Redundancy and scalability for virtualized MES systems with programmable infrastructure

• We introduce the vMES virtualization layer for manufacturing execution systems.
• Workload identification at MES layer according to ISA-95.03 functions.
• We present a process for private cloud redundancy and scaling for MES workload.
• Experimental results showing perturbation handling in cloud environment.
• Benefits of virtualization and programmable infrastructure for MES workloads.

Virtualization of manufacturing execution system (vMES) workloads offers a set of design and operational advantages to enterprises, the most visible being improved resource utilization and flexibility of the overall solution. This paper explores redundancy and scalability, as other important operational advantages introduced by the use of private clouds for MES virtualization in the context of the programmable infrastructure (PI) concept. PI is a new architectural approach in which the computing infrastructure, represented by resources, networks, storage, becomes dynamic and is controlled by the application, in contrast with traditional architectures where the application has to adapt to a static infrastructure. For MES applications, the adoption of PI has the potential to add a new layer of flexibility and optimization by allowing quick configuration and re-configuration based on environmental changes, especially in the context of virtualization in private cloud where workloads can be provisioned and de-provisioned in real time. In this context, this paper presents the main redundancy and scalability requirements for the workloads identified in ISA-95.03 based solutions and discusses in detail the strategies to assure the redundancy and scalability requirements of these workloads both individually and at the system level. The main contributions of this paper are therefore the introduction of PI combined with private cloud virtualization at the MES layer in order to achieve redundancy and scalability of the control solution. The pilot implementation presented is based on PI concepts and is realized in practice using SOA BPEL and IBM CloudBurst REST APIs. The MES system considered for the pilot implementation adopts a multi-agent vMES architecture having COBASA-type functionality. The experimental results presented in this paper show the system response in a set of failure scenarios, with focus on the reconfiguration time of workloads, and the dynamic response to perturbations in the system.