Cyber-physical cloud systems (CPCS) are integrations of cyber-physical systems (CPS) and cloud computing infrastructures. Integrating CPS into cloud computing infrastructures could improve the performance in many aspects. However, new reliability and security challenges are also introduced. This fact highlights the need to develop novel methodologies to tackle these challenges in CPCS. To this end, this article is oriented toward enhancing the soft-error reliability of real-time workflows on CPCS while satisfying the lifetime reliability, security, and real-time constraints. In this article, we propose a dependable algorithm for scheduling workflow applications on CPCS. The proposed algorithm uses slack to recover failed tasks and allows all tasks to share the available slack in the system. To improve soft-error reliability, the algorithm first determines the priority of tasks, then assigns the maximum frequency to each task, and finally assigns the recoveries to tasks dynamically. Slack also can be used to utilize security services for satisfying system security requirements. The lifetime reliability constraint is met by dynamically scaling down the operating frequency of low-priority tasks. Extensive experiments on real-world workflow benchmarks demonstrate that the proposed scheme reduces the probability of failure by up to $52.1\%$ and improves the scheduling feasibility by up to $83.5\%$ compared to a number of representative approaches.

With the advance in mobile computing, Internet of Things, and ubiquitous wireless connectivity, social sensing-based edge computing (SSEC) has emerged as a new computation paradigm where people and their personally owned devices collect sensor measurements from the physical world and process them at the edge of the network. This article focuses on a privacy-aware task allocation problem where the goal is to optimize the computation task allocation in SSEC systems while respecting the users' customized privacy settings. It introduces a novel game-theoretic privacy-aware task allocation (G-PATA) framework to achieve the goal. G-PATA includes: 1) a bottom-up game-theoretic model to generate the maximum payoffs at end devices while satisfying the end user's privacy settings and 2) a top-down incentive scheme to adjust the rewards for the tasks to ensure that the task allocation decisions made by end devices meet the Quality-of-Service (QoS) requirements of the applications. Furthermore, the framework incorporates an efficient load balancing and iteration reduction component to adapt to the dynamic changes in status and privacy configurations of end devices. The G-PATA framework was implemented on a real-world edge computing platform that consists of heterogeneous end devices (Jetson TX1 and TK1 boards, and Raspberry Pi3). We compare G-PATA with state-of-the-art task allocation schemes through two real-world social sensing applications. The results show that G-PATA significantly outperforms existing approaches under various privacy settings (our scheme achieved as much as 47% improvements in delay reduction for the application and 15% more payoffs for end devices compared to the baselines).

Heterogeneous multiprocessor systems on a chips (MPSoCs) consisting of cores with different performance/power characteristics are widely used in many real-time embedded systems, where both soft-error reliability and lifetime reliability are key concerns. Although existing efforts have investigated related problems, they either focus on one of the two reliability concerns or propose time-consuming scheduling algorithms that cannot adequately address runtime workload and environmental variations. This paper introduces an online framework which is adaptive to runtime variations and maximizes soft-error reliability while satisfying the lifetime reliability constraint for soft real-time systems executing on MPSoCs that are composed of high-performance cores and low-power (LP) cores. Based on each core's executing frequency and utilization, the framework performs workload migration between high-performance cores and LP cores to reduce power consumption and improve soft-error reliability. Experimental results based on different hardware platforms show that the proposed approach reduces the probability of failures due to soft errors by at least 17% and 50% on average compared to a number of representative existing approaches that satisfy the same lifetime reliability constraints.

