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Targeting global EDF scheduling, this paper proposes an optimal algorithm for parallelizing tasks with parallelization freedom. For this, we extend the interference-based sufficient schedulability analysis and derive monotonic increasing properties of both tolerance and interference for the schedulability. Leveraging those properties, we propose a one-way search based optimal algorithm with polynomial time complexity. We present a formal proof of the optimality of the proposed algorithm. We first address the single-segment task model and then extend to the multi-segment task model. Our extensive experiments through both simulation and actual implementation show that our proposed approach can significantly improve the schedulability.

Recent parallel programming frameworks such as OpenCL and OpenMP allow us to enjoy the parallelization freedom for real-time tasks. The parallelization freedom creates the time versus density tradeoff problem in fluid scheduling, i.e., more parallelization reduces thread execution times but increases the density. By system-widely exercising this tradeoff, we propose optimal parameter tuning of real-time tasks aiming at maximizing the schedulability of multicore fluid scheduling. Our experimental study by both simulation and actual implementation shows that the proposed approach well balances the time and the density, and results in up to 80 percent improvement of the schedulability.

Time difference of arrivals) (TDoA)-based location tracking systems using RF and ultrasonic signals often give unacceptable errors due to the line-of-sight limitation of the ultrasonic signal. To overcome this limitation, many researchers have devoted their efforts using various methods. To further enhance accuracy and improve performance, this paper proposes a novel TDoA-based location tracking technique that explicitly exploits ultrasonic reflections in location estimation. We also propose a reflection plane detection method, which makes system configuration easy for the proposed reflection-aware location tracking algorithm. The proposed system can achieve a high location accuracy with errors smaller than 35 cm even when the lines of sight of ultrasonic signals are frequently blocked and hence they are received through reflections.

For advanced features of next generation vehicles, the real-time programs in automotive embedded systems are dramatically increasing. For such large volume program codes, this paper proposes a novel framework to use high-density and low-cost nonvolatile memory, i.e., NAND flash memory, as a low-cost means of storing and executing hard real-time programs. Regarding this, one challenge is that NAND flash memory allows only 2 KB page-based read operations not per-byte random accesses, which requires RAM as working storage for code executions. This paper proposes two solutions, i.e., partitioned RAM solution and shared RAM solution, that minimize the RAM size required to deterministically guarantee the deadlines of all the hard real-time tasks. The proposed solutions are verified with the actual real-time programs for unmanned autonomous driving. To the best of our knowledge, this is the first work that allows us to use NAND flash memory for hard real-time program executions with the minimal usage of RAM.

This paper proposes a novel technique called mRT-PLRU (Multitasking Real-Time constrained combination of Pinning and LRU), which forms a generic framework to use inexpensive nonvolatile NAND flash memory for storing and executing real-time programs in multitasking environments. In order to execute multiple real-time tasks stored in NAND flash memory with the minimal usage of expensive RAM, the mRT-PLRU is optimally configured in two steps. In the first step, the per-task analysis finds the function of RAM size versus execution time (and the corresponding optimal pinning/LRU combination) for each individual task. Using these functions for all the tasks as inputs, the second-step called a stochastic-analysis-in-loop optimization conducts an iterative convex optimization with the stochastic analysis for the probabilistic schedulability check. As a result, the optimization loop can optimally determine the RAM sizes for multiple tasks such that their deadlines are probabilistically guaranteed with the minimal size of total RAM. The usefulness of the developed technique is intensively verified through both simulation and actual implementation. Our experimental study shows that mRT-PLRU can save up to 80 percent of RAM required by the industry-common shadowing approach.

NAND flash memory has been widely used as a nonvolatile storage for storing data. However, it is challenging to execute program codes on NAND flash memory, since NAND flash memory only supports page-based reads, not byte-level random reads. This paper proposes an automated process to find the optimal paging strategy called RT-PLRU (Real-Time constrained combination of Pinning and LRU) that allows program codes stored in NAND flash memory to be executed satisfying real-time requirements with minimal usage of RAM. Moreover, the proposed process optimally configure the RT-PLRU in a developer-transparent way without giving any burden to the program developer. The developed technique is specifically applied to a media player program targeting a portable media player (PMP). To the best of our knowledge, this is the first effort to use NAND flash memory as a code storage for storing and executing real-time programs with minimal usage of RAM.

