Data Sets Replicas Placements Strategy from Cost-Effective View in the Cloud

Wu, Xiuguo

doi:10.1155/2016/1496714

Cited by 9 publications

(10 citation statements)

References 24 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(5) Calculate the total profit rate (J) using equation (28). (6) If p and C are increasing and δ > ∝ , then update p and C using equations (30) and (31). (7) Calculate the total profit rate using equation (32).…”

Section: Resultsmentioning

confidence: 99%

“…Bi et al [29] used a combination of M/M/c and M/M/1 queuing models to dynamically manage resource allocations to cloud-based VMs. Wu proposed a cost-effective dataset management model [30]. A control theory approach for systems provides a rigorous technique for modeling, designing, analyzing and evaluating system performance, as mentioned by Zhu et al [31].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment

Kumari

Sahoo

2019

Scientific Programming

View full text Add to dashboard Cite

Cloud is a widely used platform for intensive computing, bulk storage, and networking. In the world of cloud computing, scaling is a preferred tool for resource management and performance determination. Scaling is generally of two types: horizontal and vertical. The horizontal scale connects users’ agreement with the hardware and software entities and is implemented physically as per the requirement and demand of the datacenter for its further expansion. Vertical scaling can essentially resize server without any change in code and can increase the capacity of existing hardware or software by adding resources. The present study aims at describing two approaches for scaling, one is a predator-prey method and second is genetic algorithm (GA) along with differential evolution (DE). The predator-prey method is a mathematical model used to implement vertical scaling of task for optimal resource provisioning and genetic algorithm (GA) along with differential evolution(DE) based metaheuristic approach that is used for resource scaling. In this respect, the predator-prey model introduces two algorithms, namely, sustainable and seasonal scaling algorithm (SSSA) and maximum profit scaling algorithm (MPSA). The SSSA tries to find the approximation of resource scaling and the mechanism for maximizing sustainable as well as seasonal scaling. On the other hand, the MPSA calculates the optimal cost per reservation and maximum sustainable profit. The experimental results reflect that the proposed logistic scaling-based predator-prey method (SSSA-MPSA) provides a comparable result with GA-DE algorithm in terms of execution time, average completion time, and cost of expenses incurred by the datacenter.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment

Kumari

Sahoo

2019

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…The evaluation results demonstrate that the proposed system brings forth a cost effective way to improve data availability and privacy for critical applications. In a relatively recent effort done by X. Wu [23], a cost effective data set replica placement strategy is proposed for cloud environments. The technique starts with designing a cost model for data management which includes storage cost and transfer cost.…”

Section: Related Workmentioning

confidence: 99%

On the Dynamic Maintenance of Data Replicas based on Access Patterns in A Multi-Cloud Environment

Shorfuzzaman

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Cloudcomputing provides services and infrastructures to enable end-users to access, modify and share massive geographically distributed data. There are increasing interests in developing data-intensive (big data) applications in this computing environment that need to access huge datasets. Accessing such data in an efficient way is deterred with factors such as dynamic changes in resource availability, provision of diverse service quality by different cloud providers. Data replication has already been proven to be an effective technique to overcome these challenges. Replication offers reduced response time in data access, higher data availability and improved system load balancing. Once the replicas are created in a multi-cloud environment, it is of utmost importance to continuously support maintenance of these replicas dynamically. This is to ensure that replicas are located in optimal data center locations to minimize replication cost and to meet specific user and system requirements. First, this paper proposes a novel approach to distributed placement of static replicas in appropriate data center locations. Secondly, motivated by the fact that a multi-cloud environment is highly dynamic, the paper presents a dynamic replica maintenance technique that reallocates replicas to new data center locations upon significant performance degradation. The evaluation results demonstrate the effectiveness of the presented dynamic maintenance technique with static placement decisions in a multi-cloud environment.

show abstract

“…To further illustrate the cost reduction between combination strategy and other works, we have designed specifically and exclusively for cost comparison. In the performance evaluation, in addition to the proposed algorithms (GA), we also include the no replication (NREP) scheme as the baseline, random (RAND) scheme and minimum-cost replicas placements algorithm (MCRP) [38] for comparison. As the name states, in NREP there is just the original objects in the root as a replica of data set.…”

Section: Simulation 2: Feasibility and Effectiveness Of Replicas And mentioning

confidence: 99%

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Wu¹

2017

IJCIS

Self Cite

View full text Add to dashboard Cite

In the cloud storage system, data sets replicas technology can efficiently enhance data availability and thereby increase the system reliability by replicating commonly used data sets in geographically different data centers. Most current approaches largely focus on system performance improvement by placing replicas for an independent data set, omitting the generation relationship among data sets. Furthermore, cost is an important element in deciding replicas number and their stored places, which can cause great financial burden for cloud clients because the cost for replicas storage and consistency maintenance may lead to high overhead with the number of new replicas increased in a pay-as-you-go paradigm. In this paper, we propose a combination strategy of real-replicas and pseudo-replicas (by computation from its provenance) from cost-effective view in order to achieve the minimum data set management cost, not only for the independent data sets but also for related data sets with generation relationships. We first define cost models that fit into the cloud computing paradigm, including data sets storage, computation and transfer costs, and then develop a new data set management cost model, helping to achieve a multi-criteria optimization of data set management. After that, a minimum cost benchmarking approach for the best trade-off between real-replicas and pseudo-replicas is proposed once decision to add a replica has been made. Then, a more practical and reasonable genetic algorithm as an alternative procedure for generating optimal or nearoptimal solution is given in order to identify the suitable replicas storage places. Finally, we present simulations setups and results that provide a first validation of our strategy. Both the theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications have shown efficiency and effectiveness of the improved system brought by the proposed strategy in cloud computing environment.

show abstract

Data Sets Replicas Placements Strategy from Cost-Effective View in the Cloud

Cited by 9 publications

References 24 publications

Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment

Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment

On the Dynamic Maintenance of Data Replicas based on Access Patterns in A Multi-Cloud Environment

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Contact Info

Product

Resources

About