Multi-resource scheduling for FPGA systems

In modern cloud data centers, reconfigurable devices (FPGAs) are used as an alternative to Graphics Processing Units to accelerate data-intensive computations (e.g., machine learning, image and signal processing). Currently, FPGAs are configured to execute fixed workloads, repeatedly over long periods of time. This conflicts with the needs, proper to cloud computing, to flexibly allocate different workloads and to offer the use of physical devices to multiple users. This raises the need for novel, efficient FPGA scheduling algorithms that can decide execution orders close to the optimum in a short time. In this context, we propose a novel scheduling heuristic where groups of tasks that execute together are interposed by hardware reconfigurations. Our contribution is based on gathering tasks around a high-latency task that hides the latency of tasks, within the same group, that run in parallel and have shorter latencies. We evaluated our solution on a benchmark of 37500 random workloads, synthesized from realistic designs (i.e., topology, resource occupancy). For this testbench, on average, our heuristic produces optimum makespan solutions in 47.4% of the cases. It produces acceptable solutions for moderately constrained systems (i.e., the deadline falls within 10% of the optimum makespan) in 90.1% of the cases.     

Scheduling distributed flowshops with flexible assembly and set-up time to minimise makespan

The scheduling problems under distributed production or flexible assembly settings have achieved increasing attention in recent years. This paper considers scheduling the integration of these two environments and proposes an original distributed flowshop scheduling problem with flexible assembly and set-up time. Distributed production stage is deployed several homogeneous flowshop factories that process the jobs to be assembled into final products in the flexible assembly stage. The objective is to find a schedule, including a production subschedule for jobs and an assembly subschedule for products, to minimise the makespan. Such a scheduling problem involves four successive decisions: assigning jobs to production factories, sequencing jobs at every factory, designating an assembly machine for each product and sequencing products on each assembly machine. The computational model is first established, and then a constructive heuristic (TPHS) and two hybrid metaheuristics (HVNS and HPSO) are proposed. Numerical experiments have been carried out and results validate the algorithmic feasibility and effectiveness. TPHS can obtain reasonable solutions in a shorter time, while metaheuristics can report better solutions using more yet acceptable time. HPSO is statistically comparable yet less robust compared with HVNS for small-scale instances. For the large-scale case, HPSO outperforms HVNS on both effectiveness and robustness.     

Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept ‘virtual operation’. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC.     

Cross-regional integrated energy system scheduling optimization model considering conditional value at risk

Integrated energy system is a very important way to improve energy efficiency. Based on the combined heating cooling and power system, combined with energy storage equipment, a cross-regional integrated energy system scheduling optimization problem is studied. An integrated energy system scheduling optimization model is established that meets the requirements of electrical, heating, and cooling load under a variety of energy sources while both considering the interaction of electrical, heating, and cooling load between regions, and complementation of them within one region. Meanwhile, the value at risk (VaR) theory is introduced and the operating constraints of equipment in the integrated energy system fully considered, the integrated energy system scheduling model with VaR is established. The example shows that the model can realize multi-type electrical, heating, and cooling load optimized by schedule across regions under the premise of satisfying the balance of energy supply and demand, which can reduce the system operation cost. The sensitivity analysis of the minimum expected cost and the influencing factors of conditional VaR is carried out to verify the validity and feasibility of the proposed model.

• An integrated energy system scheduling optimization model is established that meets the requirements of electrical, heating, and cooling load under a variety of energy sources while both considering the interaction of electrical, heating, and cooling load between regions, and complementation of them within one region.
• By using the conditional value at risk theory to consider various types of the integrated energy system complements and evaluates the operational risk of the system under optimal operating conditions of the system.
• The total cost of system scheduling operation is proportional to the storage capacity, which is inversely proportional to the heat storage capacity and inversely proportional to the pipeline capacity within a certain interval.

     

Effect of storage options on price-based scheduling for a hybrid trigeneration system

Trigeneration is efficient to supply cooling, heat, and power demands. Also, hydro (pumped) storage with zero fuel costs can increase profit when price-based scheduling problem is solved. The goal of this study is to investigate the effects of electric boiler, hydro storage, and heat storage tank on price-based scheduling problem for hybrid trigeneration (trigeneration-conventional-gas boiler) system. A heuristic algorithm is applied to a power system where in validation case, total cost reduction of 0.63% to 0.91% is reached as compared with reported in literature. The results show that the utilization of electric boiler, hydro storage, and heat storage tank leads to profit improvement by 4.58%. Also, a critical sensitivity analysis is conducted and the results show the significant effects of several factors on scheduling results.     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

Group search optimization for economic fuel scheduling

The economical use of fuel available for the generation of power has become a major concern of electric utilities. This paper presents an approach for economic fuel scheduling problem by using group search optimization. This is a minimization technique that includes the standard load constraints as well as the fuel constraints. The generation schedule is compared to that which would result if fuel constraints were ignored. The comparison shows that fuel consumed can be adequately controlled by adjusting the power output of various generating units so that the power system operates within its fuel limitations and within contractual constraints. It has been found that small additional amount of fuel may be required to serve the same power demand but the additional cost of this fuel may well compensate for the penalty that might otherwise be imposed for not maintaining the fuel contract. Numerical results for two test systems have been presented and the test results obtained from group search optimization are compared with those obtained from particle swarm optimization and evolutionary programming.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs

As cloud computing evolves, it is becoming more and more apparent that the future of this industry lies in interconnected cloud systems where resources will be provided by multiple “Cloud” providers instead of just one. In this way, the hosts of services that are cloud-based will have access to even larger resource pools while at the same time increasing their scalability and availability by diversifying both their computing resources and the geographical locations where those resources operate from. Furthermore the increased competition between the cloud providers in conjunction with the commoditization of hardware has already led to large decreases in the cost of cloud computing and this trend is bound to continue in the future. Scientific focus in cloud computing is also headed this way with more studies on the efficient allocation of resources and effective distribution of computing tasks between those resources. This study evaluates the use of meta-heuristic optimization algorithms in the scheduling of bag-of-tasks applications in a heterogeneous cloud of clouds. The study of both local and globally arriving jobs has been considered along with the introduction of sporadically arriving critical jobs. Simulation results show that the use of these meta-heuristics can provide significant benefits in costs and performance.