Industrial networks need to provide reliable communication services, usually in a redundant transmission (RT) manner. In the past few years, several device-redundancy-based, layer 2 solutions have been proposed. However, with the evolution of industrial networks to the Industrial Internet, these methods can no longer work properly in the non-redundancy, layer 3 environments. In this paper, an SDN-based reliable communication framework is proposed for the Industrial Internet. It can provide reliable communication guarantees for mission-critical applications while servicing non-critical applications in a best-effort transmission manner. Specifically, it first implements an RT-based reliable communication method using the Industrial Internet's link-redundancy feature. Next, it presents a redundant synchronization mechanism to prevent end systems from receiving duplicate data. Finally, to maximize the number of critical flows in it (an NP-hard problem), two ILP-based routing & scheduling algorithms are also put forward. These two algorithms are optimal (Scheduling with Unconstrained Routing, SUR) and suboptimal (Scheduling with Minimum length Routing, SMR). Numerous simulations are conducted to evaluate its effectiveness. The results show that it can provide reliable, duplicate-free services to end systems. Its reliable communication method performs better than the conventional best-effort transmission method in terms of packet delivery success ratio in layer 3 networks. In addition, its scheduling algorithm, SMR, performs well on the experimental topologies (with average quality of 93% when compared to SUR), and the time overhead is acceptable.

In order to obtain a feasible schedule of a hard real-time system, heuristic based techniques are the solution of choice. In the last few years, optimization solvers have gained attention from research communities due to their capability of handling large number of constraints. Recently, some works have used integer linear programming (ILP) for solving mono processor scheduling of real-time systems. In fact, ILP is commonly used for static scheduling of multiprocessor systems. However, two main solvers are used to solve the problem indistinctly. But, which one is the best for obtaining a schedulable system for hard real-time systems? This paper makes a comparison of two well-known optimization software packages (CPLEX and GUROBI) for the problem of finding a feasible schedule on monoprocessor hard real-time systems.

This paper presents a novel method for optimal control of timed Petri nets, introducing a novel temporal logic based constraint called a generalized mutual exclusion temporal constraint (GMETC). The GMETC is described by a metric temporal logic (MTL) formula where each atomic proposition represents a generalized mutual exclusion constraint (GMEC). We formulate an optimal control problem of the timed Petri nets under a given GMETC and solve the problem by transforming it into an integer linear programming problem where the MTL formula is encoded by linear inequalities. We show the effectiveness of the proposed approach by a numerical simulation.

One of the features of a software-defined network (SDN) is a logically centralized control plane hosting one or more SDN controllers. As SDN controller placement can impact network performance, it is widely studied as the controller placement problem (CPP). For a cost-effective network design, network providers need to minimize the number of SDN controllers used in the network since each SDN controller incurs installation and maintenance costs. Moreover, the network providers need to deal with the failure of SDN controllers. Existing studies that consider SDN controller failures use the scheme of connecting each SDN switch to one Master controller and one or more Slave controllers. The problem with this scheme is that the computing capacity of each SDN controller cannot be used efficiently since one SDN controller handles the load of all SDN switches connected to it. The number of SDN controllers required can be reduced by distributing the load of each SDN switch among multiple SDN controllers. This paper proposes a controller placement model that allows the distribution against SDN controller failures. The proposed model determines the ratios of computing capacity demanded by each SDN switch on the SDN controllers connected to it. The proposed model also determines the number and placement of SDN controllers and the assignment of each SDN switch to SDN controllers. Controller placement is determined so that a network provider can continue to manage all SDN switches if no more than a certain number of SDN controller failures occur. We develop two load distribution methods: split and even-split. We formulate the proposed model with each method as integer linear programming problems. Numerical results show that the proposed model reduces the number of SDN controllers compared to a benchmark model; the maximum reduction ratio is 38.8% when the system latency requirement between an SDN switch and an SDN controller is 100[ms], the computing capacity of each SDN controller is 6 × 106[packets/s], and the maximum number of SDN controllers that can fail at the same time is one.

