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Network slicing is a fundamental architectural technology for the fifth generation mobile network. It is challenging to design a robust end-to-end network slice spanning overall networks, where a slice is constituted by a set of virtual network functions (VNFs) and links. Bugs may accidentally occur in some VNFs, invalidating some slices, and triggering slice recovery processes. Besides, the traffic demands in each slice can be stochastic, and drastic changes of traffic demands may trigger slice reconfiguration. In this paper, we investigate robust network slicing mechanisms by addressing the slice recovery and reconfiguration in a unified framework. We first develop an optimal slice recovery mechanism for deterministic traffic demands. This optimal solution is used as a benchmark for evaluating other robust slicing algorithms. Then, we design an optimal joint slice recovery and reconfiguration algorithm for stochastic traffic demands by exploiting robust optimization. To tackle the slow convergence issue in the robust optimization algorithm, we propose a heuristic algorithm based on variable neighborhood search. Numerical results reveal that our proposed robust network slicing algorithms can provide adjustable tolerance of traffic uncertainties compared with the deterministic algorithm. [ABSTRACT FROM AUTHOR]

Workload management is the discipline of effectively monitoring, managing and controlling work flow across computing systems. In particular, workload management in database management systems (DBMSs) is the process or act of monitoring and controlling work (i.e., requests) executing on a database system in order to make efficient use of system resources in addition to achieving any performance objectives assigned to that work. In the past decade, workload management studies and practice have made considerable progress in both academia and industry. New techniques have been proposed by researchers, and new features of workload management facilities have been implemented in most commercial database products. In this paper, we provide a systematic study of workload management in today's DBMSs by developing a taxonomy of workload management techniques. We apply the taxonomy to evaluate and classify existing workload management techniques implemented in the commercial databases and available in the recent research literature. We also introduce the underlying principles of today's workload management technology for DBMSs, discuss open problems, and outline some research opportunities in this research area. [ABSTRACT FROM AUTHOR]