- Combining models of capacity supply to handle volatile demand: The economic impact of surplus capacity in cloud service environments(published online: 16 January 2013)
- Location-aware brokering for consumers in multi-cloud computing environments(published online: 11 July 2017)
- Integration of supply networks for customization with modularity in cloud and make-to-upgrade strategy(published online: 20 Jun 2013)
- Multi-disciplinary and multi-objective simulation framework to support intelligent and smart manufacturing
- A user interface for large-scale demographic simulation(published online: 12 February 2015)
- Discrete Event Simulation and Virtual Reality Use in Industry: New Opportunities and Future Trends(published online: 18 August 2016)
- Modelling and simulation as a service architecture for deploying resources in the Cloud(published online: 7 March 2016)
- SimIC: Designing a new Inter-Cloud simulation platform for integrating large-scale resource management(published online: 18 June 2013)
- ISim: A novel power aware discrete event simulation framework for dynamic workload consolidation and scheduling in infrastructure clouds
- A simulation study of logistics for disaster relief operations(published online: 2 July 2015)
1. Dorsch, C. and Häckel, B., 2014. Combining models of capacity supply to handle volatile demand: The economic impact of surplus capacity in cloud service environments. Decision Support Systems, 58, pp.3-14.
ABSTRACT: In the paper at hand we analyse the capacity planning problem of a service vendor providing a business process characterized by volatile demand to his customers. Thereby, we consider the situation that the service vendor executes certain activities by himself whereas specific parts of the business process are outsourced to external providers. For the outsourced parts, the vendor can choose between different models of capacity supply (MCS) that are offered by external providers differentiating with respect to elasticity of provided capacity and the underlying pricing model. Thereby, in addition to the two “traditional” MCS dedicated capacity and elastic capacity, recent developments in information technology enable the on-demand use of surplus capacity from an external providers’ market. Since an integrated analysis of these three MCS is still missing in common literature, we develop an optimization model allowing for the simultaneous consideration of the three different MCS within an integrated queuing system. By analysing the optimization model with help of a discrete event simulation, we study the question of how these different MCS may be combined to minimize the total operating costs of the service vendor considering volatile demand. The simulation results show that combining different MCS tends to be favourable in contrast to the stand-alone usage of a certain MCS. In particular, combining the additional option of using surplus capacity with “traditional” MCS promises cost advantages. Our optimization model therewith provides first insights in the potential economic benefits of IT-enabled MCS.
2. Heilig, L., Buyya, R. and Voß, S., 2017. Location-Aware Brokering for Consumers in Multi-Cloud Computing Environments. Journal of Network and Computer Applications.
ABSTRACT: The variety and complexity in cloud marketplaces is growing, making it difficult for cloud consumers to choose cloud services from multiple providers in an economic and suitable way by taking into account multiple objectives and constraints. In this paper, we present an extension of CloudSim implementing cloud management functionality to enable the assessment of consumer-oriented brokering schemes. The underlying discrete-event simulation framework allows evaluating their performance in more realistic operating conditions in a repeatable manner. We integrate brokering mechanisms to support a multi-criteria location-aware selection of virtual machines in multi-cloud environments by implementing a greedy heuristic and two large neighbourhood search metaheuristics. Based on micro benchmarks of real cloud offerings and a diverse set of scenarios and workloads, we conduct simulation experiments to assess the performance of our approaches. The results show that approximately 10 – 12% of the total costs can be saved by using a large neighbourhood search approach compared to the greedy heuristic. Finally, we analyse and discuss the trade-off between costs and latency as well as the impact of region constraints, showing, e.g., that latency improvements often come at a high price and a greater regional flexibility can lead to latency improvements while solely optimizing costs. Using real data of cloud marketplaces, we show that the proposed CloudSim extension can support decision makers as a tool for assessing cloud portfolios and market dynamics.
3. Mehrsai, A., Karimi, H.R. and Thoben, K.D., 2013. Integration of supply networks for customization with modularity in cloud and make-to-upgrade strategy. Systems Science & Control Engineering: An Open Access Journal, 1(1), pp.28-42.
