Unishyper is a reliable Rust-based unikernel with good performance for embedded scenarios. It relies on Rust Features to enable customization at fine granularity. To achieve high reliability, Unishyper makes full use of the Rust language features to reduce memory safety bugs, ensure safe resource management, and achieve fault handling and recovery. Finally, Unishyper achieves high performance through safe multi-threading model as well as the single-privilege-level and single-address-space design.

With the development of the IoT, modern embedded systems are evolving to general-purpose and mixed-criticality systems, where virtualization has become the key to guarantee the isolation between tasks with different criticality. Traditional server-based hypervisors (KVM and Xen) are difficult to use in embedded scenarios due to performance and security reasons. As a result, several new hypervisors (Jailhouse and Bao) have been proposed in recent years, which effectively solve the problems above through static partitioning. However, this inflexible resource isolation strategy assumes no resource sharing across guests, which greatly reduces the resource utilization and VM scalability. This prevents themselves from simultaneously fulfilling the differentiated demands from VMs conducting different tasks. This paper proposes an efficient and real-time embedded hypervisor “Shyper”, aiming at providing differentiated services for VMs with different criticality. To achieve that, Shyper supports fine-grained hierarchical resource isolation strategies and introduces several novel “VM-Exit-less” real-time virtualization techniques, which grants users the flexibility to strike a trade-off between VM's resource utilization and real-time performance. In this paper, we also compare Shyper with other mainstream hypervisors (KVM, Jailhouse, etc.) to evaluate its feasibility and effectiveness.

Seismic data processing is one of the important technologies in the oil exploration industry. The original seismic data volume is collected from the field, its size has exceeded petabyte level. The seismic data processing requires random access to the large seismic data set and usually selects different keywords for multiple index construction. Multi-keyword (in Trace Header, as shown in Fig. 1) range query and read-only are main features of seismic data access. A high-performance index is the key to improve the application processing efficiency. In PB-level data situation, the major challenges are:

LevelDB uses the merger mechanism for data integration during the writing process. During this process, the value will move together with the key, causing a lot of unnecessary data rewriting. This paper presents a structure that the key stored separately from the value. And the value is stored in a separate file (we call it Value File), with value offset in the file and length information stored in LevelDB. Test results show that the optimized LevelDBs sequential write performance is reduced by about 40%. But random write and overwrite performances improve more than 200%. And with the increase of the number of tests records, the improvement becomes more and more obvious. The amount of rewriting data and the number of merging files, depending on the length of every different record, reduce about 80% averagely, which significantly improves the performance of the original program.

Operating system (OS) experiments consolidate the understanding of the OS concepts and cultivate good engineering practices. Major challenges, however, including large class sizes, diverse software versions, and timely identification of difficulties from lab reports, hurt teaching quality. To address these, we designed an integrated environment to support OS experiments and automated the release and testing of lab code. The environment helped students to write, debug, and run code, and it collected their behavior data. These data included login hours and frequency, commands executed, files opened, and the code submission frequency. We found through analyzing the data a lack of preliminary knowledge in some students. Extra instructions and extended deadlines helped them master the subjects. For some students, the files read indicated the lack of attention to some most relevant system architecture code. This finding allowed us to provide targeted and specific help. Other data revealed an interesting “lab 2” phenomenon where the behavioral difference between the competent and average students was much more pronounced for lab 2 than that for lab 1. The data showed that login hours and frequency and log size correlated positively but submission frequency correlated negatively with grades. Analysis of students' behavior data allowed us to realize continuous improvements in the experiment process. 62% of the 152 students completed four labs and 46% all six, a significant improvement over the last semester.

The Global Positioning System (GPS) has been widely used to determine the location for a variety of different applications. However, it doesn't work well in indoor environments because it requires the line of sight to the satellites and therefore stops working when the line of sight is not available. High-precision indoor localization is critical to many personal and business applications. After Bluetooth Low Energy (BLE), an energy-efficient version of Bluetooth, is widely deployed, Bluetooth-based indoor localization turns out to be a practical method to locate Bluetooth-enabled devices due to its low battery cost. In this paper, we present two novel BLE-based localization schemes, Low-precision Indoor Localization (LIL) and High-precision Indoor Localization (HIL). Different than most of the existing localization methods that attempt to find the specific location of the object under investigation, LIL and HIL utilize the collected RSSI measurements to generate a small region in which the object is guaranteed to be found. Compared with LIL, HIL leads to smaller localization regions. However, HIL requires an extra data-training phase.

