paper presents an energy-efficient embedded system platform, called meCreate, to execute energy-critical real-time tasks. In meCreate, we consider a task has multiple versions for different energy criticality levels. At run time, the system will operate at one of the energy criticality levels dynamically according to the remaining capacity of battery. In particular, meCreate will operate at a lower energy criticality level and the corresponding version of each task will be executed when the battery capacity is plentiful. A version for a lower energy criticality level performs relatively complex operations to generate results more accurately, and/or it can shorten the period to obtain results more frequently. When the remaining capacity of battery become insufficient, the system will switch to a higher energy criticality level and the corresponding version of each task will be executed. A version for a higher energy criticality level performs simplified operations and/or a longer period in order to save more energy. We also present an example application, called ecoScout, to demonstrate how to build application upon meCreate. The energy efficiency and the performance were evaluated by a set of experiments based on the example application and randomly generated task sets, for which we have some encouraging results.

In this paper, a container-based mixed-criticality real-time system (ComityRT) platform is presented to help developers to build a MCRTS faster and easier. ComityRT supports real-time tasks with different degrees of timeliness and criticality to be deployed and executed on containers running in a multicore environment. It can be configured according to developers' preferences, e.g., a container can be created for a set of tasks with the same timing requirement and/or criticality level in a specific processing core or across multiple cores. Furthermore, task migration among different containers and cores is allowed, and the computing resources (such as CPU and memory) can be managed for each container statically or dynamically. This paper also includes an MCRTS example to show the capabilities and benefits of CommunityRT.

Online depression communities offer people with depressed symptoms new opportunities to obtain health information and provide social support for each other to fight against the depression. We sought to investigate whether usage of online community help improve depression outcomes and determine which types of usage behaviors have positive or negative effects on depression. We proposed that two dimensions of the sense of belonging (sense of identity and trust) and three dimensions of the sense of support (informational, emotional, and socializing) have significant effects on depression, and further considered gender difference and its effect on depression. We obtained a dataset consisting of 465,337 posts from 244 members from a popular online depression community to test all 10 proposed hypotheses. The results reveal that (i) the sense of shared identity, trust, informational support, and emotional support have positive effects on depression, while socializing support have negative effects on depression, and (ii) the sense of shared identity and trust have more positive effects on depression for female users than male users while socializing support has a more negative effect on depression for female users than for male users. The findings have important practical implications for designers and managers of online depression communities.

In this paper, an innovative mixed energy-criticality system (MECS) is proposed for which a set of periodic real-time tasks can be executed on a battery-powered or an energy-harvesting embedded system with different energy-efficient requirements. We assume that a task has multiple versions corresponding to different energy-criticality levels, where a version for a higher level has less computation than that for a lower level. Initially, an MECS starts with the lowest energy-criticality level, and it switches to a higher level whenever there is no sufficient energy available. Note that it also changes the version of tasks to their corresponding versions for the higher level so that the less amount of computation is executed and the lifetime can be prolonged. We also present a real-time embedded platform and an example real-life MECS application is implemented on it to demonstrate the performance and the energy efficiency, for which we have some encouraging results.

依照“资源共享,效益为先,优化配置,促进开放”的建设原则,浙江农林大学信息技术实验教学中心积极探索以专业功能分类建立的运行模式.从实验室统一空间布局、统一设备与仪器配置、统一实验队伍管理、统一经费预算开支及统一课程任务安排等5个方面介绍中心在计算机类实验室建设过程中所取得的改革与实践的经验及成果.

Real-time deferrable server (RTDS) scheduler is an experimental CPU scheduler for Xen virtualization systems since version 4.5. Under RTDS, each virtual CPU (VCPU) is guaranteed to have a predefined amount of physical CPU (PCPU) capacity so that the performance can be better predicted. However, the guaranteed capacity might not fit the requirement of a VCPU at the run-time because it is defined offline. Therefore, the performance of virtual machines (VMs) might be deteriorated at the run-time. In this paper, an RTDSbased CPU scheduler is proposed, called enhanced real-time deferrable server (ERTDS), to provide an additional amount of PCPU capacity to a VCPU when its run-time requirement is higher than expected. We have implemented ERTDS in Xen version 4.7 and a series of experiments has been conducted for which we have some encouraging results.

Nowadays, a Docker containerized system might run applications with different criticalities as well as timing constraints. Such a system, called Docker containerized mixed-criticality real-time system (DC-MC-RTS), consists of RT-Containers and NRT-Containers which are containers with real-time and non-real-time applications, respectively. Since Docker uses static CPU allocation methods, the performance of a DC-MC-RTS might be degraded when the workloads of containers are changed significantly at run-time. In this paper, we propose a new CPU allocation approach, called flexible deferrable server (FDS) scheduler, to improve the performance of a DC-MC-RTS. In particular, FDS first provides available CPU capacity to RT-Containers in order to ensure their timing constraints can be met. Then, the remaining CPU capacity is provided to NRT-Containers dynamically at run-time so that their unpredictable on-line requirements could be met as much as possible. Our proposed FDS has been implemented in Docker community edition 17.09.0 and a series of experiments has been conducted for which we have some encouraging results.

Energy-efficient real-time task synchronization has received increased attention in recent years. In this paper, we are interested in comparing the performance of two energy-efficient strategies, i.e., optimistic dual speed (ODS) and pessimistic dual speed (PDS) strategies. In particular, we choose two SRP-based energy-efficient approaches as the examples to evaluate the performance of ODS and PDS strategies: the blocking-aware two-speed (BATS) and the blocking-time stealing (BTS) algorithms. Note that BATS and BTS have been shown to outperform existing ODS and PDS strategies in terms of energy efficiency. We have evaluated BATS and BTS with dynamic- and fixed-priority task scheduling algorithms, i.e., EDF and RM. The performance evaluation results presented in this paper were obtained by an open source simulation framework for DVS-capable real-time systems, called MCRTsim. Based on the experimental results, the performance of ODS and PDS strategies and the capabilities of BATS and BTS are compared and presented in this paper.

Real-time deferrable server (RTDS) scheduler is presented since Xen 4.5. Under RTDS, a guaranteed physical CPU capacity is provided to every virtual CPU so that the performance can be better predicted. However, the guaranteed capacity is defined off-line, it might not fit the requirement of a virtual CPU at the run-time. In this paper, an RTDS-based CPU scheduler is proposed, called enhanced real-time deferrable server (ERTDS), to provide an additional capacity to virtual CPUs when their run-time requirements are higher than expected. The proposed ERTDS has been implemented in Xen 4.7 and a series of experiments has been conducted for which we have some encouraging results.

In the recent years, many energy-efficient realtime task scheduling algorithms have been proposed for multicore platforms. Most of them focus on independent tasks, however, relatively little work has been done in the presence of task synchronization. In this paper, we are interested in scheduling of real-time tasks which may make requests for accessing shared resources at the run time. A similarity-based task-to-core partitioning algorithm is proposed to allocate the tasks which may access a similar set of shared resources to the same core so that a large number of blockings can be avoided. Furthermore, we also propose speed assignment methods to assign proper processor speeds for tasks' executions with full-chip and per-core DVFS techniques so that the overall energy consumption can be reduced. The schedulability analysis of our proposed approach is provided in this paper. The capabilities of our proposed approach were evaluated by a series of experiments for which we have some encouraging results.

This paper examines the problem of geometric constraints acquisition of planar motion through a line-geometric approach. In previous work, we have investigated the problem of identifying point-geometric constraints associated with a motion task which is given in a parametric or discrete form. In this paper, we seek to extend the point-centric approach to the line-centric approach. The extracted geometric constraints can be used directly for determining the type and dimensions of a physical device such as mechanical linkage that generates this constrained motion task.

In this paper, we propose a dynamic memory allocation approach for Xen virtualization platforms, called critical amount guaranteed memory allocation (CAGMA), to expand or shrink the allocated memory of each virtual machine (VM) dynamically with a guaranteed amount of available memory. The performance of a VM might be degraded when its available memory is insufficient (i.e., it might generate a large amount of I/O activities for swapping in and out of data to disks). Under CAGMA, a critical memory amount is calculated for each VM periodically and at the time a swapping event is occurred. The allocated memory of each VM is then adjusted according to its critical memory amount so that the number of I/O activities generated for swapping could be reduced greatly and the performance degradation could be prevented. Our proposed CAGAM has been implemented in Xen 4.2.2 and a series of experiments have been conducted for which some encouraging results were obtained.

This paper presents an open source task scheduling simulator, called MCRTsim, for real-time systems with uniprocessors, multiprocessors, and multi-core processors. It contains a task set generator, a set of real-time schedulers and synchronization protocols, and a comprehensive set of tools including visualized execution tracer, schedulability analyzer, and measurement and statistic modules. Therefore, we can easy to evaluate the performance of existing scheduling algorithms as well as synchronization protocols by using MCRTsim. Furthermore, MCRTsim also contains a Java class library for supporting the design of new scheduling and synchronization protocols with minimum efforts. Another key feature of MCRTsim is the supporting of DVFS-enabled processors so that the capabilities of energy-aware scheduling algorithms and synchronization protocols can be better understand.

In this paper, we are interested in energy-efficient concurrency control for real-time tasks on a non-ideal DVS processor. Based on well-known ceiling-based concurrency control protocols (such as priority ceiling protocol (PCP) and stack resource policy (SRP)), researchers have proposed energy-efficient approaches to mange concurrent accesses to shared resources so that the energy consumption can be reduced. However, ceiling-based protocols have a problem of ceiling blocking which imposes a great impact on the performance of real-time systems. In order to achieve sufficient performance, we propose a new protocol, called conditional abortable stack resource policy (CA-SRP), to resolve the ceiling blocking problem for dynamic-priority real-time tasks by incorporating a conditional abort rule into SRP. Based on the schedulability analysis of CA-SRP, we also propose a method, called dynamic speed assignment (DSA), to dynamically calculate and assign proper processor speeds for task execution so that the energy consumption can be reduced further. The capabilities of our proposed CA-SRP and DSA have been evaluated by a series of experiments, for which we have encouraging results.

