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1. Dynamic Scheduling in Distributed Transactional Memory

   https://doi.org/10.1109/IPDPS47924.2020.00094  

   Busch, Costas ; Herlihy, Maurice ; Popovic, Miroslav ; Sharma, Gokarna ( May 2020 , 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS))

   We investigate scheduling algorithms for distributed transactional memory systems where transactions residing at nodes of a communication graph operate on shared, mobile objects. A transaction requests the objects it needs, executes once those objects have been assembled, and then sends the objects to other waiting transactions. We study scheduling algorithms with provable performance guarantees. Previously, only the offline batch scheduling setting was considered in the literature where transactions and the objects they access are known a priori. Minimizing execution time, even for the offline batch scheduling, is known to be NP-hard for arbitrary communication graphs. In this paper, we analyze for the very first time scheduling algorithms in the online dynamic scheduling setting where transactions and the objects they access are not known a priori and the transactions may arrive online over time. We provide efficient and near-optimal execution time schedules for dynamic scheduling in many specialized network architectures. The core of our technique is a method to convert offline schedules to online. We first describe a centralized scheduler which we then adapt it to a purely distributed scheduler. To our knowledge, these are the first attempts to obtain provably efficient online execution schedules for distributed transactional memory. 

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2. Blockumulus: A Scalable Framework for Smart Contracts on the Cloud

   https://doi.org/10.1109/ICDCS51616.2021.00064  

   Ivanov, Nikolay ; Yan, Qiben ; Wang, Qingyang ( July 2021 , 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS))

   Public blockchains have spurred the growing popularity of decentralized transactions and smart contracts, especially on the financial market. However, public blockchains exhibit their limitations on the transaction throughput, storage availability, and compute capacity. To avoid transaction gridlock, public blockchains impose large fees and per-block resource limits, making it difficult to accommodate the ever-growing high transaction demand. Previous research endeavors to improve the scalability and performance of blockchain through various technologies, such as side-chaining, sharding, secured off-chain computation, communication network optimizations, and efficient consensus protocols. However, these approaches have not attained a widespread adoption due to their inability in delivering a cloud-like performance, in terms of the scalability in transaction throughput, storage, and compute capacity. In this work, we determine that the major obstacle to public blockchain scalability is their underlying unstructured P2P networks. We further show that a centralized network can support the deployment of decentralized smart contracts. We propose a novel approach for achieving scalable decentralization: instead of trying to make blockchain scalable, we deliver decentralization to already scalable cloud by using an Ethereum smart contract. We introduce Blockumulus, a framework that can deploy decentralized cloud smart contract environments using a novel technique called overlay consensus. Through experiments, we demonstrate that Blockumulus is scalable in all three dimensions: computation, data storage, and transaction throughput. Besides eliminating the current code execution and storage restrictions, Blockumulus delivers a transaction latency between 2 and 5 seconds under normal load. Moreover, the stress test of our prototype reveals the ability to execute 20,000 simultaneous transactions under 26 seconds, which is on par with the average throughput of worldwide credit card transactions. 

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3. Wake-up radio-based data forwarding for green wireless networks

   https://doi.org/10.1016/j.comcom.2020.05.046  

   Koutsandria, G. ; DiValerio, V. ; Spenza, D. ; Basagni, S. ; Petrioli, C. ( April 2020 , Computer communications)

   Green wireless networks Wake-up radio Energy harvesting Routing Markov decision process Reinforcement learning 1. Introduction With 14.2 billions of connected things in 2019, over 41.6 billions expected by 2025, and a total spending on endpoints and services that will reach well over $1.1 trillion by the end of 2026, the Internet of Things (IoT) is poised to have a transformative impact on the way we live and on the way we work [1–3]. The vision of this ‘‘connected continuum’’ of objects and people, however, comes with a wide variety of challenges, especially for those IoT networks whose devices rely on some forms of depletable energy support. This has prompted research on hardware and software solutions aimed at decreasing the depen- dence of devices from ‘‘pre-packaged’’ energy provision (e.g., batteries), leading to devices capable of harvesting energy from the environment, and to networks – often called green wireless networks – whose lifetime is virtually infinite. Despite the promising advances of energy harvesting technologies, IoT devices are still doomed to run out of energy due to their inherent constraints on resources such as storage, processing and communica- tion, whose energy requirements often exceed what harvesting can provide. The communication circuitry of prevailing radio technology, especially, consumes relevant amount of energy even when in idle state, i.e., even when no transmissions or receptions occur. Even duty cycling, namely, operating with the radio in low energy consumption ∗ Corresponding author. E-mail address: koutsandria@di.uniroma1.it (G. Koutsandria). https://doi.org/10.1016/j.comcom.2020.05.046 (sleep) mode for pre-set amounts of time, has been shown to only mildly alleviate the problem of making IoT devices durable [4]. An effective answer to eliminate all possible forms of energy consumption that are not directly related to communication (e.g., idle listening) is provided by ultra low power radio triggering techniques, also known as wake-up radios [5,6]. Wake-up radio-based networks allow devices to remain in sleep mode by turning off their main radio when no communication is taking place. Devices continuously listen for a trigger on their wake-up radio, namely, for a wake-up sequence, to activate their main radio and participate to communication tasks. Therefore, devices wake up and turn their main radio on only when data communication is requested by a neighboring device. Further energy savings can be obtained by restricting the number of neighboring devices that wake up when triggered. This is obtained by allowing devices to wake up only when they receive specific wake-up sequences, which correspond to particular protocol requirements, including distance from the destina- tion, current energy status, residual energy, etc. This form of selective awakenings is called semantic addressing [7]. Use of low-power wake-up radio with semantic addressing has been shown to remarkably reduce the dominating energy costs of communication and idle listening of traditional radio networking [7–12]. This paper contributes to the research on enabling green wireless networks for long lasting IoT applications. Specifically, we introduce a ABSTRACT This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively. 

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4. Prism: Deconstructing the Blockchain to Approach Physical Limits

   https://doi.org/10.1145/3319535.3363213  

   Bagaria, Vivek ; Kannan, Sreeram ; Tse, David ; Fanti, Giulia ; Viswanath, Pramod ( November 2019 , ACM SIGSAC Conference on Computer and Communications Security)

   The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput and confirmation latency. In a decentralized setting, these measures are limited by two underlying physical network attributes: communication capacity and speed-of-light propagation delay. In this work we introduce Prism, a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in the bandwidth-delay product CD; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing Nakamoto’s blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits. 

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5. On Sharding Permissioned Blockchains.

   Mohammad Javad Amiri, Divyakant Agrawal ( July 2019 , IEEE International Conference on Blockchain)

   Permissioned Blockchain systems rely mainly on Byzantine fault-tolerant protocols to establish consensus on the order of transactions. While Byzantine fault-tolerant protocols mostly guarantee consistency (safety) in an asynchronous network using 3f+1 machines to overcome the simultaneous malicious failure of any f nodes, in many systems, e.g., blockchain systems, the number of available nodes (resources) is much more than 3f + 1. To utilize such extra resources, in this paper we introduce a model that leverages transaction parallelism by partitioning the nodes into clusters (partitions) and processing independent transactions on different partitions simultaneously. The model also shards the blockchain ledger, assigns different shards of the blockchain ledger to different clusters, and includes both intra-shard and cross-shard transactions. Since more than one cluster is involved in each cross-shard transaction, the ledger is formed as a directed acyclic graph. 

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