Scalable Analytic Models for Performance Efficiency in the Cloud

Abstract

This paper presents a scalable model-driven approach to quantify the availability of resources and optimal distribution of tasks over these resources, such that the average response time of tasks is minimized. To reduce the complexity of analysis and solution time, we use an integrated stochastic based approach. To achieve this, first we use clustering algorithm to group the tasks into distinct classes with similar characteristics in terms of resource and performance requirements. Second, we quantify the resource availability of cloud center among three states: active (running), idle (turned on, but not ready), and off (turned off). Third, we develop a queuing model for multiple heterogeneous multicore servers, and formulate and solve the optimal load distribution of tasks for multiple heterogeneous multicore servers in a cloud computing data centers. We derive equations that permit us to find optimal load distribution of tasks that their average response time is minimized. We obtain not only detailed assessment of cloud center performance, but also insights into equilibrium arrangement, capacity planning and power consumption to be kept under control.