Reconsidering the Relationship between Cloud Computing and Cloud Manufacturing

International audienceHistory shows many relations between computer science and manufacturing processes, starting with the initial idea of " digital manufacturing " in the 70's. Since then, advances in computer science have given birth to the Cloud Computing (CC) paradigm, where computing resources are seen as a service offered to various end-users. Of course, CC has been used as such to improve the IT infrastructure associated to a manufacturing infrastructure, but its principles have also inspired a new manufacturing paradigm Cloud Manufacturing (CMfg) with the perspective of many benefits for both the manufacturers and their customers. However, despite the usefulness of CC for CMfg, we advocate that considering CC as a core enabling technology for CMfg, as is often put forth in the literature, is limited and should be reconsidered. This paper presents a new core-enabling vision toward CMfg, called Cloud Anything (CA). CA is based on the idea of abstracting low-level resources, beyond computing resources, into a set of core control building blocks providing the grounds on top of which any domain could be " cloudified "

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Short-comings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature