We develop an adaptive load distribution protocol for logical volume I/O workload in clustered storage systems. It exploits data redundancy among decentralized storage servers to dynamically route I/O workload on a per-request basis, offering short-term load balancing and improved I/O performance. Our protocol builds on tunable hashing techniques and is based purely on client logic. Therefore, it does not limit system scalability and requires no change to the existing infrastructure. It distributes the I/O requests of a client to storage servers selected adoptively by a decentralized tunable hashing scheme, and, applies different policies to read and write requests. It also makes no assumption about inter-server communication latency and thus is robust to different network configurations. It supports both replication and erasure coding data redundancy schemes. Experimental results show that our protocol performs closely to a centralized load-balancing algorithm and verify the robustness of our protocol

We develop and evaluate a system for load management in shared-disk file systems built on clusters of heterogeneous computers. It balances workload by moving file sets among cluster server nodes. It responds to changing server resources that arise from failure and recovery, and dynamically adding or removing servers. It also realizes performance consistency---nearly uniform performance across all servers. The system is adaptive and self-tuning. It operates without any a priori knowledge of workload properties, or the capabilities of the servers. Rather, it continuously tunes load placement using a technique called adaptive, nonuniform (ANU) randomization. ANU randomization realizes the scalability and metadata reduction benefits of hash-based, randomized placement techniques, while avoiding hashing's drawbacks: load skew, inability to cope with heterogeneity, and lack of tunability. ANU randomization outperforms virtual-processor approaches to load balancing, while reducing the amount of shared state among servers and the amount of load movement.

We develop and evaluate a system for load management in shared-disk file systems built on clusters of heterogeneous computers. The system generalizes load balancing and server provisioning. It balances file metadata workload by moving file sets among cluster server nodes. It also responds to changing server resources that arise from failure and recovery and dynamically adding or removing servers. The system is adaptive and self-managing. It operates without any a-priori knowledge of workload properties or the capabilities of the servers. Rather, it continuously tunes load placement using a technique called adaptive, non-uniform (ANU) randomization. ANU randomization realizes the scalability and metadata reduction benefits of hash-based, randomized placement techniques. It also avoids hashing's drawbacks: load skew, inability to cope with heterogeneity, and lack of tunability. Simulation results show that our load-management algorithm performs comparably to a prescient algorithm.