4,073 research outputs found
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
Internet of Things Cloud: Architecture and Implementation
The Internet of Things (IoT), which enables common objects to be intelligent
and interactive, is considered the next evolution of the Internet. Its
pervasiveness and abilities to collect and analyze data which can be converted
into information have motivated a plethora of IoT applications. For the
successful deployment and management of these applications, cloud computing
techniques are indispensable since they provide high computational capabilities
as well as large storage capacity. This paper aims at providing insights about
the architecture, implementation and performance of the IoT cloud. Several
potential application scenarios of IoT cloud are studied, and an architecture
is discussed regarding the functionality of each component. Moreover, the
implementation details of the IoT cloud are presented along with the services
that it offers. The main contributions of this paper lie in the combination of
the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport
(MQTT) servers to offer IoT services in the architecture of the IoT cloud with
various techniques to guarantee high performance. Finally, experimental results
are given in order to demonstrate the service capabilities of the IoT cloud
under certain conditions.Comment: 19pages, 4figures, IEEE Communications Magazin
Load Balancing in Distributed Cloud Computing: A Reinforcement Learning Algorithms in Heterogeneous Environment
Balancing load in cloud based is an important aspect that plays a vital role in order to achieve sharing of load between different types of resources such as virtual machines that lay on servers, storage in the form of hard drives and servers. Reinforcement learning approaches can be adopted with cloud computing to achieve quality of service factors such as minimized cost and response time, increased throughput, fault tolerance and utilization of all available resources in the network, thus increasing system performance. Reinforcement Learning based approaches result in making effective resource utilization by selecting the best suitable processor for task execution with minimum makespan. Since in the earlier related work done on sharing of load, there are limited reinforcement learning based approaches. However this paper, focuses on the importance of RL based approaches for achieving balanced load in the area of distributed cloud computing. A Reinforcement Learning framework is proposed and implemented for execution of tasks in heterogeneous environments, particularly, Least Load Balancing (LLB) and Booster Reinforcement Controller (BRC) Load Balancing. With the help of reinforcement learning approaches an optimal result is achieved for load sharing and task allocation. In this RL based framework processor workload is taken as an input. In this paper, the results of proposed RL based approaches have been evaluated for cost and makespan and are compared with existing load balancing techniques for task execution and resource utilization.
Efficient memory management in video on demand servers
In this article we present, analyse and evaluate a new memory management technique for video-on-demand servers. Our proposal,
Memory Reservation Per Storage Device (MRPSD), relies on the allocation of a fixed, small number of memory buffers per storage device.
Selecting adequate scheduling algorithms, information storage strategies and admission control mechanisms, we demonstrate that MRPSD is suited for the deterministic service of variable bit rate streams to intolerant clients. MRPSD allows large memory savings compared to traditional memory management techniques, based on the allocation of a certain amount of memory per client served, without a significant performance penaltyPublicad
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