This paper presents robust optimization models for minimizing the required backup capacity while providing probabilistic protection against multiple simultaneous failures of physical machines under uncertain virtual machine capacities in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties: failure event and virtual machine capacity. By adopting a robust optimization technique, we formulate six mixed integer linear programming problems. Numerical results show that for a small size problem, our presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty. A simulated annealing heuristic is presented to solve large size problems. By using this heuristic, an approximate solution is obtained for a large size problem.

In this paper, we consider decouple-and-forward (DCF) relaying, where the relay encodes and amplifies decoupled data using orthogonal space-time block codes (OSTBCs), to achieve the maximum diversity gain of multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying. Since the channel status of all antennas is generally unknown and time-varying for cooperation in multi-antenna multiple-relay systems, we investigate an opportunistic relaying scheme for DCF relaying to harness distributed antennas and minimize the cooperation overheads by not using the global channel state information (CSI). In addition, for realistic wireless channels which have spatial fading correlation due to closely-spaced antenna configurations and poor scattering environments, we analyze the exact and lower bound on the symbol error probability (SEP) of the opportunistic DCF relaying over spatially correlated MIMO Rayleigh fading channels. Numerical results show that, even in the presence of spatial fading correlation, the proposed opportunistic relaying scheme is efficient and achieves additional performance gain with low overhead.

The performance of packet processing applications is dependent on the memory access speed of network systems. Table lookup requires fast memory access and is one of the most common processes in various packet processing applications, which can be a dominant performance bottleneck. Therefore, in Network Function Virtualization (NFV)-aware environments, on-chip fast cache memories of a CPU of general-purpose hardware become critical to achieve high performance packet processing speeds of over tens of Gbps. Also, multiple types of applications and complex applications are executed in the same system simultaneously in carrier network systems, which require adequate cache memory capacities as well. In this paper, we propose a packet processing architecture that utilizes interleaved 3 Dimensional (3D)-stacked Dynamic Random Access Memory (DRAM) devices as off-chip Last Level Cache (LLC) in addition to several levels of dedicated cache memories of each CPU core. Entries of a lookup table are distributed in every bank and vault to utilize both bank interleaving and vault-level memory parallelism. Frequently accessed entries in 3D-stacked DRAM are also cached in on-chip dedicated cache memories of each CPU core. The evaluation results show that the proposed architecture reduces the memory access latency by 57%, and increases the throughput by 100% while reducing the blocking probability but about 10% compared to the architecture with shared on-chip LLC. These results indicate that 3D-stacked DRAM can be practical as off-chip LLC in parallel packet processing systems.

This paper presents an improved min-sum iterative decoding scheme for regular and irregular LDPC codes. The proposed decoding scheme scales the extrinsic soft information from variable nodes to check. Different scaling factors are applied for iterations and the scaling factors are obtained by a simplified vector optimization method.

This paper presents an interference cancellation and multipath mitigation algorithm for use in Global Positioning System (GPS) with an array antenna. It is shown that interference signals and multipath signals are effectively suppressed using a serial subspace projection method without any knowledge of the incoming directional information. After the subspace projections, a beamformer is used to maximize the SNR of the received signal. The enhancement in the performance is presented in terms of the cross correlation value and beam patterns.

This paper proposes a defragmentation scheme using reroutable backup paths in toggled-based quasi 1+1 path protected elastic optical networks (EONs) to improve the efficiency of defragmentation and suppress the fragmentation effect. The proposed scheme can reallocate spectrum slots of backup paths and reroute of backup paths. The path exchange function of the proposed scheme makes the primary paths become the backup state while the backup paths become the primary. This allows utilization of the advantages of defragmentation in both primary and backup paths. We formulate a static spectrum reallocation problem with rerouting (SSRR) in the toggled-based quasi 1+1 path protected EON as an integer linear programming (ILP) problem. The decision version of SSRR is proven to be an NP-complete problem. A heuristic algorithm is introduced to solve the problem for large networks networks where the ILP problem is not tractable. For a dynamic traffic scenario, an approach that suppresses the fragmentation considering rerouting and path exchanging operations is presented. We evaluate the performances of the proposed scheme by comparing it to the conventional scheme in terms of dependencies on node degree, processing time of network operations and interval time between scheduled defragmentations. The numerical results obtained from the performance evaluation indicate that the proposed scheme increases the traffic admissibility compared to the conventional scheme.

In this paper, the performance of Tree-LDPC code [1] is presented based on the min-sum algorithm with scaling and the asymptotic performance in the water fall region is shown by density evolution. We presents that the Tree-LDPC code show a significant performance gain by scaling with the optimal scaling factor [3] which is obtained by density evolution methods. We also show that the performance of min-sum with scaling is as good as the performance of sum-product while the decoding complexity of min-sum algorithm is much lower than that of sum-product algorithm.

