Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

Day-Ahead Optimal Charging/Discharging Scheduling for Electric Vehicles in Micro-Grids

A micro-grid is an autonomous system which can be operated and connected to an external system or isolated with the help of energy storage systems (ESSs). While the daily output of distributed generators (DGs) strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the imbalance between the daily load and generation curves. In this paper, a statistical model is presented to describe daily EV charging/discharging behaviour. An optimisation problem is proposed to obtain economic operation for the micro-grid based on this model. In day-ahead scheduling, with estimated information of power generation and load demand, optimal charging/discharging of EVs during 24 hours is obtained. A series of numerical optimization solutions in different scenarios is achieved by serial quadratic programming. The results show that optimal charging/discharging of EVs, a daily load curve can better track the generation curve and the network loss and required ESS capacity are both decreased. The paper also demonstrates cost benefits for EVs and operators.

SHEPARD: Scheduling on heterogeneous platforms using application resource demands

Heterogeneous computing technologies, such as multi-core CPUs, GPUs and FPGAs can provide significant performance improvements. However, developing applications for these technologies often results in coupling applications to specific devices, typically through the use of proprietary tools. This paper presents SHEPARD, a compile time and run-time framework that decouples application development from the target platform and enables run-time allocation of tasks to heterogeneous computing devices. Through the use of special annotated functions, called managed tasks, SHEPARD approximates a task's performance on available devices, and coupled with the approximation of current device demand, decides which device can satisfy the task with the lowest overall execution time. Experiments using a task parallel application, based on an in-memory database, demonstrate the opportunity for automatic run-time task allocation to achieve speed-up over a static allocation to a single specific device. © 2014 IEEE.

Autonomic scheduling of tasks from data parallel patterns to CPU/GPU core mixes

We propose a methodology for optimizing the execution of data parallel (sub-)tasks on CPU and GPU cores of the same heterogeneous architecture. The methodology is based on two main components: i) an analytical performance model for scheduling tasks among CPU and GPU cores, such that the global execution time of the overall data parallel pattern is optimized; and ii) an autonomic module which uses the analytical performance model to implement the data parallel computations in a completely autonomic way, requiring no programmer intervention to optimize the computation across CPU and GPU cores. The analytical performance model uses a small set of simple parameters to devise a partitioning-between CPU and GPU cores-of the tasks derived from structured data parallel patterns/algorithmic skeletons. The model takes into account both hardware related and application dependent parameters. It computes the percentage of tasks to be executed on CPU and GPU cores such that both kinds of cores are exploited and performance figures are optimized. The autonomic module, implemented in FastFlow, executes a generic map (reduce) data parallel pattern scheduling part of the tasks to the GPU and part to CPU cores so as to achieve optimal execution time. Experimental results on state-of-the-art CPU/GPU architectures are shown that assess both performance model properties and autonomic module effectiveness. © 2013 IEEE.

Young children's food brand knowledge. Early development and associations with television viewing and parent's diet

Brand knowledge is a prerequisite of children's requests and choices for branded foods. We explored the development of young children's brand knowledge of foods highly advertised on television - both healthy and less healthy. Participants were 172 children aged 3-5 years in diverse socio-economic settings, from two jurisdictions on the island of Ireland with different regulatory environments. Results indicated that food brand knowledge (i) did not differ across jurisdictions; (ii) increased significantly between 3 and 4 years; and (iii) children had significantly greater knowledge of unhealthy food brands, compared with similarly advertised healthy brands. In addition, (iv) children's healthy food brand knowledge was not related to their television viewing, their mother's education, or parent or child eating. However, (v) unhealthy brand knowledge was significantly related to all these factors, although only parent eating and children's age were independent predictors. Findings indicate that effects of food marketing for unhealthy foods take place through routes other than television advertising alone, and are present before pre-schoolers develop the concept of healthy eating. Implications are that marketing restrictions of unhealthy foods should extend beyond television advertising; and that family-focused obesity prevention programmes should begin before children are 3 years of age.