Scheduling Strategies in Cloud Computing: Methods and Implementations

Nasr, Aida A.; El-Booz, Sheren A.

doi:10.3844/ajeassp.2018.426.432

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Several researchers have developed algorithms to solve the cloudlets scheduling issue [4][5][6][7][8]. However, most of the existing algorithms consider schedule length, and disregard several constraints that may affect the scheduling process like memory and processing load constraints.…”

Section: I In Nt Tr Ro Od Du Uc Ct Ti Io On Nmentioning

confidence: 99%

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Nasr

El‐Bahnasawy

Attiya

et al. 2019

Menoufia Journal of Electronic Engineering Research

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A A P Pr ro op po os se ed d M Me et ta a--H He eu ur ri is st ti ic c A Ap pp pr ro oa ac ch h f fo or r C Cl lo ou ud dl le et ts s S Sc ch he ed du ul li in ng g i in n C Cl lo ou ud d C Co om mp pu ut ti in ng g E En nv vi ir ro on nm me en nt t A Ab bs st tr ra ac ct tThis paper presents a new hybrid approach, called ACOSA, for cloudlets scheduling to enhance the scheduler behavior in Cloud computing (CC) environment and to overcome the results oscillation problem of the existing meta-heuristic scheduling algorithms. The proposed approach combines both the Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithm to improve both quality of solutions and time complexity of the scheduling algorithm. The proposed approach is evaluated by using the well-known CloudSim, and the results are compared with the ant colony and simulated annealing separately in terms of schedule length, load balancing, and time complexity. It decreases the schedule length by 29.75% with SA and 12.25% with ACO. The ACOSA provides higher load balancing degree. It improves the balancing degree ratio by 36.36% than SA and 12.13% than ACO algorithms.

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Section: I In Nt Tr Ro Od Du Uc Ct Ti Io On Nmentioning

confidence: 99%

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Nasr

El‐Bahnasawy

Attiya

et al. 2019

Menoufia Journal of Electronic Engineering Research

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“…Once the bags are scanned the officer then uploads the information onto the cloud-based database (Gautam et al, 2010). This is done in real time (Nasr and Elbooz, 2018); this information can be viewed by anyone who has authorization from any location. The system then generates a report or invoice for the farmer.…”

Section: Alignment Patternmentioning

confidence: 99%

Real-Time Data Capture Model for Accelerated Payment of Small-Scale Farmers

Mwansa¹,

Phiri²

2018

American Journal of Engineering and Applied Sciences

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Most developing countries especially in Africa face a number of challenges in the area of food security. These challenges include proliferation, theft, poor inventory system, poor transport and communication network. Zambia is one of the countries with these challenges and has been losing millions of dollars each year due to various challenges in managing the inventory system. In this paper, we are proposing a model for the inventory system based on Quick Response (QR) and cloud computing for real-time capture of grain bags brought in by farmers at the satellite depot. The government supplies farming input to local farmers and buys the grain back from the farmers. This study is looking at part of the buying process which requires the movement of grain from the local farmer to the government. The grain forms part of the national food storage reserve. The proposed system first requires tagging the grain bags then capture the details of the farmer and attach to the grain bags. Our proposed model based on cloud technologies is integrated with the mobile application used to read the QR code attached to the grain bags. These details are then linked to the details of the farmer in the database. These captured data regarding the farmer and grain bags supplied at the satellite depot are made available to the decision makers in real-time. Out results show that the proposed model will helps to address a number of challenges that the current system has been facing. These include accelerated process of paying the local farmers supplying grain to the government which used to take months. It will also help to give the grain stock statistics in real time per region and the country at large. This model will be very useful for most developing countries in managing their grain.

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Scheduling Strategies in Cloud Computing: Methods and Implementations

Cited by 2 publications

References 18 publications

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Real-Time Data Capture Model for Accelerated Payment of Small-Scale Farmers

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