16 research outputs found
Boomerang: Redundancy Improves Latency and Throughput in Payment-Channel Networks
In multi-path routing schemes for payment-channel networks, Alice transfers
funds to Bob by splitting them into partial payments and routing them along
multiple paths. Undisclosed channel balances and mismatched transaction fees
cause delays and failures on some payment paths. For atomic transfer schemes,
these straggling paths stall the whole transfer. We show that the latency of
transfers reduces when redundant payment paths are added. This frees up
liquidity in payment channels and hence increases the throughput of the
network. We devise Boomerang, a generic technique to be used on top of
multi-path routing schemes to construct redundant payment paths free of
counterparty risk. In our experiments, applying Boomerang to a baseline routing
scheme leads to 40% latency reduction and 2x throughput increase. We build on
ideas from publicly verifiable secret sharing, such that Alice learns a secret
of Bob iff Bob overdraws funds from the redundant paths. Funds are forwarded
using Boomerang contracts, which allow Alice to revert the transfer iff she has
learned Bob's secret. We implement the Boomerang contract in Bitcoin Script
Efficient Migration-Aware Algorithms for Elastic BPMaaS
International audienceAs for all kind of software, customers expect to find business process execution provided as a service (BPMaaS). They expect it to be provided at the best cost with guaranteed SLA. From the BPMaaS provider point of view it can be done thanks to the provision of an elastic cloud infrastructure. Providers still have to provide the service at the lowest possible cost while meeting customers expectation. We propose a customer-centric service model that link the BP execution requirement to cloud resources, and that optimize the deployment of customer’s (or tenants) processes in the cloud to adjust constantly the provision to the needs. However, migrations between cloud configurations can be costly in terms of quality of service and a provider should reduce the number of migrations. We propose a model for BPMaaS cost optimization that take into account a maximum number of migrations for each tenants. We designed a heuristic algorithm and experimented using various customer load configurations based on customer data, and on an actual estimation of the capacity of cloud resources