Blind Updates in Coded Caching

10/20/2020
∙
by   Suman Ghosh, et al.
∙
0
∙

We consider the centralized coded caching system where a library of files is available at the server and their subfiles are cached at the clients as prescribed by a placement delivery array (PDA). We are interested in the problem where a specific file in the library is replaced with a new file at the server, the contents of which are correlated with the file being replaced, and this replacement needs to be communicated to the caches. The server loses the original file when the replacement is done and is unaware of the differences between the two files, whereas each cache has access to specific subfiles of the original file as dictated by the PDA. We model the correlation between the two files by assuming that they differ in at the most ϵ subfiles, and aim to reduce the number of bits broadcast by the server to update the caches. We design a new elegant coded transmission strategy for the server to update the caches blindly, and also identify another simple scheme that is based on MDS codes. We then derive converse bounds on the minimum cost ℓ^* among all linear strategies. Equipped with these results, we identify ℓ^* when the caching system uses a PDA of Yan et al. For two other families of PDAs – the Maddah-Ali-Niesen scheme and a scheme by Tang & Ramamoorthy and Yan et al. – we show that our new scheme has cost ℓ^*(1 + o(1)) when the updates are sufficiently sparse, while the scheme using MDS codes has order-optimal cost when the updates are dense.

READ FULL TEXT

Please sign up or login with your details

Continue with:
Or login with email
Enter Password
Re-enter Password

Forgot password? Click here to reset
Success!
Error Icon An error occurred

Sign in with Google

×

Use your Google Account to sign in to DeepAI

×
Pro

Consider DeepAI Pro

Subscribe to DeepAI Pro
DeepAI Pro
Provides a limited generation allowance each month. When exceeded, you are charged overage rates available at deepai.org/pricing. Also includes an ad-free experience and API access. Renews automatically until canceled. Non-refundable.
Subtotal
Total due today

Payment

Add DeepAI credits
DeepAI credits
One-time purchase. Credits are added to your wallet after payment.
Subtotal
Total due today

Payment