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![Dynamic load balancing in cloud computing [28].](profile/Samia-Gamoura/publication/298791638/figure/fig1/AS:344972680286210@1459259304717/Dynamic-load-balancing-in-cloud-computing-28_Q320.jpg)
The workload automation supported by cloudified environments begins to be agreed by the industry; however there are numerous prevailing challenges which have not been wholly addressed. Primary of these issues is load balancing that is mandatory to equilibrate workload of resources and avoid services unavailability. This paper presents a new model n...
Citations
... Our exploration of literature interconnecting supply chain processes and digital transformation through AI have helped us understand that one of the major complexities in these studies is initially considering the scope and objectives of these processes (Chehbi Gamoura 2016. This investigation has consequently led us to identify a distinction between these two scopes: the processes responsible for the optimal management of stakeholders and their resources on the organizational dimension, referred to as Organizational Management Processes (OMPs) as defined in (Lam 2021), and the processes responsible for the optimal management of flows through those stakeholders and resources, termed Industrial Management Processes (IMPs) as depicted in (Reddy 2007). ...
This paper explores the dynamic landscape of supply chain processes in the contemporary era, marked by the predominance of their deep digital transformation, primarily driven by Artificial Intelligence (AI). The integration of AI into supply chain processes presents a dual challenge, necessitating the incorporation of algorithms into Information Systems (IS) while accommodating the inherent complexity and uncertainty in these processes within the rapidly disruptive changes. Despite efforts to develop unified AI integration frameworks, current literature lacks a comprehensive standardized procedure, especially in supply chain research. This paper aims to tie this gap by introducing a unified, holistic framework for integrating AI into organizational and industrial processes amidst its deep digital transformation. To demonstrate its applicability in an industrial setting, this paper examines a case study within the French Electrical Industry to illustrate how the proposed framework can be applied.
Due to the continuous evolution of the Big Data phenomenon, data processing in Business Big Data Analytics (BBDA) needs new advanced load balancing techniques. This paper proposes a new algorithm based on a non-stigmergic approach to address these concerns. The algorithm imitates the behavior of a specific species of ants that perform by acoustics in situations of threats. Besides, the research methodology in this study presents a methodic filtration of the relevant metrics before carrying out the benchmarking trials of several ant-colony algorithms (i.e. makespan, response time, throughput, memory and CPU utilization, etc.). The experimentations' outcomes show the effectiveness of the proposed approach that might empower the research efforts in big data analytics, business intelligence, and intelligent autonomous software agents. The main objective of this research is to contribute in reinforcing the resilience of the Big Data processing environment for enterprises.
For more than decades, the central drivers of business growth have been technological tools. The most important of these is Artificial Intelligence (AI). We name the paradigm "Business Artificial Intelligence (BAI)" the category of AI approaches that can create profitable new business models, earnest business-values, and more competitiveness, particularly Machine Learning (ML). However, ML algorithms are plentiful, and most of them necessitate specific Data structures, particular applicability features, and in-depth analysis of the business context. Because of these considerations, an increasing number of industrial ML-based applications fail. Therefore, it is natural to ask the question of applicability limits and ML algorithms selection, giving the business context. Accordingly, in this chapter we propose the evidence of the "No-Free-Lunch" (NFL) theorems to understand ML use's applicability in business organizations.
