Road traffic collisions are an extremely serious and often fatal issue. One promising approach to mitigate such collisions is the use of connected car services that share road traffic information obtained from vehicles and cameras over mobile networks. In connected car services, it is important for data chunks to arrive at a destination node within a certain deadline constraint. In this paper, we define a flow from a vehicle (or camera) to the same vehicle (or camera) via an MEC server, as a mission critical (MC) flow, and call a deadline of the MC flow the MC deadline. Our research objective is to achieve a higher arrival ratio within the MC deadline for the MC flow that passes through both the radio uplink and downlink. We previously developed a deadline-aware scheduler with consideration for quality fluctuation (DAS-QF) that considers chunk size and a certain deadline constraint in addition to radio quality and utilizes these to prioritize users such that the deadline constraints are met. However, this DAS-QF does not consider that the congestion levels of evolved NodeB (eNB) differ depending on the eNB location, or that the uplink congestion level differs from the downlink congestion level in the same eNB. Therefore, in the DAS-QF, some data chunks of a MC flow are discarded in the eNB when they exceed either the uplink or downlink deadline in congestion, even if they do not exceed the MC deadline. In this paper, to reduce the eNB packet drop probability due to exceeding either the uplink and downlink deadline, we propose a deadline coordination function (DCF) that adaptively sets each of the uplink and downlink deadlines for the MC flow according to the congestion level of each link. Simulation results show that the DAS-QF with DCF offers higher arrival ratios within the MC deadline compared to DAS-QF on its own

The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.

As real-time embedded systems are required to accommodate various tasks with different levels of criticality, scheduling algorithms for MC (Mixed-Criticality) systems have been widely studied in the real-time systems community. Most studies have focused on MC uniprocessor systems whereas there have been only a few studies to support MC multiprocessor systems. In particular, although the ZL (Zero-Laxity) policy has been known to an effective technique in improving the schedulability performance of base scheduling algorithms on SC (Single-Criticality) multiprocessor systems, the effectiveness of the ZL policy on MC multiprocessor systems has not been revealed to date. In this paper, we focus on realizing the potential of the ZL policy for MC multiprocessor systems, which is the first attempt. To this end, we design the ZL policy for MC multiprocessor systems, and apply the policy to EDF (Earliest Deadline First), yielding EDZL (Earliest Deadline first until Zero-Laxity) tailored for MC multiprocessor systems. Then, we develop a schedulability analysis for EDZL (as well as its base algorithm EDF) to support its timing guarantee. Our simulation results show a significant schedulability improvement of EDZL over EDF, demonstrating the effectiveness of the ZL policy for MC multiprocessor systems.

In Worldwide interoperability for Microwave Access (WiMAX) network, QoS(quality of service) is provided for service flows. For this, five classes of services are defined in IEEE 802.16. They are Unsolicited Grant Service (UGS), Extended Real-Time Polling Service (ertPS), Real-Time Polling Service (rtPS), Non Real-Time Polling Service (nrtPS) and Best Effort (BE). For real-time classes, the sent packet has a deadline. As the transmission delay is over the limitation of deadline, the packet becomes useless and will be discarded. Thus, they will be served earlier and have higher probability. Nevertheless, non-real-time packets need also to be served from time to time. The scheduler should assign proper bandwidth for non-real-time flows and send the real-time packets before they are discarded. To deicide the right allocated bandwidth, the arrival rate of each flow is a good parameter for assignment. The average µ and standard deviation σ of arrival rate correspond to the long term need and variation of load for one flow. Thus, we proposed a scheduling algorithm named BAcSOA in which µ+kσ is used as a reference to allocate bandwidth with weighted round robin for one flow [5]. Different classes of flows will be given different values of k which corresponds to the priorities of classes. In this algorithm, flow with higher priority should have larger value of k. The value of k will decide the performance of this class. In this paper, we revise the algorithm to EBAcSOA and propose a mathematical way to decide the value of k for a required performance. Then, a simulation platform is proposed to decide k such that a required performance can be obtained for an operating system. This approach may be different from other researches in which there is no required performance and the performance results are obtained only for several operating points. However, the approach proposed is more practical from the view of an operator and may become an attractive point for other researchers.

