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Hybrid Cloud
Published in Curtis Franklin, Brian J. S. Chee, Securing the Cloud, 2019
Curtis Franklin, Brian J. S. Chee
The first dynamic capacity hybrid that we heard about publically was from Microsoft and was part of the Windows® Server® 2012 R2 rollout by the Microsoft® Server and Tools Division. The announcement demonstrated how the new Microsoft Azure® Services for Windows Server 2012 R2 could dynamically move virtual machine collections between the private data center and the Azure public cloud—all based upon a set of user-definable business rules.
An effective memetic algorithm for the distributed flowshop scheduling problem with an assemble machine
Published in International Journal of Production Research, 2023
Ying-Ying Huang, Quan-Ke Pan, Liang Gao
To evaluate the performance of the proposed EMA for solving the DAPFSP with criterion, experimental studies are carried out on the basis of benchmark instances from http://soa.iti.es. All experiments are programmed using C++ language in Visual Studio 2019. The operating environment is Intel (R) Xeon (R) CPU E5-2640 v4 @ 2.40 GHz with 3.99 GB RAM in the Windows Server 2012 Operation System. The values of different instances cannot be directly compared visually, the Relative Improvement Index (RDI), which is used in most literatures (Rossi and Nagano 2020; Ribas, Companys, and Tort-Martorell 2019), is applied as the response variable to make the comparison of experimental results more intuitive and occupy less space. Its calculation is as follows. where is the result of the current algorithm. and represent the lowest and highest of the values of all the algorithms respectively that need to be compared.
Optimization–based decoding algorithms for LDPC convolutional codes in communication systems
Published in IISE Transactions, 2019
Banu Kabakulak, Z. Caner Taşkın, Ali Emre Pusane
The computations were performed on a computer with 2.0 GHz Intel Xeon E5-2620 processor and 46 GB of RAM working under the Windows Server 2012 R2 operating system. In our computational experiments, we evaluate the performance of our sliding window decoders. In our decoders, the number of the constraints and decision variables in the EM formulation is limited by the size of the window. We make use of CPLEX 12.8.0 to solve the EM for the current window (see Step 1 of Algorithm 2). We compare the performance of our sliding window decoders with the Exact Model Decoder (EMD). In EMD, the EM formulation includes all constraints and decision variables corresponding to terminated LDPC–C code. That is, for a terminated LDPC–C code of size we have many Constraints (2) and many fi decision variables in EM. We again utilize CPLEX for solving the EM of the EMD.
An improved water wave optimization algorithm with the single wave mechanism for the no-wait flow-shop scheduling problem
Published in Engineering Optimization, 2019
Fuqing Zhao, Lixin Zhang, Huan Liu, Yi Zhang, Weimin Ma, Chuck Zhang, Houbin Song
The SWWO for the NWFSP was coded using MATLAB®. The simulation experiments were carried out on a personal computer with an Intel® Core™ i7-6700 central processing unit (CPU) at 3.4 GHz and 8 GB of memory, with a Windows Server 2012 Operating System. Owing to limited space, the parametric analysis of SWWO, analysis of neighbourhood structures, computational complexity for SWWO and evaluation criteria are described in the online Supplementary material. The performance of the SWWO compared with other algorithms is tested on three benchmark sets, consisting of three sets of instances: small-scale, Carlier's instances (Carlier 1978); medium-scale, Reeve's instances (Reeves 1995); and large-scale, Taillard's instances (Taillard 1993).