Abstract-Thing-to-thing payments are a key enabler in the Internet of Things (IoT) era, to ubiquitously allow for devices to pay each other for services without any human interaction. Traditional credit card-based systems are not able to handle this new paradigm, however blockchain technology is a promising payment candidate in this context. The prominent example of blockchain technology is Bitcoin, with its decentralized structure and ease of account creation. This paper presents a proof-ofconcept implementation of a smart cable that connects to a smart socket and without human interaction pays for electricity. We identify several obstacles for the widespread use of bitcoins in thing-to-thing payments. A critical problem is the high transaction fees in the Bitcoin network when doing micro transactions. To reduce this impact, we present a single-fee micro-payment protocol that aggregates multiple smaller payments incrementally into one larger transaction needing only one transaction fee. The proof-of concept shows that trustless, autonomous, and ubiquitous thing-to-thing micro-payments is no longer a future technology.

No abstract

When two or more programs are co-scheduled on the same multicore computer they might experience a slowdown due to the limited off-chip memory bandwidth. According to our measurements, this slowdown does not depend on the total bandwidth use in a simple way. One thing we observe is that a higher memory bandwidth usage will not always lead to a larger slowdown. This means that relying on bandwidth usage as input to a job scheduler might cause non-optimal scheduling of processes on multicore nodes in clusters, clouds, and grids. To guide scheduling decisions, we instead propose a slowdown based characterization approach. Real slowdowns are complex to measure due to the exponential number of experiments needed. Thus, we present a novel method for estimating the slowdown programs will experience when co-scheduled on the same computer. We evaluate the method by comparing the predictions made with real slowdown data and the often used memory bandwidth based method. This study show that a scheduler relying on slowdown based categorization makes fewer incorrect co-scheduling choices and the negative impact on program execution times is less than when using a bandwidth based categorization method.

No abstract

No abstract