With the fast development of mobile communication technologies, mobile multimedia services like mobile Video on Demand (VOD) are becoming prevalent. However, VOD streaming requires dedicated bandwidth to satisfy Quality of Service (QoS), and the limited wireless bandwidth will become insufficient to support the increasing number of mobile VOD users. In order to solve the problem, this paper proposes a Call Admission Control (CAC) approach which can accept new users even when the system bandwidth is insufficient. Our approach also guarantees continuous playback for subscribers by taking into account the service end time and the delay bound of the users. Simulation results demonstrate that the proposed scheme can increase the number of concurrent users and reduce the connection blocking probability significantly without playback interruption.

This paper presents a novel chunk selection strategy for peer-to-peer video streaming, called enr-first selection policy, which simultaneously considers both block rarity and playback deadline. The policy intends to boost overall network-wide streaming performance, while ensuring good playback continuity of individual peers. The efficacy of the proposed scheme is validated through our performance evaluation study that demonstrates a substantial gain.

We consider wireless interactive data broadcasting environments consisting of the broadcast channel for data dissemination and the communication channels for client requests. Modeling client impatience as the soft deadline of client requests, we propose a broadcast scheduling based on a combination of periodic scheduling and priority-based scheduling. The server partitions data items into hot and cold-item sets according to the optimized cut-off point. We apply periodic and priority-based scheduling to hot and cold item sets, respectively, in order to maximize the average utility of the items. We investigate the optimized cut-off point by analyzing the average utility of items as a function of the cut-off point. Simulation results show that our proposed algorithm outperforms existing methods in various circumstances in terms of average utility as well as average response time.

This letter proves the finish time predictability of EDZL (Earliest Deadline Zero Laxity) scheduling algorithm for multiprocessor real-time systems, which is a variant of EDF. Based on the results, it also shows that EDZL can successfully schedule any periodic task set if its total utilization is not greater than (m+1)/2, where m is the number of processors.

Multiprocessor architecture becomes common on real-time systems as the workload of real-time systems increases. Recently new deadline-based (EDF-based) multiprocessor scheduling algorithms are devised, and comparative studies on the performance of these algorithms are necessary. In this paper, we compare EDZL, a hybrid of EDF and LLF, with other deadline-based scheduling algorithms such as EDF, EDF-US[m/(2m-1)], and fpEDF. We show EDZL schedules all task sets schedulable by EDF. The experimental results show that the number of preemptions of EDZL is comparable to that of EDF and the schedulable utilization bound of EDZL is higher than those of other algorithms we consider.

For real-time systems, multiprocessor support is indispensable to handle the large number of requests. Existing on-line scheduling algorithms such as Earliest Deadline First Algorithm (EDF) and Least Laxity Algorithm (LLA) may not be suitable for scheduling hard real-time tasks on multiprocessors. Although EDF has a low context switching overhead, it can produce arbitrarily low processor utilization. LLA has been shown as suboptimal, but has the potential for higher context switching overhead. We propose new on-line scheduling algorithms Earliest Deadline/Least Laxity (ED/LL) and Earliest Deadline Zero Laxity (EDZL) for identical multiprocessors. We show that ED/LL is suboptimal for multiprocessors and EDZL is suboptimal for two processors. Experimental results show that ED/LL and EDZL have low context switching overhead and low deadline miss rate.

In order for a service discipline to be used for guaranteed service networks at very high speed, its overall implementation must be scalable while it provides as wide a network schedulability region as possible. From this point of view, GPS-based service disciplines provide a narrow network schedulability region while EDF-based disciplines suffer from the implementation complexities of rate-controllers and admission control. Alternatively, although service disciplines based on service-curves can provide a wider network schedulability region than GPS-based and EDF-based disciplines, they may have even worse implementation complexities than EDF-based disciplines. In this paper, we propose to employ a service discipline based on our specific service-curves. We show that our service discipline has comparable implementation complexity to GPS-based disciplines while providing the same wide network schedulability region that EDF-based disciplines can provide. In fact, this service discipline is an SCED service discipline proposed in [14]. However, our specific service-curves provide the SCED service discipline with the same network schedulability region that EDF-based disciplines can provide, O(1) complexity for deadline calculation, and O(N) complexity for admission control where N is the number of sessions.

