Uncertain Task Execution Research Articles

Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

A fuzzy cloud resource scheduling model with time-cost constraints is built using fuzzy triangular numbers to represent uncertain task execution time. Task scheduling reduces total time and cost spent on a project. It connects virtual machines and functions. Particle swarm optimization (HPO) is used to plan cloud resources (HSOA). The approach uses orthogonal particle swarm initialization to increase the quality of the initial particle exploration, rerandomization to regulate the particle search range, and real-time updating of inertia weights to control particle speed. The suggested problem model and optimization approach are evaluated using random simulation data provided by the CloudSim simulation platform. Less overall execution time and a lower cost are shown to have fast convergence and solution capabilities in experiments.

Uncertainty-Aware Online Scheduling for Real-Time Workflows in Cloud Service Environment

Scheduling workflows in cloud service environment has attracted great enthusiasm, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the scheduling environment, such as the uncertain task start/execution/finish time, the uncertain data transfer time among tasks, the sudden arrival of new workflows. Ignoring these uncertain factors often leads to the violation of workflow deadlines and increases service renting costs of executing workflows. This study devotes to improving the performance for cloud service platforms by minimizing uncertainty propagation in scheduling workflow applications that have both uncertain task execution time and data transfer time. To be specific, a novel scheduling architecture is designed to control the count of workflow tasks directly waiting on each service instance (e.g., virtual machine and container). Once a task is completed, its start/execution/finish time are available, which means its uncertainties disappearing, and will not affect the subsequent waiting tasks on the same service instance. Thus, controlling the count of waiting tasks on service instances can prohibit the propagation of uncertainties. Based on this architecture, we develop an unceRtainty-aware Online Scheduling Algorithm (ROSA) to schedule dynamic and multiple workflows with deadlines. The proposed ROSA skillfully integrates both the proactive and reactive strategies. During the execution of the generated baseline schedules, the reactive strategy in ROSA will be dynamically called to produce new proactive baseline schedules for dealing with uncertainties. Then, on the basis of real-world workflow traces, five groups of simulation experiments are carried out to compare ROSA with five typical algorithms. The comparison results reveal that ROSA performs better than the five compared algorithms with respect to costs (up to 56 percent), deviation (up to 70 percent), resource utilization (up to 37 percent), and fairness (up to 37 percent).

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.

An optimal analytical solution for maximizing expected battery lifetime using the calculus of variations

The exponential growth in the semiconductor industry and hence the increase in chip complexity, has led to more power usage and power density in modern processors. On the other hand, most of today's embedded systems are battery-powered, so the power consumption is one of the most critical criteria in these systems. Dynamic Voltage and Frequency Scaling (DVFS) is known as one of the most effective energy-saving methods. In this paper, we propose the optimal DVFS profile to minimize the energy consumption of a battery-based system with uncertain task execution time under deadline constraints using the Calculus of Variations (CoV). The contribution of this work is to analytically calculate the lower bound of expected battery charge consumption for a given task with uncertain execution time. Most of the research in dynamic voltage and frequency scaling tends to discretize time and value factors. This is presumably because of the context of embedded systems which is mainly based on digital design and algorithms. However, important factors in power and energy, such as supply voltage, supply current, and operational frequency, are continuous functions of time. The CoV is a branch of mathematics, where system parameters are considered as continuous functions of time. So, for dealing with this kind of problems, which system parameters are continuous functions of time, we can use the CoV as a powerful way to solve continuous optimization problems. In this paper, we obtain the exact analytical solution for maximizing battery lifetime, which is applicable to any convex power model.

I- Rosa, Improved Uncertainty Aware Workflow Scheduling Algorithm

Many applications running in cloud computing environment are workflow applications which contain large number of precedence specific tasks and so require proper schedule in order to complete successfully. Efficient scheduling of workflow applications is a challenging task. In workflows, the uncertainties like ‘uncertain data transfer time’ among dependent tasks and the uncertain task execution time, if ignored may lead to deadline violation. The proposed workflow scheduling algorithms so far unconcerned these uncertainties. This paper presents an improved unceRtainty aware workflow scheduling algorithm abbreviated as i-ROSA, that considers the uncertainties of scheduling workflows such as the uncertain running time of tasks in distributed environment when focused upon gave the superior outcome in way of cost and resource utilization, for DAG when compared with the original algorithm. The compared task scheduling algorithms are implemented in Workflowsim.

Scheduling of computational processes in real-time distributed systems with uncertain task execution times

An approach to scheduling computational processes in real-time distributed computing systems is considered. It is assumed that the task execution time is inexactly; more precisely, it is assumed to belog to a certain time interval. The problem is formulated as the scheduling of jobs of which each is characterized by its priority and consists of a set of tasks (with respect to the number of processors) executing on different processors and associated by a hierarchical precedence relationship. The proposed approach is based on algorithms with low computational complexity for suboptimal scheduling of equal-priority tasks.

Dynamic Voltage Scaling for Multitasking Real-Time Systems With Uncertain Execution Time

Dynamic voltage and frequency scaling can save energy for real-time systems. Frequencies are generally assumed proportional to voltages. Previous studies consider the probabilistic distributions of tasks' execution time to assist dynamic voltage scaling in task scheduling. These studies use probability information for intratask voltage scheduling but do not sufficiently explore the opportunities for intertask scheduling to save more energy. This paper presents a new approach to combine intra- and intertask voltage scheduling for better energy savings in hard real-time systems with uncertain task execution time. Our approach takes three steps: 1) We calculate statistically the optimal voltage schedules for multiple concurrent tasks, using earliest deadline first scheduling for an ideal processor that can change the frequency continuously; 2) we then adapt the solution to a processor with a limited range of discrete frequencies, using a polynomial-time heuristic algorithm; and 3) finally, we improve our solution, considering the time and energy overheads of frequency switching for schedulability and energy reduction. Our simulation shows that the new approach can save more energy than existing solutions while meeting hard deadlines.