Modelling of Handover Priority Scheme Based on eMLPP in GSM-R

A minicomputer-based, semiquantitative, emission spectrographic system was designed to perform survey analyses (64 elements per sample), on a wide variety of geologic materials rapidly (9 s per determination). The system can analyze as many as 40 000 samples per year, while maintaining long-term consistency of results, and can provide archival storage capability (photoplate, microfiche, data bank). The minicomputer's partitioned memory allows simultaneous execution of programs to acquire 92 000 sequential, digitized, transmittance-readings per spectrum from a precision scanning microphotometer in 70 s, and to reduce these data to the peak and background transmittances, the location, and a profile code of as many as 500 analytical lines. The plate emulsion is calibrated in 10 equal segments between 2300 and 4700 Å. Intensities and preliminary concentrations based on prestored analytical curve coefficients are calculated for each line. Corrections for spectral interferences are made, and final results are selected according to a predetermined priority scheme. A report form for every 10 samples is printed within 5 min after a plate is recorded. All the preliminary data are stored on magnetic tape for production of microfiche within 24 h. Spectra on a second plate can be scanned while analysis of the first plate is being performed.

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Resource allocation and task admission control in cloud systems

10.32920/ryerson.14645862.v1 ◽

2021 ◽

Author(s):

Haleh Khojasteh

Keyword(s):

Resource Allocation ◽

Performance Metrics ◽

Cloud Model ◽

Blocking Probability ◽

Arrival Rate ◽

Cloud System ◽

Wide Range ◽

Priority Scheme ◽

The Impact ◽

And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

Download Full-text

Resource allocation and task admission control in cloud systems

10.32920/ryerson.14645862 ◽

2021 ◽

Author(s):

Haleh Khojasteh

Keyword(s):

Resource Allocation ◽

Performance Metrics ◽

Cloud Model ◽

Blocking Probability ◽

Arrival Rate ◽

Cloud System ◽

Wide Range ◽

Priority Scheme ◽

The Impact ◽

And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

Download Full-text