Information-Theoretic Lower Bounds on the Storage Cost of Shared Memory   Emulation
  The focus of this paper is to understand storage costs of emulating an atomic shared memory over an asynchronous, distributed message passing system. Previous literature has developed several shared memory emulation algorithms based on replication and erasure coding techniques. In this paper, we present information-theoretic lower bounds on the storage costs incurred by shared memory emulation algorithms. Our storage cost lower bounds are universally applicable, that is, we make no assumption on the structure of the algorithm or the method of encoding the data.   We consider an arbitrary algorithm $A$ that implements an atomic multi-writer single-reader (MWSR) shared memory variable whose values come from a finite set $\mathcal{V}$ over a system of $N$ servers connected by point-to-point asynchronous links. We require that in every fair execution of algorithm $A$ where the number of server failures is smaller than a parameter $f$, every operation invoked at a non-failing client terminates. We define the storage cost of a server in algorithm $A$ as the logarithm (to base 2) of number of states it can take on; the total-storage cost of algorithm $A$ is the sum of the storage cost of all servers.   Our results are as follows. (i) We show that if algorithm $A$ does not use server gossip, then the total storage cost is lower bounded by $2 \frac{N}{N-f+1}\log_2|\mathcal{V}|-o(\log_2|\mathcal{V}|)$. (ii) The total storage cost is at least $2 \frac{N}{N-f+2} \log_{2}|\mathcal{V}|-o(\log_{2}|\mathcal{V}|)$ even if the algorithm uses server gossip. (iii) We consider algorithms where the write protocol sends information about the value in at most one phase. We show that the total storage cost is at least $\nu^* \frac{N}{N-f+\nu^*-1} \log_2( |\mathcal{V}|)- o(\log_2(|\mathcal{V}|),$ where $\nu^*$ is the minimum of $f+1$ and the number of active write operations of an execution. 
Viveck R. Cadambe and Zhiying Wang and Nancy Lynch
cs.DC cs.IT math.IT