Wireless sensor networks have great potentials to be applied to mission critical real-time applications such as target tracking, emergency detection, and disaster management. For such real-time applications, sensory data should be delivered within the end-to-end deadlines; otherwise, the data may become useless or misleading. In order to provide such deterministic guarantee of end-to-end deadlines, not only packet scheduling within a single hop but also routing across multiple hops have to be jointly considered. In this paper, we propose a combined scheduling and routing mechanism that provides a deterministic guarantee of end-to-end deadlines leveraging real-time scheduling and analysis techniques. Our experimental study shows that the proposed mechanism can accommodate a larger workload meeting all the end-to-end deadlines than a pretty comprehensive set of other possible solutions of scheduling and routing that are known to deterministically guarantee end-to-end deadlines.

This paper proposes a safe stochastic analysis for fixed-priority scheduling, which is applicable to a broader spectrum of periodic tasks than the ones analyzable by any of the existing techniques. The proposed analysis can find a safe upper-bound of deadline miss probability for periodic tasks with (1) arbitrary execution time distributions, (2) varying interrelease times with the period as the minimum, and (3) the maximum utilization factor Umax that can be greater than 1. One challenge for this is that the release times of tasks are not known a priori because we are not limiting the interrelease times of each task to a constant, i.e., the period. In such a situation, the relative phases of task instances at run time can be arbitrary. Thus, we need to consider all possible phase combinations among jobs to find the worst case deadline miss probability, which is not tractable. To handle this difficulty, we first derive the worst case phase combination for harmonic task sets. Then, we present a safe way to transform a nonharmonic task set to a harmonic task set such that the deadline miss probabilities obtained with the worst case phase combination for the transformed harmonic task set are guaranteed to be worse than those for the original nonharmonic task set with all possible phase combinations. Therefore, the worst case deadline miss probabilities of the transformed harmonic tasks can be used as safe upper-bounds of deadline miss probabilities of the original nonharmonic tasks. Through experiments, we show that the safe upper-bound computed by the proposed analysis is tight enough for practical uses.

As the elderly population increases, elderly care using inexpensive technological means is becoming critical. This paper presents our prototype system that provides real-time indoor tracking of elderly residents and their belongings, which is essential to assisting and securing their independent living. For high-fidelity real-time tracking, we propose novel scheduling algorithms. Our scheduling algorithms are designed by harmonizing both sensing and communication signals and leveraging location awareness and mobility consciousness in order to improve tracking accuracy while reducing the energy consumption. We performed extensive experiments through both simulation and actual implementation. Our experimental result says that our scheduling algorithms can provide real-time tracking of residents within a 20 cm error bound in the typical range of human mobility.

Recent computing applications such as videoconferencing and grid computing run their tasks on distributed computing resources connected through networks. For such applications, knowledge of the network status such as delay, jitter, and available bandwidth can help them select proper network resources to meet the Quality-of-Service (QoS) requirements. Also, the applications can dynamically change the resource selection if the current selection is found to experience poor performance. For such purposes, Internet Service Providers (ISPs) have started to instrument their networks with Network Measurement Infrastructures (NMIs) that run active measurement tasks periodically and/or on demand. However, one problem that most network engineers have overlooked is the measurement conflict problem, which happens when multiple active measurement tasks inject probing packets into the same network segment at the same time, resulting in misleading reports of network performance due to their combined effects. This paper proposes enhanced Earliest Deadline First (EDF) algorithms that allow "Concurrent Executions" to orchestrate offline/online measurement jobs in a conflict-free manner. The simulation study shows that our measurement scheduling mechanism can improve the schedulable utilization of offline measurement tasks up to 300 percent and the response time of on-demand jobs up to 50 percent. Further, we implement and deploy our scheduling mechanism in a real working NMI for monitoring the Internet2 Abilene network. As a case study, we show the utility of our algorithms in the widely used Network Weather Service (NWS).

Increased access to broadband networks has led to a fast-growing demand for voice and video over IP (VVoIP) applications such as Internet telephony (VoIP), videoconferencing, and IP television (IPTV). For pro-active troubleshooting of VVoIP performance bottlenecks that manifest to end-users as performance impairments such as video frame freezing and voice dropouts, network operators cannot rely on actual end-users to report their subjective quality of experience (QoE). Hence, automated and objective techniques that provide real-time or online VVoIP QoE estimates are vital. Objective techniques developed to-date estimate VVoIP QoE by performing frame-to-frame peak-signal-to-noise ratio (PSNR) comparisons of the original video sequence and the reconstructed video sequence obtained from the sender-side and receiver-side, respectively. Since processing such video sequences is time consuming and computationally intensive, existing objective techniques cannot provide online VVoIP QoE. In this paper, we present a novel framework that can provide online estimates of VVoIP QoE on network paths without end-user involvement and without requiring any video sequences. The framework features the "G AP-model", which is an offline model of QoE expressed as a function of measurable network factors such as bandwidth, delay, jitter, and loss. Using the GAP-model, our online framework can produce VVoIP QoE estimates in terms of "Good", "Acceptable", or "Poor" (GAP) grades of perceptual quality solely from the on-line measured network conditions.

