In this paper, by adopting the analytical framework of dynamical systems theory, a new congestion control (CC) algorithm for Multipath TCP (MPTCP) streaming services is developed.The proposed nonlinear algorithm, following an onoff principle, is formally demonstrated robust with respect to variable network conditions.It maintains stream consistency at the negotiated video rate despite a priori unknown delay fluctuations.Also, precise guidelines for buffer size allocation at the receiver are provided.The designed algorithm is compared against the established MPTCP CC algorithms: LIA, OLIA, BaLIA, and wVegas.The tests, conducted in open networks using real devices and equipment, show that the on-off controller reduces protocol delay, jitter, and Head-of-Line blocking, which is crucial for ensuring high-quality streaming in mobile networks. Index Terms-MPTCP, congestion control,

Multipath communication has become a viable alternative to costly quality-of-service networking solutions. Despite multiple advantages, multipath transport protocols suffer from aggravated protocol delay and jitter originating from path heterogeneity. The algorithms responsible for splitting the data over the channels developed so far rely on the evaluation of the most recent channel properties. Taking into account the time lag between the decision on the path allocation and experiencing its effects, the standard approaches are vulnerable to the temporal fluctuations of transmission delays. The paper shows how to use the past and current information about the receiver state to form a zero-sum game among the paths. Contrary to the typical scheduler logic, the path is rewarded and penalized by the receiver irrespective of the channel-related readings. As a result, a prompt reaction to the changes in networking conditions is obtained, and the surges of protocol delay owing to Head-of-Line blocking are mitigated.

Susceptibility to weather conditions combined with different consumption and energy generation schemes threatens the large-scale adoption of renewable sources of energy. Moreover, the associated price fluctuation lowers economic gain. As a countermeasure, one may consider energy depots, although not in the form of bulky industrial installations that impose substantial costs themselves. This work proposes to involve a system of connected prosumer hydro plants with ponds acting as energy reservoirs to create the desirable buffer. For that purpose, a dynamic model of a hydro multi-cascade is constructed, and a cost-optimal control law is established via a formal analytical procedure. In addition to economic benefits, the proposed control policy counteracts droughts in periods of high vaporization, e.g., during sunny summer days or in arid climates. The prosumers are estimated to increase their revenue by as much as 5%, whereas the operators gain from reduced load variation on the grid.

In the face of the recent surge in energy prices, intensified use of free renewable sources of energy (RSE) gains much importance.Unfortunately, the operation of RSE highly depends on weather conditions, which perturb the balance between the industrial and home energy dissipation patterns.This disparity induces price fluctuations or even destabilizes the energy supply system, yet can be alleviated by the installation of energy depots.While electrochemical depots are hardly cost-effective, they may be supplemented or replaced by small hydro plants with the ponds located above the plant recognized as energy reservoirs.However, inappropriate use of the plant is likely to cause floods or droughts down the river.In this paper, following a rigorous mathematical argument, a cost-optimal controller of a cascade of hydro plants is designed and its properties are formally proved.It is shown to flatten the price pattern, by reducing the load fluctuation of the legacy supply system, as well as provide a concrete revenue for prosumers.

In order to improve supply system robustness, or gain cost advantage, multiple delivery modes may be used. In this work, the (r, Q) inventory policy is formally investigated as a tool to control the resupply process of a remote depot that answers uncertain market demand. In the considered scenario, the goods are relocated using multiple channels with diverse properties concerning transportation delay, lot size, and quality, e.g., trucks, or trains. The lot partitioning strategy is adapted on the fly to the current channel characteristics. A dynamic model of the supplier-depot interaction is built and used to evaluate the policy properties in a robust control framework. Using the constructed framework, it is explicitly shown how to select the policy parameters to obtain full demand realization, thus eliminating backorders, irrespective of the pattern of delay and demand fluctuations. It is also discussed how to assign the warehouse space at the depot so that emergency storage will not be required even though, as a result of channel disruptions, deliveries from a few periods arrive simultaneously, or out of order.

Efficient transfer of time-sensitive data plays an essential role in entertainment, personal and business communication, and in industry, especially in the systems of monitoring and robotic automation. In those application areas high network throughput together with short latency, and resiliency from network problems are expected. One of the most promising methods of answering those challenges is to simultaneously engage multiple paths of communication. Currently, only two protocols provide such a possibility in public networks - the multipath version of TCP (MPTCP) and the multipath version of QUIC (MPQUIC). Both protocols split data units over available paths via a component named scheduler. The default scheduler favors throughput over protocol delay, which is unsuitable in industrial applications. In the paper, a balanced solution - a synchronizing scheduler - that reduces the protocol delay is proposed. Its properties are analyzed formally and tested experimentally in the framework of both MPTCP and MPQUIC in the open Internet.

Providing an efficient service of time-sensitive data transfer plays an essential role in entertainment, personal, and business communication, and industry, especially in the systems of monitoring and robotic automation. Those applications require high network throughput and short latency, rarely available within one data channel. Until recently, nonhomogeneous channels could not have been concurrently engaged owing to the protocol legacy restrictions. In order to answer the current challenges, new protocols, e.g., Multipath TCP (MPTCP), have been designed. MPTCP splits the data stream over a number of channels in the way dictated by a low-level component – the scheduler. However, the default scheduler aims at increasing throughput rather than constraining the transfer delay. Thus, for time-sensitive applications, other solutions are needed. In this work, a new scheduler, explicitly targeting the transmission aspects related to protocol delay, is designed. The scheduler properties are analyzed formally and its performance is tested experimentally.

