in the emerging economies differs greatly with EE in the developed economies and they produced distinctive characteristics as follows: lack of a dense network, resource scarcities and structural gaps and weak institutional policies. Research limitations/implications The research is based on a case study of tech-based EE in Yogyakarta, Indonesia. Therefore, the authors encourage other researchers to investigate networks and connections in other EEs in emerging economies. This research contributes a conceptual framework to better understand the network of connections in an emerging-economies-based EE. Practical implications The research shows grants provision alone cannot contribute to the functioning of EE. The authors argue strategic networks which promote collaboration among actors can reduce holes and structural gaps, as well as resource scarcities in the ecosystem. In addition to that, strong institutional policies and effective policy integration are needed to create a successful EE. Social implications This research promotes the importance of networks, particularly networks between tech startups and strategic organizations to provide resources and support productive entrepreneurship in hopes of sustaining and accelerating tech startup growth within an EE. Originality/value The research proposes to add to the existing EE literature by shedding light on governance of EE, as well as exploring network of connection and interaction among actors within the ecosystem. As a result, the study addresses the need for a more micro or operational-level understanding of an EE. Recent calls for EEs literature have also focused on a certain actor’s dynamic function in the ecosystem. By focusing on the role of the government, the research added to the underdeveloped EE literature.

A Proposal to Distinguish DDoS Traffic in Flash Crowd Environments

A Flash Crowd (FC) event occurs when network traffic increases suddenly due to a specific reason (e.g. e-commerce sale). Despite its legitimacy, this kind of situation usually decreases the network resource performance. Furthermore, attackers may simulate FC situations to introduce undetected attacks, such as Distributed Denial of Service (DDoS), since it is very difficult to distinguish between legitimate and malicious data flows. To differentiate malicious and legitimate traffic we propose applying zero inflated count data models in conjunction with the Correlation Coefficient Flow (CCF) method – a well-known method used in FC situations. Our results were satisfactory and improve the accuracy of CCF method. Furthermore, since the environment toggles between normal and FC situations, our method has the advantage of working in both situations.

A Fat Client OS Architecture Supported by Semi-network Resources

Purpose–This study is to explore a way toretainthe strengths and eliminatethe weaknesses of the existingarchitecture oflocal OS and cloud OS,then create an innovativeone, which is referredto as semi-network OS architecture.Method–The elements of semi-network OS architecture includes networkresources, localresources, and semi-mobile hardware resources; among them, networkresources are the expanded portionof OS, which is used to ensure the scalability of OS; local resources are the base portion of OS, which is used to ensure the stability of local computing, as well as the autonomy of user operations; the semi-mobile hardware resource is OSPU, which is used to ensure the positioning and security of dataflow.Results–Thefat client OS relies on the network shared resources,local exclusive resources,and semi-mobilehardware resources (OSPU), not relies solely on a single resource, to perform its tasks on a fat client, in thisarchitecture, most of the system files of OS on a fat client isderived from OS server, which is a network shared resources, and the rest of system files of OS is derived from OSPUof a fat client, which is a non-network resource, so the architecture of OShas "semi-network" attribute, wherein the OSPU is a key subordinate component for data processing and security verification,the OS server is a storage place rather than operating a placeof system files, and system files that stored on a server can only be downloaded to a fat client to carry out their mission.Conclusion–A complete OS is divided into base portion and expanded portion, and this "portion" division of OS enables a fat client to be dually supported by remote network resources and local non-network resources, therefore, it is expected to make a fat client more flexible, safer and more reliable, and more convenient to be operated.

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

Static/dynamic input-based event-triggered control for a network resource constraint environment

The work proposes static and dynamic input-based event-triggered controllers for a network resource-constrained environment. The controller is designed for a discrete-time system using a low-gain approach, where feedback gain is designed as a function of a user-defined parameter. Depending on the event density, the low-gain parameter can be adjusted to increase the inter-event time between two consecutive events at a particular instant. Thus the demand for computational and network resources can be reduced

Machine Learning Based User Retention and Channel Allocation: 6G Aspect

Future 6G wireless network will be focused on Artificial Intelligence (AI) based network selection, resource allocation and user satisfaction. A user has multiple options to switch one service provider to another service provider in case of network quality degradation. The new schemes/policies are required to retain their valuable users. This paper proposed supervised machine learning methods such as Decision Tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), etc., to classify and identify the loyal user. The decision tree algorithm has been identified as the best classification technique in order to identify the type of user (loyal, normal, and recent). A threshold-based algorithm is proposed to allocate the resource, particularly to loyal users. The performance of the algorithm is measured in terms of average waiting time (AWT), and the number of particular types of user’s services dropped. Priority is given to the loyal user when only 10% network resource is available. The simulation environment is created by SimPy implemented in Python. The result of the simulation run represents that no loyal user’ services have been interrupted during communication. Loyal users achieved less AWT as 32.51s compare to the normal user and recent user.

An Edge-Based Approach for Virtual Network Embedding Based on the Graph Edit Distance

Virtualization and resource isolation techniques have enabled the efficient sharing of networked resources. How to control network resource allocation accurately and flexibly has gradually become a research hotspot due to the growth in user demands. Therefore, this paper presents a new edge-based virtual network embedding approach to studying this problem that employs a graph edit distance method to accurately control resource usage. In particular, to manage network resources efficiently, we restrict the use conditions of network resources and restrict the structure based on common substructure isomorphism and an improved spider monkey optimization algorithm is employed to prune redundant information from the substrate network. Experimental results showed that the proposed method achieves better performance than existing algorithms in terms of resource management capacity, including energy savings and the revenue-cost ratio.

A price-aware congestion control protocol for cloud services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.