A Review Of Cloud Manufacturing: Issues And Opportunities

Cloud Manufacturing (CM) is the latest manufacturing paradigm that enables manufacturing to be looked upon as a service industry.The aim is to offer manufacturing as a service so that an individual or organization is willing to manufacture products and utilize this service without having to make capital investment.However,industry adoption of CM paradigm is still limited.This paper compared the current adoption of CM by the industry with the ideal CM environment.The gaps between the two were identified and related research topics were reviewed. This paper also outlined research areas to be pursued to facilitate CM adoption by the manufacturing industry.This will also improve manufacturing resource utilization efficiencies not only within an organization but globally.At the end,the cost benefits will be passed down to end customer

A Review of Cloud Manufacturing: Issues and Opportunities

Cloud Manufacturing (CM) is the latest manufacturing paradigm that enables manufacturing to be looked upon as a service industry. The aim is to offer manufacturing as a service so that an individual or organization is willing to manufacture products and utilize this service without having to make capital investment. However, industry adoption of CM paradigm is still limited.  This paper compared the current adoption of CM by the industry with the ideal CM environment.  The gaps between the two were identified and related research topics were reviewed.  This paper also outlined research areas to be pursued to facilitate CM adoption by the manufacturing industry.  This will also improve manufacturing resource utilization efficiencies not only within an organization but globally.  At the end, the cost benefits will be passed down to end customer

Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. When compared with genetic algorithm (GA) and fast elitist non-dominated sorting genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO algorithm is more effective than the existing solutions to solve the problem of resource allocation with a large number of the requested tasks. Furthermore, we provide the performance comparison of the HMAO algorithm with the first-fit greedy approach in on-line resource allocation