53,963 research outputs found
A Review on Software Architectures for Heterogeneous Platforms
The increasing demands for computing performance have been a reality
regardless of the requirements for smaller and more energy efficient devices.
Throughout the years, the strategy adopted by industry was to increase the
robustness of a single processor by increasing its clock frequency and mounting
more transistors so more calculations could be executed. However, it is known
that the physical limits of such processors are being reached, and one way to
fulfill such increasing computing demands has been to adopt a strategy based on
heterogeneous computing, i.e., using a heterogeneous platform containing more
than one type of processor. This way, different types of tasks can be executed
by processors that are specialized in them. Heterogeneous computing, however,
poses a number of challenges to software engineering, especially in the
architecture and deployment phases. In this paper, we conduct an empirical
study that aims at discovering the state-of-the-art in software architecture
for heterogeneous computing, with focus on deployment. We conduct a systematic
mapping study that retrieved 28 studies, which were critically assessed to
obtain an overview of the research field. We identified gaps and trends that
can be used by both researchers and practitioners as guides to further
investigate the topic
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
On Distributed Storage Allocations for Memory-Limited Systems
In this paper we consider distributed allocation problems with memory
constraint limits. Firstly, we propose a tractable relaxation to the problem of
optimal symmetric allocations from [1]. The approximated problem is based on
the Q-error function, and its solution approaches the solution of the initial
problem, as the number of storage nodes in the network grows. Secondly,
exploiting this relaxation, we are able to formulate and to solve the problem
for storage allocations for memory-limited DSS storing and arbitrary memory
profiles. Finally, we discuss the extension to the case of multiple data
objects, stored in the DSS.Comment: Submitted to IEEE GLOBECOM'1
Efficient Resource Matching in Heterogeneous Grid Using Resource Vector
In this paper, a method for efficient scheduling to obtain optimum job
throughput in a distributed campus grid environment is presented; Traditional
job schedulers determine job scheduling using user and job resource attributes.
User attributes are related to current usage, historical usage, user priority
and project access. Job resource attributes mainly comprise of soft
requirements (compilers, libraries) and hard requirements like memory, storage
and interconnect. A job scheduler dispatches jobs to a resource if a job's hard
and soft requirements are met by a resource. In current scenario during
execution of a job, if a resource becomes unavailable, schedulers are presented
with limited options, namely re-queuing job or migrating job to a different
resource. Both options are expensive in terms of data and compute time. These
situations can be avoided, if the often ignored factor, availability time of a
resource in a grid environment is considered. We propose resource rank
approach, in which jobs are dispatched to a resource which has the highest rank
among all resources that match the job's requirement. The results show that our
approach can increase throughput of many serial / monolithic jobs.Comment: 10 page
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