Nowadays, one of the most challenging design issues of battery-driven real-time embedded systems is how to reduce energy consumption such that the battery life can be prolonged. Based on dynamic voltage scaling technology, many energy-efficient real-time task scheduling algorithms have been proposed, however, relatively little work is done in the presence of task synchronization. In this paper, energy-efficient synchronization approaches for real-time embedded systems have been investigated under various characteristics of shared resources (e.g., non-preemptible, preemptible, and abortable critical sections) and system architectures (e.g., uniprocessor and multi-core processor).

This paper explores the energy-efficient scheduling of real-time tasks on a non-ideal DVS processor in the presence of resource sharing. We assume that tasks are periodic, preemptive and may access to shared resources. When dynamic-priority and fixed-priority scheduling are considered, we use the earliest deadline first (EDF) algorithm and the rate monotonic (RM) algorithm to schedule the given set of tasks. Based on the stack resource policy (SRP), we propose an approach, called blocking-aware two-speed (BATS) algorithm, to synchronize the tasks with shared resources and to calculate appropriate execution speeds so that the shared resources can be accessed in a mutual exclusive manner and the energy consumption can be reduced. Particularly, BATS uses a static low speed to execute tasks initially, and then it switches to a high speed dynamically whenever a task blocks a higher priority task. More specifically, the processor runs at the high speed from the beginning of the blocking until the deadline of the blocked task or the processor becomes idle. In order to guarantee that the deadlines of tasks are met, the static low speed and the dynamic high speeds are derived based on the theoretical analysis of the schedulability of tasks. Compared with existing work, BATS achieves more energy saving because its dynamic high speeds are lower than that of existing work and the processor has less chance to execute tasks at the high speeds. The schedulability analysis and the properties of our proposed BATS are provided in this paper. We also evaluated the capabilities of BATS by a series of experiments, for which we have some encouraging results.

Xen is a powerful virtualization solution which enables multiple virtual machines to run in a single physical machine isolatedly. Since the hardware resources of a physical machine (e.g., the CPU) are limited and they have to be shared by multiple virtual machines, the performance of virtual machines might be degraded. How to allocate the underlying hardware resources to virtual machines appropriately has become an active research topic. In this paper, we are interested in CPU scheduling algorithm for a Xen virtualized system. The default CPU scheduling algorithm of Xen, called Credit scheduler, is a fairness strategy which provides proportional fair shared CPU time to each virtual machine based on its predefined weight value. However, the Credit scheduler might deteriorate the performance of a virtual machine when its predefined weight value cannot meet its current requirement of the physical CPU. It is because the dynamic workload of a virtual machine is unpredictable. In this paper, a credit-based CPU scheduling algorithm, called load-awareness credit (LA-Credit), is proposed to adjust the weight values of virtual machines dynamically such that the overall system performance could be improved. In particular, the LA-Credit algorithm monitors the current workload of each virtual machine and then it calculates and adjusts an appropriate weight for each virtual machine so that a virtual machine with heavy workload can obtain more CPU share. We have implemented the LA-Credit algorithm in Xen and evaluated the performance, for which some encouraging results were obtained.

In this paper, we are interested in the energy-efficient scheduling of real-time tasks on a non-ideal DVS processor. We assume that tasks are periodic, preemptive, dynamic priority, and may concurrent access to a set of shared resources. Based on the well-known earliest deadline first (EDF) algorithm and the stack resource policy (SRP), we propose an approach, called blocking-aware two-speed (BATS) algorithm, to calculate appropriate speeds for task execution. In particular, tasks are executed at a static low speed when blocking does not occur, and a high speed will be calculated dynamically whenever a task blocks a higher priority task. The processor must run at the high speed from the beginning of the blocking until the deadline of the blocked task or the processor becomes idle. The low speed and the high speed are derived based on the theoretical analysis of the schedulability of tasks so that the energy consumption can be reduced without violating the timing constraints of tasks. Compare with existing work, BATS achieves more energy saving because its derived high speeds are lower than that of existing work and the processor has less time to execute tasks at the high speeds.

Internet of Things (IoT) is expected to offer promising solutions to transform the operation and role of many existing systems such as transportation systems, manufacturing systems, and enables many applications in many domains. IoT aims to connect different things over the network. The goal of IoT is to provide a good and efficient service for many applications. A real-time IoT applications must react to stimuli from its environment within time intervals dictated by its environment. The instant when a result must be produced is called a deadline. Wireless Sensor Networks (WSN) have recently used been in the limelight for many domains. The IoT can be explained as a general purpose sensor network. WSNs will constitute an integral part of the IoT paradigm, spanning many different application areas. Since sensor nodes usually are developed by low-cost hardware, one major challenge in the development of many sensor-network applications is to provide high-security features with limited resources. In this paper, we propose a path generation framework with deadline considerations for real-time query processing. To meet the deadline, the framework will assign the time budget to the routing path, and then, derive a feasible path with the assigned time budget. In order to evaluate the performance of the proposed RTQP scheme, we construct a simulation model using ns 2.35. The performance of the RTQP scheme is compared with that of other related mechanisms, for which we have very encouraging results.