ABSTRACT: Today, integration of supply networks (SNs) out of heterogeneous entities is quite challenging for industries. Individualized demands are getting continuously higher values in the global business and this fact forces traditional businesses for restructuring their organizations. In order to contribute to new performances in manufacturing networks, in this paper a collaborative approach is recommended out of modularity structure, cloud computing, and make-to-upgrade concept for improving flexibility as well as coordination of entities in networks. A cloud-based framework for inbound and outbound manufacturing is introduced for complying with the production of individualized products in the turbulent global market, with local decision-makings and integrated performances. Additionally, the complementary aspects of these techniques with new features of products are conceptually highlighted. The compatibility of this wide range of theoretical concepts and practical techniques is explained here. A discrete-event simulation out of an exemplary cloud-based SN is set up to define the applicability of the cloud and the recommended strategy.
4. Longo, F., Nicoletti, L. and Padovano, A., Multi-disciplinary and multi-objective simulation framework to support Intelligent and Smart Manufacturing.
ABSTRACT: The proposed approach leverages on well-established paradigms (i.e. numerical simulation, discrete event simulation, etc.) as well as on new concepts (i.e. agent-driven simulation, distributed and interoperable simulation, human-computer vocal interaction) to promote the integration of the Industry 4.0 paradigm with the Intelligent (Smart) factory concept. The reduction of the time to production and optimization of the supply chains is a mandatory step for those manufacturing system that aims to respond and react quickly to the market demand as well as to the market instability. In such a context, the proposed framework seeks to push the available frontiers of knowledge forward proposing a holistic, multidisciplinary M&S approach. It can be used as predictive tool to see how changes implemented in the manufacturing system and along the supply chain may affect the overall system performances.
5. Montañola-Sales, C., Casanovas-Garcia, J., Onggo, B.S. and Li, Z., 2014, December. A user interface for large-scale demographic simulation. In Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on (pp. 723-726). IEEE.
ABSTRACT: Agent-based modelling is one of the promising modelling tools that can be used in the study of population dynamics. Two of the main obstacles hindering the use of agent based simulation in practice are its scalability when the analysis requires large-scale models as in policy research, and its ease-of use especially for users with no programming experience. While there has been a significant work on the scalability issue, ease-of use aspect has not been addressed in the same intensity. This paper presents a graphical user interface designed for a simulation tool which allows modellers with no programming background to specify agent-based demographic models and run them on parallel environments. The interface eases the definition of models to describe individual and group dynamics processes with both qualitative and quantitative data. The main advantage is to allow users to transparently run the models on high performance computing infrastructures.
6. Turner, C.J., Hutabarat, W., Oyekan, J. and Tiwari, A., 2016. Discrete Event Simulation and Virtual Reality Use in Industry: New Opportunities and Future Trends. IEEE Transactions on Human-Machine Systems, 46(6), pp.882-894.
ABSTRACT: This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within industry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This paper reviews active research topics such as VR and DES real-time integration, communication protocols, system design considerations, model validation, and applications of VR and DES. While summarizing future research directions for this technology combination, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization requirements of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recognition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets.
7. Wang, S. and Wainer, G., 2016. Modeling and simulation as a service architecture for deploying resources in the Cloud. International Journal of Modeling, Simulation, and Scientific Computing, 7(01), p.1641002.
ABSTRACT: In recent years, Cloud Computing has become popular to facilitate the use of Modelling and Simulation (M&S) resources. Nevertheless, there are still various issues to solve, including the structure constrain of chosen web service frameworks, the sharing of varied resources in the Cloud, and the difficulties in reproducing experiments. We show a new architecture based on Cloud Computing and new modelling methods to deal with these issues. This layered architecture, called Cloud Architecture for Modelling and Simulation as a Service (CAMSaaS), simplifies the deployment of M&S resources as services in the Cloud. CAM-SaaS supports hierarchical resource services, experimental frameworks, and scalable infrastructure and makes everything as a service. We deploy varied M&S resources as services in the Cloud, and build a Modelling and Simulation as a Service (MSaaS) middleware called Cloud-RISE to manage a variety of M&S resources. We also use the experimental framework concept to simplify the management of experiment environments. We present a case study for crowd evacuation application using the architecture.
8. Sotiriadis, S., Bessis, N., Antonopoulos, N. and Anjum, A., 2013, March. SimIC: Designing a new inter-cloud simulation platform for integrating large-scale resource management. In Advanced Information Networking and Applications (AINA), 2013 IEEE 27th International Conference on (pp. 90-97). IEEE.
