Summary Blockchain technologies are expected to make a significant impact on a variety of industries. However, one issue holding them back is their limited transaction throughput, especially compared to established solutions such as distributed database systems. In this paper, we rearchitect a modern permissioned blockchain system, Hyperledger Fabric, to increase transaction throughput from 3000 to 20 000 transactions per second. We focus on performance bottlenecks beyond the consensus mechanism, and we propose architectural changes that reduce computation and I/O overhead during transaction ordering and validation to greatly improve throughput. Notably, our optimizations are fully plug‐and‐play and do not require any interface changes to Hyperledger Fabric.

Understanding the energy usage of buildings is crucial for policymaking, energy planning, and achieving sustainable development. Unfortunately, instrumenting buildings to collect energy usage data is difficult and all publicly available datasets typically include only a few hundred homes within a region. Due to their relatively small size, these datasets provide limited insight and are insufficient for analyses that require a larger representation, such as an entire city or town. In recent years, utility companies have installed advanced electric and gas meters, i.e., "smart meters" that enable energy data collection on a massive scale. In this paper, we analyze such a dataset from a utility company that includes energy data from 14,836 smart meters covering a small city. We conduct a wideranging analysis of the city's gas and electric data to gain insights into the energy consumption of both individual homes and the city as a whole. In doing so, we demonstrate how city-scale smart meter datasets can answer a variety of questions on building energy consumption, such as the impact of weather on energy usage, the correlation between the size and age of a building and its energy usage, the impact of increasing levels of renewable penetration, etc. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we observe that 700 homes are highly energy inefficient as its energy demand variability is twice that of the aggregate grid demand. Finally, we study the impact of increasing level of renewable integration in homes and show that solar penetration rates higher than 20% of demand increases the risk of over-generation and may impact utility operations.

Infrastructure-as-a-Service (IaaS) cloud platforms rent resources, in the form of virtual machines (VMs), under a variety of contract terms that offer different levels of risk and cost. For example, users may acquire VMs in the spot market that are often cheap but entail significant risk, since their price varies over time based on market supply and demand and they may terminate at any time if the price rises too high. Currently, users must manage all the risks associated with using spot servers. As a result, conventional wisdom holds that spot servers are only appropriate for delay-tolerant batch applications. In this paper, we propose a derivative cloud platform, called SpotCheck, that transparently manages the risks associated with using spot servers for users.SpotCheck provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports all types of applications, including interactive ones. SpotCheck's design combines the use of nested VMs with live bounded-time migration and novel server pool management policies to maximize availability, while balancing risk and cost. We implement SpotCheck on Amazon's EC2 and show that it i) provides nested VMs to users that are 99.9989% available, ii) achieves nearly 5× cost savings compared to using equivalent types of ondemand VMs, and iii) eliminates any risk of losing VM state.

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Electric vehicles (EV) are rapidly increasing in popularity, which is signicantly increasing demand on the distribution infrastructure in the electric grid. This poses a serious problem for the grid, as most distribution transformers were installed during the pre-EV era, and thus were not sized to handle large loads from EVs. In parallel, smart grid technologies have emerged that actively regulate demand to prevent overloading the grid's infrastructure, in particular by optimizing the use of grid-scale energy storage. In this paper, we rst analyze the load on distribution transformers across a small city and study the potential impact of EVs as their penetration levels increase. Our real-world dataset includes the energy demand from 1,353 transformers and charging proles from 91 EVs over a 1 year period, and thus provides an accurate snapshot of the grid's current state, and allows us to examine the potential impact of increasing EV penetrations. We then evaluate the benets of using smart grid technologies, such as smart EV charging and energy storage, to mitigate the eects of increasing the EV-based load.