With the demand for energy-efficient and high- performance computing, multicore architecture has become more appealing than ever. Multicore task scheduling is one of domains in parallel computing which exploits the parallelism of multicore. Unlike traditional scheduling, multicore task scheduling has recently been studied on the assumption that tasks have inherent parallelism and can be split into multiple sub-tasks in data parallel fashion. However, it is still challenging to properly determine the degree of parallelism of tasks and mapping on multicores. Our proposed scheduling techniques determine the degree of parallelism of tasks, and sub-tasks are decided which type of cores to be assigned to heterogeneous multicores. In addition, two approaches to hardware/software codesign for heterogeneous multicore systems are proposed. The works optimize the types of cores organized in the architecture simultaneously with scheduling of the tasks such that the overall energy consumption is minimized under a deadline constraint, a warm start approach is also presented to effectively solve the problem. The experimental results show the simultaneous scheduling and core-type optimization technique remarkably reduces the energy consumption.

The Software-defined network (SDN) uses a centralized SDN controller to store flow entries in the flow table of each SDN switch; the entries in the switch control packet flows. When a multicast service is provided in an SDN, the SDN controller stores a multicast entry dedicated for a multicast group in each SDN switch. Due to the limited capacity of each flow table, the number of flow entries required to set up a multicast tree must be suppressed. A conventional multicast routing scheme suppresses the number of multicast entries in one multicast tree by replacing some of them with unicast entries. However, since the conventional scheme individually determines a multicast tree for each request, unicast entries dedicated to the same receiver are distributed to various SDN switches if there are multiple multicast service requests. Therefore, further reduction in the number of flow entries is still possible. In this paper, we propose a multicast routing model for multiple multicast requests that minimizes the number of flow entries. This model determines multiple multicast trees simultaneously so that a unicast entry dedicated to the same receiver and stored in the same SDN switch is shared by multicast trees. We formulate the proposed model as an integer linear programming (ILP) problem. In addition, we develop a heuristic algorithm which can be used when the ILP problem cannot be solved in practical time. Numerical results show that the proposed model reduces the required number of flow entries compared to two benchmark models; the maximum reduction ratio is 49.3% when the number of multicast requests is 40.

The machine-to-machine (M2M) service network platform that accommodates and controls various types of Internet of Things devices has been presented. This paper investigates program file placement strategies for the M2M service network platform that achieve low blocking ratios of new task requests and accommodate as many tasks as possible in the dynamic scenario. We present four strategies for determining program file placement, which differ in the computation method and whether the relocation of program files being used by existing tasks is allowed or not. Simulation results show that a strategy based on solving a mixed-integer linear programming model achieves the lowest blocking ratio, but a heuristic algorithm-based strategy can be an attractive option by allowing recomputation of the placement when the placement cannot be obtained at the timing of new task request arrival.

This paper proposes a Petri net based mathematical programming approach to combinatorial optimization, in which we generate integer linear programming problems from Petri net models instead of the direct mathematical formulation. We treat two types of combinatorial optimization problems, ordinary problems and time-dependent problems. Firstly, we present autonomous Petri net modeling for ordinary optimization problems, where we obtain fundamental constraints derived from Petri net properties and additional problem-specific ones. Secondly, we propose a colored timed Petri net modeling approach to time-dependent problems, where we generate variables and constraints for time management and for resolving conflicts. Our Petri net approach can drastically reduce the difficulty of the mathematical formulation in a sense that (1) the Petri net modeling does not require deep knowledge of mathematical programming and technique of integer linear model formulations, (2) our automatic formulation allows us to generate large size of integer linear programming problems, and (3) the Petri net modeling approach is flexible for input parameter changes of the original problem.

The separating redundancy is an important property in the analysis of the error-and-erasure decoding of a linear block code. In this work, we investigate the separating redundancy of the duals of first-order generalized Reed-Muller (GRM) codes, a class of nonbinary linear block codes that have nice algebraic properties. The dual of a first-order GRM code can be specified by two positive integers m and q and denoted by R(m,q), where q is the power of a prime number and q≠2. We determine the first separating redundancy value of R(m,q) for any m and q. We also determine the second separating redundancy values of R(m,q) for any q and m=1 and 2. For m≥3, we set up a binary integer linear programming problem, the optimum of which gives a lower bound on the second separating redundancy of R(m,q).

