Deadline Violation Probability Research Articles

Efficient Dependent Task Offloading for Multiple Applications in MEC-Cloud System

With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the MEC/Cloud becomes a dramatic technique due to the limited resources and high user experience requirements at mobile devices. However, most existing works design their task offloading schemes without considering the dependence of tasks and the orchestration of the MEC and Cloud, and thus may limit the system performance. In this paper, we propose a dependent task offloading framework for multiple mobile applications, named COFE, where mobile devices can offload their compute-intensive tasks with dependent constraints to the MEC-Cloud system. It can assign the offloaded tasks to the MEC and Cloud adaptively to improve the user experience. Based on COFE, we formulate the task offloading problem as an average makespan minimization problem, which is proved to be NP-hard. Then, we propose a heuristic ranking-based algorithm to assign the offloaded tasks according to their bottom levels. Theoretical analysis proves the stability of the system under the proposed algorithm and extensive simulations validate that the proposed algorithm can significantly reduce the average makespan and deadline violation probabilities of offloaded applications.

Online Multi-Workflow Scheduling under Uncertain Task Execution Time in IaaS Clouds

Cloud has become an important platform for executing numerous deadline-constrained scientific applications generally represented by workflow models. It provides scientists a simple and cost-efficient method of running workflows on their rental Virtual Machines (VMs) anytime and anywhere. Since pay-as-you-go is a dominating pricing solution in clouds, extensive research efforts have been devoted to minimizing the monetary cost of executing workflows by designing tailored VM allocation mechanisms. However, most of them assume that the task execution time in clouds is static and can be estimated in advance, which is impractical in real scenarios due to performance fluctuation of VMs. In this paper, we propose an onli N e multi-workfl O w S cheduling F ramework, named NOSF, to schedule deadline-constrained workflows with random arrivals and uncertain task execution time. In NOSF, workflow scheduling process consists of three phases, including workflow preprocessing, VM allocation and feedback process. Built upon the new framework, a deadline-aware heuristic algorithm is then developed to elastically provision suitable VMs for workflow execution, with the objective of minimizing the rental cost and improving resource utilization. Simulation results demonstrate that the proposed algorithm significantly outperforms two state-of-the-art algorithms in terms of reducing VM rental costs and deadline violation probability, as well as improving the resource utilization efficiency.

Task Replication for Deadline-Constrained Vehicular Cloud Computing: Optimal Policy, Performance Analysis, and Implications on Road Traffic

In vehicular cloud computing (VCC) systems, the computational resources of moving vehicles are exploited and managed by infrastructures, e.g., roadside units, to provide computational services. The offloading of computational tasks and collection of results rely on successful transmissions between vehicles and infrastructures during encounters. In this paper, we investigate how to provide timely computational services in VCC systems. In particular, we seek to minimize the deadline violation probability given a set of tasks to be executed in vehicular clouds. Due to the uncertainty of vehicle movements, the task replication methodology is leveraged which allows one task to be executed by several vehicles, and thus trading computational resources for delay reduction. The optimal task replication policy is of key interest. We first formulate the problem as a finite-horizon sampled-time Markov decision problem and obtain the optimal policy by value iterations. To conquer the complexity issue, we propose the balanced-task-assignment (BETA) policy which is proved optimal and has a clear structure: it always assigns the task with the minimum number of replicas. Moreover, a tight closed-form performance upper bound for the BETA policy is derived, which indicates that the deadline violation probability follows the Rayleigh distribution approximately. Applying the vehicle speed-density relationship in the traffic flow theory, we find that vehicle mobility benefits VCC systems more compared with road traffic systems, by showing that the optimum vehicle speed to minimize the deadline violation probability is larger than the critical vehicle speed in traffic theory which maximizes traffic flow efficiency.

Application-Level Scheduling With Probabilistic Deadline Constraints

Opportunistic scheduling of delay-tolerant traffic has been shown to substantially improve spectrum efficiency. To encourage users to adopt delay-tolerant scheduling for capacity -improvement, it is critical to provide guarantees in terms of completion time. In this paper, we study application-level scheduling with deadline constraints, where the deadline is pre-specified by users/applications and is associated with a deadline violation probability. To address the exponentially-high complexity due to temporally-varying channel conditions and deadline constraints, we develop a novel asymptotic approach that exploits the largeness of the network to our advantage. Specifically, we identify a lower bound on the deadline violation probability, and propose simple policies that achieve the lower bound in the large-system regime. The results in this paper thus provide a rigorous analytical framework to develop and analyze policies for application-level scheduling under very general settings of channel models and deadline requirements. Further, based on the asymptotic approach , we propose the notion of Application-Level Effective Capacity region, i.e., the throughput region that can be supported subject to deadline constraints, which allows us to quantify the potential gain of application-level scheduling. Simulation results show that application-level scheduling can improve the system capacity significantly while guaranteeing the deadline constraints.

