Abstract
Cloud service providers (CSPs) can offer infinite storage space with cheaper maintenance cost compared to the traditional storage mode. Users tend to store their data in geographical and diverse CSPs so as to avoid vendor lock-in. Static data placement has been widely studied in recent works. However, the data access pattern is often time-varying and users may pay more cost if static placement is adopted during the data lifetime. Therefore, it is a pending problem and challenge of how to dynamically store users’ data under time-varying data access pattern. To this end, we propose ADPA, an adaptive data placement architecture that can adjust the data placement scheme based on the time-varying data access pattern and subject for minimizing the total cost and maximizing the data availability. The proposed architecture includes two main components: data retrieval frequency prediction module based on LSTM and data placement optimization module based on Q-learning. The performance of ADPA is evaluated through several experimental scenarios using NASA-HTTP workload and cloud providers information.
Highlights
With the development of cloud computing, more and more companies adapt the cloud to store their data for low maintenance costs and reliable SLAs (Service Level Agreements) comparing to the traditional data storage mode
In addition to the constraints of high migration costs, a single cloud faces the risk of vendor lock-in; i.e., major risks include the price of cloud services and the interruption of SLA. ese situations may result in making users pay expensive migration costs
In order to verify the universality of the ADPA algorithm, we extend the time range of NASA-HTTP request data to one year (24/ Oct/1994–11/Oct/1995) and perform data retrieving frequency (DAF) statistics based on one-day cycles
Summary
With the development of cloud computing, more and more companies adapt the cloud to store their data for low maintenance costs and reliable SLAs (Service Level Agreements) comparing to the traditional data storage mode. ADPA, which can dynamically host the data object with cost-effective and high availability based on time-varying data workload, is an adaptive data placement architecture proposed in our study. Wang et al [1] propose an approach based on ant colony algorithm that minimizes total cost and enhances data availability through erasure coding It is still a single objective optimization problem. Zhang et al [3] propose a data hosting scheme which contains a transition of storage modes based on changes of data access frequency They only put forward a transfer condition without considering the global data placement sequence optimization problem. In [18], Papaioannou et al propose a cloud storage brokerage solution that can periodically recompute the best provider set using data access statics of the last sampling periods It can adjust data placement for dynamically changing data access pattern. In data placement optimization module, an approach based on Q-learning is used to get a sequential data placement solution according to the prediction data object workload
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