Khai Leong Yong

Concurrent Regeneration Code with Local Reconstruction in Distributed Storage Systems

Reed-Solomon (RS) codes are a standard erasure code choice and their repair cost is so high that it is a penalty for storage efficiency and high reliability. In this paper, we propose a novel class of Concurrent Regeneration codes with Local reconstruction (CRL), that enjoy three advantages: (1) to minimize the network bandwidth for node repair, (2) to minimize the number of accessed nodes, and (3) faster reconstruction in distributed storage systems. We show how they overcome the limitation of RS codes, and we demonstrate that they are optimal on a trade-off between minimum distance and locality. By conducting performance evaluation in both memory and JBOD environments, experimental results demonstrate the performance of the CRL codes.

C2: Adaptive Load Balancing for Metadata Server Cluster in Cloud-Scale Storage Systems

Big data is an emerging term in the storage industry, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale storage systems, load balancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improving quality of services. Many good approaches have been proposed for load balancing in distributed storage systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request distributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication. In this paper, we propose Cloud Cache (C 2), an adaptive load balancing scheme for metadata server cluster in Cloud-scale storage systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balancing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. By conducting a performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C 2.

Adaptive and scalable load balancing for metadata server cluster in cloud-scale file systems

Big data is an emerging term in the storage industry, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale (or EB-scale) file systems, load balancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improving quality of services.Many good approaches have been proposed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request distributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication. In this paper, we propose Cloud Cache (C2), an adaptive and scalable load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balancing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C2 runs as follows: 1) to run adaptive cache diffusion first, if a node is overloaded, loadshedding will be used; otherwise, load-stealing will be used; and 2) to run adaptive replication scheme second, if there is a very popular metadata item (or at least two items) causing a node be overloaded, adaptive replication scheme will be used, in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack property. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C2.