Scheduling arbitrary-deadline sporadic task systems on multiprocessors

A new algorithm is proposed for scheduling preemptible arbitrary-deadline sporadic task systems upon multiprocessor platforms, with interprocessor migration permitted. This algorithm is based on a task-splitting approach - while most tasks are entirely assigned to specific processors, a few tasks (fewer than the number of processors) may be split across two processors. This algorithm can be used for two distinct purposes: for actually scheduling specific sporadic task systems, and for feasibility analysis. Simulation- based evaluation indicates that this algorithm offers a significant improvement on the ability to schedule arbitrary- deadline sporadic task systems as compared to the contemporary state-of-art. With regard to feasibility analysis, the new algorithm is proved to offer superior performance guarantees in comparison to prior feasibility tests.

A VLF to UHF propagation model for evaluation of high-speed railway electromagnetic interference in RF monitoring base-stations

Considering that recent european high-speed railway system has a traction power system of kV 50 Hz, which causes electromagnetic emission for the outside world, it is important to dimension the railway system emissions, using a frequency/distance dependent propagation model. This paper presents an enhanced theoretical model for VLF to UHF propagation, railway system oriented. It introduces the near field approach (crucial in low frequency propagation) and also considers the source characteristics and type of measuring antenna. Simulations are presented, and comparisons are set with earlier far field models. Using the developed model, a real case study was performed in partnership with Refer Telecom (portuguese telecom operator for railways). The new propagation model was used in order to predict the future high-speed railway electromagnetic emissions in the Lisbon north track. The results show the model's prediction capabilities and also its applicability to realistic scenarios.

Automatic tunning of Okumura-Hata model on railway communications

This paper presents the Genetic Algorithms (GA) as an efficient solution for the Okumura-Hata prediction model tuning on railways communications. A method for modelling the propagation model tuning parameters was presented. The algorithm tuning and validation were based on real networks measurements carried out on four different propagation scenarios and several performance indicators were used. It was shown that the proposed GA is able to produce significant improvements over the original model. The algorithm developed is currently been used on real GSM-R network planning process for an enhanced resources usage.

The utilization bound of static-priority preemptive partitioned multiprocessor scheduling is 50%

This paper studies static-priority preemptive scheduling on a multiprocessor using partitioned scheduling. We propose a new scheduling algorithm and prove that if the proposed algorithm is used and if less than 50% of the capacity is requested then all deadlines are met. It is known that for every static-priority multiprocessor scheduling algorithm, there is a task set that misses a deadline although the requested capacity is arbitrary close to 50%.

Multiprocessor scheduling with few preemptions

Consider the problem of scheduling a set of periodically arriving tasks on a multiprocessor with the goal of meeting deadlines. Processors are identical and have the same speed. Tasks can be preempted and they can migrate between processors. We propose an algorithm with a utilization bound of 66% and with few preemptions. It can trade a higher utilization bound for more preemption and in doing so it has a utilization bound of 100%.

Beacon scheduling in cluster-tree IEEE 802.15.4/ZigBee wireless sensor networks

The recently standardized IEEE 802.15.4/Zigbee protocol stack offers great potentials for ubiquitous and pervasive computing, namely for Wireless Sensor Networks (WSNs). However, there are still some open and ambiguous issues that turn its practical use a challenging task. One of those issues is how to build a synchronized multi-hop cluster-tree network, which is quite suitable for QoS support in WSNs. In fact, the current IEEE 802.15.4/Zigbee specifications restrict the synchronization in the beacon-enabled mode (by the generation of periodic beacon frames) to star-based networks, while it supports multi-hop networking using the peer-to-peer mesh topology, but with no synchronization. Even though both specifications mention the possible use of cluster-tree topologies, which combine multi-hop and synchronization features, the description on how to effectively construct such a network topology is missing. This report tackles this problem, unveils the ambiguities regarding the use of the cluster-tree topology and proposes two collisionfree beacon frame scheduling schemes.

