New scheduling problems with interfering and independent jobs

33 pages. Paper submitted to Journal of scheduling the 8 September 2009.We consider the problems of scheduling independent jobs, when a subset of jobs has its own objective function to minimize. The performance of this subset of jobs is in competition with the performance of the whole set of jobs and compromise solutions have to be found. Such a problem arises for some practical applications like ball bearing production problems. This new scheduling problem is positioned within the literature and the differences with the problems with competing agents or with interfering job set problems are presented. Classical and regular scheduling objective functions are considered and epsilon-constraint approach and linear combination of criteria approach are used for finding compromise solutions. The study focus on single machine and identical parallel machine environments and for each environment, the complexity of several problems is established and some dynamic programming algorithms are proposed

A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud

Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page

Numerical Analysis of the Dynamic Responses of Multistory Structures Equipped with Tuned Liquid Dampers Considering Fluid-Structure Interactions

Aims: The paper analyzes the effectiveness of tuned liquid damper in controlling the vibration of high rise building. The new contribution is considering the fluid-structure interaction of a water tank as a Tuned Liquid Dampers (TLD). Background: Currently, buildings are being built higher and higher, which requires TLDs to be larger as well. Therefore, the fluid pressure acting on the tank wall is more significant. In previous studies of liquid sloshing in TLDs, researchers simply ignored the effect of liquid pressure acting on the tank walls by making the assumption that the tanks are rigid. Currently, the failure of a tank because of FSI occurs regularly, so this phenomenon cannot be ignored when designing the tanks in general and TLDs in particular. Objective: To investigate the thickness of the tank wall affect to the TLD mechanism. Method: Numerical method was used for this research. Results: A TLD could be easy to design; however one could not bypass the fluid-structure interaction by assuming the tank wall is rigid. Conclusion: This kind of damper is very good to mitigate the dynamic response of structrure

CROWD-2-CLOUD – Remote Sensing Land Cover Verification With Crowd-Sourcing Data

Nowadays, advanced remote sensing technologies provide huge amount of Earth Observation (EO) data timely. Growing quickly in terms of size and structure, EO data require a new way of handling and processing as it is considered big data. Cloud-computing platform proved to be a reliable and scalable platform that suits various user demands in remote sensing data processing. To verify the ambiguity of information derived solely from remote sensing, ground data is vital. The only way to keep pace with big remote sensing data is to exploit the crowdsourced data, which has been recently proposed elsewhere. In this study, we developed a prototype of an integrated location based service on top of cloud computing platform to detect land cover features and engage the crowd of volunteers during training and verification process. Relying on open-source tools, the proposed system provides location-based data collection and satellite image classification. The prototype was tested over the rapid on-going landscape surrounding the University of Nottingham, Malaysia campus. More advanced functions will be developed and a full system will be deployed and tested in further study

Neighborhood search for solving personal scheduling problem in available time windows with split-min and deadline constraints

The scheduling of individual jobs with certain constraints so that efficiency is a matter of concern. Jobs have deadlines to complete, can be broken down but not too small, and will be scheduled into some available time windows. The goal of the problem is to find a solution so that all jobs are completed as soon as possible. This problem is proved to be a strongly $NP$-hard problem. The implementation of the proposed MILP model using a CPLEX solver was also conducted to determine the optimal solution for the small-size dataset. For large-size dataset, heuristic algorithms are recommended such as First Come First Served (FCFS), Earliest Deadline (EDL), and neighborhood search including  Stochastic Hill Climbing (SHC), Random Restart Hill Climbing (RRHC), Simulated Annealing (SA) to determine a good solution in an acceptable time. Experimental results will present in detail the performance among the groups of exact, heuristic, and neighborhood search methods

Minimizing makespan of Personal Scheduling problem in available time-windows with split-min and setup-time constraints

This paper deals with personal scheduling problem in available time-windows with split-min and setup-time constraints. The jobs are splitable into sub-jobs and a common lower bound on the size of each sub-job is imposed. The objective function aims to find a feasible schedule that minimizes the maximum completion time of all jobs. The proposed scheduling problem was proved to be strongly NP-hard by a reduction to 3-SAT problem in the preliminary results. We propose in this paper an exact method based on MILP model to find optimal solution, some heuristics to find feasible solution and a meta-heuristic based on tabu search algorithm to find good solution. The computational results show the performance of proposed exact method, some heuristics and tabu search algorithm