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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). This paper uses continuous time computer simulation tools to analyze the maintenance scheduling problem of a fleet of assets that is subjected to a CBM (Condition-Based Maintenance) program for critical components under a maintenance 4.0 environment. Detection of component anomalies, their diagnosis and prognosis are considered as built-in capabilities of the organization. Once the remaining useful life (RUL) of a component at risk is known, the organization must react and determine when the component’s on-condition maintenance can be released. Maintenance on-condition activities are released controlling a variable named accumulated excess of anomalies over the CBM capacity constraint. When the existing CBM capacity is not enough to service all the components with their lowest possible RUL, components could be replaced with a higher RUL, as few times as possible, to ensure the largest component´s lifecycle. The paper explores the implications of different CBM capacity levels modelling a cost function. Cost function factors considered are cost of lost RUL, cost of CBM capacity, cost of overdue CBM and cost of asset unavailability due to overdue CBM. Empirically, the paper shows how capacity can be optimized to minimize this cost function. Once all different possibilities to schedule CBM activities are modeled, together with the cost of the selected CBM strategy, the paper compare results with those obtained for a base case where the organization could detect anomalies in components but not schedule CBM activities according to their RUL limitations and the maintenance organization capacity constraints. The paper demonstrates the different benefits of this opportunistic CBM task scheduling, according to assets stops for their predetermined PM activities. The tool that is developed has been tested in the railway sector, for a fleet of trains. Interesting results are obtained for different strategies, and they are discussed to understand possible implications of changes in the different factors and parameters of the problem.

Standing on the brink of the fourth industrial revolution, Cyber Physical Systems (CPS) are considered the basic components of the Smart Factory. One important challenge in cyber physical production systems is dynamic scheduling that can handle random disruptions such as failures, raw material shortages and quality defects. To achieve dynamic scheduling, we have proposed a Supervised and Distributed Holonic architecture we called SUDIHA. This architecture incorporates three Holons: Product Holon, Resource Holon and Order Holon and combines global supervision, achieved by Product Holon, with dynamic local control, achieved by Resource Holon. The Digital Twin (DT) concept is generally used to design CPS; it is virtual copies of the system that can interact with the physical counterparts in a bi-directional way. It seems to be promising to tackle the complexity and increase manufacturing system flexibility. In this paper, we use a DT Model to improve the SUDIHA architecture. We propose a Digital Twin based SUDIHA architecture (DT-SUDIHA). The paper will describe Digital Twins’ configuration of each Holon of the SUDIHA Architecture, and the intelligent and real time data driven operation control of this architecture. A case study is carried out at the ENSAM-Meknes flexible workshop to prove the effectiveness of the proposed approach.

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team’s operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of “one spray and three defenses” for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system.

Real-time dynamic workload consists of tasks that can arbitrarily join and leave the system at run-time. To avoid incurring deadline misses, tasks that request to join the system must pass an admission test, which has to cope with potential scheduling transients originated by the residual effect of the tasks that previously left the system. This phenomenon may require some tasks to suffer an admission delay before being accepted for execution. This paper focuses on uniprocessor earliest-deadline first (EDF) scheduling with constrained deadlines and explicitly considers methods for handling scheduling transients in the presence of dynamic real-time workload. A generalized analysis framework is first presented to overcome several limitations of the existing approaches (including the support for overlapping transients), and is then used to derive methods for computing bounds on the admission delays incurred by tasks. Building on such results, an on-line protocol is proposed to handle the admission control of a dynamic workload, which also comes with a variant that can execute in polynomial time to favor its practical application. Furthermore, the paper shows how the presented analysis can be used off-line for analyzing mode-changes among static task sets. Experimental results are finally presented to evaluate the proposed algorithms.

There is a widely held belief that future Radio Access Network (RAN) architectures will be characterized by increased levels of virtualization, whereby base station functionalities, traditionally residing at a single location, will be scattered across different logical entities while being interfaced via high-speed fronthaul (FH) links. For the deployment of such FH links, operators are faced with the challenge of maintaining acceptable radio access performance while at the same time keeping deployment costs low. A common practice is to exploit statistical multiplexing by allowing several cells to utilize the same FH link. As a result, in order to cope with the resulting aggregated traffic, different techniques can be used to reduce the required FH data rates. Herein, we focus on FH compression control strategies for multiple-cell/multiple-user scenarios sharing a common FH link. We propose various methods for sounding reference signal (SRS) handling and analyze different FH-aware modulation data compression and scheduling strategies. Considering a full system setup, including the radio and FH access networks, numerical evaluation is conducted using a 5G NR system-level simulator implemented in ns-3. Simulation results show that, under stringent FH capacity constraints, optimized modulation compression strategies provide significant user-perceived throughput gains over baseline strategies (between 5.2x and 6.9x). On top of them, SRS handling methods achieve additional 2% to 41% gains., paper to appear in IEEE Communications Magazine

Production scheduling can be affected by many disturbances in the manufacturing system, and consequently, the feasible schedules previously defined became obsolete. Emerging of new technologies associated with Industry 4.0, such as Cyber-Physical Production Systems, as a paradigm of implementation of control and support in decision making, should embed the capacity to simulate different environment scenarios based on the data collected by the manufacturing systems. This paper presents the evaluation of environment dynamics effect on production scheduling, considering three scheduling models and three environment scenarios, through a case study. Results show that environment dynamics affect production schedules, and a very strong or strong positive correlation between environment dynamics scenarios and total completion time with delay, over three scheduling paradigms. Based on these results, the requirement for mandatory inclusion of a module for different environment dynamics scenarios generation and the corresponded simulations, of a Cyber-Physical Production Systems architecture, is confirmed., This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

The paper presents balanced heuristic techniques of static tasks scheduling in multi-core real-time system architecture. The main objective was to minimize the energy consumed by the system without causing deadlines to be missed. The authors proposed a few scheduling scenarios based on modifications of different parameters of the system and defined appropriate algorithms. The methodology was verified and tested on the original hardware platform of the system developed by the authors. The system consists of the reconfigurable set of cores based on an interleaved pipeline processing scheme. The entire system was modeled as original IP at the RTL level in VERILOG and implemented on a Virtex7 FPGA platform. The tasks were executed as a set of programs based on Mälardalen WCET benchmarks; commonly used by the PRET community for validation time predictable systems and worst-case analyses. Many series of experiments were carried out and the results validated the approach and showed that it is possible to radically reduce the energy consumed by the system while retaining timing conditions (i.e., deadlines). The authors gathered and discussed results and formulated a set of recommendations for the safety real-time system’s design flowchart aimed to minimize the energy consumed by the system.

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task scheduling, this paper constructs a multi-constraint mathematical model for multi-UAV dynamic task scheduling, involving task demands and platform capabilities. Three objectives are considered, which are to maximize the total profit of scheduled tasks, to minimize the time consumption, and to balance the number of scheduled tasks for multiple UAVs. The multi-objective problem is converted into single-objective optimization via the weighted sum method. Then, a novel dynamic task scheduling method based on a hybrid contract net protocol is proposed, including a buy-sell contract, swap contract, and replacement contract. Finally, extensive simulations are conducted under three scenarios with emergency tasks, pop-up obstacles, and platform failure to verify the superiority of the proposed method.

Stream workflow is a network of big data streaming applications that acts as key enabler for real-time analysis from Internet of Things data. Smart traffic management and smart grid are examples of stream workflow. The focus of existing work is on streaming operator graphs which differs from stream workflow and handling data fluctuations without significant consideration of different dynamic forms that could happen in the structure of data pipelines. This paper investigates the scheduling problem of stream workflow to support runtime alterations of stream workflow deployment, so that scheduling plans will be revised to handle stream workflow applications with continuously changing characteristics. It proposes a pluggable dynamic scheduling technique that accepts user-defined algorithms to handle stream workflow runtime changes. It also presents three different plug-in algorithms and methods to enable auto-scaling of this workflow in a Multicloud environment. The experimental results of the quality of the solution showed that the proposed plug-in optimisation technique is more efficient than baseline and dynamic fair-share techniques to handle runtime changes. Refereed/Peer-reviewed

Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system. In this paper, reinforcement learning (RL)-based dynamic scheduling and a selection mechanism for multiple dynamic scheduling rules (MDSRs) are proposed to support the operating characteristics of a flexible manufacturing system (FMS) and semiconductor wafer fabrication (FAB). The proposed RL-based dynamic scheduling MDSR selection mechanism consisted of initial MDSR KB generation and revision phases. According to various performance criteria, the presented approach yields a system performance that is superior to those of the fixed-decision scheduling approach, the machine learning classification approach, and the classical MDSR selection mechanism.

Network slicing allows different applications and network services to be deployed on virtualized resources running on a common underlying physical infrastructure. Developing a scalable system for the orchestration of end-to-end (E2E) mobile network slices requires careful planning and very reliable algorithms. In this paper, we propose a novel E2E Network Slicing Orchestration System (NSOS) and a Dynamic Auto- Scaling Algorithm (DASA) for it. Our NSOS relies strongly on the foundation of a hierarchical architecture that incorporates dedicated entities per domain to manage every segment of the mobile network from the access, to the transport and core network part for a scalable orchestration of federated network slices. The DASA enables the NSOS to autonomously adapt its resources to changes in the demand for slice orchestration requests (SORs) while enforcing a given mean overall time taken by the NSOS to process any SOR. The proposed DASA includes both proactive and reactive resource provisioning techniques). The proposed resource dimensioning heuristic algorithm of the DASA is based on a queuing model for the NSOS, which consists of an open network of G/G/m queues. Finally, we validate the proper operation and evaluate the performance of our DASA solution for the NSOS by means of system-level simulations., This research work is partially supported by the European Union’s Horizon 2020 research and innovation program under the 5G!Pagoda project, the MATILDA project and the Academy of Finland 6Genesis project with grant agreement No. 723172, No. 761898 and No. 318927, respectively. It was also partially funded by the Academy of Finland Project CSN - under Grant Agreement 311654 and the Spanish Ministry of Education, Culture and Sport (FPU Grant 13/04833), and the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (TEC2016-76795-C6- 4-R).

At present, discrete workshops demand higher transportation efficiency, but the traditional scheduling strategy of the logistics systems can no longer meet the requirements. In a transportation system with multiple automated guided vehicles (multi-AGVs), AGV path conflicts directly affect the efficiency and coordination of the whole system. At the same time, the uncertainty of the number and speed of AGVs will lead to excessive cost. To solve these problems, an AGVs Multi-Objective Dynamic Scheduling (AMODS) method is proposed which is based on the digital twin of the workshop. The digital twin of the workshop is built in the virtual space, and a two-way exchange and real-time control framework based on dynamic data is established. The digital twin system is adopted to exchange data in real time, create a real-time updated dynamic task list, determine the number of AGVs and the speed of AGVs under different working conditions, and effectively improve the efficiency of the logistics system. Compared with the traditional scheduling strategy, this paper is of practical significance for the scheduling of the discrete workshop logistics systems to improve the production efficiency, utilization rate of resources, and dynamic response capability.

Writing and optimizing application software for heterogeneous platforms including GPU units is a very difficult task that requires designer efforts and resources to consider several key elements to obtain good performance. Dataflow programming has shown to be a good approach for accomplishing such a difficult task for its properties of portability and the possibility of arbitrary partitioning a dataflow network on each unit of heterogeneous platforms. However, such a design methodology is not sufficient by itself to obtain good performance. The paper describes some methodological steps for improving the performance of dataflow programs written in RVC-CAL and synthesized to execute on heterogeneous CPU/GPU co-processing platforms. The steps do include the optimization of the performance of the communication tasks between processing elements, a strategy for the efficient scheduling of independent GPU partitions, and the introduction of dynamic programming for leveraging the SIMD nature of GPU platforms. The approach is validated qualitatively and quantitatively using dataflow application program examples executed by applying several partitioning configurations.

Dynamic scheduling of parallel tasks is one of the efficient techniques to achieve high performance in multicore systems. Most existing algorithms for dynamic task scheduling assume that a task runs on one of the multiple cores or a fixed number of cores. Existing researches on dynamic task scheduling methods have evaluated their methods in different experimental environments and models. In this paper, the dynamic task scheduling methods are systematically rearranged and evaluated.

Interbay Automated Material Handling Systems (AMHS) are widely adopted especially in Semiconductor Wafer Fabrication Systems (SWFS). The dispatching method plays a major role in the control of AMHS. This paper proposes an efficient multi-objective dynamic dispatching method which will dynamically adjust vehicle-load assignments according to the real-time situation of the system. A multi-objective cost function with variable weights is established, taking into account various performance indices (i.e., transport time, throughput, cycle time, vehicle utilization, movement, and waiting time), and the corresponding mathematical model is formulated. Then, in order to obtain the suitable weights according to the real-time condition, an advanced method is developed based on fuzzy theory. After that, a Hungarian algorithm is adopted to solve the model. Finally, simulations are conducted to validate the proposed method. The results demonstrate that it has better comprehensive performance compared to the previous dispatching methods.

In this paper, we propose a novel approach to schedule conditional DAG parallel tasks, with which we can derive safe response time upper bounds significantly better than the state-of-the-art counterparts. The main idea is to eliminate the notorious timing anomaly in scheduling parallel tasks by enforcing certain order constraints among the vertices, and thus the response time bound can be accurately predicted off-line by somehow “simulating” the runtime scheduling. A key challenge to apply the timing-anomaly free scheduling approach to conditional DAG parallel tasks is that at runtime it may generate exponentially many instances from a conditional DAG structure. To deal with this problem, we develop effective abstractions, based on which a safe response time upper bound is computed in polynomial time. We also develop algorithms to explore the vertex orders to shorten the response time bound. The effectiveness of the proposed approach is evaluated by experiments with randomly generated DAG tasks with different parameter configurations. Accepted version This work is supported by the Research Grants Council of Hong Kong (GRF 15204917 and 15213818) and National Natrual Science Foundation of China (Grant No. 61672140), and Nanyang Assistant Professorship (NAP) M4082282 and Start-Up Grant (SUG) M4082087 from Nanyang Technological University, Singapore.

The Minimum Cost Flow (MCF) problem is a well-known problem in the area of network optimisation. To tackle this problem, Network Simplex Algorithm (NSA) is the fastest solution method. NSA has three extensions, namely Network Simplex plus Algorithm (NSA+), Dynamic Network Simplex Algorithm (DNSA) and Dynamic Network Simplex plus Algorithm (DNSA+). The objectives of the research reported in this paper are to simulate and investigate the advantages and disadvantages of NSA compared with those of the three extensions in practical situations. To perform the evaluation, an application of these algorithms to scheduling problem of automated guided vehicles in container terminal is used. In the experiments, the number of iterations, CPU-time required to solve problems, overheads and complexity are considered.

High-level synthesis (HLS) tools automatically trans-form a high-level program, for example in C/C++, into a low-level hardware description. A key challenge in HLS tools is scheduling, i.e. determining the start time of all the operations in the untimed program. There are three approaches to scheduling :static, dynamic and hybrid. A major shortcoming of existing approaches to scheduling is that the tools either assume the worst-case timing behaviour, which can cause significant performance loss or area overhead, or use simulation-based approaches, which take a long time to explore enough program traces. In this paper, we propose a probabilistic model that allows HLS tools to efficiently explore the timing behaviour of hardware generated from all these scheduling approaches. We capture the performance of the hardware using Petri nets, allowing us to leverage off-the-shelf Petri net analysis tools to make HLS decisions. We demonstrate the utility of our approach by using it to automatically infer the optimal initiation interval (II) for statically scheduled components that form part of a larger dynamically scheduled circuit. An empirical evaluation on a range of benchmarks suggests that by using this approach, on average we incur a 2% overhead in area-delay product (ADP)compared to optimal designs. In contrast, the static analysis in Vitis HLS incurs a 112% ADP overhead, while the through put analysis in the dynamically scheduled Dynamatic tool incurs a17% ADP overhead.

Conventional High-Level Synthesis (HLS) tools exploit parallelism mostly at the Instruction Level (ILP). They statically schedule the input specifications and build centralized Finite State Machine (FSM) controllers. However, aggressive exploitation of ILP in many applications has diminishing returns and, usually, centralized approaches do not efficiently exploit coarser parallelism, because FSMs are inherently serial. In this paper we present an HLS framework able to synthesize applications that, beside ILP, also expose Task Level Parallelism (TLP). An application can expose TLP through annotations that identify the parallel functions (i.e., tasks). To generate accelerators that efficiently execute concurrent tasks, we need to solve several issues: devise a mechanism to support concurrent execution flows, exploit memory parallelism, and manage synchronization. To support concurrent execution flows, we introduce a novel adaptive controller. The adaptive controller is composed of a set of interacting control elements that independently manage the execution of a task. These control elements check dependencies and resource constraints at runtime, enabling as soon as possible execution. To support parallel access to shared memories and synchronization, we integrate with a novel Hierarchical Memory Interface (HMI). With respect to previous solutions, the proposed interface supports multi-ported memories and atomic memory operations, which commonly occur in parallel programming. Our framework can generate the hardware implementation of C functions by employing two different approaches, depending on its characteristics. If a function exposes TLP, then the framework generates hardware implementations based on the adaptive controller. Otherwise, the framework implements the function through the FSM approach, which is optimized for ILP exploitation. We evaluate our framework on a set of parallel applications and show substantial performance improvements (average speedup of 4.7) with limited area overheads (average area increase of 5.48 times)

Wireless recharging by autonomous power delivery vehicles is an attractive maintenance solution for Internet of Things devices. Improving the operating efficiency of power delivery vehicles is challenging due to complex dynamic environments and the need to solve difficult optimization problems to determine the best combination of routes, number of vehicles, and numerous safety thresholds prior to deployment. The optimal recharge scheduling problem considers minimizing discharged energy of drones while maximizing devices’ recharged energy. In this paper, a configurable optimal recharge scheduler is proposed that incorporates several evolutionary and clustering approaches. A modified version of the Black Hole algorithm is presented, which is shown to execute on average 35% faster than the state of the art genetic approach, while delivering comparable performance in simulation across 18 scenarios with varying area and density of sensor nodes deployed under different initialization scenarios.

Optical network optimization involves an algorithm and a physical layer model (PLM) to estimate the quality of transmission of connections while examining candidate optimization operations. In particular, the algorithm typically calculates intermediate solutions until it reaches the optimum, which is then configured to the network. If it uses a PLM that was aligned once to reflect the starting network configuration, then the algorithm within its intermediate calculations can project the network into states where the PLM suffers from low accuracy, resulting in a suboptimal optimization. In this paper, we propose to solve dynamic multivariable optimization problems with an iterative closed control loop process, where after certain algorithm steps we configure the intermediate solution so that we monitor and realign/retrain the PLM to follow the projected network states. The PLM is used as a digital twin, a digital representation of the real system, which is realigned during the dynamic optimization process. Specifically, we study the dynamic launch power optimization problem, where we have a set of established connections, and we optimize their launch powers while the network operates. We observed substantial improvements in the sum and the lowest margin when optimizing the launch powers with the proposed approach over optimization using a one-time trained PLM. The proposed approach achieved near-to-optimum solutions as found by optimizing and continuously probing and monitoring the network, but with a substantial lower optimization time. Funding: Horizon 2020 Framework Programme (765275). This work is a part of the Future Optical Networks for Innovation, Research and Experimentation (ONFIRE) project (https://h2020-onfire.eu/), supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Actions.

Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the problem of cloud-RAN (C-RAN) joint slice admission control and resource allocation by first formulating it as a Markov decision process (MDP). We then invoke an advanced continuous deep reinforcement learning (DRL) method called twin delayed deep deterministic policy gradient (TD3) to solve it. In this intent, we introduce a multi-objective approach to make the central unit (CU) learn how to re-configure computing resources autonomously while minimizing latency, energy consumption and virtual network function (VNF) instantiation cost for each slice. Moreover, we build a complete 5G C-RAN network slicing environment using OpenAI Gym toolkit where, thanks to its standardized interface, it can be easily tested with different DRL schemes. Finally, we present extensive experimental results to showcase the gain of TD3 as well as the adopted multi-objective strategy in terms of achieved slice admission success rate, latency, energy saving and CPU utilization., Comment: 6 pages, 4 figures, accepted for publication at IEEE GLOBECOM 2020

Proceeding of: 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt 2020) Network slicing is a key capability for next generation mobile networks. It enables one to cost effectively customize logical networks over a shared infrastructure. A critical component of network slicing is resource allocation, which needs to ensure that slices receive the resources needed to support their services while optimizing network efficiency. In this paper, we propose a novel approach to slice-based resource allocation named Guaranteed seRvice Efficient nETwork slicing (GREET). The underlying concept is to set up a constrained resource allocation game, where (i) slices unilaterally optimize their allocations to best meet their (dynamic) customer loads, while (ii) constraints are imposed to guarantee that, if they wish so, slices receive a pre-agreed share of the network resources. The resulting game is a variation of the well-known Fisher market, where slices are provided a budget to contend for network resources (as in a traditional Fisher market), but (unlike a Fisher market) prices are constrained for some resources to provide the desired guarantees. In this way, GREET combines the advantages of a share-based approach (high efficiency by flexible sharing) and reservation-based ones (which provide guarantees by assigning a fixed amount of resources). We characterize the Nash equilibrium, best response dynamics, and propose a practical slice strategy with provable convergence properties. Extensive simulations exhibit substantial improvements over network slicing state-of-the-art benchmarks. The work of J. Zheng and G. de Veciana were supported in part by NSF CNS-1731658 and an Army Futures Command Grant W911NF1920333. The work of A. Banchs was funded in part by the Spanish Ministry of Education, Culture, and Sports with a Salvador de Madariaga grant together with the H2020 5G-TOURS European project (Grant Agreement No. 856950).