The industrial applications, especially in the context of communication-intensive Industry 4.0, require high throughput, good robustness, and low latency in the data exchange among the system components. These objectives can be achieved through multipath transfer. In order to overcome the restrictions of the primary networking protocols with respect to engaging multiple network interfaces, a new version of transmission control protocol (TCP) has recently been standardized—multipath TCP (MPTCP). Still though, only one type of congestion control algorithm is used to manage the flow of data despite various choices available in the modern operating systems, which leads to nonoptimal transfer dynamics. In this article, the influence of TCP congestion control algorithms, operating at the path and MPTCP level, on the transmission quality in industrial applications is examined. The conducted experiments involving physical devices and real, public networks lead to a set of recommendations for the algorithm selection so that the premises of robustness and efficiency are met. The default algorithm configuration is found ill-suited, yielding priority to nonhomogeneous settings. Only a few algorithms can maintain the benefits of multiplicity for an on-going transmission when the transfer conditions at one or more paths deteriorate. Motivated by the deficiency of common solutions, a new MPTCP congestion control algorithm has been developed. While retaining their good properties in the general networking context, this new–robust–algorithm yields improved performance in the uncertain, industrial communication environment.

TCP provides the principal framework for the Internet traffic control. Following the protocol development, however, the communicating parties have not benefitted from the potential of multiple interfaces, now commonly installed at the end-point devices. In order to grasp new opportunities, a multipath version of TCP - multipath TCP (MPTCP) - has recently been introduced. It permits the simultaneous use of a few, diverse interfaces for enhancing the user experience. This article provides a discrete-time model of the data exchange process in the MPTCP framework and new flow control algorithms. The algorithms, applicable to the paths with nonhomogenous properties with respect to delays, loss rate, etc., are designed following the rules of discrete sliding-mode control (DSMC). As a result, direct implementation in discrete networking context and robust transmission system performance are guaranteed in uncertain operating conditions. It is formally demonstrated that a finite data queue length in the end-point buffers and a feasible (non-negative and bounded) input signal are ensured despite input and output perturbations affecting the control process. It is also shown that the classical DSMC design approach results in decreased performance for unanticipated surge in the intensity of network-related perturbations. A second, improved algorithm, with a well-chosen sliding variable, eliminates this drawback.

In the effort to achieve high convergence rate, at the same time avoiding implementation difficulties and poor robustness of time-optimal controllers, the concept of soft Variable Structure Control (VSC) may be applied. The classical formulation of soft VSC in continuous time domain assumes smooth switching among an infinite number of controllers. Since nowadays control laws are implemented digitally, changing the control structure is limited to sampling instances, which leads to quasi-soft VSC. The paper investigates how the favourable characteristics of dynamic soft VSC can be extended to input-constrained systems with finite sampling. The design procedure and stability analysis are conducted directly in discrete time domain. The resulting nonlinear control law is synthesised into a form substantially different from its continuous-time counterpart. However, smooth control action and fast convergence of continuous soft VSC is retained. The properties of the obtained control system are formally proved and confirmed experimentally.

In this paper, inventory control problem in goods distribution networks with non-negligible transshipment delay is addressed. In contrast to the majority of earlier approaches, system modeling and policy design do not assume simplified system structure, such as a serial, or a tree-like one. The network nodes, in addition to satisfying market demand, answer internal requests with delay spanning multiple periods. The stock in the network is refilled from uncapacited outside sources. A dynamic model of the considered class of goods distribution systems is constructed and a new inventory policy is formulated. The proposed policy shares similarity with the classical order-up-to one, yet provide improved performance owing to the networked perspective assumed in the design process.

This paper addresses the problem of establishing an efficient (fast yet robust) inventory management policy for production-inventory systems in which the stock deteriorates with time and the replenishment process is unreliable. The stock used to satisfy uncertain, variable demand is refilled using multiple supply options. Owing to information distortion, product defects, manufacturing faults, or improper transportation, the goods quantity obtained from the suppliers differs from the ordered one. The setting is modeled as an uncertain, time-varying discrete-time system with multiple time delays. A new compensation mechanism, which provides a smooth ordering pattern and ensures closed-loop stability for a delay spanning arbitrary number of review periods is proposed. The developed strategy outperforms the classical order-up-to policy in mitigating demand variations and prevents the bullwhip effect despite unpredictable distortions of the supply process.

In this paper, discrete sliding-mode (DSM) control concepts are applied to develop an efficient control algorithm for a class of uncertain systems with delay-logistic networks. In the analyzed systems, the stock used to satisfy unknown, variable demand placed at a goods distribution center is replenished with delay from a number of distant supply sources. In the considered setting, the order quantity is fixed and the instants of placing the order serve as the decision variable, which perfectly suits the switching input signals in DSM control systems. It is proved that, with the proposed nonlinear algorithm applied, the stock level does not exceed the assigned warehouse space. Moreover, it is also shown that the stock is never depleted, which ensures full demand satisfaction and maximum service level. Comparison with the classical inventory policy is provided, and improved performance of the DSM algorithm is demonstrated.

In this paper, we apply control-theoretic approach to the design of inventory policy for systems with perishable goods. In the considered systems, the stock used to fulfill unknown, variable demand is subject to exponential decay. It is replenished from multiple supply sources characterized by different lead times. The challenge is to achieve high demand satisfaction with minimum costs when replenishment orders are realized with non-negligible delay. In contrast to the classical stochastic, or heuristic approaches, a formal design methodology based on discrete time sliding mode (DSM) control is employed. The proposed DSM controller with the sliding plane selected for a dead-beat scheme ensures full demand satisfaction for arbitrary bounded demand. It achieves a given service level with smaller holding costs and reduced order-to-demand variance ratio as compared to the classical order-up-to policy.