Hierarchical Scheduling Framework Research Articles

Collaborative hierarchical scheduling model of interconnected multi-microgrid and ADN considering DR with different strategies

The demand response (DR) resources of multiple entities have different operation strategies, which makes it difficult to realize the co-optimization. This paper proposes a cooperative hierarchical scheduling optimization model of interconnected multi-microgrid and an active distribution network (ADN) considering DR with different operation strategies. First, the collaborative hierarchical scheduling framework is proposed and the interaction characteristics and cooperative scheduling mode of multi-interconnected microgrid and ADN are analyzed. Second, the two-layer cooperative hierarchical scheduling model is established, considering DR with different strategies. The upper layer model takes the minimum operating cost of the ADN as the objective to optimize the trading tariff between the ADN and microgrids, and its own DR, while the lower layer model takes the operating cost of multi-microgrid as the objective to optimize the purchasing and selling of electrical energy for the ADN, the DR, and the interaction power. Finally, the case studies with three microgrids and an ADN are used to demonstrate the validity and effectiveness of the proposed model.

Price-based coordination strategy for copper smelting process

Rigorous scheduling that uses hard heuristics and allows forceful shutdown of process units, whether intentionally or unintentionally, in the copper smelting process always harms the process operation. This way of process scheduling often results in sub-optimal process operation. Although a flexible scheduling framework that empowers process units to make independent operational decisions is desirable, the inter-dependencies among these units continue to pose a challenge in its design. This study presents a hierarchical scheduling framework for the copper smelting process, which uses price-based heuristics to resolve process inter-dependencies among the process units. These inter-dependencies arise due to the bottlenecks present in the process. Taking advantage of market theory, the coordinator uses PI controllers to find the appropriate prices to address the inter-dependencies. Two case studies are presented to demonstrate that the proposed framework effectively resolves all the process inter-dependencies, indicating its potential for application in other large-scale scheduling applications.

Optimal Selection of Matte Grade in Copper Smelting Process

This work presents the sensitivity analysis of a hierarchical scheduling framework for the copper smelting process with respect to the change in matte grade. The aim is to find the matte grade that can maximize the throughput of the copper smelting process. This study considers multiple-PSC units and a multiple-batch hierarchical scheduling framework that finds an optimal schedule for the process using promising heuristics. The process is briefly discussed, and the simulation results are discussed in detail to provide a comprehensive view of the effects of the matte grade on the operation of the copper smelting process.

Distribution locational marginal price based hierarchical scheduling framework for grid flexibility from virtual energy storage systems

• VESS's are modeled using residential air-conditioners for DSM. • Local ancillary services TSO-DSO coordination scheme for cooperative management in grids. • Hierarchical framework for scheduling aggregated VESS. • Network constrained optimal VESS participation in ancillary service markets. • DSM based practical market model for SOs, maximizing overall market benefits. Maintaining generation and demand equilibrium under large scale renewable energy integration is challenging. Conventional flexibility providers such as grid-scale Energy Storage Systems (ESS) have potential but are costly solutions. With evolving smart grid scenario, aggregated demand response from Thermostatically Controlled Loads (TCLs) is utilized as Virtual Energy Storage Systems (VESS) and considered as innovative and cost efficient solution to provide fast response reserves. VESS units are often scheduled and controlled in a centralized manner considering limited distribution system constraints. However, accurate operational flexibility potential of diversified VESS units for various grid services needs to consider distribution network constraints. It can be fully utilized when Distribution System Operators (DSO) coordinate with Transmission System Operators (TSO) to transfer grid services from VESS of its area. Local ancillary service TSO-DSO coordination scheme ensures in this perspective significant flexibility of TSO and allows both TSO and DSO to access VESS flexibility. For such controlling of diversified VESS units where DSO is responsible for aggregate flexibility and local area balancing, a hierarchical scheduling framework is more practical. In this context, this paper proposes a two-stage hierarchical approach for optimal scheduling of TCLs as VESS to optimize energy and provide grid flexibility. At the lower stage, TCLs are scheduled based on Distribution Locational Marginal Price (DLMP) provided by DSO, while ensuring consumers’ comfort. At the upper level, DSO provides additional flexibility to TSO through optimal scheduling of aggregated VESS considering local ancillary services TSO-DSO coordination scheme. The simulation results indicate that VESS scheduling improves system flexibility by maintaining a generation-demand balance.