The 5G networks are expected to satisfy diverse use cases and business models with significant advancements in terms of capacity, reliability, and latency. The allocation and provisioning of network resources pose a challenge for this novel architecture to guarantee higher flexibility and quality of service. As a potential enabler, network slicing was proposed as an innovative approach for the control of the network resources. Although a static slicing approach can be suitable for the transport and core network, the stochastic behavior of the wireless channel requires fast and secure slicing techniques for resource allocation. In this paper, we propose a dynamic slicing approach for the radio access network, where the network resources are carefully assigned to guarantee the service level agreements and increase the number of served users. To prove the performance of our approach, we implemented a fronthaul testbed to emphasize the strength of our method in terms of throughput and resource utilization, compared to static slicing., © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Nowadays, industries cope with a wide range of situations and/or perturbations that endanger the manufacturing productivity. Traditionally, manufacturing control systems are responsible for managing the manufacturing scheduling and execution, as these have the capability of maintaining the production operations regardless of a given perturbation. Still, the challenge of these systems is to achieve an optimal performance after the perturbations occur. For this reason, manufacturing control systems must incorporate a mechanism with intelligent capabilities to look for optimal performance and operation reactivity regardless of any scenario. This paper proposes a generic control strategy for a manufacturing control system for piloting the execution of a dynamic scheduling problem, considering a new job arrival as the manufacturing perturbation. The study explores a predictive-reactive approach that couples a genetic algorithm for the predictive scheduling and an adaptive genetic algorithm for reactivity control aiming to minimize the weighted tardiness in a dynamic manufacturing scenario. The results obtained from this proposal verify that the effectiveness was improved by using adaptive metaheuristic in a dynamic scheduling problem, considering absorbing the degradation caused by the perturbation.

As performance and energy efficiency have become the main challenges for next-generation high-performance computing, asymmetric multi-core architectures can provide solutions to tackle these issues. Parallel programming models need to be able to suit the needs of such systems and keep on increasing the application's portability and efficiency. This paper proposes two task scheduling approaches that target asymmetric systems. These dynamic scheduling policies reduce total execution time either by detecting the longest or the critical path of the dynamic task dependency graph of the application, or by finding the earliest executor of a task. They use dynamic scheduling and information discoverable during execution, fact that makes them implementable and functional without the need of off-line profiling. In our evaluation we compare these scheduling approaches with two existing state-of the art heterogeneous schedulers and we track their improvement over a FIFO baseline scheduler. We show that the heterogeneous schedulers improve the baseline by up to 1.45× in a real 8-core asymmetric system and up to 2.1× in a simulated 32-core asymmetric chip. © 2016 IEEE.

The growing increase of renewable generation worldwide is posing new challenges for a secure, reliable, and economic operation of power systems. In order to face the uncertain and intermittent production of renewable sources, operating reserves must be allocated efficiently and accurately. Nowadays, these reserves are mainly assigned to thermal units, especially gas-fired generators, due to their operation flexibility and fast response. However, the ramping capabilities of these units define the grade of flexibility offered to the system operation. In practical applications, ramping limits are dynamic, i.e., they are a function of the unit's generating output. Omitting this feature leads to suboptimal or even infeasible reserve allocations, thus increasing not only operating reserve requirements but also transactions in real-time balancing markets needed to back up deviations of renewable generation. This paper contributes with a mixed-integer linear programming model for units’ dynamic ramping allowing intraperiod changes in the unit commitment problem. As a result, operating reserves are better allocated and the units’ flexibility is managed more efficiently than traditional ramping models found in the literature. Different case studies illustrate the functioning and benefits of the proposed formulation.

International audience; In recent wireless communication standards (4G, 5G), the growing need for dynamic adjustments of transmission parameters (e.g., modulation, bandwidth, channel coding rate) makes traditional static scheduling approaches less and less efficient. The reason being that precomputed fixed mapping and scheduling prevent the system from dynamically adapting to changes of the operating conditions (e.g. wireless channel quality, available bandwidth). In this paper, we present Odyn, a hybrid approach for the scheduling and memory management of periodic dataflow applications on parallel, heterogeneous, Non-Uniform Memory Architecture (NUMA) platforms. In Odyn, the ordering of tasks and memory allocation are distributed and computed simultaneously at run-time for each Processing Element. Odyn also proposes a mechanism to prevent deadlocks caused by attempts to allocate buffers in size-limited memories. This technique, based on the static computation of exclusion relations among buffers in a target application, removes the need for backtracking that is typical of dynamic scheduling algorithms. We demonstrate the effectiveness of Odyn on a testbench that simulates the interactions of randomly generated concurrent applications. We also demonstrate its deadlock prevention technique on a selection of use cases.

The integration of economic and environmental objectives is crucial in the waste collection sector, especially for flows characterized by a high economic value like waste from electric and electronic equipment (WEEE). WEEE needs a complex and flexible reverse logistics system to face high uncertainty and variability of waste flows, while keeping a high efficiency. A few efforts in the literature have focused on planning an efficient collection service on a local scale. In this paper, a simulation-based methodology is adopted to compare different alternatives for a WEEE collection service in Italy. A dynamic collection scheme (i.e. with variable collection frequencies based on the actual level of waste flow) is simulated in two different logistics configurations, i.e. one based on direct connection and one based on an “hub-and-spoke” network. The impact of adopting electric vehicles is also evaluated. Alternatives are compared through economic and environmental key performance indicators to assess the level of sustainability. Results show the advantages of a “hub-and-spoke” configuration with the use of electric vehicles.

