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Elastic optical networks (EONs) virtualization can allow the virtual optical networks (VONs) to utilize all the physical resources of EONs, and can attain a high performance of the networks. However, the optimal scheme for VONs mapping should be determined. To tackle these challenges, a bi-level mathematical model is established. leader's and follower's objectives are to minimize energy consumption and the maximum index of used frequency slots, respectively. The bi-level mathematical model can determine the optimal schemes of VONs mapping. To solve the mathematical model effectively, a uniform design method is applied to generate initial population for the lower level problem. In addition, To solve the whole model effectively, a tailor-made encoding, population initialization, genetic operators and local search operator are designed. An efficient genetic algorithm with local search operator is proposed for the bi-level mathematical model. To evaluate the mathematical model and the designed algorithm, a large number of experiments are performed on three kinds of the widely used networks, and the experimental results indicate that the effectiveness of the proposed bi-level mathematical model and designed algorithms.

Time discretization is an important part of time-varying problems solving that determines convergence, real-time performance and accuracy for solution models. It is a challenging work compared with relatively simple derivative approximation due to unknown future information and stability constraint. To the best of the authors’s knowledge, no effective time discretization was developed other than Euler finite difference formula before recently development of ZeaD formulas. Existing work presents some ZeaD formulas including specific time-discretization formulas having third order accuracy. In this work, $N$ -instant general third-order-accuracy formula is proposed, and it leads to different general third-order-accuracy formulas when different instant number $N$ is considered. Stability and convergence are analyzed, and effective domains for parameters in 5-instant and 6-instant general third-order-accuracy formula are given to guarantee effective time discretization. Furthermore, $N$ -instant general third-order-accuracy formula is employed to solve time-varying optimization, and $N$ -instant general solution model is proposed. Finally, comparative experimental results are presented to substantiate the effectiveness and superiority of proposed general formulas and models.

Virtual network function (VNF) can provide various network services and is widely deployed in inter-data centers elastic optical networks (Inter-DC EONs). Routing and VNF deployment for VNF service chain (VNF-SC) in Inter-DC EONs is a very important and well-known NP-hard problem. For this problem, if determining the number and locations of data centers is additionally considered, it will be more complicated. In this paper, we investigate a network planning problem in Inter-DC EONs by determining all these factors, i.e, by determining not only the optimal routing and the optimal VNF deployment for VNF-SCs, but also the optimal number and locations of data centers. To achieve this purpose, we first establish a bi-level programming model in which the leader's objective is to minimize the number of data centers and find the best locations of data centers so that we can get a balanced VNF deployment on data centers. To determine the optimal routing and VNF deployment for VNF-SCs, the follower's objective is to minimize the maximum index of used frequency slots and the number of used frequency slots. Then, to solve the proposed model effectively, tailor-made crossover, mutation and local search operators are designed, and based on these operators, an efficient bi-level hybrid memetic algorithm (BiHMA) is proposed. Finally, to test the effectiveness of the proposed model and the efficiency of the proposed algorithm, the simulation experiments are conducted on two widely used networks, and experimental results indicate that the proposed algorithm has a higher efficiency than compared algorithms.

Signcryption is a basic cryptographic primitive that simultaneously captures the functions of encryption and signature. To realize comprehensive information security against quantum computing attacks, lattice-based signcryption schemes have been successively proposed. However, the performance of signcryption schemes should be improved in the lattice setting. An efficient lattice-based signcryption scheme in the standard model is proposed in this paper. Under the ring learning with errors (RLWE) assumption and the ideal short integer solution (ISIS) assumption, the proposed signcryption scheme achieves indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) and existential unforgeability under an adaptive chosen-message attack (EUF-ACMA). Our scheme not only reduces the communication and computational overhead but also realizes a new design that combines the partitioning technique with the idea of tag-based key encapsulation. The performance analysis results show that our scheme is more efficient than previous lattice-based signcryption schemes in the standard model.

