Dynamic Job Scheduling on the Grid Environment using the Great Deluge Algorithm

The utilization of the computational Grid processor network has become a common method for researchers and scientists without access to local processor clusters to avail of the benefits of parallel processing for compute-intensive applications. As a result, this demand requires effective and efficient dynamic allocation of available resources. Although static scheduling and allocation techniques have proved effective, the dynamic nature of the Grid requires innovative techniques for reacting to change and maintaining stability for users. The dynamic scheduling process requires quite powerful optimization techniques, which can themselves lack the performance required in reaction time for achieving an effective schedule solution. Often there is a trade-off between solution quality and speed in achieving a solution. This paper presents an extension of a technique used in optimization and scheduling which can provide the means of achieving this balance and improves on similar approaches currently published.

A Hybrid Swarm Based Approach in Addressing University Timetabling Problems

This paper is concerned with the application of an automated hybrid approach in addressing the university timetabling problem. The approach described is based on the nature-inspired artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-inspired optimization approach, which has been widely implemented in solving a range of optimization problems in recent years such as job shop scheduling and machine timetabling problems. Although the approach has proven to be robust across a range of problems, it is acknowledged within the literature that there currently exist a number of inefficiencies regarding the exploration and exploitation abilities. These inefficiencies can often lead to a slow convergence speed within the search process. Hence, this paper introduces a variant of the algorithm which utilizes a global best model inspired from particle swarm optimization to enhance the global exploration ability while hybridizing with the great deluge (GD) algorithm in order to improve the local exploitation ability. Using this approach, an effective balance between exploration and exploitation is attained. In addition, a traditional local search approach is incorporated within the GD algorithm with the aim of further enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two diverse university timetabling datasets are investigated, i.e., Carter's examination timetabling and Socha course timetabling datasets. It should be noted that both problems have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed method is capable of producing high quality solutions across both these benchmark problems, showing a good degree of generality in the approach. Moreover, the proposed method produces best results on some instances as compared with other approaches presented in the literature.

Compiler and Runtime Support for Hybrid Static/Dynamic Scheduling

One of the outstanding issues in parallel computing is the selection of task granularity. This work proposes a solution to the task granularity problem by lowering the overhead of the task scheduler and as such supporting very fine-grain tasks. Using a combination of static (compile-time) scheduling and dynamic (run-time) scheduling, we aim to make scheduling decisions as fast as with static scheduling while retaining the dynamic load- balancing properties of fully dynamic scheduling. We present an example application and discuss the requirements on the compiler and runtime system to realize hybrid static/dynamic scheduling.

Comparison of a centralised and distributed approach for a generic scheduling system

PEGS (Production and Environmental Generic Scheduler) is a generic production scheduler that produces good schedules over a wide range of problems. It is centralised, using search strategies with the Shifting Bottleneck algorithm. We have also developed an alternative distributed approach using software agents. In some cases this reduces run times by a factor of 10 or more. In most cases, the agent-based program also produces good solutions for published benchmark data, and the short run times make our program useful for a large range of problems. Test results show that the agents can produce schedules comparable to the best found so far for some benchmark datasets and actually better schedules than PEGS on our own random datasets. The flexibility that agents can provide for today's dynamic scheduling is also appealing. We suggest that in this sort of generic or commercial system, the agent-based approach is a good alternative.

Dynamic global security-aware synthesis using System-C

A dynamic global security-aware synthesis flow using the SystemC language is presented. SystemC security models are first specified at the system or behavioural level using a library of SystemC behavioural descriptions which provide for the reuse and extension of security modules. At the core of the system is incorporated a global security-aware scheduling algorithm which allows for scheduling to a mixture of components of varying security level. The output from the scheduler is translated into annotated nets which are subsequently passed to allocation, optimisation and mapping tools for mapping into circuits. The synthesised circuits incorporate asynchronous secure power-balanced and fault-protected components. Results show that the approach offers robust implementations and efficient security/area trade-offs leading to significant improvements in turnover.