Multiprocessor systems on a chip consisting of integrated CPUs and GPUs are suitable platforms for real-time embedded applications requiring massively parallel processing. For such applications, lifetime reliability due to permanent faults and soft-error reliability due to transient faults are major concerns. Detailed execution profiling has revealed that a CUDA task's CPU execution time significantly increases if the task executes on a different core than the operating system (OS). Based on this observation, an extended task model is introduced to consider the execution time dependencies among tasks and the OS. A hybrid framework is proposed to improve soft-error reliability while satisfying a lifetime reliability constraint for soft real-time systems executing on integrated CPU and GPU platforms. This framework: 1) reduces the total utilization of cores and improves soft-error reliability via off-line task mapping; 2) achieves a higher lifetime reliability through task migration at run time; and 3) improves soft-error reliability by dynamically scaling frequencies of CPU and GPU cores. The experimental results show that the proposed framework leads to a system that can execute without soft errors for at least 4 days (4 times) and 6 days (6 times) longer, on average, than existing approaches.

CMOS scaling has greatly increased concerns for both lifetime reliability due to permanent faults and soft-error reliability due to transient faults. Most existing works only focus on one of the two reliability concerns, but often times techniques used to increase one type of reliability may adversely impact the other type. A few efforts do consider both types of reliability together and use two different metrics to quantify the two types of reliability. However, for many systems, the user's concern is to maximize system availability by improving the mean time to failure (MTTF), regardless of whether the failure is caused by permanent or transient faults. Addressing this concern requires a uniform metric to measure the effect due to both types of faults. This paper introduces a novel analytical expression for calculating the MTTF due to transient faults. Using this new formula and an existing method to evaluate system MTTF, we tackle the problem of maximizing availability for multicore real-time systems with consideration of permanent and transient faults. A framework is proposed to solve the system availability maximization problem. Experimental results on a hardware board and simulation results of synthetic tasks show that our scheme significantly improves system MTTF (and hence availability) compared with existing techniques.

With the rapid growth of medical and biomedical image data, energy-efficient solutions for analyzing such image data that can be processed fast and accurately on platforms with low power budget are highly desirable. This paper uses segmenting glial cells in brain microscopy images as a case study to demonstrate how to achieve biomedical image segmentation with significant energy saving and minimal comprise in accuracy. Specifically, we design, train, implement, and evaluate Fully Convolutional Networks (FCNs) for biomedical image segmentation on IBM's neurosynaptic DNN processor - TrueNorth (TN). Comparisons in terms of accuracy and energy dissipation of TN with that of a low power NVIDIA TX2 mobile GPU platform have been conducted. Experimental results show that TN can offer at least two orders of magnitude improvement in energy efficiency when compared to TX2 GPU for the same workload.

Technology scaling has increased concerns about transient faults due to soft errors and permanent faults due to lifetime wear processes. Although researchers have investigated related problems, they have either considered only one of the two reliability concerns or presented simple recovery allocation algorithms that cannot effectively use available time slack to improve soft-error reliability. This paper introduces a framework for improving soft-error reliability while satisfying lifetime reliability and real-time constraints. We present a dynamic recovery allocation technique that guarantees to recover any failed task if the remaining slack is adequate. Based on this technique, we propose two scheduling algorithms for task sets with different characteristics to improve system-level soft-error reliability. Lifetime reliability requirements are satisfied by reducing core frequencies for appropriate tasks, thereby reducing wear due to temperature and thermal cycling. Simulation results show that the proposed framework reduces the probability of failure by at least 8% and 73% on average compared to existing approaches.

Both soft-error reliability (SER) due to transient faults and lifetime reliability (LTR) due to permanent faults are key concerns in real-time MPSoCs. Existing works have investigated related problems, however, most of them only focus on one of the two reliability concerns. A few efforts do consider both types of reliability together, but ignore the impacts of hardware- and application-level variations on reliability, thus are not applicable to state-of-the-art MPSoCs under variations. In this paper, we focus on increasing SER without sacrificing LTR since transient faults occur much more frequently than permanent faults. Specifically, we propose a novel task allocation and scheduling scheme to maximize SER while satisfying a LTR constraint for soft real-time MPSoCs. Considering that SER is the objective while LTR is a constraint in our problem, and LTR is highly related to core temperature profiles, we dedicate to investigating the effects of variations in core soft-error rate, task vulnerability to soft errors, and task execution time on SER. To the best of our knowledge, our work is the first attempt that jointly handles the two reliability issues as well as taking into account the effects of variations on reliability. Experimental results show that our scheme improves the SER by up to 66% as compared to a number of representative existing approaches while meeting the same LTR constraint.

