Scheduling independent stochastic tasks on heterogeneous cloud platforms

Abstract

This work introduces scheduling strategies to maximize the expected number of independent tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The cloud platform is composed of several types of virtual machines (VMs), where each type has a unit execution cost that depends upon its characteristics. The amount of budget spent during the execution of a task on a given VM is the product of its execution length by the unit execution cost of that VM. The execution lengths of tasks follow a variety of standard probability distributions (exponential, uniform, half-normal, lognormal, gamma, inverse-gamma and Weibull) whose mean and standard deviation both depend upon the VM type. Finally, there is a global available budget and a deadline constraint, and the goal is to successfully execute as many tasks as possible before the deadline is reached or the budget is exhausted (whichever comes first). On each VM, the scheduler can decide at any instant to interrupt the execution of a (long) running task and to launch a new one, but the budget already spent for the interrupted task is lost. The main questions are which VMs to enroll, and whether and when to interrupt tasks that have been executing for some time. We assess the complexity of the problem by showing its NP-completeness and providing a 2-approximation for the asymptotic case where budget and deadline both tend to infinity. Then we introduce several heuristics and compare their performance by running an extensive set of simulations.

Resource-Constrained Scheduling Algorithms for Stochastic Independent Tasks With Unknown Probability Distribution

“…Furthermore, the present study appears to be unique because it uses a fully non-clairvoyant framework and assumes an overall deadline in addition to a budget constraint. Our previous works [5,14] had the same setting under homogeneous [5] or heterogeneous [14] platforms. But in these works, we assumed that the distribution of execution times was known in advance, while the key problem studied in the current paper is to learn the distribution of task execution times on the fly and to decide when interrupting unfinished tasks.…”

An Optimal Virtual Machine Placement Method in Cloud Computing Environment

“…Resource provisioning is provided for accessing the tasks with execution time. The stochastic-based scheme uses multi-objective scheduling criteria for making energy-efficient in a cloud [20]. A stochastic approach is used for energy and cost minimization purposes.…”