RISS 검색 - 국내학술지논문

1
A Survey on SLA Management for Cloud Computing and Cloud-Hosted Big Data Analytic Applications

Radhya Sahal,Mohamed H. Khafagy,Fatma A. Omara 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.4
Cloud computing is a new generation of computing based on the layered model which provides different services to cloud customer. Conceptually, cloud computing offers a scalable platform for Big Data Analytic Applications (BDAAs) which can elastically provision resources based on data growth and complexity of analytic applications. This complexity of analysis increases with vast volumes of data in big enterprises which prefer effective and fast decision-making. Therefore, the importance of Service Level Agreement (SLA) is appeared which clarifies the roles between a customer (i.e., cloud user or Big Data analyst) and a provider for particular service provision comes. The main components of the SLAs are Quality of Service parameters which must be monitored to achieve a set of Service Level Objectives (SLOs) and detect violations. Indeed, many SLA management approaches have been developed as solutions for preventing SLA violations to avoid costly penalties. Consequently, many interesting solutions developed works of SLA violation management in cloud technology and cloud-hosted BDAAs. A survey about the existed works in terms of idea, strengths and weaknesses is introduced in this paper. Meanwhile, the challenges and new research directions in this area which require further investigation will be discussed to provide a comprehensive overview and big-picture.
2
Task Scheduling Using PSO Algorithm in Cloud Computing Environments

Ali Al-maamari,Fatma A. Omara 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.5
The Cloud computing has become the fast spread in the field of computing, research and industry in the last few years. As part of the service offered, there are new possibilities to build applications and provide various services to the end user by virtualization through the internet. Task scheduling is the most significant matter in the cloud computing because the user has to pay for resource using on the basis of time, which acts to distribute the load evenly among the system resources by maximizing utilization and reducing task execution Time. Many heuristic algorithms have been existed to resolve the task scheduling problem such as a Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Cuckoo search (CS) algorithms, etc. In this paper, a Dynamic Adaptive Particle Swarm Optimization algorithm (DAPSO) has been implemented to enhance the performance of the basic PSO algorithm to optimize the task runtime by minimizing the makespan of a particular task set, and in the same time, maximizing resource utilization. Also, .a task scheduling algorithm has been proposed to schedule the independent task over the Cloud Computing. The proposed algorithm is considered an amalgamation of the Dynamic PSO (DAPSO) algorithm and the Cuckoo search (CS) algorithm; called MDAPSO. According to the experimental results, it is found that MDAPSO and DAPSO algorithms outperform the original PSO algorithm. Also, a comparative study has been done to evaluate the performance of the proposed MDAPSO with respect to the original PSO.
3
An Enhanced Task Scheduling Algorithm on Cloud Computing Environment

Hussin M. Alkhashai,Fatma A. Omara 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.7
Cloud computing is the technology that moves the information technology (IT) services out of the office. Unfortunately, Cloud computing has faced some challenges. The task scheduling problem is considered one of the main challenges because a good mapping between the available resources and the users' tasks is needed to reduce the execution time of the users’ tasks (i.e., reduce make-span), and increase resource utilization. The objective of this paper is to introduce and implement an enhanced task scheduling algorithm to assign the users' tasks to multiple computing resources. The aim of the proposed algorithm is to reduce the execution time, and cost, as well as, increase resource utilization. The proposed algorithm is considered an amalgamation of the Particle Swarm Optimization (PSO),the Best–Fit (BF), and Tabu-Search (TS) algorithms; called BFPSOTS. According to the proposed BFPSOTS algorithm, the BF algorithm has been used to generate the initial population of the standard PSO algorithm instead of to be random. The Tabu-Search (TS) algorithm has been used to improve the local research by avoiding the trap of the local optimality which could be occurred using the standard PSO algorithm. The proposed hybrid algorithm (i.e., BFPSOTS) has been implemented using Cloudsim. A comparative study has been done to evaluate the performance of the proposed algorithm relative to the standard PSO algorithm using five problems with different number of independent task, and Virtual Machines (VMs). The performance parameters which have been considered are the execution time (Makspan), cost, and resources utilization. The implementation results prove that the proposed hybrid algorithm (i.e., BFPSOTS) outperforms the standard PSO algorithm..
4
Comparative Study of Multi-query Optimization Techniques using Shared Predicate-based for Big Data

Radhya Sahal,Mohamed H. Khafagy,Fatma A. Omara 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.5
Big data analytical systems, such as MapReduce, have become main issues for many enterprises and research groups. Currently, multi-query which translated into MapReduce jobs is submitted repeatedly with similar tasks. So, exploiting these similar tasks can offer possibilities to avoid repeated computations of MapReduce jobs. Therefore, many researches have addressed the sharing opportunity to optimize multi-query processing. Consequently, the main goal of this work is to study and compare comprehensively two existed sharing opportunity techniques using predicate-based filters; MRShare and relaxed MRShare. The comparative study has been performed over TPC-H benchmark and confirmed that the relaxed MRShare technique significantly outperforms the MRShare for shared data in terms of predicate-based filters among multi-query.

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