Abstract
In cloud computing, a large-scale parallel-distributed processing service is provided where a huge task is split into a number of subtasks and those subtasks are processed on a cluster of machines called workers. In such a processing service, a worker which takes a long time for processing a subtask makes the response time long (the issue of stragglers). One of efficient methods to alleviate this issue is to execute the same subtask by another worker in preparation for the slow worker (backup tasks). In this paper, we consider the efficiency of backup tasks. We model the task-scheduling server as a single-server queue, in which the server consists of a number of workers. When a task enters the server, the task is split into subtasks, and each subtask is served by its own worker and an alternative distinct worker. In this processing, we explicitly derive task processing time distributions for the two cases that the subtask processing time of a worker obeys Weibull or Pareto distribution. We compare the mean response time and the total processing time under backup-task scheduling with those under normal scheduling. Numerical examples show that the efficiency of backup-task scheduling significantly depends on workers' processing time distribution.
Highlights
IntroductionCloud computing has attracted considerable attention due to the availability of huge computing resources and its significant cost efficiency
Cloud computing has attracted considerable attention due to the availability of huge computing resources and its significant cost efficiency.In [1], cloud computing is defined as the sum of the existing concepts, software as a service (SaaS) and utility computing
Β = 4.702 β = 3.159 β = 2.414 β = 2.095 β = 2.007. These results suggest that the coefficient of variation of the worker processing time distribution significantly affects the response time performance of the largescale parallel-distributed processing
Summary
Cloud computing has attracted considerable attention due to the availability of huge computing resources and its significant cost efficiency. Focusing on the percentile of the response time as a performance measure of cloud computing, they approximately analyze the response time distribution In their model, the service-center part is modeled as a single-server queue with a fixed service rate, and this model is too simple to describe a large-scale parallel-distributed processing of a task. We consider the efficiency of backup-task scheduling on two performance measures: the response time of a task and the total processing time of workers. On the other hand, each subtask is processed by its own worker and by an alternative distinct worker, and the subtask service ends when either of the two workers’ processes is completed In both scheduling policies, we explicitly derive task processing time distributions when the subtask processing time of a worker follows Weibull or Pareto distribution. The assumption of subtasks of an equal size becomes accurate when a huge-sized input data is almost split into data pieces [4]
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