The Experts below are selected from a list of 2247 Experts worldwide ranked by ideXlab platform
Rijnveld Justus - One of the best experts on this subject based on the ideXlab platform.
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Maturity model for the IT-department when migrating to a Saas-Environment
2019Co-Authors: Rijnveld JustusAbstract:In a period where digitization rapidly influences the corporate world, Cloud Computing (CC) has emerged over the past years as well. CC refers to offering hardware, software, and data by a provider over a network and can benefit organizations enormously regarding cost efficiency, operational excellence, and innovation. Software-as-a-Service (Saas), a delivery model of CC, allows organizations to deploy and use complete applications over the internet, which are managed by an external provider. The use of Saas can bring firms several benefits, such as scalability, transparency of costs, access to high-end applications, and avoidance of up-front costs. However, it is unclear how the migration to Saas impacts the IT-department on an organization level, and how IT-departments should adapt to perform in a Saas-Environment. This research contributes to this knowledge gap by investigating which resources and capabilities of the IT-department should change for the IT-department to perform. Propositions were formulated based on a literature research and a questionnaire, and empirical data was collected by means of a case study research. The results show that financial assets, technological tools, organizational structure, management systems, skills, knowledge, organizational culture, contractual governance, and relational governance are important resources of the IT- department, that should change in order to perform. The insights gained from this study can support organizations’ decision-makers improving the organization of the IT-department when migrating to a Saas-Environment.Complex Systems Engineering and Managemen
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Maturity model for the IT-department when migrating to a Saas-Environment
2019Co-Authors: Rijnveld JustusAbstract:In a period where digitization rapidly influences the corporate world, Cloud Computing (CC) has emerged over the past years as well. CC refers to offering hardware, software, and data by a provider over a network and can benefit organizations enormously regarding cost efficiency, operational excellence, and innovation. Software-as-a-Service (Saas), a delivery model of CC, allows organizations to deploy and use complete applications over the internet, which are managed by an external provider. The use of Saas can bring firms several benefits, such as scalability, transparency of costs, access to high-end applications, and avoidance of up-front costs. However, it is unclear how the migration to Saas impacts the IT-department on an organization level, and how IT-departments should adapt to perform in a Saas-Environment. This research contributes to this knowledge gap by investigating which resources and capabilities of the IT-department should change for the IT-department to perform. Propositions were formulated based on a literature research and a questionnaire, and empirical data was collected by means of a case study research. The results show that financial assets, technological tools, organizational structure, management systems, skills, knowledge, organizational culture, contractual governance, and relational governance are important resources of the IT- department, that should change in order to perform. The insights gained from this study can support organizations’ decision-makers improving the organization of the IT-department when migrating to a Saas-Environment.Complex Systems Engineering and Management (CoSEM
Barry E Mullins - One of the best experts on this subject based on the ideXlab platform.
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cloud chamber a self organizing facility to create exercise and examine software as a service tenants
Hawaii International Conference on System Sciences, 2012Co-Authors: Brent M Reynolds, Don R Hulce, Kenneth M Hopkinson, Mark E Oxley, Barry E MullinsAbstract:Cloud Chamber is a test bed for understanding how web services behave as tenants in a Software as a Service (Saas) Environment. This work describes the Cloud Chamber test bed to investigate autonomic resource management of web services in a cloud Environment. Cloud Chamber is a virtualized Environment which provides web servers as services, facilities to apply loads to the tenant services, algorithms for autonomic organization and reconfiguration of service assignments as demand changes, and sensors to capture resource consumption and performance metrics. The test bed inserts sensors into web servers to collect the resource utilization of CPU cycles, memory consumption, and bandwidth consumption of the individual web services, the web server, and the operating system. This high resolution performance data generates profiles of the resource usage of each web service and the resource availability of each server. The test bed, as described in this work, utilizes these profiles to efficiently place services on servers, thus balancing resource consumption, service performance, and service availability. Once services have been placed, the test bed monitors changes such as traffic levels, server churn, and the introduction of new services. The information gathered is used to calculate configurations of service placement which better meet the changing requirements of the Environment.