Social sensing has emerged as a new sensing application paradigm where measurements about the physical world are collected from humans or devices on their behalf. A key limitation in the current social sensing solution space is that data processing and analytics are often done in a "backend" mode (e.g., on dedicated servers or commercial cloud platforms). Such mode ignores the rich processing capability of increasingly powerful edge devices (e.g., mobile phones and nodes in the Internet of Things). Exploiting such edge devices in the social sensing setting introduces new challenges to real-time resource management. In this work, we develop a Bottom-up Game-theoretic Task Allocation (BGTA) framework to solve the critical problem of allocating real-time social sensing tasks to self-aware and non-cooperative edge computing nodes. In particular, we address two important challenges in solving this problem. The first one is "conflicting interest" where the objectives of applications and edge nodes may be at odds with each other. The second challenge is "asymmetric and incomplete information" where the application is often unaware of the detailed status (e.g., energy profile, utilization, CPU frequency) and compliance level of the edge nodes. To address these challenges, we first design a non-cooperative task allocation game model to address the conflicting objectives of the applications and edge nodes. We then develop a decentralized Fictitious Play scheme to allow each edge node to make its own decision on which task to execute in a non-cooperative context. Finally, we design a dynamic incentive mechanism to ensure the decisions made by the edge nodes meet objectives of the application. We implement a system prototype deployed on Nvidia Jetson TX1 and Jetson TK1 boards and evaluate our task allocation framework using two real-world social sensing applications. The results show that our scheme can well satisfy Quality of Service (QoS) requirement of the applications while providing optimized payoffs to edge nodes compared to the state-of-the-art baselines.

Heterogeneous MPSoCs consisting of cores with different performance/power behaviors are widely used in many power-constrained real-time systems. Both soft-error reliability and lifetime reliability are key concerns in such systems. Although existing work have investigated related problems, they either focus on one of the two reliability concerns or propose complicated scheduling algorithms that cannot adequately address run-time workload and environment variations. This paper introduces an on-line heuristic to maximize soft-error reliability while satisfying a lifetime reliability constraint for soft real-time systems executed on MPSoCs composed of high-performance cores and low-power cores. Based on the run-time cores' frequencies and utilizations, the heuristic performs workload migration between the high-performance cores and low-power cores to achieve improved soft-error reliability. Experimental results from both a hardware platform and a simulator show that the proposed algorithm reduces the probability of faults by at least 30% compared to a number of representative existing approaches while satisfying the same lifetime reliability constraints.

This paper studies the problem of maximizing multicore system lifetime reliability, an important design consideration for many real-time embedded systems. Existing work has investigated the problem, but has neglected important failure mechanisms. Furthermore, most existing algorithms are too slow for online use, and thus cannot address runtime workload and environment variations. This paper presents an online framework that maximizes system lifetime reliability through reliability-aware utilization control. It focuses on homogeneous multicore soft real-time systems. It selectively employs a comprehensive reliability estimation tool to deal with a variety of failure mechanisms at the system level. A model-predictive controller adjusts utilization by manipulating core frequencies, thereby reducing temperature, and an online heuristic adjusts the controller sampling window length to decrease the reliability effects of thermal cycling. Experiments with a real quad-core ARM processor and a simulator demonstrate that the proposed approach improves system mean time to failure by 50% on average and 141% in the best case, compared with existing techniques.

