Compact, Popularity-Aware and Adaptive Hybrid Data Placement Schemes for Heterogeneous Cloud Storage

Abstract

Cloud storage systems frequently have a large user base that requires huge cloud resources. Sometimes, cloud devices become overloaded because of an imbalance in input/output (I/O) or space demand. How can data with different popularity be distributed over heterogeneous devices? The key to resolving this problem is to balance the workload of multi-dimension resources. A consistent hash-aware cloud storage system constitutes a good solution for data placement. It can achieve only 1-D balance, usually the balance of the space resource. However, it is not straightforward to obtain a balance of space, I/O, and other resources simultaneously. Many users have experienced the overloading of devices in these systems. We focus mainly on this problem in this paper. In this paper, we discuss the factors that cause the overload of devices that occurs in the hash-aware cloud. Furthermore, we design some schemes with three algorithms to facilitate the assignment of hybrid data of different size and popularity to the heterogeneous cloud. The system can reduce the probability of an overload occurring. Most systems do not easily accommodate the movement of data. However, we argue that relocating part of the necessary data is helpful. This relocation can achieve a balance of resource usage and use fewer resources, without the need for replicas. Our system can provide a better quality of service, because the imbalance in the usage of resources is reduced. We performed an evaluation using extensive simulations driven by real-world traces. We demonstrate that our system can effectively reduce the overload probability of devices in cloud storage systems.