Localization is a fundamental operation in wireless networks. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor applications. For indoor localization, GPS does not work due to the lack of the line of sight to satellites. High-precision indoor localization is critical to many personal and business applications. WiFi-based indoor localization was proposed to be a practical method to locate WiFi-enabled devices due to the popularity of WiFi networks. However, it suffers from large localization errors. Our experimental results indicate that this scheme consistently leads to an average error around 3 meters. The existence of different locations with similar WiFi signal strength is the reason behind the large errors. To improve the localization precision, a hybrid indoor localization scheme, HILL, is proposed in this paper. Inspired by the fact that a large number of WiFi-enabled mobile devices have been deployed, HILL uses 3 phases to improve the precision of WiFi-based localization. First of all, it measures the distances between each pair of peer devices through acoustic ranging. Secondly, the Classical Metric Multidimensional Scaling (MDS) method is applied to the collected distances, which results in a graph consistent with the distances. Finally, the graph generated by MDS is embedded onto the graph corresponding to WiFi-based localization in order to achieve high localization precision. Our experimental results indicate that the average localization error of HILL is about 1 meter.

In today's data centers supporting Internet-scale computing and I/O services, increasingly more network-intensive applications are deployed on the network as a service. To this end, it is critical for the applications to quickly retrieve requests from the network and send their responses to the network. To facilitate this network function, operating system usually provides an event notification mechanism so that the applications (or the library) know if the network is ready to supply data for them to read or to receive data for them to write. As a widely used and representative notification mechanism, epoll in Linux provides a scalable and high-performance implementation by allowing applications to specifically indicate which connections and what events on them need to be watched. As epoll has been used in some major systems, including KV systems, such as Redis and Memcached, and web server systems such as NGINX, we have identified a substantial performance issue in its use. For the sake of efficiency, applications usually use epoll's system calls to inform the kernel exactly of what events they are interested in and always keep the information up-to-date. However, in a system with demanding network traffic, such a rigid maintenance of the information is not necessary and the excess number of system calls for this purpose can substantially degrade the system's performance. In this paper, we use Redis as an example to explore the issue. We propose a strategy of informing the kernel of the interest events in a manner adaptive to the current network load, so that the epoll system calls can be reduced and the events can be efficiently delivered. We have implemented the strategy, named as FlexPoll, in Redis without modifying any kernel code. Our evaluation on Redis shows that the query throughput can be improved by up to 46.9% on micro benchmarks, and even up to 67.8% on workloads emulating real-world access patterns. FlexPoll can be extended to other applications and event libraries built on the epoll mechanism in a straightforward manner.

SUMMARY This paper presents a novel method to study the evolution of Linux kernel components using complex networks to understand how Linux kernel components evolve over time. After analyzing the node degree distribution, clustering coefficient, and average path length of the call graphs corresponding to the kernel components of 130 development versions and 94 stable versions (V1.0 to V2.4.35), we found that the call graphs of the file system, driver, kernel, memory management, and net components are scale‐free, small‐world complex networks. In addition, all of the five components exhibit very strong preferential attachment tendency. With such in‐depth understanding of the features of the Linux kernel components, we propose a generic method that could be used to find major structural changes that occur during the evolution of software systems. Copyright © 2012 John Wiley & Sons, Ltd.

In recent years, many graphs have turned out to be complex networks. This paper presents a novel method to study Linux kernel evolution - using complex networks to understand how Linux kernel modules evolve over time. After studying the node degree distribution and average path length of the call graphs corresponding to the kernel modules of 223 different versions (V1.1.0 to V2.4.35), we found that the call graphs of the file system and drivers module are scale-free small-world complex networks. In addition, both of the file system and drivers module exhibit very strong preferential attachment tendency. Finally, we proposed a generic method that could be used to find major structural changes that occur during the evolution of software systems.