# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 1 Introduction  
As new businesses emerge and existing businesses expand, there is a heavy reliance on the need to keep costs low and maximise profits. Queueing systems play a fundamental role that helps businesses within the supply chain domain reduce cost by supporting high availability, resilient connectivity and operational efficiency. Further costs can be achieved by adopting smartness to these existing queueing applications and Supply Chain environments that will advance the Intelligence Age of these systems [1].  
When demand exceeds supply, queueing systems provide a more streamlined experience by preventing job loss and supporting queue and job prioritisation. However, like all computing systems, queues are not immune to performance and reliability problems, including latency, bottlenecks, scalability, and the challenges faced with the unpredictable arrival of incoming messages [2]. Real-word queueing systems also have challenges not encountered by abstract models, such as re-processing failed jobs and malformed messages.  
Queueing systems can support the effective passing of business-to-business (B2B) transactions through a supply chain network. With the demand for digital having goneglobal, there is an increased need for digitised environments that can support these B2B transactions alongside their associated paperless contracts and traceability of goods.  
Millions of B2B messages can be ingested by a queueing application. When modelling these messages, it is imperative that we can capture the real-time aspects, including the temporal ordering of message arrivals, which was first studied by [PERSON] [3]. Rounding or truncating an event's timestamp at the time of writing to a systems log file or software application can obfuscate the exact ordering of events, cause quantisation noise and lead to challenges fitting particular distributions [4].  
Stress testing queueing systems is crucial to uncovering functional and performance problems in large-scale systems. The messages order, volume, pace, and dependencies are critical for distribution modelling. Understanding these features is important before applying queueing models, such as the GI/GI/1 model, which assumes message independence of the inter-arrival and service times [5]. If messages are dependent whereby the service times are dependent on the messages' inter-arrival times, then a G/G/1, in its most general sense, might be more appropriate.  
For any queueing system, different products are always looking for ways to make product improvements. IBM has developed \"Uniform Clusters\". It can automatically create connection channels and support horizontal scaling across multiple matching queues [6]. It does, however, have some limitations. Load balancing problems may occur if there are more queue managers than applications [7].  
Another application that IBM has developed for MQ is \"Streaming Queues,\" which allows copying every message to a second queue. If the customer wants a carbon copy of their data for later retrieval, it can be used [8].  
In Apache Kafka, a broker may be alive but unable to establish a new connection due to a failed DNS resolution which may be hard to detect. In the latest 3.1 release, new functionality has been implemented to help identify this type of problem by including KIP-748 [9].  
\"Pre-staging messages at a remote location\" is an innovative patent that, if applied, supports remotely storing a vast number of messages in a data storage system that will form a message queue. Many queue managers can then access these messages [10].