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

Integrated Green Cloud Computing Architecture

Arbitrary usage of cloud computing, either private or public, can lead to uneconomical energy consumption in data processing, storage and communication. Hence, green cloud computing solutions aim not only to save energy but also reduce operational costs and carbon footprints on the environment. In this paper, an Integrated Green Cloud Architecture (IGCA) is proposed that comprises of a client-oriented Green Cloud Middleware to assist managers in better overseeing and configuring their overall access to cloud services in the greenest or most energy-efficient way. Decision making, whether to use local machine processing, private or public clouds, is smartly handled by the middleware using predefined system specifications such as service level agreement (SLA), Quality of service (QoS), equipment specifications and job description provided by IT department. Analytical model is used to show the feasibility to achieve efficient energy consumption while choosing between local, private and public Cloud service provider (CSP).Comment: 6 pages, International Conference on Advanced Computer Science Applications and Technologies, ACSAT 201

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

A Literature Survey on Resource Management Techniques, Issues and Challenges in Cloud Computing

Cloud computing is a large scale distributed computing which provides on demand services for clients. Cloud Clients use web browsers, mobile apps, thin clients, or terminal emulators to request and control their cloud resources at any time and anywhere through the network. As many companies are shifting their data to cloud and as many people are being aware of the advantages of storing data to cloud, there is increasing number of cloud computing infrastructure and large amount of data which lead to the complexity management for cloud providers. We surveyed the state-of-the-art resource management techniques for IaaS (infrastructure as a service) in cloud computing. Then we put forward different major issues in the deployment of the cloud infrastructure in order to avoid poor service delivery in cloud computing

A Review on Various Energy Efficient Techniques in Cloud Environment

Cloud computing is web based mostly development and use of engineering. it is a mode of computing within which dynamically scalable and sometimes virtualized resources are provided as a service over the web. Users needn't have data of, experience in, or management over the technology infrastructure "in the cloud" that supports them. programming is one of the core steps to with efficiency exploit the capabilities of heterogeneous computing systems. On cloud computing platform, load equalisation of the whole system will be dynamically handled by using virtualization technology through that it becomes potential to remap virtual machine and physical resources in step with the modification in load. However, so as to boost performance, the virtual machines ought to totally utilize its resources and services by adapting to computing setting dynamically. The load balancing with correct allocation of resources should be bonded so as to boost resource utility and energy efficiency

A Survey on Resource Allocation Techniques in Cloud Computing

Cloud is an important and emerging technology utilized by various fields for storing, processing and retrieving of data anywhere and anytime without any interruption. Cloud is now acting as a platform for many companies for storing and other computational purposes to reduce infrastructure and maintenance cost similarly they can utilize their application widely based on pay per use. To make available of data to all cloud users Resource Allocation (RA) is mandatory process. In cloud hardware, software and platform are the resources utilized to satisfy user needs hence sharing these resources according to users need is a difficult task. Cloud service provider and cloud service consumer plays the major role in RA. The parameters under resource allocation, its issues and challenges are needed to be analyzed deeply before implementing any optimizing approach in RA. Hence in this work various resource allocation methods have been studied and issues in it is analyzed and presented as a survey. This work is useful for both cloud users and researchers in overcoming the challenges faced in RA

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

EQUAL: Energy and QoS Aware Resource Allocation Approach for Clouds

The popularity of cloud computing is increasing by leaps and bounds. To cope with resource demands of increasing number of cloud users, the cloud market players establish large sized data centers. The huge energy consumption by the data centers and liability of fulfilling Quality of Service (QoS) requirements of the end users have made resource allocation a challenging task. In this paper, energy and QoS aware resource allocation approach which employs Antlion optimization for allocation of resources to virtual machines (VMs) is proposed. It can operate in three modes, namely power aware, performance aware, and balanced mode. The proposed approach enhances energy efficiency of the cloud infrastructure by improving the utilization of resources while fulfilling QoS requirements of the end users. The proposed approach is implemented in CloudSim. The simulation results have shown improvement in QoS and energy efficiency of the cloud

A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.Comment: arXiv admin note: text overlap with arXiv:1006.0308 by other author