Partial utility-driven scheduling for flexible SLA and pricing arbitration in clouds

Cloud SLAs compensate customers with credits when average availability drops below certain levels. This is too inflexible because consumers lose non-measurable amounts of performance being only compensated later, in next charging cycles. We propose to schedule virtual machines (VMs), driven by range-based non-linear reductions of utility, different for classes of users and across different ranges of resource allocations: partial utility. This customer-defined metric, allows providers transferring resources between VMs in meaningful and economically efficient ways. We define a comprehensive cost model incorporating partial utility given by clients to a certain level of degradation, when VMs are allocated in overcommitted environments (Public, Private, Community Clouds). CloudSim was extended to support our scheduling model. Several simulation scenarios with synthetic and real workloads are presented, using datacenters with different dimensions regarding the number of servers and computational capacity. We show the partial utility-driven driven scheduling allows more VMs to be allocated. It brings benefits to providers, regarding revenue and resource utilization, allowing for more revenue per resource allocated and scaling well with the size of datacenters when comparing with an utility-oblivious redistribution of resources. Regarding clients, their workloads’ execution time is also improved, by incorporating an SLA-based redistribution of their VM’s computational power.

A hybrid genetic algorithm for the multi-mode resource-constrained project scheduling

A construction project is a group of discernible tasks or activities that are conduct-ed in a coordinated effort to accomplish one or more objectives. Construction projects re-quire varying levels of cost, time and other resources. To plan and schedule a construction project, activities must be defined sufficiently. The level of detail determines the number of activities contained within the project plan and schedule. So, finding feasible schedules which efficiently use scarce resources is a challenging task within project management. In this context, the well-known Resource Constrained Project Scheduling Problem (RCPSP) has been studied during the last decades. In the RCPSP the activities of a project have to be scheduled such that the makespan of the project is minimized. So, the technological precedence constraints have to be observed as well as limitations of the renewable resources required to accomplish the activities. Once started, an activity may not be interrupted. This problem has been extended to a more realistic model, the multi-mode resource con-strained project scheduling problem (MRCPSP), where each activity can be performed in one out of several modes. Each mode of an activity represents an alternative way of combining different levels of resource requirements with a related duration. Each renewable resource has a limited availability for the entire project such as manpower and machines. This paper presents a hybrid genetic algorithm for the multi-mode resource-constrained pro-ject scheduling problem, in which multiple execution modes are available for each of the ac-tivities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme. It is evaluated the quality of the schedules and presents detailed comparative computational re-sults for the MRCPSP, which reveal that this approach is a competitive algorithm.

Renewable power forecast to scheduling of thermal units

In this work is discussed the importance of the renewable production forecast in an island environment. A probabilistic forecast based on kernel density estimators is proposed. The aggregation of these forecasts, allows the determination of thermal generation amount needed to schedule and operating a power grid of an island with high penetration of renewable generation. A case study based on electric system of S. Miguel Island is presented. The results show that the forecast techniques are an imperative tool help the grid management.

A genetic algorithm for the resource constrained multi-project scheduling problem

This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

Self-Scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach

This paper is on the self-scheduling for a power producer taking part in day-ahead joint energy and spinning reserve markets and aiming at a short-term coordination of wind power plants with concentrated solar power plants having thermal energy storage. The short-term coordination is formulated as a mixed-integer linear programming problem given as the maximization of profit subjected to technical operation constraints, including the ones related to a transmission line. Probability density functions are used to model the variability of the hourly wind speed and the solar irradiation in regard to a negative correlation. Case studies based on an Iberian Peninsula wind and concentrated solar power plants are presented, providing the optimal energy and spinning reserve for the short-term self-scheduling in order to unveil the coordination benefits and synergies between wind and solar resources. Results and sensitivity analysis are in favour of the coordination, showing an increase on profit, allowing for spinning reserve, reducing the need for curtailment, increasing the transmission line capacity factor. (C) 2014 Elsevier Ltd. All rights reserved.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Project scheduling using a competitive genetic algorithm

- The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm