Adaptive Dispatching of Tasks in the Cloud

Lan Wang,Erol Gelenbe

doi:10.1109/tcc.2015.2474406

Abstract

The increasingly wide application of Cloud Computing enables the consolidation of tens of thousands of applications in shared infrastructures. Thus, meeting the quality of service requirements of so many diverse applications in such shared resource environments has become a real challenge, especially since the characteristics and workload of applications differ widely and may change over time. This paper presents an experimental system that can exploit a variety of online quality of service aware adaptive task allocation schemes, and three such schemes are designed and compared. These are a measurement driven algorithm that uses reinforcement learning, secondly a "sensible" allocation algorithm that assigns jobs to sub-systems that are observed to provide a lower response time, and then an algorithm that splits the job arrival stream into sub-streams at rates computed from the hosts' processing capabilities. All of these schemes are compared via measurements among themselves and with a simple round-robin scheduler, on two experimental test-beds with homogeneous and heterogeneous hosts having different processing capacities.

Highlights

C LOUD computing enables elasticity and scalability of computing resources such as networks, servers, storage, applications, and services, which constitute a shared pool, providing on-demand services at the level of infrastructure, platform and software [1]
The Sensible Decision Algorithm (e) uses a weighted average of Gi of the goal function that we wish to minimise, which is estimated from on-going measurements at each host i, and updated each time t that Task Allocation Platform (TAP) receives a measurement that can be used to update the goal function
In this paper we have first reviewed the area of task allocation to Cloud servers, and presented TAP, an Standard deviation of job response times

Summary

Introduction

C LOUD computing enables elasticity and scalability of computing resources such as networks, servers, storage, applications, and services, which constitute a shared pool, providing on-demand services at the level of infrastructure, platform and software [1]. This makes it realistic to deliver computing services in a manner similar to utilities such as water and electricity where service providers take the responsibility of constructing IT infrastructure and endusers make use of the services through the Internet in a pay-as-you-go manner.

Methods

Results

Conclusion