For multiple heterogeneous multicore server processors across clouds and data centers, the
aggregated performance of the cloud of clouds can be optimized by load distribution and …

The technique of using workload dependent dynamic power management (ie, variable
power and speed of processor cores according to the current workload) to improve system …

When multiple manycore processors in a data center for cloud computing are shared by a
large number parallel tasks simultaneously, we are facing the problem of allocating the …

Many of today's data centers experience physical limitations on the power needed to run the
data center. The first problem that we study is maximizing the performance (quantified by the …

We address scheduling independent and precedence constrained parallel tasks on multiple
homogeneous processors in a data center with dynamically variable voltage and speed as …

Cloud is the state-of-the-art back-end infrastructure for most large-scale web services. This
paper studies what effect energy proportionality has on the energy savings of cloud data …

As data centers are consuming massive amount of energy, improving the energy efficiency
of cloud computing has emerged as a focus of research. However, it is challenging to reduce …

In this paper, we introduce a model of task scheduling for a cloud-computing data center to
analyze energy-efficient task scheduling. We formulate the assignments of tasks to servers …

Server power and cooling power amount to a significant fraction of modern data centers'
recurring costs. While data centers provision enough servers to guarantee response times …

This paper focuses on power minimization in a data center accounting for both the
information technology equipment and the air conditioning power usage. In particular we …