Dynamic Buffer Management for Multimedia Services in Beyond 3G Wireless Networks

This paper investigates a dynamic buffer man-agement scheme for QoS control of multimedia services in be-yond 3G wireless systems. The scheme is studied in the context of the state-of-the-art 3.5G system i.e. the High Speed Downlink Packet Access (HSDPA) which enhances 3G UMTS to support high-speed packet switched services. Unlike earlier systems, UMTS-evolved systems from HSDPA and beyond incorporate mechanisms such as packet scheduling and HARQ in the base station necessitating data buffering at the air interface. This introduces a potential bottleneck to end-to-end communication. Hence, buffer management at the air interface is crucial for end-to-end QoS support of multimedia services with multi-plexed parallel diverse flows such as video and data in the same end-user session. The dynamic buffer management scheme for HSDPA multimedia sessions with aggregated real-time and non real-time flows is investigated via extensive HSDPA simulations. The impact of the scheme on end-to-end traffic performance is evaluated with an example multimedia session comprising a real-time streaming flow concurrent with TCP-based non real-time flow. Results demonstrate that the scheme can guar-antee the end-to-end QoS of the real-time streaming flow, whilst simultaneously protecting the non real-time flow from starva-tion resulting in improved end-to-end throughput performance

Dynamic Buffer Management for Multimedia Services in 3.5G Wireless Networks

This paper presents and investigates a dynamic
buffer management scheme for QoS control of multimedia
services in a 3.5G wireless system i.e. the High Speed Downlink
Packet Access (HSDPA). HSDPA was introduced to enhance
UMTS for high-speed packet switched services. With HSDPA,
packet scheduling and HARQ mechanisms in the base station
require data buffering at the air interface thus introducing a
potential bottleneck to end-to-end communication. Hence, for
multimedia services with multiplexed parallel diverse flows
such as video and data in the same end-user session, buffer
management schemes in the base station are essential to support
end-to-end QoS provision. We propose a dynamic buffer management
scheme for HSDPA multimedia sessions with aggregated real-time and non real-time flows in the paper. The end-to-end performance impact of the scheme is evaluated with an example multimedia session comprising a real-time streaming
flow concurrent with TCP-based non real-time flow via extensive HSDPA simulations. Results demonstrate that the scheme can guarantee the end-to-end QoS of the real-time streaming flow, whilst simultaneously protecting non real-time flow from starvation resulting in improved end-to-end throughput performance

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Efficiently Scheduling Task Dataflow Parallelism: A Comparison Between Swan and QUARK

Increased system variability and irregularity of parallelism in applications put increasing demands on the ef- ficiency of dynamic task schedulers. This paper presents a new design for a work-stealing scheduler supporting both Cilk- style recursively parallel code and parallelism deduced from dataflow dependences. Initial evaluation on a set of linear algebra kernels demonstrates that our scheduler outperforms PLASMA’s QUARK scheduler by up to 12% on a 16-thread Intel Xeon and by up to 50% on a 32-thread AMD Bulldozer.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

An efficient harmony search with new pitch adjustment for dynamic economic dispatch

A simple yet efficient harmony search (HS) method with a new pitch adjustment rule (NPAHS) is proposed for dynamic economic dispatch (DED) of electrical power systems, a large-scale non-linear real time optimization problem imposed by a number of complex constraints. The new pitch adjustment rule is based on the perturbation information and the mean value of the harmony memory, which is simple to implement and helps to enhance solution quality and convergence speed. A new constraint handling technique is also developed to effectively handle various constraints in the DED problem, and the violation of ramp rate limits between the first and last scheduling intervals that is often ignored by existing approaches for DED problems is effectively eliminated. To validate the effectiveness, the NPAHS is first tested on 10 popular benchmark functions with 100 dimensions, in comparison with four HS variants and five state-of-the-art evolutionary algorithms. Then, NPAHS is used to solve three 24-h DED systems with 5, 15 and 54 units, which consider the valve point effects, transmission loss, emission and prohibited operating zones. Simulation results on all these systems show the scalability and superiority of the proposed NPAHS on various large scale problems.