The resource allocation for bag-of-tasks in the heterogeneous distributed system is to distribute the tasks to proper processors such that the makespan is minimized. It is a well-known NP-hard problem, and is even more complex and challenging when the processors have off-line time. To tackle this challenging problem, first, we set up a mathematical model for this problem which minimizes the makespan of the bag-of-tasks with the off-line time segment of the processors. Second, to solve the model efficiently, we propose two new algorithms: a new scheduling algorithm referred to as sorting-allocation-pulling scheduling algorithm which first allocate the tasks to available time segment on proper processors and then pulls them to the formerly available time segment for the sake of minimizing the makespan, and an effective genetic algorithm with a novel local search operator and a well-designed modify operator. Finally, the numerical simulation experiments are conducted, and the two proposed algorithms are compared. The experimental results indicate the effectiveness of the proposed model and algorithms.

Divisible task fault-tolerant scheduling problems for a heterogeneous system on a general and realistic platform are addressed in this paper, where the communication is in non-blocking message receiving mode, and the processors and communication links may have different speeds and startup overheads. For this kind of problems, the optimal sequence and the fraction of task for each processor are derived first when the fault checkout overhead and checkout time consumption are considered. Then, to decrease the time consumption and checkout overheads, a checkout strategy, which is more suitable for divisible task, is employed. Moreover, an efficient algorithm with the fault fraction units re-allocated is proposed. Finally, the experiments on some simulation examples are conducted and the experimental results indicate that the proposed algorithm is effective, can minimize the expected execution time, and can save the time on fault-tolerant consumption.

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [ABSTRACT FROM AUTHOR]

As a new cloud service mode, mobile edge computing distributes the traditional centralized deployment and management of cloud resources to the wireless access network. Mobile services can be processed for the sake of obtaining a good service experience and reduce the network load of the backtrip network. In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing (MEC) network, an improved virtual network function (VNF) service chain deployment algorithm based on Q-learning was proposed. Based on the planning model, a spatial-temporal optimization model of VNF service chain deployment was established by markov decision process, and an improved BPQ-learning algorithm was designed to solve the model. The method considered both the virtual deployment of MEC service chain in space dimension and the life cycle management of VNF in time dimension. The optimization of VNF deployment is realized. The possible network congestion is avoided by deployment service nodes in advance. Experimental results show that the proposed strategy can provide end-to-end services with lower latency and better experience for delaysensitive mobile services under different VNF service chain scale, service node scale, cluster number and logical connection relationship among the virtual network functions. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of next generation communication. Resource scheduling for virtual network function service chain (VNF-SC) mapping is the key issue of the NFV. A virtual network function service chain placement multi-objective optimization model and algorithm based on improved genetic algorithm is proposed. Firstly, a multi-objective optimization model, which minimizes deployment cost, transmission delay and maximizes the load balance, is established. On this basis, an improved genetic algorithm based on MOEA/D is proposed to solve the established multiobjective model. In this algorithm, the combination scheme and mapping scheme of the service request are coded by the two-level coding method in the mapping process, and then the improved sparrow search algorithm is used to obtain the service function chain deployment scheme of the request and calculate the mapping weight. In addition, an efficient individual generation strategy is proposed to generate some superior individual. Finally, some simulation experiments are conducted and the experimental results show that the algorithm can effectively reduce deployment cost and transmission delay than the compared algorithm. [ABSTRACT FROM AUTHOR]

A fuzzy identity-based signature (FIBS) allows a user with identity i to produce a signature that can be verified under identity j when and only when i and j are close to each other. Lattice-based cryptography is thus of high importance. Aiming to solve the unwanted disclosure of biometrics data in the biometrics applications and to enhance the computing efficiency and authentication security, adaptive security based on FIBS from lattices is proposed. On the basis of the definition and security model of the fuzzy identity fully homomorphic signature (FIFHS), the key homomorphism and partitioning technology can be used to assign values to any circuit of the signature message. The correctness and security of the scheme are derived and proved strictly, and the adaptive security existential unforgeability of the scheme under adaptive chosen message and identity attacks is reduced to the module short integer solution (MSIS) problem, which is as difficult as approximating the worst-case module-generalized independent vectors problem (Mod-GIVP). [ABSTRACT FROM AUTHOR]

