High availability of containerized applications requires to perform robust storage of applications' state. Since basic replication techniques are extremely costly at scale, storage space requirements can be reduced by means of erasure or repairing codes. In this paper we address storage regeneration using repair codes, a robust distributed storage technique with no need to fully restore the whole state in case of failure. In fact, only the lost servers' content is replaced. To do so, new cleanslate storage units are made operational at a cost for activating new storage servers and a cost for the transfer of repair data. Our goal is to guarantee maximal availability of containers' state files by a given deadline. activation of servers and communication cost. Upon a fault occurring at a subset of the storage servers, we aim at ensuring that they are repaired by a given deadline. We introduce a controlled fluid model and derive the optimal activation policy to replace servers under such correlated faults. The solution concept is the optimal control of regeneration via the Pontryagin minimum principle. We characterise feasibility conditions and we prove that the optimal policy is of threshold type. Numerical results describe how to apply the model for system dimensioning and show the tradeoff between
In the rest of the paper, we consider an assigned deadline for failsafe operations, as proposed in @cite_20 : in that work, recovery time limits are imposed on the parallel failover of virtual machines based on customers' SLA plans. Also, in this work we adopt a system perspective close to @cite_19 . To the best of the authors' knowledge, this is the first paper describing optimal control of failsafe operations for storage regeneration.
@cite_19 @cite_20
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The explosion of the amount of data stored in cloud systems calls for more efficient paradigms for redundancy. While replication is widely used to ensure data availability, erasure correcting codes provide a much better trade-off between storage and availability. Regenerating codes are good candidates for they also offer low repair costs in term of network bandwidth. While they have been proven optimal, they are difficult to understand and parameterize. In this paper we provide an analysis of regenerating codes for practitioners to grasp the various trade-offs. More specifically we make two contributions: (i) we study the impact of the parameters by conducting an analysis at the level of the system, rather than at the level of a single device; (ii) we compare the computational costs of various implementations of codes and highlight the most efficient ones. Our goal is to provide system designers with concrete information to help them choose the best parameters and design for regenerating codes. In this paper, we present a scalable parallel virtual machine planning and failover method for high availability at a virtual machine (VM) level in a data center. The solution is implemented in IBM's Cloud Managed Services (CMS) enterprise cloud offering for rapid failover in large data centers with a large number of servers, VMs, and disks. The failover system enables failover-time planning and execution and keeps the recovery time within limits of service level agreement (SLA) allowed time budget. The initial serial failover time is reduced by a factor of up to 11 for parallel implementation, and by a factor of up to 44 for parallel failover - parallel storage mapping implementation.