Erasure coding provides fault tolerance in a storage-efficient manner, yet it introduces a high repair penalty. We uncover via trace-driven experiments that the substantial repair traffic in erasure coding is prone to entangling with the foreground traffic, thereby slowing down repair progress and downgrading service quality. We present ChameleonEC, a general mechanism that can assist a variety of erasure codes in realizing low-interference repair. ChameleonEC comprises the following design techniques: (i) repair task assignment, which decomposes a repair plan into multiple repair tasks and makes them coexist harmoniously with the foreground traffic, so as to saturate unoccupied bandwidth and avoid bandwidth contentions; (ii) repair path establishment, which orchestrates elastic transmission routings over the dispatched repair tasks to instruct the repair; and (iii) straggler-aware re-scheduling, which timely re-tunes task transmissions and repair plans to bypass unexpected stragglers emerging in repair. We conduct extensive experiments on Amazon EC2, showing that ChameleonEC can accelerate the repair by 4.9–498.2% for various erasure codes under different real-world traces. ChameleonEC can also speed up the repair process by 25.4–73.5% under the storage-bottlenecked scenarios.

Erasure coding is a storage-efficient means to guarantee data reliability in today's commodity storage systems, yet its repair performance is seriously hindered by the substantial repair traffic. Repair in clustered storage systems is even complicated because of the scarcity of the cross-cluster bandwidth. We present${\sf ClusterSR}$, a cluster-aware scattered repair approach.${\sf ClusterSR}$minimizes the cross-cluster repair traffic by carefully choosing the clusters for reading and repairing chunks. It further balances the cross-cluster repair traffic by scheduling the repair of multiple chunks. Large-scale simulation and Alibaba Cloud ECS experiments show that${\sf ClusterSR}$can reduce 5.6-52.7 percent of the cross-cluster repair traffic and improve 14.4–68.8 percent of the repair throughput.

SUMMARY Lock‐based resource sharing protocols for single processor systems are well understood and supported in programming languages such as Ada and the Real‐Time Specification for Java, and in Real‐Time Operating Systems, such as those that conform to the Real‐Time POSIX standard. In contrast, multiprocessor resource sharing protocols are still in their infancy with no agreed best practices, and yet current real‐time programming languages and operating systems claim to be suitable for multiprocessor applications. This paper reviews the currently available multiprocessor resource allocation policies and analyzes their applicability to the main industry standard real‐time programming languages. It then proposes a framework that allows programmers to define and implement their own locking policy. A prototype implementation of the framework for Ada is presented and evaluated. Copyright © 2012 John Wiley & Sons, Ltd.