Ahmed Hadj Kacem - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Provisioning of Service Composition in a Multi-Tenant Saas Environment
Journal of Network and Systems Management, 2020Co-Authors: Wael Sellami, Hatem Hadj Kacem, Ahmed Hadj KacemAbstract:Multi-tenant service composition has become a common delivery model for business processes in cloud computing. To dynamically support the workload tenant variation, elasticity holds the promise of ensuring the quality of service (QoS) of the business process by providing the involved service instances at a low cost. However, integrating both of multi-tenancy and elasticity during service composition is a key problem for serving multiple tenants from a single process instance. Nowadays, existing approaches in the field of cloud service composition, although numerous, still fall short since they cannot adequately address issues related to supporting the scalability of the composed service and adapting it to the workload fluctuation. In this paper, we propose a holistic approach which makes the dynamic multi-tenant services matching and manages their elasticity in distributed business processes. This approach is based on a generic service pattern that integrates multi-tenancy property and handles elasticity at the process and service levels. Furthermore, we present elastic composition algorithms to compose multi-tenant cloud services and perform their elasticity through the proposed service pattern. The evaluation of our approach, compared to the baseline approach, proves that the latency taken to provide an elastic multi-tenant service composition and detect its SLA (Service Level Agreements) violation are reasonably short. We also show that the CPU overhead of using our approach is negligible. Furthermore, experimental results demonstrate the merits of our approach in terms of minimizing the memory consumption through the deployed service instances.
Justus Rijnveld - One of the best experts on this subject based on the ideXlab platform.
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maturity model for the it department when migrating to a Saas Environment
2019Co-Authors: Justus RijnveldAbstract:In a period where digitization rapidly influences the corporate world, Cloud Computing (CC) has emerged over the past years as well. CC refers to offering hardware, software, and data by a provider over a network and can benefit organizations enormously regarding cost efficiency, operational excellence, and innovation. Software-as-a-Service (Saas), a delivery model of CC, allows organizations to deploy and use complete applications over the internet, which are managed by an external provider. The use of Saas can bring firms several benefits, such as scalability, transparency of costs, access to high-end applications, and avoidance of up-front costs. However, it is unclear how the migration to Saas impacts the IT-department on an organization level, and how IT-departments should adapt to perform in a Saas-Environment. This research contributes to this knowledge gap by investigating which resources and capabilities of the IT-department should change for the IT-department to perform. Propositions were formulated based on a literature research and a questionnaire, and empirical data was collected by means of a case study research. The results show that financial assets, technological tools, organizational structure, management systems, skills, knowledge, organizational culture, contractual governance, and relational governance are important resources of the IT- department, that should change in order to perform. The insights gained from this study can support organizations’ decision- makers improving the organization of the IT-department when migrating to a Saas-Environment.
Harry G Perros - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of the reserve capacity policy for dynamic vm allocation in a Saas Environment
Simulation Modelling Practice and Theory, 2019Co-Authors: Brian Bouterse, Harry G PerrosAbstract:Abstract We consider a periodic-review provision scheme with constant inspection intervals for allocating dynamically virtual machines (VMs) in a Software-as-a Service (Saas) Environment. At each interval, we determine how many virtual machines (VMs) to provisioned or de-provision using a simple heuristic referred to as the reserve capacity policy, since it maintains a fixed reserve capacity of VMs. We analyze the performance of the reserve capacity policy within the context of a periodic-review provision scheme using a Markov Chain embedded at the inspection intervals. We assume a single stream of jobs with each job requiring a single VM. Jobs arrive in a Poisson fashion and the execution time of a job in a VM is exponentially distributed. We calculate the probability distribution of the number of customers in the system, the number of in-service VMs, the utilization, and the queue-length distribution of the waiting customers. The embedded Markov Chain is solved numerically. For cases where the underlying transition matrix is very large, we have proposed approximations and showed that they have a root mean square error (RMSE) of less than 2%.
