3,519 research outputs found
A Simulated Annealing Method to Cover Dynamic Load Balancing in Grid Environment
High-performance scheduling is critical to the achievement of application performance on the computational grid. New scheduling algorithms are in demand for addressing new concerns arising in the grid environment. One of the main phases of scheduling on a grid is related to the load balancing problem therefore having a high-performance method to deal with the load balancing problem is essential to obtain a satisfactory high-performance scheduling. This paper presents SAGE, a new high-performance method to cover the dynamic load balancing problem by means of a simulated annealing algorithm. Even though this problem has been addressed with several different approaches only one of these methods is related with simulated annealing algorithm. Preliminary results show that SAGE not only makes it possible to find a good solution to the problem (effectiveness) but also in a reasonable amount of time (efficiency)
Maximising the Utility of Enterprise Millimetre-Wave Networks
Millimetre-wave (mmWave) technology is a promising candidate for meeting the
intensifying demand for ultra fast wireless connectivity, especially in
high-end enterprise networks. Very narrow beam forming is mandatory to mitigate
the severe attenuation specific to the extremely high frequency (EHF) bands
exploited. Simultaneously, this greatly reduces interference, but generates
problematic communication blockages. As a consequence, client association
control and scheduling in scenarios with densely deployed mmWave access points
become particularly challenging, while policies designed for traditional
wireless networks remain inappropriate. In this paper we formulate and solve
these tasks as utility maximisation problems under different traffic regimes,
for the first time in the mmWave context. We specify a set of low-complexity
algorithms that capture distinctive terminal deafness and user demand
constraints, while providing near-optimal client associations and airtime
allocations, despite the problems' inherent NP-completeness. To evaluate our
solutions, we develop an NS-3 implementation of the IEEE 802.11ad protocol,
which we construct upon preliminary 60GHz channel measurements. Simulation
results demonstrate that our schemes provide up to 60% higher throughput as
compared to the commonly used signal strength based association policy for
mmWave networks, and outperform recently proposed load-balancing oriented
solutions, as we accommodate the demand of 33% more clients in both static and
mobile scenarios.Comment: 22 pages, 12 figures, accepted for publication in Computer
Communication
A Multi-Criteria Meta-Fuzzy-Scheduler for Independent Tasks in Grid Computing
The paradigm of distributed computation in heterogeneous resources, grid computing, has given rise to a large amount of research on resource scheduling. This paper presents a Meta-Scheduler for grid computing that does not need any given information about tasks length or tasks arrival time unlike traditional dynamic heuristics. Our Meta-Scheduler is of multi-criteria type, because it solves two conflicting objectives: minimize the makespan of a set of tasks and distribute these tasks in a balanced way among the resources of the Grid. Experimental results using fuzzy scheduler show that, through our proposal, we achieve these two objectives and improve dynamic heuristics presented in prior literature
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are required or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high. This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. The proposed algorithm can overcome stagnation problem, minimize processing time, match jobs with suitable resources, and balance entire resources in grid environment. This research follows the experimental research methodology that consists of problem analysis, developing the proposed framework, constructing the simulation environment, conducting a set of experiments and evaluating the results. There are three new mechanisms in this proposed framework that are used to organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone. This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid job scheduling. The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job. EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form. The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. Resources with high pheromone value are selected to process the submitted jobs. Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. Experimental results show that EACO produced better grid resource management solution compared to other algorithms
Biologically Inspired Energy Manager Design For The Greatt Residential Microgrid
A biologically inspired method, involving the design of an energy manager, for coordinating the operation of a hybrid renewable residential micro-grid is presented. Flexible optimization procedures that minimize the cost of renewable distribution generators based upon the climate and location of the load profile have been developed and modeled in simulation. A novel design of a dual channel converter system and its control system forms the distributed energy storage (DES) system that features the capability of balancing the power flow in the micro-grid (even in the grid-off mode). The proposed energy management system utilizes a back propagation neural network in order to predict the state of charge (SOC) of the DES, yielding the reference value of control variables, which allows the micro-grid to respond to the desired operation conditions rapidly fast with acceptable controller error. Preliminary results indicate that the DES system allows for the implementation of energy management strategies in a technically viable manner
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