With the development of chiplet technology, the architecture of Non-Uniform Memory Access (NUMA) has become increasingly intricate. The placement of memory page significantly influences application performance in NUMA systems. We found that memory access bottlenecks occur between high-level NUMA domains consisting of multiple chiplets. In this paper, we introduce a Traffic-Aware Page Mapping Method (TAPMM) designed for multi-level NUMA systems. TAPMM conceptualizes the multi-level NUMA system as a memory access tree, utilizing hardware performance events to be aware of system traffic and identify the optimal page mapping method for bandwidth efficiency. Our experiments demonstrate that TAPMM achieves a speedup of up to 2.12× on a real commodity machine compared to existing optimization tools.

Influence maximization (IM) problem is an extensively studied problem in social networks. It aims to find a small set of users in the social network to initiate the diffusion process and maximize the expected influence spread. Existing works on conformity-aware IM focus on the interaction between influence and conformity in a single-influence setting and ignore the role of conformity in a competitive and multiple-influence setting. This paper proposes a conformity-aware independent cascade (C-IC) model that considers the competition among multiple influences as well as the role of conformity in a user’s decision-making. It is proved that the adoption of an influence under the C-IC model is monotone and submodular. Meanwhile, we formulate two adoption maximization (AM) problems, O-AM and S-AM, which are both NP-hard. Because estimating the adoption through diffusion simulations is very time-consuming, we propose a reverse adoption estimation (RAE) method based on a reverse multiple influence sampling (RMIS) technology for the C-IC model and integrate it into the D-SSA-fix (Nguyenet al., 2018) framework, DSSA for short, to compute a solution with approximation guarantee. To further boost the performance, we present a fast one-hop adoption estimation (OAE) method and develop a heuristic algorithm based on OAE, called GOAE. Extensive experiments on eight real-world social networks show that the C-IC model is superior to a non-conformity diffusion model and that RAE+DSSA and GOAE are efficient and effective. In most cases, GOAE finds comparable solutions to RAE+DSSA and CELF with less time and memory overhead. GOAE is five to six orders of magnitude faster than CELF and RAE+DSSA is up to three orders of magnitude faster than CELF on NetHEPT. GOAE runs up to four to five orders of magnitude faster than RAE+DSSA with at most two orders of magnitude less memory usage. GOAE is more scalable than RAE+DSSA in terms of the number of seeds and the size of the social network.

Summary With the rapid development of the internet, cyberspace security issues have become increasingly prominent. The importance of constructing a cyberspace security system is self‐evident, but compared with attackers, defenders in cyberspace are in a castle‐like passive defense state in most cases. Therefore, building a reliable, accurate, timely, and active defense system is challenging. The key is to accurately focus on defense priorities, the anticipation of attackers who will likely succeed, and blocking attacks in a timely manner. In this article, we propose an active defense model based on the interaction of situational awareness and firewalls. First, by biasing the integrity, confidentiality, and availability of assets to get the score of assets, and using the Common Vulnerability Scoring System to assess the threat level of assets, we combine the two to determine the maximum system damage that the asset will suffer if it is lost, and then focus on defense. Meanwhile, log analysis of the network situational awareness platform can predict successful attackers, and then the linked firewall strategy can block these attacks in time before the attackers obtain attack gains. After that, we force the attackers to give up their attacks on the target by increasing the attack cost. We compared our model with iptables auto‐blocking and nginx auto‐blocking, and our model excelled them across the board in terms of comprehensiveness and false positive rate. The experimental results verify thar our active defense model proposed in this article can better reduce the defense cost and increase the attack cost, thus achieving the relatively defense goal.

The blockchain establishes trust by maintaining a distributed appending-only ledger, which is widely applied to the nodes lacking trust in the edge environment. However, the full-replication storage mode of blockchain is a big challenge for resource-constrained edge devices. What is worse, system performance is also affected by the large storage overhead. Existing solutions to reduce blockchain storage overhead often require additional security assumptions, lack incentives, or fail to account for resource heterogeneity. To overcome these limitations, we design an auction-based storage resource allocation scheme. Winners are selected to store blocks, taking into account the block preferences of nodes, and the fairness of the system. Nodes are incentivized by implementing fairness and equity in distributed auctions and data transactions through smart contracts. Finally, extensive experiments show 65%–81% savings in storage overhead compared to fully replicated storage.

Abstract Nowadays, a new paradigm named mobile edge computing (MEC) is capable of supplying some cloud‐like functions at the edges of wireless networks, which enables vehicles to offload the computation intensive tasks on MEC servers with low latency. However, new challenges posed by the complex network environment and the mobility of vehicles are usually not covered by traditional offloading schemes. To solve such problems, we propose a heuristic task migration computation offloading (TMCO) scheme. Compared with traditional ones, TMCO can dynamically choose suitable places to offload the tasks for moving vehicles within deadline. For this purpose, the mobility of vehicle and strict delay deadline are considered comprehensively. We use hash table to store the number of tasks on the corresponding server and use random function to simulate the probability of task offloading. In terms of latency, experimental results suggest that the performance of TMCO is on average 10% higher than that of traditional full offloading schemes.

Task scheduling in cloud environments is the problem of assigning and executing computational tasks on the available cloud resources. Effective task scheduling can improve processor utilization, reduce processor energy consumption, and improve user experience. Large-scale task scheduling under multiple constraints is an NP-complete problem. The traditional task scheduling algorithm cannot be applied to large-scale scheduling, either because of high time complexity or because its heuristic algorithm cannot be applied to complexly large-scale scenarios. The problem of large-scale task scheduling is gradually becoming a challenge in cloud computing. The article proposes a topology-based multilevel algorithm for large-scale task scheduling in clouds. Based on the topological order of the graph, multi-level coarsening is performed on the large-scale graphs, and then uses the traditional scheduling algorithm for the initial scheduling of the coarse graph, and then refine the initial scheduling result during its uncoarsen phrase. It can perform fast and efficient scheduling of large-scale task graphs. At the same time, it has good compatibility, which can be combined with excellent traditional scheduling algorithms.

Closer to mobile users geographically, mobile edge computing (MEC) can provide some cloud-like capabilities to users more efficiently. This enables it possible for resource-limited mobile users to offload their computation-intensive and latency-sensitive tasks to MEC nodes. For its great benefits, MEC has drawn wide attention and extensive works have been done. However, few of them address task migration problem caused by distributed user mobility, which can't be ignored with quality of service (QoS) consideration. In this article, we study task migration problem and try to minimize the average completion time of tasks under migration energy budget. There are multiple independent users and the movement of each mobile user is memoryless with a sequential decision-making process, thus reinforcement learning algorithm based on Markov chain model is applied with low computation complexity. To further facilitate cooperation among users, we devise a distributed task migration algorithm based on counterfactual multi-agent (COMA) reinforcement learning approach to solve this problem. Extensive experiments are carried out to assess the performance of this distributed task migration algorithm. Compared with no migrating (NM) and single-agent actor-critic (AC) algorithms, the proposed distributed task migration algorithm can achieve up 30-50 percent reduction about average completion time.