Scheduling Parallel Real-Time Tasks on Virtual Processors

In many popular parallel programming models, e.g., OpenMP (OpenMP, 2013), applications are usually dispatched into several dedicated scheduling entities (named ”threads” in common) for which the processor time of physical platform is provided through the OS schedulers. This behavior requires for a hierarchical scheduling framework, considering each thread as a virtual processor (VP). Moreover, hierarchical scheduling allow separate applications to execute together on a common hardware platform, with each application having the “illusion” of executing on a dedicated component. However, the problem for scheduling parallel real-time tasks on virtual multiprocessor platform has not been addressed yet. An analogous approach to virtual scheduling for parallel real-time tasks is federeted scheudling, where each task exclusively executes on a set of dedicated physical processors. However, federated scheduling suffers significant resource wasting. In this article, we study the scheduling of real-time parallel task on virtual multiprocessors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings.

A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units

Nowadays, decentralized microgrids (DC-MGs) have become a popular topic due to the effectiveness and the less complexity. In fact, DC-MGs resist to share their internal information with the distribution system operator (DSO) to protect their privacy and compete in the electricity market. Further, lack of information sharing among MGs in normal operation conditions leads to form a competitive market. However, in emergency operation conditions, it results numerous challenges in managing network outages. Therefore, this paper presents a hierarchical model consisting of three stages to enhance the resilience of DC-MGs. In all stages, the network outage management is performed considering the reported data of MGs. In the first stage, proactive actions are performed with the aim of increasing the network readiness against the upcoming windstorm. In the second stage, generation scheduling, allocation of mobile units and distribution feeder reconfiguration (DFR) are operated by DSO to minimize operating costs. In the final stage, the repair crew is allocated to minimize the energy not served (ENS). Uncertainties of load demand, wind speed and solar radiation are considered, and the effectiveness of the proposed model is investigated by integrating to the 118-bus distribution network. Finally, the results of the simulation indicate that DFR and proactive actions decrease the ENS by 19,124 kWh and 4101 kWh, respectively. Further, the sharing of information among MGs leads to a 48.16% growth in the supply service level to critical loads, and consequently a 3.47% increase in the resilience index.

Hierarchical management strategy for electric vehicles charging schedule considering the scarcity of charging resources

Charging resources are scarce due to the limited capacity of charging stations and the congestion of distribution networks. Here, a hierarchical scheduling framework that considers the privacy of participants is established to optimize the charging power with a fair payment mechanism. The charging resource allocation model is established considering the scarcity of charging resources. Furthermore, the model is divided into three sub problems, including the congestion management problem of distribution network operator, the energy management problem of charging station and the charging mode problem of electric vehicle. Then the collaborative optimization algorithms are proposed to ensure the privacy of participants. Finally, a hierarchical payment mechanism based on the opportunity cost theory is proposed. The performance of the proposed hierarchical scheduling framework is evaluated using the Roy Billiton Test System with 2000 EVs. On the premise of ensuring distribution network economic operation, the proposed hierarchical scheduling strategy can satisfy the requirement for privacy protection and eliminate the overload problem caused by electric vehicle charging. The simulation results have shown that the additional capacity cost has an obvious levelling effect. With the application of the payment mechanism, charging resources is equitably allocated. Also, the equilibrium of a market and the stable operation of power system can be guaranteed.

The short-term optimal resource allocation approach for electric vehicles and V2G service stations

As more electric vehicles (EVs) participate in vehicle-to-grid (V2G) service, the large-scale EV-pile resource allocation problem is becoming a key issue that affects system operation and user participation. EV user preference and decision-making uncertainty can affect V2G scheduling, potentially causing an imbalance between the dispatchable capacities of aggregated EVs and the power required in service stations. To improve the utilization rates of EV batteries and charging/discharging piles, this study proposes a vehicle-pile resource allocation approach based on a two-stage categorical hierarchical scheduling framework to solve the vehicle-pile assignment problem in near real time. It also develops a new hybrid clustering algorithm and a vehicle-pile resource assignment model that considers user preferences and requirements in the upper layer, and operational cost reduction in the lower layer. The effectiveness of the proposed algorithm and model are verified by simulation cases to achieve an 88% actual power matching degree and a 25% cost reduction. Moreover, the credit priority strategy is proposed and designed for the selection of EVs with higher dispatchability to ensure the effective implementation of allocation solutions.

