An adaptive scheduling scheme for fair bandwidth allocation

Class-based service differentiation is provided in DiffServ networks. However, this differentiation will be disordered under dynamic traffic loads due to the fixed weighted scheduling. An adaptive weighted scheduling scheme is proposed in this paper to achieve fair bandwidth allocation among different service classes. In this scheme, the number of active flows and the subscribed bandwidth are estimated based on the measurement of local queue metrics, then the scheduling weights of each service class are adjusted for the per-flow fairness of excess bandwidth allocation. This adaptive scheme can be combined with any weighted scheduling algorithm. Simulation results show that, comparing with fixed weighted scheduling, it effectively improve the fairness of excess bandwidth allocation.

The influence of harvest and storage on the properties of and fast pyrolysis products from Miscanthus x giganteus

The research investigates the fuel property variations associated with the time of harvest and the duration of storage of Miscanthus x giganteus over a one year period. The crop has been harvested at three different times: early (September 2009), conventional (April 2010) and late (June 2010). Once harvested the crop was baled and stored. Biomass properties of samples taken from different storage zones were compared. The thermochemical properties have been investigated using a range of analytical equipment including thermogravimetric analysis (TGA) and pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS). In addition, bio-oil has been produced from the early, conventional and late harvest using a laboratory scale (300gh) fast pyrolysis unit. The potential organic liquid yield (ondry basis, also excluding the reaction water generated) based on the laboratory fast pyrolysis processing undertaken in this study, was found to vary between 2.82 and 3.18 dry tha for the early and the late harvest respectively. The bio-oil organic yield was reduced by approximately 11% (0.36tha) between the early and the late harvest. Char yield was also reduced by approximately 18% (0.61tha). The highest gas yield (18.03%-1.60tha) was observed for the conventional harvest. Gas chromatography-mass spectrometry (GC-MS) analysis of the bio-oil shows that levoglucosan, methylbenzaldehyde and 1,2-benzenediol all increase as a consequence of delayed harvest. It was also observed that by delaying the harvest time the O:C atomic ratio is reduced and a more carbonaceous feedstock is produced. © 2013 Elsevier Ltd.

Scheduling of aircraft crews of a commercial airline

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Some aspects of scheduling in a group technology manufacturing system

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

Transparent Scheduling of Composite Web Services

Composite Web Services (CWS) aggregate multiple Web Services in one logical unit to accomplish a complex task (e.g. business process). This aggregation is achieved by defining a workflow that orchestrates the underlying Web Services in a manner consistent with the desired functionality. Since CWS can aggregate atomic and other CWS they foster the development of service layers and reuse of already existing functionality. An important issue in the deployment of services is their run-time performance under various loads. Due to the complex interactions of the underlying services, a CWS they can exhibit problematic and often difficult to predict behaviours in overload situations. This paper focuses on the use of request scheduling for improving CWS performance in overload situations. Different scheduling policies are investigated in regards to their effectiveness in helping with bulk arrivals.

Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop

The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem. © 2013 Published by Elsevier Ltd.

Learning Technology in Scheduling Based on the Mixed Graphs

We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus.

Multi-agent Systems in the Harvest Prognosis

The paper presents a case study of geo-monitoring a region consisting in the capturing and encoding of human expertise into a knowledge-based system. As soon as the maps have been processed, the data patterns are detected using knowledge-based agents for the harvest prognosis.

Expansion, harvest and cryopreservation of human mesenchymal stem cells in a serum-free microcarrier process

Human mesenchymal stem cell (hMSC) therapies are currently progressing through clinical development, driving the need for consistent, and cost effective manufacturing processes to meet the lot-sizes required for commercial production. The use of animal-derived serum is common in hMSC culture but has many drawbacks such as limited supply, lot-to-lot variability, increased regulatory burden, possibility of pathogen transmission, and reduced scope for process optimization. These constraints may impact the development of a consistent large-scale process and therefore must be addressed. The aim of this work was therefore to run a pilot study in the systematic development of serum-free hMSC manufacturing process. Human bone-marrow derived hMSCs were expanded on fibronectin-coated, non-porous plastic microcarriers in 100mL stirred spinner flasks at a density of 3×10⁵cells.mL^-1 in serum-free medium. The hMSCs were successfully harvested by our recently-developed technique using animal-free enzymatic cell detachment accompanied by agitation followed by filtration to separate the hMSCs from microcarriers, with a post-harvest viability of 99.63±0.03%. The hMSCs were found to be in accordance with the ISCT characterization criteria and maintained hMSC outgrowth and colony-forming potential. The hMSCs were held in suspension post-harvest to simulate a typical pooling time for a scaled expansion process and cryopreserved in a serum-free vehicle solution using a controlled-rate freezing process. Post-thaw viability was 75.8±1.4% with a similar 3h attachment efficiency also observed, indicating successful hMSC recovery, and attachment. This approach therefore demonstrates that once an hMSC line and appropriate medium have been selected for production, multiple unit operations can be integrated to generate an animal component-free hMSC production process from expansion through to cryopreservation.

The Production Scheduling in Assembly System with Evolutionary Algorithm

In this paper an evolutionary algorithm is proposed for solving the problem of production scheduling in assembly system. The aim of the paper is to investigate a possibility of the application of evolutionary algorithms in the assembly system of a normally functioning enterprise producing household appliances to make the production graphic schedule.

Recent research trend of economic-lot scheduling problems

Purpose - This research note aims to present a summary of research concerning economic-lot scheduling problem (ELSP). Design/methodology/approach - The paper's approach is to review over 100 selected studies published in the last 15 years (1997-2012), which are then grouped under different research themes. Findings - Five research themes are identified and insights for future studies are reported at the end of this paper. Research limitations/implications - The motivation of preparing this research note is to summarize key research studies in this field since 1997, when the ELSP problems have been verified as NP-hard. Originality/value - ELSP is an important scheduling problem that has been studied since the 1950s. Because of its complexity in delivering a feasible analytical closed form solution, many studies in the last two decades employed heuristic algorithms in order to come up with good and acceptable solutions. As a consequence, the solution approaches are quite diversified. The major contribution of this paper is to provide researchers who are interested in this area with a quick reference guide on the reviewed studies. © Emerald Group Publishing Limited.

Variable Neighborhood Search for the File Transfer Scheduling Problem

ACM Computing Classification System (1998): I.2.8, G.1.6.

A new scheduling method for enhanced quality of experience in LTE systems

In this paper a new approach to the resource allocation and scheduling mechanism that reflects the effect of user's Quality of Experience is presented. The proposed scheduling algorithm is examined in the context of 3GPP Long Term Evolution (LTE) system. Pause Intensity (PI) as an objective and no-reference quality assessment metric is employed to represent user's satisfaction in the scheduler of eNodeB. PI is in fact a measurement of discontinuity in the service. The performance of the scheduling method proposed is compared with two extreme cases: maxCI and Round Robin scheduling schemes which correspond to the efficiency and fairness oriented mechanisms, respectively. Our work reveals that the proposed method is able to perform between fairness and efficiency requirements, in favor of higher satisfaction for the users to the desired level. © VDE VERLAG GMBH.