With the rapidly deployment of 5G communication, network functional virtualization has became one of key technology. However, how to achieve virtual network function service chaining (VNF-SC) adaptively and cost-effectively in an inter datacenter elastic optical network (interDC-EON) has become an interesting and challenging problem. In addition, network function virtualization has became one of the key technology for 5G Commons. In this paper, we propose a resource scheduling framework based on artificial intelligence. In addition, an effective VNF-SC deployment algorithm based on deep reinforcement learning (RL) is designed. In this algorithm, VNF-SC deployed scheme is obtained by using deep Q-learning according to the state of the network and the reward of the action. To verify the efficient of the proposed algorithm, a large number of experiments have been conducted. Experimental results demonstrate that the RL achieves better performance than several benchmarks, in terms of balancing the tradeoff among the overall resource utilization, the vNF-SC request blocking probability. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of 5G communication. Resource scheduling for virtual network function service chain (VNF-SC) is the key issue of the NFV. In this paper, the problem of routing and VNFs deployment for VNF service chain (VNF-SC) in inter-data center elastic optical networks (inter-DC EONs) is investigated.We investigate the inter-DC EONs that each data center only can provide some specific VNFs, and the system resources of all the data centers are limited. In addition, part of requiblack VNFs in each VNF-SC is dependent, i.e., the relative order of requiblack VNFs is fixed. To solve the challenging problem, we first establish a mix-integer programming model. Then, to make the population distributed on the search domain uniformly, uniform design method was developed. In addition, an improved artificial raindrop algorithm (IARA) for the established model, which inspired by particle swarm optimization and differential evolution, is proposed to solve the maximum model efficiently. Finally, to demonstrate high performance of the designed algorithm, a series of experiments are conducted in several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain the better results than compared algorithm. [ABSTRACT FROM AUTHOR]

Virtual network function (VNF) is the key issue and can provide various network services and is widely deployed in 5G communication. Routing and VNF deployment for the VNF service chain (VNF-SC) is a very important and wellknown NP-hard problem. For this problem, if determining the number and locations of data centers is additionally considered, it will be more complexity. In this paper, we investigate a network planning problem by determining all these factors, i.e, by determining not only the optimal routing and the optimal VNF deployment for VNF-SCs, but also the optimal number and locations of data centers. To achieve this purpose, a three objectives optimization model, which minimizes capital expenditure, the maximum index of used frequency slots and the number of deployed VNFs on all data centers, is estimated. To solve this model efficiency, we integrate three objectives into one objective by using a weighted sum strategy. Then, a high-performance memetic algorithm with three populations (tp-MA), which includes well-designed crossover, mutation, and local search operators, is proposed. To demonstrate reasonable of the model and high performance of the designed algorithm, a series of experiments are conducted in several different experimental scenes. Experimental results indicate that the effectiveness of the proposed model and the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The resource allocation for tasks in heterogeneous distributed system is a well known NP-hard problem. For the sake of making the makespan is minimized, it is hard to distribute the tasks to proper processors. The problem is even more complex and challenging when the processors have unavailable time and the tasks type are various. This paper investigates a resource allocation problem for hybrid tasks comprising both divisible and bag-of-tasks(BoT) in heterogeneous distributed system when the processors has unavailable time. First, the mathematical model, which minimizes the makespan of the hybrid tasks when the processors have unavailable time, is established. Second, we propose a scheduling algorithm referred to as bag-of-tasks allocate-pull and divisible task allocation (BoTAPDTA) algorithm for handling hybrid tasks on heterogeneous distributed systems. In addition, to solving the optimization model efficiently, a generic algorithm(GA) is proposed. For the sake of reducing the search space and solving the optimization model effectively, a two step scheduling algorithm(TSGA), which first allocate bag-of-tasks(BoT) using generic algorithm and then assign divisible task to processors like BoTAPDTA, is designed. Finally, numerical simulation experiments are conducted, and experimental results indicate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]