Online Learning for Hierarchical Scheduling to Support Network Slicing in Cellular Networks

We study a learning-based hierarchical scheduling framework in support of network slicing for cellular networks. This addresses settings where users and/or service classes are grouped into slices, and resources are allocated hierarchically. The hierarchy is implemented by combining a slice-level scheduler which allocates resources to slices, and flow-level schedulers within slices which opportunistically allocate resources to users/services. Optimizing the slice-level scheduler to maximize system utility is typically hard due to underlying heterogeneity and uncertainty in user channels and performance requirements. We address this by reformulating the problem as an online black-box optimization where slice-level schedulers (parameterized by a weight vector) combined with flow-level schedulers result in user/service level stochastic rewards representing performance fitness; the goal is to learn the best weight vector. We develop a bandit algorithm based on queueing cycles by building on Hierarchical Optimistic Optimization (HOO). The algorithm guides the system to improve the choice of the weight vector based on observed rewards. Theoretical analysis of our algorithm shows a sub-linear regret with respect to an omniscient genie. Finally through simulations, we show that the algorithm adaptively learns the optimal weight vectors when combined with opportunistic and/or utility-maximizing flow-level schedulers.

CAMDNN: Content-Aware Mapping of a Network of Deep Neural Networks on Edge MPSoCs

Machine Learning (ML) workloads are increasingly deployed at the edge. Enabling efficient inference execution while considering model and system heterogeneity remains challenging, especially for ML tasks built with a network of DNNs. The challenge is to maximize the utilization of all available resources on the multiprocessor system on a chip (MPSoC) at the same time. This becomes even more complicated because the optimal mapping for the network of DNNs can vary with input batch sizes and scene complexity. In this paper, a holistic hierarchical scheduling framework is presented to optimize the execution time for a network of DNN models on an edge MPSoC at runtime, considering varying input characteristics. The framework consists of a local and a global scheduler. The local scheduler maps individual DNNs in the inference pipeline to the best-performing hardware unit while the global scheduler customizes an Integer Linear Programming (ILP) solution to instantiate DNN remapping. To minimize scheduler runtime overhead, an imitation learning (IL) based scheduler is used that approximates the ILP solutions. The proposed scheduling framework (CAMDNN) was implemented on a Qualcomm Robotic RB5 platform. CAMDNN resulted in lower execution time of up to 32% than HEFT, and by factors of 6.67X, 5.6X and 2.17X than the CPU-only, GPU-only and Central Queue schedulers.

Active and reactive power coordinated optimal dispatch in active distribution network based on model predictive control

With the proliferation of the distributed energy resources (DERs), the scheduling and control of the distribution network have become more complicated. To cope with the uncertainty nature of distributed generation, a multi-timescale optimal dispatch method in active distribution network (ADN) based on the model predictive control (MPC) is proposed in this paper. First, based on MPC, a hierarchical scheduling framework for ADN is established, including long-timescale stage, and short-timescale stage. Then, via coordinated control of various resources in the ADN, i.e., distributed generators, energy storage, capacitor banks and OLTC transformer, the impact of intermittent renewable energy and load forecast errors can be reduced. Finally, considering the coupling characteristics of active and reactive power in the ADN, a joint active and reactive power optimization model is proposed to further reduce the network loss. Numerical simulation on a modified IEEE-33 distribution network system verifies the correctness and superiority of the proposed scheduling approach.

Online learning for hierarchical scheduling to support network slicing in cellular networks

We study a learning-based hierarchical scheduling framework in support of network slicing for cellular networks. This addresses settings where users and/or service classes are grouped into slices, and resources are allocated hierarchically. The hierarchy is implemented by combining a slice-level scheduler which allocates resources to slices, and flow-level schedulers within slices which opportunistically allocate resources to users/services. Optimizing the slice-level scheduler to maximize system utility is typically hard due to underlying heterogeneity and uncertainty in user channels and performance requirements. We address this by reformulating the problem as an online black-box optimization where slice-level schedulers (parameterized by a weight vector) combined with flow-level schedulers result in user/service level stochastic rewards representing performance fitness; the goal is to learn the best weight vector. We develop a bandit algorithm based on queueing cycles by building on Hierarchical Optimistic Optimization (HOO). The algorithm guides the system to improve the choice of the weight vector based on observed rewards. Theoretical analysis of our algorithm shows a sub-linear regret with respect to an omniscient genie. Finally through simulations, we show that the algorithm adaptively learns the optimal weight vectors when combined with opportunistic and/or utility-maximizing flow-level schedulers.