In this paper, we introduce dynamic time-frequency-division duplex (D-TFDD), which is a novel duplexing scheme that combines time-division duplex (TDD) and frequency-division duplex (FDD). In D-TFDD, a user receives from the base station (BS) on the downlink in one frequency band and transmits to the BS on the uplink in another frequency band, as in FDD. Next, the user shares its uplink transmission (downlink reception) on the corresponding frequency band with the uplink transmission or the downlink reception of another user in a D-TDD fashion. Hence, in a given frequency band, the BS communicates with user 1 (U1) and user 2 (U2) in a D-TDD fashion. The proposed D-TFDD scheme does not require inter-cell interference (ICI) knowledge and only requires channel state information (CSI) of the local BS-U1 and BS-U2 channels. Thereby, it is practical for implementation. The proposed D-TFDD scheme increases the throughput region between the BS and the two users in a given frequency band, and significantly decreases the outage probabilities on the corresponding BS-U1 and BS-U2 channels. Most importantly, the proposed D-TFDD scheme doubles the diversity gain on both the corresponding BS-U1 and the BS-U2 channels compared to the diversity gain of existing duplexing schemes, which results in very large performance gains., Comment: Content presented in this article is subjected to Australian Provisional Patent Application 2019903224, filing date 2/Sep/2019, see http://pericles.ipaustralia.gov.au/ols/auspat/applicationDetails.do?applicationNo=2019903224. arXiv admin note: text overlap with arXiv:1701.05275

Reverse time migration (RTM) is an algorithm widely used in the oil and gas industry to process seismic data. It is a computationally intensive task that suits well in parallel computers. Methods such as RTM can be parallelized in shared memory systems through scheduling iterations of parallel loops to threads. However, several aspects, such as memory size and hierarchy, number of cores, and input size, make optimal scheduling very challenging. In this paper, we introduce a run-time strategy to automatically tune the dynamic scheduling of parallel loops iterations in iterative applications, such as the RTM, in multicore systems. The proposed method aims to reduce the execution time of such applications. To find the optimal granularity, we propose a coupled simulated annealing (CSA) based auto-tuning strategy that adjusts the chunk size of work that OpenMP parallel loops assign dynamically to worker threads during the initialization of a 3D RTM application. Experiments performed with different computational systems and input sizes show that the proposed method is consistently better than the default OpenMP schedulers, static , auto , and guided , causing the application to be up to 33% faster. We show that the possible reason for this performance is the reduction of cache misses, mainly level L3, and low overhead, inferior to 2%. Having shown to be robust and scalable for the 3D RTM, the proposed method could also improve the performance of similar wave-based algorithms, such as full-waveform inversion (FWI) and other iterative applications.

International audience; Checkpointing-Aided Parallel Execution (CAPE) is a framework that is based on checkpointing technique and serves to automatically translates and execute OpenMP programs on distributed-memory architectures. In some comparisons with MPI, CAPE have demonstrated high-performance and the potential for fully compatibility with OpenMP on distributed-memory systems. However, it should be continued to improve the performance, flexibility, portability and capability. This paper presents the new execution model for CAPE that improves its performance and makes CAPE even more flexible

Multi-layer microgrids cluster (MLMC) has been formed in some certain areas with microgrids (MGs) merging into smart distributed system. It relies on an energy management system (EMS) to manage and schedule the optimal power flow among MGs and public power system in a stable and balanced way. Compared with EMS for single MG, energy management optimization for MLMC is much more complex that the interactions with other MGs require to be considered. In this paper a multi-time-scales EMS is proposed to minimize the total operating cost of MLMC. It consists of two parts: day-ahead static scheduling and real-time dynamic compensation. The day-ahead scheduling is an economic optimization based on the forecasting data within 24 hours. The real-time dynamic compensation is presented to compensate scheduling signal based on the real-time operating information, which is caused by the predictive error. Furthermore, charge-discharge protections of energy storage system (ESS) for each single MG are taken into account. A general-structure MLMG is studied as an example, and this problem is solved by mixed integer linear programming (MILP). The effectiveness of proposed EMS is verified in the MG Research Laboratory in Aalborg University, Denmark. The hardware-in-loop simulation results show the proposed EMS has a good convergence and performance in grid-connected mode.

Commodity processors are comprised of several CPU cores and one integrated GPU. To fully exploit this type of architectures, one needs to automatically determine how to partition the workload between both devices. This is specially challenging for irregular workloads, where each iteration's work is data dependent and shows control and memory divergence. In this paper, we present a novel adaptive partitioning strategy specially designed for irregular applications running on heterogeneous CPU-GPU chips. The main novelty of this work is that the size of the workload assigned to the GPU and CPU adapts dynamically to maximize the GPU and CPU utilization while balancing the workload among the devices. Our experimental results on an Intel Haswell architecture using a set of irregular benchmarks show that our approach outperforms exhaustive static and adaptive state-of-the-art approaches in terms of performance and energy consumption.

