Blockchain, as a distributed ledger technology that can establish trust in untrusted environments, has garnered increasing attention in recent years. Enhancing system performance by improving the concurrency of blockchain systems is a current research focus. However, at the same time, the high transaction abortion rate in high-concurrency scenarios has become a new urgent problem to be solved. In this article, our primary focus lies in addressing the issue of high transaction abort rates in Hyperledger Fabric within concurrent scenarios. We propose MoryFabric, a conflict transaction optimization scheme based on transaction actual validity verification and reordering based on keys. Through solutions involving inter-block transaction conflict detection, transaction actual validity detection, and key-based transaction reordering, we aim to minimize transaction abort rates to the greatest extent possible. We integrate the proposed solution into Hyperledger Fabric.Benchmark tests using SmallBank indicate that in scenarios characterized by a high number of conflicting transactions, the proposed approach effectively reduces the average transaction abort rate and exhibits favorable latency performance.

The consensus mechanism, as a core technology of blockchain, plays a crucial role in ensuring system consistency and reliability. Existing consensus algorithms adopt a Directed Acyclic Graph (DAG) structure to improve the throughput of blockchain systems. However, when unstable network connections or network partitions occur, a large number of blocks may be lost, and work may be wasted, resulting in a significant drop in system performance. We propose an asynchronous Byzantine fault-tolerant consensus protocol, Komorebi, based on DAG and sharding. The protocol divides the blockchain network into multiple shards, allowing for parallel processing of different transaction sets to enhance the scalability and network partition tolerance of the blockchain, thus avoiding the bottleneck problem of a single chain in the blockchain. Komorebi utilizes structured DAG inside each shard to achieve parallel broadcasting and transaction processing. Nodes only broadcast and store transaction blocks of their local shards, which improves transaction processing efficiency and reduces storage overhead. For inter-shard communication, nodes transmit block events instead of blocks, significantly reducing communication overhead between shards. Furthermore, through inter-shard communication, nodes in different shards can maintain a consistent global block event status, solving security degradation issues caused by sharding. Experimental results show that Komorebi achieves a throughput of over 190,000 tx/s with a delay of less than 2 seconds in a 64-node network with 16 shards.

As cloud computing is a market-oriented utility, optimal virtual machine (VM) scheduling in cloud computing should take into account the incentives for both cloud users and the cloud provider. However, most of existing studies on VM scheduling only consider the incentive for one party, i.e., either the cloud users or the cloud provider. Very few related studies consider the incentives for both parties, in which the cost, one of the most attractive incentives for cloud users, is not well addressed. In this paper, we investigate the problem of VM scheduling in cloud computing by optimizing the incentives for both parties. The problem is formulated as a multi-objective optimization model, i.e., maximizing the successful execution rate of VM requests and minimizing the combined cost (incentives for cloud users), and minimizing the fairness deviation of profits (incentive for the cloud provider). The proposed multi-objective optimization model can offer sufficient incentives for the two parties to stay and play in the cloud and keep the cloud system sustainable. A heuristic-based scheduling algorithm, called cost-greedy dynamic price scheduling, is then developed to optimize the incentives for both parties. Experimental results show that, compared with some popular algorithms, the developed algorithm can achieve higher successful execution rate, lower execution cost, smaller fairness deviation and most important, higher degree of user satisfaction in most cases.

Summary We consider a large‐scale online service system of placing resources geographically distributed over multiple regional cloud data centers. Service providers need to place the resources in these regions so as to maximize profit, accounting for demand granting revenues minus resource placement costs. The challenge is how to optimally place these resources to fulfill varying demands (e.g., multidimensional and stochastic demands) among these cloud data centers. Considering demand stochasticity will significantly increase time complexity of resource placement algorithm, resulting in inefficiency when handling a large number of resources. We propose a fast resource placement algorithm (FRP) to obtain the maximum resource revenue from distributed cloud systems. Experiments show that in scenarios with general settings, FRP can achieve up to 99.2% revenue of existed best solution while reducing execution time by two orders of magnitude. Therefore, FRP is an effective supplement to existing algorithms under time‐tense scheduling scenarios with a large number of resources. Copyright © 2015 John Wiley & Sons, Ltd.

In duty-cycled wireless sensor networks, the nodes switch between active and dormant states, and each node may determine its active/dormant schedule independently. This complicates the Minimum-Energy Multicasting (MEM) problem, which was primarily studied in always-active wireless ad hoc networks. In this paper, we study the duty-cycle-aware MEM problem in wireless sensor networks both for one-to-many multicasting and for all-to-all multicasting. In the case of one-to-many multicasting, we present a formalization of the Minimum-Energy Multicast Tree Construction and Scheduling (MEMTCS) problem. We prove that the MEMTCS problem is NP-hard, and it is unlikely to have an approximation algorithm with a performance ratio of (1 - 0(1)) ln Δ, where Δ is the maximum node degree in a network. We propose a polynomial-time approximation algorithm for the MEMTCS problem with a performance ratio of O (H(Δ + 1)), where H(·) is the harmonic number. In the case of all-to-all multicasting, we prove that the Minimum-Energy Multicast Backbone Construction and Scheduling (MEMBCS) problem is also NP-hard and present an approximation algorithm for it, which has the same approximation ratio as that of the proposed algorithm for the MEMTCS problem. We also provide a distributed implementation of our algorithms, as well as a simple but efficient collision-free scheduling scheme to avoid packet loss. Finally, we perform extensive simulations, and the results demonstrate that our algorithms significantly outperform other known algorithms in terms of the total transmission energy cost, without sacrificing much of the delay performance.

Wireless Sensor Networks (WSNs) have acquired new features recently, i.e., both the sensor and the antenna of a node can be directional. This brings new challenges to the Connected Coverage (CoCo) problem, where a finite set of targets needs to be monitored by some active sensor nodes, and the connectivity of these active nodes with the sink must be retained at the same time. In this paper, we study the Minimum-Energy Connected Coverage (MeCoCo) problem in WSNs with Omni-directional (O) and Directional (D) features, aiming at minimizing the total energy cost of both sensing and connectivity. Considering different combinations of O and D features, we study the MeCoCo problem under four cases, namely: O-Antenna and O-Sensor (OAOS), O-Antenna and D-Sensor (OADS), D-Antenna and D-Sensor (DADS), as well as D-Antenna and O-Sensor (DAOS). We prove that the MeCoCo problem is NP-hard under all these cases, and present approximation algorithms with provable approximation ratios. In particular, we propose a constant-approximation for OAOS, and polylogarithmic approximations for all other cases. Finally, we conduct extensive simulations and the results strongly confirm the effectiveness of our approach.