A Simulation Model for Decision Support in Business Continuity Planning

Enterprises with a global supply network are at risk of lost revenue as a result of disruptive disasters at supplier locations. Various strategies exist for addressing this risk, and a variety of types of research has been done regarding the identification, assessment and response to the risk of disruption in a supply chain network. This thesis establishes a decision model to support Business Continuity Planning at the first-tier supplier level. The decision model incorporates discrete-event simulation of supply chain networks (through Simio software), Monte Carlo simulation, and risk index optimization. After modeling disruption vulnerability in a supply chain network, costs of implementing all combinations of Business Continuity Plans are ranked and then tested in discrete-event simulation for further insight into inventory levels, unmet customer demand, production loss and related costs. A case study demonstrates the implementation of the decision support process and tests a historical set of data from a large manufacturing company. Discrete-event simulation modeling of loss is confirmed to be accurate. The relevance of the model concept is upheld and recommendations for future work are made

QoS Based Service Selection and Provisioning in Cloud Computing

Cloud computing has become a disruptive technology which has seen significant growth among consumers of various sizes. Consumers can now have access to seemingly unlimited computing resources over the Internet without making significant investment in computing infrastructure. Consequently, this trend has seen a rise in the number of cloud computing providers. Most of these providers offer various services to consumers, which are commonly classified into three main types of service provisioning models such as, Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Cloud computing providers offer multiple services to consumers using one or more of models. However, the large number of services offered by cloud providers introduces a new set of problems for the consumers. Consumers have to choose from a wide range of services and providers such that they meet consumers’ requirements. The problem is further complicated as a large number of consumers do not necessarily have adequate knowledge of cloud services’ concepts and terminologies. To add to the complexity, there is no standard benchmark for cloud services. Therefore cloud providers can package the same cloud services in different ways for consumers. Furthermore, there are no standard service level agreements defined for cloud services selection. Each cloud provider has different service level agreements which make consumer selection process more cumbersome. This research aims to bridge this gap by proposing and developing new methods and techniques that take into account different cloud services and providers as well as quality of services attributes that make it easier for consumers to rank and select cloud services which are tailored to their requirements. This thesis makes various contributions to the current state of knowledge in the cloud service selection and provisioning area. It proposes a new model in order to systematically represent the quality of service (QoS) attributes of cloud services that cover both technical and non-technical aspects of cloud computing. The new model succinctly represents QoS attributes which cloud consumers can easily use and understand when selecting cloud services. The thesis also proposes a new framework for cloud service selection which improves and simplifies the cloud service selection process. It takes into account the level of user’s knowledge of cloud computing technologies. The major benefit is to simplify the selection process for ‘Beginner’ cloud service consumers (who have little knowledge of cloud computing) by presenting the main QoS attributes to them with brief explanations. The other benefit is to give an Intermediate/Expert cloud service consumers an opportunity to go through more details of QoS sub-parameters. Unlike existing approaches, the ii framework developed in this thesis also ensures the credibility of the service selection by utilizing information from three different sources, including, information from cloud service providers’ websites, online monitoring tools and users’ reviews of cloud services. Furthermore, the framework integrates Service Level Agreement (SLA) which is an integral part of cloud services as it is important for the consumer to be able to view it as part of their decision making process. The framework is validated by developing a prototype tool using Python, MongoDB and Amazon AWS EC2 server. The tool is then evaluated using various real life scenarios to rank cloud service providers and also by comparing it against existing tools. The results show that the proposed tool outperforms existing tools using a set of criteria such as operability, mode of data selection and number of cloud providers among others for ranking and selecting cloud services