CMOS scaling has greatly increased concerns for lifetime reliability due to permanent faults and soft-error reliability due to transient faults. Most existing works only focus on one of the two reliability concerns, but often times techniques used to increase one type of reliability may adversely impact the other type. A few efforts do consider both types of reliability together and use two different metrics to quantify the two types of reliability. However, for many systems, the concern of the user is to maximize system availability by improving the mean time to failure (MTTF), regardless of whether the failure is caused by permanent faults or transient faults. Addressing this concern requires a uniform metric to measure the effect due to both types of faults. In this paper, we derive a novel analytical expression for calculating the MTTF due to transient faults. Using this new formula and an existing method to evaluate system MTTF, we formulate and solve the problem of maximizing system availability with consideration of permanent faults, transient faults, and throughput constraint. Extensive simulations of synthetic task sets and benchmarks based on real-world applications were performed to validate our algorithm.

With the continuous scaling of CMOS devices, the increase in power density and system integration level have not only resulted in huge energy consumption but also led to elevated chip temperature. Thus, energy efficient task scheduling with thermal consideration has become a pressing research issue in computing systems, especially for real-time embedded systems with limited cooling techniques. In this paper, we design a two-stage energy-efficient temperature-aware task scheduling scheme for heterogeneous real-time multiprocessor system-on-chip (MPSoC) systems. In the first stage, we analyze the energy optimality of assigning real-time tasks to multiple processors of an MPSoC system, and design a task assignment heuristic that minimizes the system dynamic energy consumption under the constraint of task deadlines. In the second stage, the optimality of minimizing the peak temperature of a processor is investigated, and a slack distribution heuristic is proposed to improve the temperature profile of each processor under the thermal constraint, thus the temperature-dependent system leakage energy consumption is reduced. Through the extensive efforts made in two stages, the system overall energy consumption is minimized. Experimental results have demonstrated the effectiveness of our scheme.

System lifetime reliability is an important design consideration for many real-time embedded systems. Increasing integrated circuit power density and the subsequent rise in chip temperature negatively impact the lifetime reliability of such systems. Although existing thermal-aware methods are effective in reducing temperature, they cannot increase, and may even hamper, the system lifetime reliability. The complicated relationship between temperature and system lifetime requires that reliability be considered explicitly during system design. This paper presents a reliability-aware utilization control framework for homogeneous multicore soft real-time systems. The framework employs a model predictive controller to increase the system lifetime by manipulating the utilization of real-time tasks. An online heuristic algorithm is introduced to adjust the controller's sampling window in order to reduce the effects of thermal cycling on reliability. Simulation results show that the proposed approach can improve the system mean time to failure by at least 43% and as much as 369% compared to existing techniques.

In this work, we address the emerging scheduling problem existed in the design of secure and energy-efficient real-time embedded systems. The objective is to minimize the energy consumption subject to security and schedulability constraints. Due to the complexity of the problem, we propose a dynamic programming based approximation approach to find the near-optimal solutions with respect to predefined security constraint. The proposed technique has polynomial time complexity which is about half of traditional approximation approaches. The efficiency of our algorithm is validated by extensive experiments.

In this paper, we focus on task scheduling problems in security-critical real-time systems. We consider that all of the critical tasks are equipped with RC5 algorithm to reduce the vulnerability when facing security attacks. The relationships among security level, vulnerability and execution time of each task are firstly deduced. Then, a Multiple Task Vulnerability Optimization Method (MTVOM), which is based on dynamic programming algorithm, is devised to obtain minimal vulnerability under strict timing constraints. Furthermore, we take task criticality into consideration because each task is not equally important to its system. Finally, simulation experiments demonstrate the effectiveness of this method.

