Thanks to high-density flash memory and high parallelism, multitenant solid-state drives (MSSDs) have become a popular high-performance storage device for enhancing cache resource utilization and reducing operational costs within these SSDs. The competition for limited cache resources inside the MSSD among multiple tenants, however, can lead to performance interference among the tenants, and prior studies focused on quality of service (QoS) in MSSDs. An efficient caching scheme is crucial for optimizing SSD performance and lifetime. Existing caching schemes aim to shorten response time by the virtue of improved cache hit rates, which offer limited performance improvement as well as low cache resource efficiency. In this article, we propose an intelligent cache allocation scheme named iCache, which employs a long short-term memory (LSTM) model to capture the I/Os access patterns of workloads and dynamically allocates cache resources inside an MSSD according to maximum benefit point (MBP) and optimal allocation point (OAP). The extensive experimental results demonstrate that iCache reduces response time by up to 87%, 24%, and 20% compared against the existing caching schemes—Shared, Justitia, and MLCache, respectively. The empirical study confirms that the new traits of iCache immensely improve system performance by enhancing the cache efficiency of MSSDs and guaranteeing fairness in performance across varying workloads.

Facing the difficulty and inefficiency of creating and maintaining High-Definition (HD) maps in our commercial deployments, we have developed an edge-based crowdsourcing task distribution framework for HD Map in autonomous driving. Our key observation is that: HD map data crowdsourcing exhibits the diminishing marginal utility thus there exists an inflection point for maximum utility, meanwhile its premature convergence of utility will leave some map updates not notified in time. Based on this observation, we develop a periodic crowdsourcing task distribution framework. It discretizes the demands for collecting source data into different periods and uses an optimal stopping rule to terminate the data collection for the maximum crowdsourcing utility. The experimental results verify that our crowdsourcing framework can achieve high time coverage and high efficiency with lower cost.

In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume of data in a streaming way that often need to be processed in real time. Therefore, streaming data computing technology plays an indispensable role in the real-time processing of sensor data in high throughput but low latency. However, there are two problems in deploying streaming data process ability in cloud computing data centre. Firstly, massive sensor nodes simultaneously upload data to the remote cloud computing data centre, which requires a large number of bandwidth resources supports. The existing network infrastructure cannot provide enough bandwidth at a reasonable price. Secondly, due to the geographical distribution characteristics of the cloud computing data centre, there will inevitably be large transmission delay during the process of data transmission. Such end-to-end delay is intolerable to mobile applications especially for those latency sensitive tasks. In view of the above problems, this paper proposes an autonomous driving oriented edge streaming data processing framework, which migrates the computing and storage capability from the remote cloud data centre to the edge data centre. It focuses on the change of vehicle flow in a specific geographical area, and uses the computing power sunk to edge node to process the massive streaming data generated by autonomous vehicles nearby. The proposed framework is implemented on top of Spark Streaming, which builds up a gray model based traffic flow monitor, a traffic prediction orientated prediction layer and a fuzzy control based Batch Interval dynamic adjustment layer for Spark Streaming. It could forecast the variation of sensors data arrive rate, make streaming Batch Interval adjustment in advance and implement real-time streaming process by edge. Therefore, it can realise the monitor and prediction of the data flow changes of the autonomous driving vehicle sensor data in geographical coverage of edge computing node area, meanwhile minimise the end-to-end latency but satisfy the application throughput requirements. The experiments show that it can predict short-term traffic with no more than 4% relative error in a whole day. By making batch consuming rate close to data generating rate, it can maintain system stability well even when arrival data rate changes rapidly. The Batch Interval can be converged to a suitable value in two minutes when data arrival rate is doubled. Compared with vanilla version Spark Streaming, where there has serious task accumulation and introduces large delay, it can reduce 35% latency by squeezing Batch Interval when data arrival rate is low; it also can significantly improve system throughput by only at most 25% Batch Interval increase when data arrival rate is high.

The search for the equilibrium states of a given conflict is a major issue in conflict analyses. There are several traditional methodologies to determine the equilibrium states of a strategic conflict, such as logical definitions within the graph model for conflict resolution and the matrix representation for conflict resolution. However, these methods depend on a graphical or mathematical representation and need analytical expressions to calculate the equilibrium states. Reinforcement learning (RL) is a type of machine learning method that can search for the equilibrium states by trial-and-error without the need for a precise mathematical model of the conflict problem. This paper proposes a novel multiple RL technique that deals with conflict resolution problems for the case of two decision makers. Moreover, the proposed method cannot only find equilibria, but also track all paths from any status quo to the equilibria in conflicts when these paths exist. This method is evaluated using two well-known conflict analysis examples. The experimental results show that the proposed method can quickly, correctly, and efficiently find the equilibria and track the evolution paths.