An Evolutionary Approach for the Hierarchical Scheduling of Safety- and Security-Critical Multicore Architectures

The aerospace and defense industry is facing an end-of-life production issue with legacy embedded uniprocessor systems. Most, if not all, embedded processor manufacturers have already moved towards system-on-a-chip multicore architectures. Current scheduling arrangements do not consider schedules related to safety and security. The methods are also inefficient because they arbitrarily assign larger-than-necessary windows of execution. This research creates a hierarchical scheduling framework as a model for real-time multicore systems to integrate the scheduling for safe and secure systems. This provides a more efficient approach which automates the migration of embedded systems’ real-time software tasks to multicore architectures. A novel genetic algorithm with a unique objective function and encoding scheme was created and compared to classical bin-packing algorithms. The simulation results show the genetic algorithm had 1.8–2.5 times less error (a 56–71% difference), outperforming its counterparts in uniformity in utilization. This research provides an efficient, automated method for commercial, private and defense industries to use a genetic algorithm to create a feasible two-level hierarchical schedule for real-time embedded multicore systems that address safety and security constraints.

Adia: Achieving High Link Utilization with Coflow-Aware Scheduling in Data Center Networks

Link utilization has received extensive attention since data centers become the most pervasive platform for data-parallel applications. A specific job of such applications involves communication among multiple machines. The recently proposed coflow abstraction depicts such communication through a group of parallel flows, and captures application performance through corresponding communication requirements. Existing techniques to improve link utilization, however, either restrict themselves to achieving work conservation, or merely focus on flow-level metrics and ignore coflow-level performance. In this paper, we address the coflow-aware scheduling problem with the objective of maximizing link utilization. Through theoretic analyses, we formulate the coflow-aware scheduling problem as a NP-hard open shop scheduling problem with heterogeneous concurrency. We design Adia, a hierarchical scheduling framework to conduct both inter- and intra- link scheduling. The design of Adia leverages priority-based scheduling while guarantees work-conserving and starvation-free bandwidth allocation at the same time. We also prove Adia's algorithm is two-approximate in terms of link utilization. Extensive simulation results on ns3 further show that Adia outperforms both per-flow mechanisms coflow schemes in terms of link utilization, and achieves similar coflow performance in comparison with the state-of-art coflow scheduling schemes.

Adaptive Hierarchical Scheduling Framework for TiRTOS

The complexity of embedded real-time systems has increased, and most applications have large diversity in execution times of their tasks. Therefore, most traditional scheduling techniques do not satisfy requirements of such applications. This article proposes an adaptive hierarchical scheduling framework for a set of independent concurrent applications composing of soft and hard real time tasks, that run on a single processor. It ensures temporal partitioning between independent applications with budget adaption feature, where CPU time of each application is periodically and dynamically assigned. Implemented in the kernel of TI-RTOS on a resource constrained platform, experiments show that proposed scheme provides good performance for multiple applications with dynamic tasks under overload conditions. Compared with traditional priority scheduler originally implemented in TI-RTOS and EDF scheduler, it achieves low miss ratio with minimal overhead while yielding temporal partitioning.

Building real-time parallel task systems on multi-cores: A hierarchical scheduling approach

Abstract Parallel real-time scheduling on multi-core platforms is widely studied and focused in academic and industrial fields. DAG (directed acyclic graph) is a widely-used parallel model. But existing scheduling algorithms for DAG suffer schedulability analysis of significant inaccuracy and resource waste. In this paper, we propose a hierarchical scheduling framework for DAG tasks with constrained deadlines that it provides a much more efficient resource sharing solution without distinguishing the types of tasks. We have evaluated and implemented our proposed hierarchical scheduling and the results show that our proposed method has promising performance.