With many advantages like low cost, faster and non-volatile, NAND flash memory has become a critical component in building security-critical real-time embedded devices. In this paper, we are interested in online optimization of security-sensitive storage applications, whose workloads are unpredictable but have explicit deterministic or probabilistic timing constraints and certain security constraints. Sensitive data must be stored before a specific deadline, otherwise it will lose its validity. To address these challenges, this paper presents a Feedback Vulnerability and Utilization Control (FVUC) mechanism. FVUC employs two proportional-integral controllers, the Utilization Controller and Vulnerability Controller, to build a big feedback loop that dynamically monitors the system run-time status as well as decides how many flash pages would be encrypted by a cryptography algorithm. Relied on the accurate model and design, FVUC can make a balance between the utilization and vulnerability, and achieve a better overall performance. The advantages of FVUC are verified by a series of simulation experiments under a broad range of system configurations and run-time uncertainties.

Traditional mechanisms for risk control and task scheduling may not be very suitable for security-critical embedded real-time systems under dynamic operation environments. In this paper, we are interested in design of an adaptive risk control and management mechanism of the uniprocessor system for real-time embedded applications. With the purpose of obtaining adaptive capability to reduce system risks without sacrificing soft real-time performance, a two-level feedback scheduling framework was deployed at runtime. The upper-level component automatically monitors the status of the waiting queue, then admits or rejects the input workloads and assigns a reasonable security level for ready tasks. The lower-level scheduling framework can dynamically adjust the security level of ready tasks based on the runtime situation. Simulation results show the superiority of the proposed mechanism, especially the good adaptivity under dynamic system workloads.

Since the real-time systems are becoming increasingly networked, there are some growing critical challenges of security management and risk control. For the unpredicted and especially unsecured environments, such security-critical distributed real-time embedded systems need to enforce security on multiple nodes in order to against the potential threats as well as satisfying the real-time requirements. Unlike the traditional ad hoc static designing approaches, based on feedback theory, this paper proposes an Adaptive Risk Control and Security Management (ARCSM) mechanism for centralized distributed systems. ARCSM has the ability to guarantee the soft real-time and provide as strong security protection as possible. To meet this requirement, the host server is able to refuse to serve for the deadline missed tasks. In each node, a two-level scheduling framework is designed to implement the feedback control twice. In the first feedback, an appropriate initial security level is assigned for new task. At the runtime, the second feedback can dynamically adjust the ready tasks' security level based on runtime status. Considering features of embedded systems, we measured the energy and time costs of cryptographic algorithms and established a method to quantify the security risk. Moreover, the relationship between time and energy costs analyzed in this paper leaves its significance for the future researches. Experimental results demonstrate that our mechanism can be adaptive to the fluctuation of input workloads and has better performance than open-loop schemes.

In this paper, we are interested in resource allocation for energy constrained and security-critical embedded systems. Tasks in such systems need to be successfully executed under certain energy budget and be robust against serious security threatens. Different to former energy minimal scheduling problem, we introduce a new optimization problem for a set of tasks with energy constraint and multiple security choices. We present a dynamic programming based approximation algorithm to minimize the security risk of the system while statistically guaranteeing energy consumption constraints for given energy slack ratio. The proposed algorithm is very efficient in both time and space dimensions, and achieves good solutions. Extensive simulations demonstrate the superiority of our algorithm over other approaches.

Distributed Real-time Embedded (DRE) systems are facing great challenges in networked, unpredictable and especially unsecured environments. In such systems, there is a strong need to enforce security on distributed computing nodes in order to guard against potential threats, while satisfying the real-time requirements. This paper proposes a Security-Aware Feedback Control Mechanism (SAFCM) which has the ability to dynamically change the security level to guarantee soft real-time requirements and make the security protection as strong as possible. In order to widely support distributed real-time systems, a multi-input multi-output feedback loop is designed and a model predictive controller is deployed based on an equation model that describes the dynamic behavior of the DRE systems. This control loop uses security level scaling to globally control the CPU utilization and security performance for the whole system. We propose a "security level" metric based on an evolution of cryptography algorithms used in embedded systems. Experimental results demonstrate that SAFCM not only has the excellent adaptivity compared to open-loop mechanism, but also has a better overall performance than PID control mechanism.