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Copyright of JMD: Journal für Mathematik-Didaktik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The evolvement of data intensive applications in the cloud computing platform increased the burden of the cloud and resulted in the violation of the service level agreement (SLA). Moreover, the single cloud scenario offers no flexibility to switch between cloud service providers (CPs), leading to limited resource utilization and slower execution. To resolve these issues, this article introduces a hierarchical framework for task scheduling in a federated cloud scenario with multiple datacenters (DCs). The dynamic tasks with different resource requirements are considered to arrive at the federated cloud environment for successful execution. Initially, the task agent receives the tasks and clusters them using the proposed enhanced density peaks clustering (EDPC) algorithm. The proposed clustering algorithm clusters the tasks based on their resource requirements and results in two clusters such as CPU demanding tasks and I/O demanding tasks. After clustering, the tasks are sent to the federation layer, where the scheduling decisions are taken for the tasks. The federated cloud manager (FCM) within the cloud service provider (CP) communicates with the available DCs regarding resource availability. If the resources cannot satisfy the needs, they extend communication with the neighboring FCMs within the federation to complete the execution of tasks. After acquiring the availability status of the entire federation, the FCM executes the African vultures optimization algorithm (AVOA) based task scheduling framework to make the scheduling decision. The performance of the proposed model is evaluated using the GWT T-12 Bit Brains dataset by implementation in the CloudSim tool. The proposed model proved its efficacy over the compared metaheuristics in different metrics. [ABSTRACT FROM AUTHOR]

The classification of development tasks for innovative industrial clusters in regional innovation systems is considered. The importance of such clusters in regional economic development is established. A classification of development tasks in four planes is proposed. Each plane corresponds to a different direction of development. The benefits of cluster development are identified at the level of the cluster constituent, the cluster, and the region. [ABSTRACT FROM AUTHOR]

Principles of goal setting are proposed for use in developing innovative industrial clusters. The need for a cluster-based economy is established, and the influence of the goals on the process and outcome of cluster development is investigated. A definition is offered for the goals of cluster development. The effects of the goals are considered at the micro-, meso-, and macroscopic levels, not only for cluster participants but for the regional innovation system. [ABSTRACT FROM AUTHOR]