The paper will present the key role of the cloud solution for obtaining scalability, interoperability and flexibility, and for enabling the integration of different services for the automation of the distribution system.
In summary, all of the three in-situ schemes obtain high scalability in network size.
In order to obtain better scalability, the proposed method makes use of a dynamic data partitioning method.
The aim of cJVM is to obtain improved scalability for Java server applications by distributing the application's work among the cluster's computing resources.
A variety of techniques addressed to obtain a large scalability on the Xeon Phi were implemented in p DPM. Maximum scalabilities of 84.2× and 107.5× were obtained in the Xeon Phi for simulations of electron and photon beams, respectively.
Another issue is that to obtain a reasonable scalability with the Intel Phi coprocessor (Phi is the coprocessor that implements the MIC architecture) in a cluster configuration it is necessary to use the so-called offload mode which reduces the performances of the single system.
Moreover, we obtain insights about the scalability of the network coding gain with the network size.
In this paper, to obtain a high factor of scalability, we partition network search space using a consistent static shared upper ontology.
Below, we determine a suitable number of scalable basis filters, J, derive the closed-form C j (r), and obtain the interpolation functions for steerability and scalability.
We demonstrate our methods with two examples, namely quantum phase estimation and quantum-gate design, for which we achieve superior fidelity and scalability than obtained using greedy algorithms.