Most of current codesign tools or methodologies only support validation in the form of cosimulation and testing of design alternatives. The results of hardware-software codesign of a distributed system are often not verified, because they are not easily verifiable. In this paper, we propose a new formal coverification approach based on linear hybrid automata, and an algorithm for automatically converting codesign results to the linear hybrid automata framework. Our coverification approach allows automatic verification of real-time constraints such as hard deadlines. Another advantage is that the proposed approach is suitable for verifying distributed systems with arbitrary communication patterns and system architecture. The feasibility of our approach is demonstrated through several application examples. The proposed approach has also been successfully used in verifying deadline violations when there are inter-task communications between tasks with different period lengths.

When a failure or upset occurring in a controller computer induces a task failure durable for a substantial period, system dynamics apparently deviates from its desirable sample paths, and loses its stability in an extreme case for the period to exceed the hard deadline in a real-time control system. In the paper, we propose an algorithm to combine the deadlines of all elementary tasks (derived formerly by our work) executed in several operation modes with multi-sampling periods. This results in computing the hard deadline of the entire system through modifying task-state equations to capture the effects of task failures and inter-correlations among tasks.

Estimating the deadline of a real-time task is a necessary prerequisite to the applications that have strict timing constraints, such as real-time systems design. This paper shows how Monte-Carlo simulation can be used as a space-efficient way of analyzing Timed Petri nets to predict whether the system specified can satisfy its real-time deadlines. For the purpose, Extended Timed Petri Net (XTPN), an extension of conventional Timed Petri net, and its execution rule, using Monte-Carlo technique, are newly defined. A simple simulation scheme with less memory space is presented as a way of estimating the deadline of a real-time task modeled in XTPN. And the comparison between the analytical and simulation results is given. The problem addressed here is to find the probabilities of meeting given deadlines.

The problem of non-preemptive scheduling of real-time periodic tasks with specified release times on a uniprocessor system is known as NP-hard problem. In this paper we propose a new non-preemptive scheduling algorithm and a new static scheduling strategy which use the repetitiveness and the predictability of periodic tasks in order to improve schedulabilities of real-time periodic tasks with specified release times. The proposed scheduling algorithm schedules periodic tasks by using the heuristic that precalculates if the scheduling of the selected task leads to the case that a task misses a deadline when tasks are scheduled by the non-preemptive EDF algorithm. If so, it defers the scheduling of the selected task to avoid the precalculated deadline-missing. Otherwise, it schedules the selected task in the same way as the non-preemptive EDF algorithm. Our scheduling algorithm can always find a feasible schedule for the set of periodic tasks with specified release times which is schedulable by the non-preemptive EDF algorithm. Our static sheduling strategy transforms the problem of non-preemptive scheduling for periodic tasks with specified release times into one with same release times for all tasks. It suggests dividing the given problem into two subproblems, making a non-preemptive scheduling algorithm to find two feasible subschedules for the two subproblems in the forward or backward scheduling within specific time intervals, and then combining the two feasible subschedules into a complete feasible schedule for the given problem. We present the release times as a function of periods for the efficient problem division. Finally, we show improvements of schedulabilities of our scheduling algorithm and scheduling strategy by simulation results.

The integration of both real-time systems and fault-tolerant systems has been emerged as one of the greatest challenges of this decade. It is called a responsive system, which has the objective to optimeze both timeliness and reliability. The performance measure in responsive systems is responsiveness that tells how probable a system executes correctly on time with faults occurred. While there have been some achievements in communication protocols and specification, we believe that scheduling problems in responsive systems are not understood deeply and sufficiently, yet. In this paper, we discuss the scheduling problem in responsive systems. At first, we investigate the issues in the scheduling and propose the precise definition of the responsiveness. We also suggest a scheduling algorithm called Responsive Earliest Deadline First (REDF) for preemptive aperiodic tasks in a uniprocessor system. We show that REDF is optimal to obtain the maximum responsiveness, and the time complexity is analyzed to be (N 2N). By illustrating a contradictory example, it is shown that REDF can be enhanced if a constraint on tasks is released.

In this paper, we propose the media scheduler employing an adaptive estimator, which uses a posteriori information of data traffic characteristics to facilitate scheduling, when available, to provide on-line scheduling of dynamic scene change based on its statistical characteristics. Especially, a new adaptive scheduling scheme showed good persistent to the arrival message with bursty characteristics. And we confirmed the performance through the computer simulation when QOS requirements are given.