Cost-effective cloud storage services are attracting users with their convenience, but there is a trade-off between service availability and usage cost. We develop two cloud provider selection models for cloud storage services to minimize the total cost of usage. The models select multiple cloud providers to meet the user requirements while considering unavailability. The first model, called a user-copy (UC) model, allows the selection of multiple cloud providers, where the user copies its data to multiple providers. In addition to the user copy function of the UC model, the second model, which is called a user and cloud-provider copy (UCC) model, allows cloud providers to make copies of the data to deliver them to other cloud providers. The cloud service is available if at least one cloud provider is available. We formulate both models as integer linear programming (ILP) problems. Our performance evaluation observes that both models reduce the total cost of usage, compared to the single cloud provider selection approach. As the cost of bandwidth usage between a user and a cloud provider increases, the UCC model becomes more beneficial than the UC model. We implement the prototype for cloud storage services, and demonstrate our models via Science Information Network 5.

Task mapping is one of the most important design processes in embedded manycore systems. This paper proposes a static task mapping technique for manycore real-time systems. The technique minimizes the number of cores while satisfying deadline constraints of individual tasks.

Task scheduling is one of the most important processes in the design of multicore computing systems. This paper presents a technique for scheduling of malleable tasks. Our scheduling technique decides not only the execution order of the tasks but also the number of cores assigned to the individual tasks, simultaneously. We formulate the scheduling problem as an integer linear programming (ILP) problem, and the optimal schedule can be obtained by solving the ILP problem. Experiments using a standard task-set suite clarify the strength of this work.

In this paper, the authors propose an integer linear programming (ILP) model for static multi-car elevator operation problems. Here, “static” means that all information which make the behavior of the elevator system indeterministic is known before scheduling. The proposed model is based on the trip-based ILP model for static single-car elevator operation problems. A trip of an elevator is a one-directional movement of that elevator, which is labaled upward or downward. In the trip-based ILP model, an elevator trajectory is scheduled according to decision variables which determine allocations of trips to users of an elevator system. That model has such an advantage that the difficulty in solving ILP formulations resulted by that model does not depend on the length of the planning horizon nor the height of the considered building, thus is effective when elevator trajectories are simple. Moreover, that model has many variables relevant to elevators' positions. The proposed model is resulted by adding 3 constraints which are basically based on those variables and make it possible to prevent elevators in a same shaft from interfering. The first constraint simply imposes the first and last floors of an upper trip to be above those of its lower trip. The second constraint imagines the crossing point between upper and lower trips and imposes it ahead of or behind the lower trip according to their directions. The last constraint estimates future positions of elevators and imposes the upper trip to be above floors of passengers on the lower trip. The basic validity of the proposed model is displayed by solving 90 problem instances and examining elevator trajectories generated from them, then comparing objective function values of elevator trajectories on a multi-car elevator system with those on single-car elevator systems.

As semiconductor technologies have advanced, the reliability problem caused by soft-errors is becoming one of the serious issues in LSIs. Moreover, multiple component errors due to single soft-errors also have become a serious problem. In this paper, we propose a method to synthesize multiple component soft-error tolerant application-specific datapaths via high-level synthesis. The novel feature of our method is speculative resource sharing between the retry parts and the secondary parts for time overhead mitigation. A scheduling algorithm using a special priority function to maximize speculative resource sharing is also an important feature of this study. Our approach can reduce the latency (schedule length) in many applications without deterioration of reliability and chip area compared with conventional datapaths without speculative resource sharing. We also found that our method is more effective when a computation algorithm possesses higher parallelism and a smaller number of resources is available.

This paper proposes a static application mapping technique, based on integer linear programming, for non-hierarchical manycore embedded systems. Unlike previous work which was designed for hierarchical manycore SoCs, this work allows more flexible application mapping to achieve higher performance. The experimental results show the effectiveness of this work.

In general-synchronous framework, in which the clock is distributed periodically to each register but not necessarily simultaneously, circuit performance is expected to be improved compared to complete-synchronous framework, in which the clock is distributed periodically and simultaneously to each register. To improve the circuit performance more, logic synthesis for general-synchronous framework is required. In this paper, under the assumption that any clock schedule is realized by an ideal clock distribution circuit, when two or more cell libraries are available, a technology mapping method which assigns a cell to each gate in the given logic circuit by using integer linear programming is proposed. In experiments, we show the effectiveness of the proposed technology mapping method.

