FRAC: a flexible resource allocation for vehicular cloud system

Beam hopping technology is considered to provide a high level of flexible resource allocation to manage uneven traffic requests in multi-beam high throughput satellite systems. Conventional beam hopping resource allocation methods assume constant rainfall attenuation. Different from conventional methods, by employing genetic algorithm this paper studies dynamic beam hopping time slots allocation under the effect of time-varying rain attenuation. Firstly, a beam hopping system model as well as rain attenuation time series based on Dirac lognormal distribution are provided. On this basis, the dynamic allocation method by employing genetic algorithm is proposed to obtain both quantity and arrangement of time slots allocated for each beam. Simulation results show that, compared with conventional methods, the proposed algorithm can dynamically adjust time slots allocation to meet the non-uniform traffic requirements of each beam under the effect of time-varying rain attenuation and effectively improve system performance.

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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.

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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.

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Time-Sliced Flexible Resource Allocation for Optical Low Earth Orbit Satellite Networks

IEEE Access ◽

10.1109/access.2019.2913441 ◽

2019 ◽

Vol 7 ◽

pp. 56753-56759 ◽

Cited By ~ 1

Author(s):

Zelin Zheng ◽

Nan Hua ◽

Zhizhen Zhong ◽

Jialong Li ◽

Yanhe Li ◽

...

Keyword(s):

Resource Allocation ◽

Low Earth Orbit ◽

Satellite Networks ◽

Earth Orbit ◽

Orbit Satellite ◽

Low Earth Orbit Satellite ◽

Flexible Resource

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A Safety Resource Allocation Mechanism against Connection Fault for Vehicular Cloud Computing

Mobile Information Systems ◽

10.1155/2016/2747808 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Tianpeng Ye ◽

Zhou Su ◽

Jun Wu ◽

Longhua Guo ◽

Jianhua Li ◽

...

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Real Time ◽

Traffic Control ◽

Intelligent Transportation System ◽

Probability Model ◽

Allocation Mechanism ◽

Vehicular Cloud Computing ◽

Vehicular Cloud ◽

Resource Allocation Mechanism

The Intelligent Transportation System (ITS) becomes an important component of the smart city toward safer roads, better traffic control, and on-demand service by utilizing and processing the information collected from sensors of vehicles and road side infrastructure. In ITS, Vehicular Cloud Computing (VCC) is a novel technology balancing the requirement of complex services and the limited capability of on-board computers. However, the behaviors of the vehicles in VCC are dynamic, random, and complex. Thus, one of the key safety issues is the frequent disconnections between the vehicle and the Vehicular Cloud (VC) when this vehicle is computing for a service. More important, the connection fault will disturb seriously the normal services of VCC and impact the safety works of the transportation. In this paper, a safety resource allocation mechanism is proposed against connection fault in VCC by using a modified workflow with prediction capability. We firstly propose the probability model for the vehicle movement which satisfies the high dynamics and real-time requirements of VCC. And then we propose a Prediction-based Reliability Maximization Algorithm (PRMA) to realize the safety resource allocation for VCC. The evaluation shows that our mechanism can improve the reliability and guarantee the real-time performance of the VCC.

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How to get the most bang for your buck: the evolution and physiology of nutrition-dependent resource allocation strategies

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2017.0445 ◽

2017 ◽

Vol 284 (1857) ◽

pp. 20170445 ◽

Cited By ~ 12

Author(s):

Enoch Ng'oma ◽

Anna M. Perinchery ◽

Elizabeth G. King

Keyword(s):

Resource Allocation ◽

Natural Populations ◽

Analytical Models ◽

Major Question ◽

Proximate Mechanisms ◽

Level Data ◽

Flexible Resource ◽

Empirical Tests ◽

Underlying Mechanisms ◽

Allocation Strategies

All organisms use resources to grow, survive and reproduce. The supply of these resources varies widely across landscapes and time, imposing ultimate constraints on the maximal trait values for allocation-related traits. In this review, we address three key questions fundamental to our understanding of the evolution of allocation strategies and their underlying mechanisms. First, we ask: how diverse are flexible resource allocation strategies among different organisms? We find there are many, varied, examples of flexible strategies that depend on nutrition. However, this diversity is often ignored in some of the best-known cases of resource allocation shifts, such as the commonly observed pattern of lifespan extension under nutrient limitation. A greater appreciation of the wide variety of flexible allocation strategies leads directly to our second major question: what conditions select for different plastic allocation strategies? Here, we highlight the need for additional models that explicitly consider the evolution of phenotypically plastic allocation strategies and empirical tests of the predictions of those models in natural populations. Finally, we consider the question: what are the underlying mechanisms determining resource allocation strategies? Although evolutionary biologists assume differential allocation of resources is a major factor limiting trait evolution, few proximate mechanisms are known that specifically support the model. We argue that an integrated framework can reconcile evolutionary models with proximate mechanisms that appear at first glance to be in conflict with these models. Overall, we encourage future studies to: (i) mimic ecological conditions in which those patterns evolve, and (ii) take advantage of the ‘omic’ opportunities to produce multi-level data and analytical models that effectively integrate across physiological and evolutionary theory.

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