Parallel computation is fundamental to satisfy the performance requirements of advanced safety-critical systems. OpenMP is a good candidate to exploit the performance opportunities of parallel platforms. However, safety-critical systems are often based on static allocation strategies, whereas current OpenMP implementations are based on dynamic schedulers. This paper proposes two OpenMP-compliant static allocation approaches: an optimal but costly approach based on an ILP formulation, and a sub-optimal but tractable approach that computes a worst-case makespan bound close to the optimal one. This work is funded by the EU projects P-SOCRATES (FP7-ICT-2013-10) and HERCULES (H2020/ICT/2015/688860), and the Spanish Ministry of Science and Innovation under contract TIN2015-65316-P.

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.

Supply and production uncertainties can affect the scheduling and inventory performance of final production systems. Facing such uncertainties, production managers normally choose to maintain the original production schedule, or follow the first-in-first-out policy. This paper develops a new, dynamic algorithm policy that considers scheduling and inventory problems, by taking advantage of real-time shipping information enabled by today’s advanced technology. Simulation models based on the industrial example of a chemical company and the Taguchi’s method are used to test these three policies under 81 experiments with varying supply and production lead times and uncertainties. Simulation results show that the proposed dynamic algorithm outperforms the other two policies for supply chain cost. Results from Taguchi’s method show that companies should focus their long-term effort on the reduction of supply lead times, which positively affects the mitigation of supply uncertainty.

This study experiments with the manufacturing efficiency by layout change of a factory by means of agent-based autonomous production scheduling, using the virtual factory on a multi-agent simulation system. As infrastructure software for agent based simulation, the artisoc(c) is used. In this virtual factory, three types of agents are equipped. Users can alter a configuration such as input new jobs, adjusting a machine setting, etc, with monitoring conditions of agents. As a result, by adjustment of the agent's behavior with shop floor detail, the assembly schedule becomes more effective. The experiment is carried out to show that local negotiations contribute to global optimization when resources in the factory are effectively distributed and shared. In this paper, the effectiveness of job-list cleanup method is shown. In addition, the scheduling influence is simulated by the communication range of agents. A part agent chooses a machine, by the length of a job list and the conveyance cost. But the communication cost between agents increases with the size of the communication range. From experimental results, when extending the communication range simply, the conclusion is reached that optimization did not necessarily result in progress.

Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.

Scheduling product batches in pipelines is a very complex task with many constraints to be considered. Several papers have been published on the subject during the last decade. Most of them are based on large-size MILP discrete time scheduling models whose computational efficiency greatly diminishes for rather long time horizons. Recently, anMILPcontinuous problem representation in both time and volume providing better schedules at much lower computational cost has been published.However, all model-based scheduling techniques were applied to examples assuming a static market environment, a short single-period time horizon and a unique due-date for all deliveries at the horizon end. In contrast, pipeline operators generally use a monthly planning horizon divided into a number of equal-length periods and a cyclic scheduling strategy to fulfill terminal demands at period ends. Moreover, the rerouting of shipments and time-dependent product requirements at distribution terminals force the scheduler to continuously update pipeline operations. To address such big challenges facing the pipeline industry, thiswork presents an efficient MILP continuous-time framework for the dynamic scheduling of pipelines over a multiperiod moving horizon. At the completion time of the current period, the planning horizon moves forward and the re-scheduling process based on updated problem data is triggered again over the newhorizon. Pumping runs may extend over two or more periods and a different sequence of batches may be injected at each one. The approach has successfully solved a real-world pipeline scheduling problem involving the transportation of four products to five destinations over a rolling horizon always comprising four 1-week periods. Fil: Cafaro, Diego Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina

Este trabajo describe nuestra contribución para la ejecución de bucles paralelos en arquitecturas multi-core/multi-GPU de forma que la carga computacional se distribuya de forma balanceada entre todas las unidades de computación. This paper explores the possibility of efficiently using multicores in conjunction with multiple GPU accelerators under a parallel task programming paradigm. In particular, we address the challenge of extending a parallel_for template to allow its exploitation on heterogeneous systems. The extension is based on a two-stages pipeline engine which is responsible for partitioning and scheduling the chunks into the computational resources. Under this engine, we propose a dynamic scheduling strategy coupled with an adaptive partitioning heuristic that resizes chunks to prevent underutilization and load unbalance of CPUs and GPUs. In this paper we introduce the adaptive partitioning heuristic which is derived from an analytical model that minimizes the load unbalance while maximizes the throughput in the system. Using two benchmarks we evaluate the overhead introduced by our template extensions finding that it is negligible. We also evaluate the efficiency of our adaptive partitioning strategies and compared them with related work. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. TIN2010-16144, P08-TIC-3500, P11-TIC-08144

This paper makes three contributions to research on QoS-enabled middleware for open distributed real-time embedded (DRE) systems. First, it describes the design and implementation of a dynamic scheduling framework based on the OMG Real-Time CORBA 1.2 specification (RTC1.2) that provides capabilities for (1) propagating QoS parameters and a locus of execution across endsystems via a distributable thread abstraction and (2) enforcing the scheduling of multiple distributable threads dynamically using standard CORBA middleware. Second, it examines the results of empirical studies that show how adaptive dynamic scheduling and management of distributable threads can be enforced efficiently in standard middleware for open DRE systems. Third, it presents results from case studies of multiple adaptive middleware QoS management technologies to monitor and control the quality, timeliness, and criticality of key operations adaptively in a representative DRE avionics system.

Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. The composite rule is obtained by exploring various states of a semiconductor production line (machine status, queue size, etc.), where such indicators as makespan and equipment efficiency are used to judge performance. A model of the response surface, as a function of key variables, is then developed to find the optimized parameters of a composite rule for various production states. Furthermore, dynamic scheduling of semiconductor manufacturing is studied based on support vector regression (SVR). This approach dynamically obtains a composite dispatching rule (i.e., parameters of the composite dispatching rule) that can be used to optimize production performance according to real-time production line state. Following optimization, the proposed dynamic scheduling approach is tested in a real semiconductor production line to validate the effectiveness of the proposed composite rule set.