Counting the number of differentially active S-boxes is of great importance in evaluating the security of a block cipher against differential attack. Mouha et al. proposed a technique based on Mixed-Integer Linear Programming (MILP) to automatically calculate a lower bound of the number of differentially active S-boxes for word-oriented block ciphers, and applied it to symmetric ciphers AES and Enocoro-128v2. Later Sun et al. extended the method by introducing bit-level representations for S-boxes and new constraints in the MILP problem, and applied the extended method to PRESENT-80 and LBlock. This kind of methods greatly depends on the constraints in the MILP problem describing the differential propagation of the block cipher. A more accurate description of the differential propagation leads to a tighter bound on the number of differentially active S-boxes. In this paper, we refine the constraints in the MILP problem describing XOR operations, and apply the refined MILP modeling to determine a lower bound of the number of active S-boxes for the Lai-Massey type block cipher FOX in the model of single-key differential attack, and obtain a tighter bound in FOX64 than existing results. Experimental results show that 6, instead of currently known 8, rounds of FOX64 is strong enough to resist against basic single-key differential attack since the differential characteristic probability is upper bounded by 2-64, and thus the maximum differential characteristic probability of 12-round FOX64 is upper bounded by 2-128, where 128 is the key-length of FOX64. We also get the lower bound of the number of differentially active S-boxes for 5-round FOX128, and proved the security of the full-round FOX128 with respect to single-key differential attack.

Peer-to-Peer (P2P) file distribution systems can efficiently disseminate massive contents, such as disk images of operating systems, from a server to many users in a piece-by-piece manner. In particular, the BitTorrent protocol optimizes each peer's download speed by applying the tit-for-tat (TFT) strategy, where each peer preferentially uploads piece(s) to peer(s) from which it can download missing pieces faster. To the best of our knowledge, however, the optimality of TFT-based P2P file distribution has not been studied sufficiently. In this paper, we aim to understand the optimal scheduling in TFT-based P2P file distribution. First, we develop a discrete-time model of TFT-based P2P file distribution and formulate its optimal scheduling as a two-step integer linear programming problem. The first step is to minimize the average file retrieval time among peers, and the second step is to improve fairness among peers. We analyze the optimal solution obtained by the existing solver and reveal the characteristics of the optimal scheduling. Specifically, we show that it is crucial to distribute pieces from the server indirectly to peers with large upload capacity via those with small upload capacity.

The notion of multiple constant multiplication (MCM) is extensively adopted in digital signal processing (DSP) applications such as finite impulse filter (FIR) designs. A set of adders is utilized to replace regular multipliers for the multiplications between input data and constant filter coefficients. Though many algorithms have been proposed to reduce the total number of adders in an MCM block for area minimization, they do not consider the actual bitwidth of each adder, which may not estimate the hardware cost well enough. Therefore, in this article we propose a bitwidth-aware MCM optimization algorithm that focuses on minimizing the total number of adder bits rather than the adder count. It first builds a subexpression graph based on the given coefficients, derives a set of constraints for adder bitwidth minimization, and then optimally solves the problem through integer linear programming (ILP). Experimental results show that the proposed algorithm can effectively reduce the required adder bit count and outperforms the existing state-of-the-art techniques.

1+1 protection provides instantaneous proactive recovery from any single link failure by duplicating and sending the same source data onto two disjoint paths. Other resource efficient recovery techniques to deal with single link failure require switching operations at least at both ends, which restrict instantaneous recovery. However, the 1+1 protection technique demands at least double network resources. Our goal is to minimize the resources required for 1+1 protection while maintaining the advantage of instantaneous recovery. It was reported that the network coding (NC) technique reduces resource utilization in 1+1 protection, and in order to determine an optimum NC aware set of routes that minimizes the required network resources for 1+1 protection, an Integer Quadratic Programming (IQP) formulation has already been addressed. Solving an IQP problem requires large amount of memory (cannot be determined exactly) and special algorithms by the mathematical programming solver. In this paper our contributions consist of two parts. First, we formulate the optimization problem, corresponding to the IQP model, as an Integer Linear Programming (ILP) formulation, which is solvable by any linear programming solver, and so its memory and time requirements are smaller. However, the presented ILP model works well in small-scale and medium-scale networks, but fails to support large-scale networks due to excessive memory requirements and calculation time. Second, to deal with these issues, a heuristic algorithm is proposed to determine the best possible NC aware set of routes in large-scale networks. Numerical results show that our strategies achieve almost double the resource saving effect than the conventional minimal-cost routing policy in the examined medium-scale and large scale networks.