Statistical admission control for real-time services under earliest deadline first scheduling

The design of call admission control (CAC) mechanisms has been a critical issue in providing quality-of-service (QoS) guarantees for heterogeneous traffic flows over integrated service (IntServ) and/or differentiated service (DiffServ) networks. Earliest deadline first (EDF) is an ideal scheduler for real-time services because of its optimal admissible region and delay bound properties. The major difficulty in developing an effective and efficient CAC algorithm for statistical services is the analysis of per-class deadline violation (loss) probabilities with respect to the delay bounds. In this paper, we provide an analytical approach to evaluate the aggregate and per-class deadline violation probabilities of an EDF scheduler. Based on these theoretical foundations, we derive the admission control conditions and then propose a CAC algorithm for statistic services under EDF scheduling. In addition, we show that the QoS metrics that an EDF scheduler actually guarantees have an asymptotic ordering property, which provides an important insight into the design and control of EDF networks. The effectiveness and performance of our proposed algorithm have been validated by trace-driven simulation experiments using MPEG and H.263 encoded video sources.

A statistical framework for EDF scheduling

Earliest deadline first (EDF) has become one of the most promising scheduling schemes for providing quality-of-service differentiation over high speed networks. In this letter, we study the deadline violation (loss) probability at an EDF scheduling switch. An analytical framework has been developed for estimating the loss probabilities for the aggregated traffic and the individual flows. This enables us to determine whether a given flow can meet its deadline with the required loss probability. As illustrated from the simulation results using real network traffic, the asymptotic approximations presented are accurate enough to predict the real metrics.

Scheduling algorithm for MPEG-2 TS multiplexers in CATV networks

We study the scheduling algorithm for multiplexers in CATV networks. First we propose the scheduling matrix concept, and then two kinds of scheduling algorithms are given, which are described with the scheduling matrix. Especially, we focus on the VBR scheduling algorithm being able to achieve a better display of the MPEG-2 program in CATV networks. The scheduler applying our VBR scheduling algorithm maintains, for each input stream, a counter to determine how many TS packets should be transmitted in the current scheduling cycle (one frame period) in order to prevent deadline violation. The real-time bit rate of each input stream is used to update the parameters of the scheduling matrix. Our VBR scheduling algorithm is compared numerically with the traditional CBR scheduling algorithm. The results of the simulation show that our VBR scheduling algorithm can achieve significantly lower delay, i.e., deadline violation probability, in comparison with the CBR scheduling algorithm. Also it is shown that a higher utilization of bandwidth can be achieved by means of our VBR scheduling algorithm than by the CBR scheduling algorithm.

Buffer losses vs. deadline violations for ABR traffic in an ATM switch: A computational approach

The B?ISDN will carry a variety of traffic types: the Variable Bit Rate traffic (VBR), of which compressed video is an example, Continuous Bit Rate traffic (CBR), of which telemetry is an example, Data traffic, and Available Bit Rate traffic (ABR) that represents aggregate data traffic with very limited guarantees on quality. Of these, VBR and CBR have timing constraints and need synchronous bandwidth; data traffic is relatively delay insensitive. In this paper, we consider the VBR, Data and ABR traffic types and obtain the cumulative distribution function (cdf) of the queueing delay experienced by a burst of ABR traffic in the output buffer of an ATM switch. The cdf is used to trade off buffer loss probabilities against deadline violation probabilities through adjusting the buffer size and (delay) deadline values. Large buffers result in low losses but queueing delays can become excessive and cause a high level of deadline violations. Both losses and violations are detrimental and an operating point must be chosen to achieve a balance. In this paper we study the nature of the trade off. We develop a stochastic Petri net model assuming periodic burst arrivals for VBR and Poisson arrival processes for the Data and ABR traffic types at the burst level, and solve the model analytically (numerically) using a decomposition approach. This decomposition, along with the inherent decomposability of the tagged customer approach for obtaining the cdf opens up a possibility of carrying out fast computations using a parallel machine for selecting the operating point each time that a call is admitted.