Kishori Konwar and Prakash Nayarana Moorthy: A Layered Architecture for Erasure-Coded Consistent Distributed Storage

Friday, April 7, 2017 - 1:00pm to 2:30pm
Kishori Konwar and Prakash Nayarana Moorthy

Motivated by emerging applications to the \emph{edge computing} paradigm, we introduce a two-layer erasure-coded fault-tolerant distributed storage system offering atomic access for read and write operations. In edge computing, clients interact with an edge-layer of servers that is geographically near; the edge-layer in turn interacts with a back-end layer of servers. The edge-layer provides low latency access and temporary storage for client operations, and uses the back-end layer for persistent storage. Our algorithm, termed Layered Data Storage (LDS) algorithm, offers several features suitable for edge-computing systems, works under asynchronous message-passing environments, supports multiple readers and writers, and can tolerate $f_1 < n_1/2$ and $f_2 < n_2/3$ crash failures in the two layers having $n_1$ and $n_2$ servers, respectively. We use a class of erasure codes known as regenerating codes for storage of data in the back-end layer. The choice of regenerating codes, instead of popular choices like Reed-Solomon codes, not only  optimizes the cost of back-end storage, but also helps in optimizing communication cost of read operations, when the value needs to be recreated all the way from the back-end. The two-layer architecture permits a modular  implementation of atomicity and erasure-code protocols; the implementation of erasure-codes is mostly limited to interaction between the two layers. We prove liveness and atomicity of LDS, and also compute performance costs associated with read and write operations. In a system with $n_1 = \Theta(n_2), f_1 = \Theta(n_1), f_2 = \Theta(n_2)$, the write and read costs are respectively given by $\Theta(n_1)$ and $\Theta(1)  + n_1\mathcal{I}(\delta > 0)$. Here $\delta$ is a parameter closely related to the number of write operations that are concurrent with the read operation. The cost of persistent storage in the back-end layer is $\Theta(1)$. The impact of temporary storage is minimally felt in a multi-object system running $N$ independent instances of \calc, where only a small fraction of the objects undergo concurrent accesses at any point during the execution. For the multi-object system, we identify a condition on the rate of concurrent writes in the system such that the overall storage cost is dominated by that of persistent storage in the back-end layer, and is given by $\Theta(N)$.