PSO Based Multiobjective Reliable Optimization Model for Cloud Storage

Liu, Xiyang; Fan, Lei; Wang, Liming; Meng, Sha

doi:10.1109/cit/iucc/dasc/picom.2015.334

Cited by 4 publications

(7 citation statements)

References 15 publications

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“…In our evaluation, an instance is considered as small if it can be evaluated within an hour using the exact method. We compared our contribution results with the standard version of NSGAII and a multi-objective version of Particle Swarm Optimization used in [46] (designated as PSO in the sequel). Finally, we evaluated the success rate of the repair function.…”

Section: Dcs Specificationsmentioning

confidence: 99%

“…Regarding distributed clouds, several studies have been proposed [30,43,46,50,58,74,75,83,84]. They considered different applications such as bigdata analysis, workflow optimization and geo-replication, etc.…”

Section: Related Workmentioning

confidence: 99%

“…The placement problem in these studies have considered the diversity of storage and network services prices offered by the federated clouds. Some of them have used multi-objective optimization [30,43,46,58,84].The placement problem in these works have been modeled under different objective functions and constraints according to the faced issues. However, generally the storage cost was only related to the capacity (Price/GB) and only [50] have considered the workload at a high level.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of data placement has been widely investigated in the Cloud environment. The proposed techniques have considered many issues related to the hybrid storage system of a centralized cloud as in [17,35,41,57,62,78,81] and distributed clouds like [30,43,46,50,72,76,79,83]. State-of-the-art studies have considered different objectives under different constraints.…”

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confidence: 99%

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Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

et al. 2021

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Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.

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Section: Dcs Specificationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

et al. 2021

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“…The simulation results from using the ACO scheme with cloud datasets in virtual machines confirm a better security level compared to the traditional baselines. Further, SI-empowered algorithms such as PSO can also build algorithms for providing data access reliability in cloud storage [152], by optimizing the data transfer trust and storage stability under cloud storage capability and storage cost.…”

Section: A Access Controlmentioning

confidence: 99%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

Preprint

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Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

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Security and storage improvement in distributed cloud data centers by increasing reliability based on particle swarm optimization and artificial immune system algorithms

Chamkoori

Katebi

2023

Concurrency and Computation

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Summary One of the major concerns in security and storage improvement of data centers is due to the fact that there is a possibility of access to cloud information, and the distance between data centers for data transfer causes cloud storage problems. In this article, an encryption method using artificial immune system (AIS) is leveraged to secure sensitive data in data centers and particle swarm optimization (PSO) algorithm is exploited to improve the selection of storage services in distributed cloud data centers to store and transfer particular a data set between data centers. To obtain satisfied performance considering the transmitting cost, and smallest transmitting distance, we exploited one discrete PSO algorithm and AIS method and called PSO_AIS. To evaluate the proposed PSO_AIS algorithm, we designed the experiments and simulated it in MATLAB software. In the simulation, we conducted experiments on 13 data centers located in different locations in Iran. The experimental results expose that the proposed algorithm reduces 30% the distance and cost to select a particular data center for a particular data set. Experiments have been carried‐out to demonstrate the effectiveness of the proposed encryption method, which is based on AIS. The results showed that the encoding performance of the proposed method is better than other methods.

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PSO Based Multiobjective Reliable Optimization Model for Cloud Storage

Cited by 4 publications

References 15 publications

Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Security and storage improvement in distributed cloud data centers by increasing reliability based on particle swarm optimization and artificial immune system algorithms

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