In order to improve the noise robustness of automatic speaker recognition, many techniques on speech/feature enhancement have been explored by using deep neural networks (DNN). In this work, a DNN multi-level enhancement (DNN-ME), which consists of the stages of signal enhancement, cepstrum enhancement and i-vector enhancement, is proposed for text-independent speaker recognition. Given the fact that these enhancement methods are applied in different stages of the speaker recognition pipeline, it is worth exploring the complementary role of these methods, which benefits the understanding of the pros and cons of the enhancements of different stages. In order to use the capabilities of DNN-ME as much as possible, two kinds of methods called Cascaded DNN-ME and joint input of DNNs are studied. Weighted Gaussian mixture models (WGMMs) proposed in our previous work is also applied to further improve the model's performance. Experiments conducted on the Speakers in the Wild (SITW) database have shown that DNN-ME demonstrated significant superiority over the systems with only a single enhancement for noise robust speaker recognition. Compared with the i-vector baseline, the equal error rate (EER) was reduced from 5.75 to 4.01.

This paper presents a distributed server allocation model with preventive start-time optimization against a single server failure. The presented model preventively determines the assignment of servers to users under each failure pattern to minimize the largest maximum delay among all failure patterns. We formulate the proposed model as an integer linear programming (ILP) problem. We prove the NP-completeness of the considered problem. As the number of users and that of servers increase, the size of ILP problem increases; the computation time to solve the ILP problem becomes excessively large. We develop a heuristic approach that applies simulated annealing and the ILP approach in a hybrid manner to obtain the solution. Numerical results reveal that the developed heuristic approach reduces the computation time by 26% compared to the ILP approach while increasing the largest maximum delay by just 3.4% in average. It reduces the largest maximum delay compared with the start-time optimization model; it avoids the instability caused by the unnecessary disconnection permitted by the run-time optimization model.

Density evolution has recently been used to analyze the iterative decoding of Low Density Parity Check (LDPC) codes, Turbo codes, and Serially Concatenated Convolutional Codes (SCCC). The density evolution technique makes it possible to explain many characteristics of iterative decoding including convergence of performance and preferred structures for the constituent codes. While the analytic density evolution methods were applied to LDPC codes, the simulation based density evolution methods were used for Turbo codes and SCCC due to analytic difficulties. In this paper, several density evolution ideas in the literature are used to analyze common code structures and it is shown that those ideas yield consistent results. In order to do that, we derive expressions for density evolution of SCCC with a simple 2-state constituent code. The analytic expressions are based on the sum-product and min-sum algorithms, and the thresholds are evaluated for both message passing algorithms. Particularly, for the min-sum algorithm, the density evolution with Gaussian approximation is derived and used to analyze the effect of scaling soft information. The scaling of extrinsic information slows down the convergence of soft information or avoids an overestimation effect of it and results in better performance, and its gain is maximized in particular constituent codes. Similar approaches are made for LDPC code. We show that the scaling gain is noticeable in the LDPC code as well. This scaling gain is analyzed with both density evolution and simulation performance. The expected scaling gain by density evolution matches well with the achievable scaling gain from simulation results. These results can be extended to the irregular LDPC codes based on the degree distribution for the min-sum algorithm. All density evolution algorithms used in this paper are based on the Gaussian approximation for the exchanged messages.

This paper proposes a migration model for distributed server allocation. In distributed server allocation, each user is assigned to a server to minimize the communication delay. In the conventional model, a user cannot migrate to another server to avoid instability. We develop a model where each user can migrate to another server while receiving services. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP-complete. We introduce a heuristic algorithm. Numerical result shows that the proposed model reduces the average communication delay by 59% compared to the conventional model at most.

In this letter, we study a scenario based on decoupled RF energy harvesting networks (DRF-EHNs) that separate energy sources from information sources to overcome the doubly near-far problem and improve harvesting efficiency. We propose an algorithm to maximize energy efficiency (EE) while satisfying constraints on the maximum transmit power of the hybrid access point (H-AP) and power beacon (PB), while further satisfying constraints on the minimum quality of service and minimum amount of harvested power in multi-user Rayleigh fading channel. Using nonlinear fractional programming and Lagrangian dual decomposition, we optimize EE with four optimization arguments: the transmit power from the H-AP and PB, time-splitting ratio, and power-splitting ratio. Numerical results show that the proposed algorithm is more energy-efficient compared to baseline schemes.

This paper proposes a robust optimization model for probabilistic protection under uncertain capacity demands to minimize the total required capacity against multiple simultaneous failures of physical machines. The proposed model determines both primary and backup virtual machine allocations simultaneously under the probabilistic protection guarantee. To express the uncertainty of capacity demands, we introduce an uncertainty set that considers the upper bound of the total demand and the upper and lower bounds of each demand. The robust optimization technique is applied to the optimization model to deal with two uncertainties: failure event and capacity demand. With this technique, the model is formulated as a mixed integer linear programming (MILP) problem. To solve larger sized problems, a simulated annealing (SA) heuristic is introduced. In SA, we obtain the capacity demands by solving maximum flow problems. Numerical results show that our proposed model reduces the total required capacity compared with the conventional model by determining both primary and backup virtual machine allocations simultaneously. We also compare the results of MILP, SA, and a baseline greedy algorithm. For a larger sized problem, we obtain approximate solutions in a practical time by using SA and the greedy algorithm.