Research on the optimal dispatch of wind power consumption based on combined heat and power with thermal energy storage

In this paper, a hierarchical scheduling framework is proposed to dispatch electricity and thermal in CHP system with thermal energy storage for the consumption of wind power. An optimization mathematical model is proposed to demonstrate the characteristics of the whole system. The modified PSO algorithm is used to solve the coordinated dispatching problem. The traditional way of working between electricity and thermal is independent while the proposed mathematical model combined electricity and thermal, it’s more accurate to take the relationship between electricity and thermal into consideration. This model has good performance in solving the cross coupling problem. The simulation results indicate that the proposed optimization mathematical model has good dispatching performance on reducing coal consumption. The most important contribution of this paper are accelerating the consumption of wind power in Northern China and saving coal consumption, which can save plenty of money and prevent air pollution. Further work will be reflected in the demonstration project.

Energy-Aware Joint User Scheduling and Power Control for Two-Tier Femtocell Networks: A Hierarchical Game Approach

Femtocells are considered as promising technologies for the next generation wireless networks to improve system capacity and enhance indoor coverage. The dense deployment of femtocells brings a lot of challenges in terms of interference management and energy consumption. To alleviate co-channel interference, we investigate interference management based on hierarchically joint user scheduling and power control in downlink femtocell networks considering the impact of energy cost. Then, we formulate this problem as a Stackelberg game with one leader and multiple followers. Specifically, macro base station and femtocell base stations select their optimal scheduled users and determine the optimal transmission power, based on their own net utilities considering power cost. To obtain the Stackelberg equilibrium (SE) in a distributed manner, a hierarchical and iterative user scheduling and power update framework is proposed. Moreover, we prove that the optimization of user-scheduling and power control can be decoupled in our scheme and the proposed scheme converges to a unique SE under mild conditions. Simulation results are presented to show the convergence and effectiveness of the proposed hierarchical game.

Predictable integration and reuse of executable real-time components

We present the concept of runnable virtual node (RVN) as a means to achieve predictable integration and reuse of executable real-time components in embedded systems. A runnable virtual node is a coarse-grained software component that provides functional and temporal isolation with respect to its environment. Its interaction with the environment is bounded both by a functional and a temporal interface, and the validity of its internal temporal behaviour is preserved when integrated with other components or when reused in a new environment. Our realization of RVN exploits the latest techniques for hierarchical scheduling to achieve temporal isolation, and the principles from component-based software-engineering to achieve functional isolation. It uses a two-level deployment process, i.e. deploying functional entities to RVNs and then deploying RVNs to physical nodes, and thus also gives development benefits with respect to composability, system integration, testing, and validation. In addition, we have implemented a server-based inter-RVN communication strategy to not only support the predictable integration and reuse properties of RVNs by keeping the communication code in a separate server, but also increasing the maintainability and flexibility to change the communication code without affecting the timing properties of RVNs. We have applied our approach to a case study, implemented in the ProCom component technology executing on top of a FreeRTOS-based hierarchical scheduling framework and present the results as a proof-of-concept.

Reliable transmission scheduling for multi‐channel wireless sensor networks with low‐cost channel estimation

The shared-medium nature and complex wireless environment of wireless sensor networks (WSNs) poses fundamental challenges to the design of effective transmission scheduling algorithms that are optimised with respect to superframe length and reliability. In this study, the authors propose an adaptive and reliable transmission scheduling algorithm for WSNs based on low-cost estimation of channel states. The authors establish a hierarchical scheduling framework on global centralised timeslot scheduling and local distributed channel scheduling. On the one hand, global centralised timeslot scheduling aims to guarantee global optimality of resource allocation, during which a mathematical reliability model is built to avoid resource waste by the stationary allocation method and improve the reliability of packet transmission. On the other hand, local distributed channel scheduling shares the responsibility of resource allocation. During channel scheduling, the channel model is constructed by the dynamic programming method and takes both probing cost and channel quality into consideration, which alleviates the uncertain and time-varying interference and overcomes the blindness of traditional methods. In contrast with previous works that do not consider link reliability and channel probing cost and often assume two channel states, the scheduling algorithm performs reliably for an arbitrary number of channels and arbitrary number of channel states. Extensive simulations and experiments under a variety of network environments have been conducted to validate our theoretical claims.