In this paper, we present a novel packet delivery mechanism called Multi-Path and Multi-SPEED Routing Protocol (MMSPEED) for probabilistic QoS guarantee in wireless sensor networks. The QoS provisioning is performed in two quality domains, namely, timeliness and reliability. Multiple QoS levels are provided in the timeliness domain by guaranteeing multiple packet delivery speed options. In the reliability domain, various reliability requirements are supported by probabilistic multipath forwarding. These mechanisms for QoS provisioning are realized in a localized way without global network information by employing localized geographic packet forwarding augmented with dynamic compensation, which compensates for local decision inaccuracies as a packet travels towards its destination. This way, MMSPEED can guarantee end-to-end requirements in a localized way, which is desirable for scalability and adaptability to large scale dynamic sensor networks. Simulation results show that MMSPEED provides QoS differentiation in both reliability and timeliness domains and, as a result, significantly improves the effective capacity of a sensor network in terms of number of flows that meet both reliability and timeliness requirements up to 50 percent (12 flows versus 18 flows).

This paper describes a stochastic analysis framework which computes the response time distribution and the deadline miss probability of individual tasks, even for systems with a maximum utilization greater than one. The framework is uniformly applied to fixed-priority and dynamic-priority systems and can handle, tasks with arbitrary relative deadlines and execution time distributions.

In many practical real-time applications, there is a given set of task frequencies (i.e., inverse of task periods) corresponding to predetermined QoS options that the applications can choose. For example, in audio applications, the typical choices of playback frequencies are for CD quality, radio quality, and phone quality. Similar configurations can be found in streaming video and in control applications. In such systems, applications dynamically arrive requesting one of the periods provided by the QoS manager. Thus, the accurate and efficient online schedulability test is essential for any task set whose periods are chosen from the QoS period set. For this purpose, we propose new utilization bounds as a function of given QoS periods. As long as there is a set of given periods that can be chosen by applications, the bounds developed can be used by the QoS manager to quickly determine the schedulability of dynamic applications.

Cache memory is used in almost all computer systems today to bridge the ever increasing speed gap between the processor and main memory. However, its use in multitasking computer systems introduces additional preemption delay due to the reloading of memory blocks that are replaced during preemption. This cache-related preemption delay poses a serious problem in realtime computing systems where predictability is of utmost importance. We propose an enhanced technique for analyzing and thus bounding the cache-related preemption delay in fixed-priority preemptive scheduling focusing on instruction caching. The proposed technique improves upon previous techniques in two important ways. First, the technique takes into account the relationship between a preempted task and the set of tasks that execute during the preemption when calculating the cache-related preemption delay. Second, the technique considers the phasing of tasks to eliminate many infeasible task interactions. These two features are expressed as constraints of a linear programming problem whose solution gives a guaranteed upper bound on the cache-related preemption delay. This paper also compares the proposed technique with previous techniques using randomly generated task sets. The results show that the improvement on the worst-case response time prediction by the proposed technique over previous techniques ranges between 5 percent and 18 percent depending on the cache refill time when the task set utilization is 0.6. The results also show that as the cache refill time increases, the improvement increases, which indicates that accurate prediction of cache-related preemption delay by the proposed technique becomes increasingly important if the current trend of widening speed gap between the processor and main memory continues.

We propose a new framework for the link assignment (i.e., topological design) problem that arises from the use of intersatellite links (ISL's) in low-earth orbit (LEO) satellite networks. In the proposed framework, we model an LEO satellite network as a finite state automaton (FSA), where each state corresponds to an equal-length interval in the system period of the LEO satellite network. This FSA-based framework allows the link assignment problem in LEO satellite networks to be treated as a set of link assignment problems in fixed topology networks. Within this framework, we study various link assignment and routing schemes. In particular, both regular link assignment and link assignment optimized by simulated annealing are considered. For each link assignment, both static and dynamic routing schemes are considered. Our simulation results show that the optimized link assignment combined with static routing achieves the best performance in terms of both newly initiated call blocking probability and ongoing call blocking probability. The results also show that when the link assignment is the same, static routing gives better performance than dynamic routing since the latter requires a substantial amount of time to stabilize its routing table after a state transition.