In this letter, we propose an efficient on-the-fly algorithm for maximum-likelihood decoding of Raptor codes used over the binary erasure channel. It is shown that our proposed decoding algorithm can reduce the actual elapsed decoding time by more than two-thirds with respect to an optimized conventional maximum-likelihood decoding.

With the network function virtualization technology, a middlebox can be deployed as software on commercial servers rather than on dedicated physical servers. A backup server is necessary to ensure the normal operation of the middlebox. The workload can affect the failure rate of backup server; the impact of workload-dependent failure rate on backup server allocation considering unavailability has not been extensively studied. This paper proposes a shared backup allocation model of middlebox with consideration of the workload-dependent failure rate of backup server. Backup resources on a backup server can be assigned to multiple functions. We observe that a function has four possible states and analyze the state transitions within the system. Through the queuing approach, we compute the probability of each function being available or unavailable for a certain assignment, and obtain the unavailability of each function. The proposed model is designed to find an assignment that minimizes the maximum unavailability among functions. We develop a simulated annealing algorithm to solve this problem. We evaluate and compare the performances of proposed and baseline models under different experimental conditions. Based on the results, we observe that, compared to the baseline model, the proposed model reduces the maximum unavailability by an average of 29% in our examined cases.

This paper introduces a deep reinforcement learning approach to solve the virtual network function scheduling problem in dynamic scenarios. We formulate an integer linear programming model for the problem in static scenarios. In dynamic scenarios, we define the state, action, and reward to form the learning approach. The learning agents are applied with the asynchronous advantage actor-critic algorithm. We assign a master agent and several worker agents to each network function virtualization node in the problem. The worker agents work in parallel to help the master agent make decision. We compare the introduced approach with existing approaches by applying them in simulated environments. The existing approaches include three greedy approaches, a simulated annealing approach, and an integer linear programming approach. The numerical results show that the introduced deep reinforcement learning approach improves the performance by 6-27% in our examined cases.

This paper proposes a backup resource allocation model for virtual network functions (VNFs) to minimize the total allocated computing capacity for backup with considering the service delay. If failures occur to primary hosts, the VNFs in failed hosts are recovered by backup hosts whose allocation is pre-determined. We introduce probabilistic protection, where the probability that the protection by a backup host fails is limited within a given value; it allows backup resource sharing to reduce the total allocated computing capacity. The previous work does not consider the service delay constraint in the backup resource allocation problem. The proposed model considers that the probability that the service delay, which consists of networking delay between hosts and processing delay in each VNF, exceeds its threshold is constrained within a given value. We introduce a basic algorithm to solve our formulated delay-constraint optimization problem. In a problem with the size that cannot be solved within an acceptable computation time limit by the basic algorithm, we develop a simulated annealing algorithm incorporating Yen's algorithm to handle the delay constraint heuristically. We observe that both algorithms in the proposed model reduce the total allocated computing capacity by up to 56.3% compared to a baseline; the simulated annealing algorithm can get feasible solutions in problems where the basic algorithm cannot.

This paper presents a performance and thresholds for Irregular Tree-LDPC codes. We obtain optimal irregular degree distributions and threshold by the density evolution technique. It is presented that Irregular Tree-LDPC code has performance gain at low SNR.

The aim of this letter is to guarantee the ability of low probability of intercept (LPI) and anti-jamming (AJ) by maximizing the energy efficiency (EE) to improve wireless communication survivability and sustain wireless communication in jamming environments. We studied a scenario based on one transceiver pair with a partial-band noise jammer in a Rician fading channel and proposed an EE optimization algorithm to solve the optimization problem. With the proposed EE optimization algorithm, the LPI and AJ can be simultaneously guaranteed while satisfying the constraint of the maximum signal-to-jamming-and-noise ratio and combinatorial subchannel allocation condition, respectively. The results of the simulation indicate that the proposed algorithm is more energy-efficient than those of the baseline schemes and guarantees the LPI and AJ performance in a jamming environment.

This paper proposes two algorithms, namely Server-User Matching (SUM) algorithm and Extended Server-User Matching (ESUM) algorithm, for the distributed server allocation problem. The server allocation problem is to determine the matching between servers and users to minimize the maximum delay, which is the maximum time to complete user synchronization. We analyze the computational time complexity. We prove that the SUM algorithm obtains the optimal solutions in polynomial time for the special case that all server-server delay values are the same and constant. We provide the upper and lower bounds when the SUM algorithm is applied to the general server allocation problem. We show that the ESUM algorithm is a fixed-parameter tractable algorithm that can attain the optimal solution for the server allocation problem parameterized by the number of servers. Numerical results show that the computation time of ESUM follows the analyzed complexity while the ESUM algorithm outperforms the approach of integer linear programming solved by our examined solver.