HTC‐Sim: a trace‐driven simulation framework for energy consumption in high‐throughput computing systems

Thomas

Proceedings of the 2nd International Workshop on Energy-Aware Simulation

2016

In the recent years, the advance wireless networks and Internet of Things (IoT) are becoming very prominent in assorted domains. In the traffic system, the problem of congestion control is very common, and it is classically handled by the global positioning systems by the drivers as well as traffic administrative authorities. But as the traffic density is increasing day by day, it is becoming difficult to handle and view all the possibilities in the prospective traffic area where the driver is willing to move. Moreover, the problem of security and integrity is also increasing rapidly as there are number of attacks in VANET and GPS systems being used by the crackers by sending the malicious code or fake packets. Ubiquitous computing is one of the recent technologies that is in the phase of implementation under Internet of Things (IoT). In this research manuscript, the approach to integrate the dynamic key exchange with the Elephant Herd Optimization (EHO) is presented to achieve the higher degree of energy optimization and overall lifetime of the network communication. The key concept of the cluster head shuffling using EHO and inner modules of key exchange are simulated in Contiki-Cooja that is open source simulator for advance wireless networks.

Section: Evaluation Methodologiesmentioning

confidence: 99%

The case for energy-aware simulation and modelling of internet of things (IoT)

Thomas

Proceedings of the 2nd International Workshop on Energy-Aware Simulation

2016

“…HTC-Sim is a trace-driven simulation framework for energy consumption in High Throughput Computing systems [5]. The simulation handles two types of users -interactive users who can sit down in-front of a computer and use it along with high-throughput users who submit multiple tasks through a batch submission system which use the computers when idle.…”

Section: A Htc-simmentioning

confidence: 99%

“…We exemplify this for the digital twin tailored from the HTC-Sim [5] simulation system of a high-throughput computing (HTC) setup -specifically HTCondor [6] at Newcastle University. Our scheduler, which chooses which resources should run which tasks 2 , employs a Reinforcement Learning [7] (RL) approach based on the work by McGough et al [8].…”

Section: Introductionmentioning

confidence: 99%

Optimising energy and overhead for large parameter space simulations

Kell

2019 Tenth International Green and Sustainable Computing Conference (IGSC)

2019

Many systems require optimisation over multiple objectives, where objectives are characteristics of the system such as energy consumed or increase in time to perform the work. Optimisation is performed by selecting the 'best' set of input parameters to elicit the desired objectives. However, the parameter search space can often be far larger than can be searched in a reasonable time. Additionally, the objectives are often mutually exclusive -leading to a decision being made as to which objective is more important or optimising over a combination of the objectives. This work is an application of a Genetic Algorithm to identify the Pareto frontier for finding the optimal parameter sets for all combinations of objectives. A Pareto frontier can be used to identify the sets of optimal parameters for which each is the 'best' for a given combination of objectives -thus allowing decisions to be made with full knowledge. We demonstrate this approach for the HTC-Sim simulation system in the case where a Reinforcement Learning scheduler is tuned for the two objectives of energy consumption and task overhead. Demonstrating that this approach can reduce the energy consumed by ∼36 % over previously published work without significantly increasing the overhead.

“…The HTC-Sim System [8] is a trace-driven simulation framework for a generic High Throughput Computing system. It is capable of simulating both dedicated computer resources and computers which are provided on a voluntary basis.…”

Section: Htc-sim Systemmentioning

confidence: 99%

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments

2018 Ninth International Green and Sustainable Computing Conference (IGSC)

Brennan

et al. 2018

High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utilising their idle time -volunteer computing. This has great advantages as it gives access to vast quantities of computational power for little or no cost. The downside is that running tasks are sacrificed if the computer is needed for its primary use. Normally terminating the task which must be restarted on a different computer -leading to wasted energy and an increase in task completion time. We demonstrate, through the use of simulation, how we can reduce this wasted energy by targeting tasks at computers less likely to be needed for primary use, predicting this idle time through machine learning. By combining two machine learning approaches, namely Random Forest and MultiLayer Perceptron, we save 51.4% of the energy without significantly affecting the time to complete tasks.