Ultra-dense network (UDN) is considered as a promising technology in 5G wireless networks. In an UDN network, dynamic traffic patterns can lead to a high computational complexity and an excessive communications overhead with traditional resource allocation schemes. In this paper, a new resource allocation scheme presenting a low computational overhead and a low subband handoff rate in a dynamic ultra-dense heterogeneous network is presented. The scheme first defines a new interference estimation method that constructs network interference state map, based on which a radio resource allocation scheme is proposed. The resource allocation problem is a MAX-K cut problem and can be solved through a graph- theoretical approach. System level simulations reveal that the proposed scheme decreases the subband handoff rate by 30% with less than 3.2% network throughput degradation.

International audience; Mapping an application on a coarse grained reconfigurable architecture (CGRA) is a complex task which is still often completely or partially realized manually. This paper presents an automated synthesis flow based on simultaneous scheduling and binding steps. The proposed method uses a backward traversal of the formal model obtained after compilation and dynamically transforms it when needed. Our approach is compared with state of the art techniques and its interest is shown through the mapping of several applications from digital signal and image processing domain.

The delivery of video services in a controllable and resource efficient manner while meeting the various QoE/QoS requirements in mobile networks is a challenging task, especially in a multiclass wireless environment. This paper proposes an intelligent and context-aware application level fair scheduler based on reinforcement-learning, which can dynamically adjust relevant scheduling parameters in reaction to specific events or context information. The implemented Q-learning method is analyzed with reference to the delivery of progressive video streaming services. We first highlight the performance issues during progressive video streaming over TCP to multiple users under resource constrained environment. We then demonstrate the utilization of employing Q-learning method in our scheduler for intelligent orchestration between multiple concurrent flows to ensure against buffer starvation and thus enable smooth playback. We also demonstrate the effectiveness of our context-aware dynamic scheduler to provide service separation between the user classes and fairness within a user class.

The tremendous increase in the size and heterogeneity of supercomputers makes it very difficult to predict the performance of a scheduling algorithm. Therefore, dynamic solutions, where scheduling decisions are made at runtime have overpassed static allocation strategies. The simplicity and efficiency of dynamic schedulers such as Hadoop are a key of the success of the MapReduce framework. Dynamic schedulers such as StarPU, PaRSEC or StarSs are also developed for more constrained computations, e.g. task graphs coming from linear algebra. To make their decisions, these runtime systems make use of some static information, such as the distance of tasks to the critical path or the affinity between tasks and computing resources (CPU, GPU,...) and of dynamic information, such as where input data are actually located. In this paper, we concentrate on two elementary linear algebra kernels, namely the outer product and the matrix multiplication. For each problem, we propose several dynamic strategies that can be used at runtime and we provide an analytic study of their theoretical performance. We prove that the theoretical analysis provides very good estimate of the amount of communications induced by a dynamic strategy and can be used in order to efficiently determine thresholds used in dynamic scheduler, thus enabling to choose among them for a given problem and architecture., Comment: Accepted for publication in HPDC 2014

Structures of parallel programs are usually represented by task graphs in the scheduling literature. Such graphs are sometimes obtained at compile time. In many other cases, however, they can be determined only at run time. In this paper, we consider the scheduling of parallel computations whose task graphs are generated at run time. We analyze the case where the task graph is a random out-tree. When the number of offspring of a task has a geometric distribution whose parameter is decreasing and convex in the level, then the breadth-first policy stochastically minimizes the makespan. If, however, this parameter is increasing and concave, then the depth-first policy stochastically minimizes the makespan.

Recent logic programming languages employ dynamic scheduling of calls to improve efficiency of programs. Dynamic scheduling is realized by allowing some calls to be dynamically ''delayed'' until their arguments are sufficiently instantiated. To this end, logic languages are extended with constructs such as delay declarations. However, many declarative properties that hold for logic and pure Prolog programs do not apply any longer in this extended setting. In particular, the equivalence between the model-theoretic and operational semantics does not hold. In this paper, we study the class of input-consuming programs. Firstly, we argue that input-consuming logic programs are suitable for modeling programs employing delay declarations. Secondly, we show that-under some syntactic restrictions-the S-semantics of a program is correct and fully abstract also for input-consuming programs. This allows us to conclude that for a large class of programs employing delay declarations there exists a model-theoretic semantics which is equivalent to the operational one. Thus, input-consuming programs are shown to be the right answer for conjugate efficiency and declarativeness.

Naval Research Logistics (NRL) V. 56, No. 2, pp 113-126. This paper concerns scheduling policies in a surveillance system aimed at detecting a terrorist attack in time. Terrorist suspects arriving at a public area are subject to continuous monitoring, while a surveillance team takes their biometric signatures and compares them with records stored in a terrorist database. Because the surveillance team can screen only one terrorist suspect at a time, the team faces a dynamic scheduling problem among the suspects. We build a model consisting of an M/G/1 queue with two types of customers--red and white--to study this problem. Both types of customers are impatient, but the reneging time distributions are different. The server only receives a reward by serving a red customer, and can use the time a customer has spent in the queue to deduce its likely type. In a few special cases, a simple service rule--such as a first-come-first-serve--is optimal. We explain why the problem is in general different, and develop a heuristic policy motivation by the fact that terrorist attacks tend to be rare events.