ABSTRACT: Simulating the Inter-Cloud’ (SimIC) is a discrete event simulation toolkit based on the process oriented simulation package of SimJava. The SimIC aims of replicating an inter-cloud facility wherein multiple clouds collaborate with each other for distributing service requests with regards to the desired simulation setup. The package encompasses the fundamental entities of the inter-cloud meta-scheduling algorithm such as users, meta-brokers, local brokers, datacentres, hosts, hypervisors and virtual machines (VMs). Additionally, resource discovery and scheduling policies together with VMs allocation, re-scheduling and VM migration strategies are included as well. Using the SimIC a modeller can design a fully dynamic inter-cloud setting wherein collaboration is founded on meta-scheduling inspired characteristics of distributed resource managers that exchange user requirements as driven events in real-time simulations. The SimIC aims of achieving interoperability, flexibility and service elasticity while at the same time introducing the notion of heterogeneity of multiple clouds’ configurations. In addition, it accepts an optimization of a variety of selected performance criteria for a diversity of entities. The crucial factor of dynamics consideration has implemented by allowing reactive orchestration based on current workload of already executed heterogeneous user specifications. These are in the form of text files that the modeller can load in the toolkit and occurs in real-time at different simulation intervals. Finally, a unique request is scheduled for execution to an internal cloud datacentre host VM that is capable of performing the service contract. This is formally designed in Service Level Agreements (SLAs) based upon user profiling.
9. Jeyarani, R., Nagaveni, N., Srinivasan, S. and Ishwarya, C., 2013. ISim: A novel power aware discrete event simulation framework for dynamic workload consolidation and scheduling in infrastructure Clouds. Advances in Computing and Information Technology, pp.375-384.
ABSTRACT: Today’s cloud environment is hosted in mega datacentres and many companies host their private cloud in enterprise datacenters. One of the key challenges for cloud computing datacentres is to maximize the utility of the Processing Elements (PEs) and minimize the power consumption of the applications hosted on them. In this paper we propose a framework called ISim, wherein a Datacentre manager playing the role of a Meta-scheduler minimizes power consumption by exploiting different power saving states of the processing elements. The considered power management techniques by the ISim framework are dynamic workload consolidation and usage of low power states on the processing elements. The meta-scheduler aims at maximizing the utility of the cores by performing dynamic workload consolidation using context switching between the cores inside the chip. The Datacentre manager makes use of a prediction algorithm to predict the number of cores that are required to be kept in active state to fulfil the input service request at a given moment, thus maximizing the CPU utilization. The simulation results show, how power can be conserved from the host level till the core level in a datacentre with the optimal usage of different power saving states without compromising the performance.
10. D’Uffizi, A., Simonetti, M., Stecca, G. and Confessore, G., 2015. A simulation study of logistics for disaster relief operations. Procedia CIRP, 33, pp.157-162.
ABSTRACT: Natural events, climate change and urbanization are pushing the pressure on disaster relief operations. Decision science and ICT technologies can be effectively used to face humanitarian logistics issues. This concerns both man-made threats (accidents) and natural hazards such as e.g. floods, storms, earthquakes and volcanic eruptions. The purpose of this paper is to analyse the operation strategies for rescue in emergency situations deployed by specialized rescue teams. Usually standard procedures are applied for rescue operations. These procedures can fail in disaster relief situations where an abnormal number of rescue operations are to be fulfilled. It is important to study mechanisms able to give more flexibility to these procedures and to study the effectiveness of the procedures in planning phase. At this aim we used discrete event simulation as decision support for planning different strategies of action to apply in emergency and risk situations. In particular several scenarios have been developed as simulation models combining different initial hypothesis with the aim to build a generalized and flexible procedure to apply in different scenarios. As a result, the simulation is able to allocate efficiently different resources under emergency situations (multiple scenarios for specific events). Tests and sensitivity analysis have been performed using instances related to a GIS of the Italian Sicily region and a typical set of facilities and rescue team. The simulation system works considering the different typologies of vehicles and staff to choose the best solution available in that specific time. The work is part of a national research project aimed to develop cloud-based systems and sensor networks for multi-risk management. The developed simulation system can provide crucial help to the rescuers in order to planning the best relief strategy for mitigate the effects of natural and industrial disasters.