Major space companies are developing satellite mega-constellations to provide global Internet coverage and services. Limited battery capacity is one of the biggest obstacles on mega-constellations due to the restricted weight and volume of satellites. Massive Internet packet routing tasks pose a big challenge to the energy system in such mega constellations. Incorrect use of satellite batteries during routing phases may significantly increase the energy consumption and cause node failure quickly. Existing state-of-the-art works on energy-saving routing for satellite networks paid much attention on traffic distribution and end-to-end delay issues. However, these methodologies were using many real-time network information for optimization which is not practical in mega-constellations. Note also that these works did not consider energy efficiency and guaranteed end-to-end delay simultaneously. In this paper, we propose a novel deep reinforcement learning based energy-efficient routing protocol called DRL-ER, which avoids the battery energy imbalance of constellations and can also guarantee a required bounded end-to-end delay. In DRL-ER, satellites can learn a routing policy that will balance energy usage among satellites. Extensive simulation results show that our proposed DRL-ER protocol reduces the energy consumption of satellites in average by more than 55% compared to the current state-of-the-art work, and prolongs the lifetime of constellations significantly.
Nowadays, web servers are suffering from flash crowds and application layer DDoS attacks that can severely degrade the availability of services. It is difficult to prevent them because they comply with the communication protocol. Peer-to-peer (P2P) networks have been exploited to amplify DDoS attacks, but we believe their available resource, such as distributed storage and network bandwidth, can be used to mitigate both flash crowds and DDoS attacks. In this paper, we propose a server initiated approach to employ deployed P2P networks as distributed web caches, so that the workload directed to web servers can be reduced. In experiments, we use Kad as the particular P2P network for the realization of a large-scale distributed web cache. We performed comprehensive evaluation on the feasibility, efficiency and robustness of our scheme, through experiments and simulations on the prototype we implemented. The evaluation results show that our scheme can increase the capacity of the protected web servers at least 10 times at the same cost of connection and bandwidth consumption. The web contents cached in Kad remain reachable even under churn of peers and targeted DoS attack, and the access latency is comparable to normal direct access to web servers. It also achieves good load balancing under the heavy-tailed distribution of object popularity.
Microservice system is a web application architecture that divides a single application into a suite of service nodes running as separate processes and communicating with lightweight message mechanisms. Although microservice can improve the abstraction, modularity and extensibility of web applications, it makes the anomaly detection and fault root cause localization more challenging for operational staff. To this end, in this paper, we first introduce the concept of service dependency graph (SDG) to depict the complex calling relationship between nodes and then develop an anomaly detection and root cause localization framework called TraceModel which consists of TraceVAE and ModelCoder. TraceVAE divides user requests into different request classes according to well-constructed trace and analysis them separately with variational autoencoder(VAE) to figures out abnormal requests. Based on the anomaly detection results of TraceVAE, ModelCoder localizes the root cause of unknown faults by comparing their fault features with the predefined fault models. By evaluating TraceModel on a realworld microservice system monitoring data set spanning 15 days, it is revealed that TraceModel can detect the anomaly and localize the fault root cause nodes within 110 seconds on average. Furthermore, it improves the root cause localization accuracy (to 97%) by 17.5% compared with the state-of-the-art root cause localization algorithm.
This paper proposes a pre-estimation based enhanced multidimensional resource allocation model(PE-EMRA) and its algorithm for network resource allocation.The PE-EMRA approach allocates both bandwidth and buffer resource for different traffic classes,balancing and limiting the arrival rate of traffic class and also maximizing the system utilization.The advantage of this approach is that it can maximize the system utilization and provide QoS guarantee while being flexible and easy to realize.
With the rapid development of the web services, e-commerce and social network applications, a robust reputation system to establish trustworthiness between mutually unknown entities is becoming increasingly important. This paper proposes a general self-adaptive reputation model, which uses the weight factor of each feedback to inherently support the defense of fake feedbacks. Moreover, we design a reputation system by using the improved Kalman Filter based on the factor of weight. With this method, we can not only get an accurate prediction for the service provider, but also resist malicious feedback attacks. Our reputation system is proved to be more robust and accurate compared with the traditional methods in the simulation and experiment.
Blockchain-based healthcare IoT technology research promotes increased security for smart healthcare services such as real-time monitoring and remote disease diagnosis. In a healthcare IoT service system, it remains a challenging problem to explore effective incentive mechanisms throughout the healthcare service process and feedback to facilitate the efficiency of the blockchain consensus process. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and stores the assessment results credibly on the blockchain. Then, we propose an improved PBFT algorithm based on the reputation assessment to enhance blockchain consensus efficiency. Finally, BtRaI designs a token reward and punishment mechanism to motivate all parties to participate in the blockchain honestly, inhibit potential misbehavior, and encourage the healthcare system to provide better service quality. Theoretical analysis and experimental evaluation demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services, improves blockchain node failure tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 seconds in a 100-node consortium chain. The reputation assessment and token incentive mechanism of BtRaI have a realistic differentiation granularity and change curve, making it suitable for dynamic and complex healthcare service scenarios.
Attribute-based signature (ABS) is a new cryptographic primitive, in which a signer can sign a message with his attributes, and the verifier can only known whether the signer owns attributes satisfying his policy. Moreover, the signature cannot be forged by any user not having attributes satisfying the policy. ABS has many applications, such as anonymous authentication, and attribute-based messaging systems. But many applications may require a user obtaining attributes from different authorities, which calls for multi-authority ABS schemes.
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