scholarly journals MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Rahul Yadav ◽  
Weizhe Zhang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Large Scale ◽  
Data Centers ◽  
Mobile Cloud Computing ◽  
Virtual Machines ◽  
Mobile Cloud ◽  
Energy Aware ◽  
Cpu Utilization ◽  
Selection Algorithms

Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.

scholarly journals Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization

2016 ◽  
Vol 6 (6) ◽  
pp. 3283 ◽  
Author(s):  
Mahendra Kumar Gourisaria ◽  
S. S. Patra ◽  
P. M. Khilar
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Carbon Dioxide Emission ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Cpu Utilization

<p>Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy.  Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption.</p>

Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization

2016 ◽  
Vol 6 (6) ◽  
pp. 3283
Author(s):  
Mahendra Kumar Gourisaria ◽  
S. S. Patra ◽  
P. M. Khilar
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Carbon Dioxide Emission ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Cpu Utilization

<p>Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy.  Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption.</p>

scholarly journals Virtual Machine Selection Algorithm Based on User Requirements in Mobile Cloud Computing Environment

2018 ◽  
Vol 17 (2) ◽  
pp. 7335-7349
Author(s):  
Rashid Alakbarov
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Mobile Cloud Computing ◽  
Virtual Machines ◽  
Mobile User ◽  
Base Stations ◽  
Mobile Cloud ◽  
Software Applications ◽  
Wireless Metropolitan Area Networks ◽  
Cloud Technologies

The article analyzes the advantages of mobile cloud technologies and problems emerging during the use of those. The network infrastructure created based on cloudlets at the second level of mobile cloud computing with hierarchical structure is analyzed. At the same time, the article explores the issues of satisfaction of demand of mobile equipment for computing and memory resources by using these technologies. The article presents one solution for the allocation of mobile user requests in virtual machines created in cloudlets located near base stations of wireless metropolitan area networks (WMAN) in a balanced way by considering the technical capacity of those. Alongside, the article considers the solution of user problem during designated time and the issue of determining virtual machines satisfying other requirements. For this purpose, different characteristics of the stated problem, virtual machines, as well as communication channels between a user and virtual machines are considered. By using possible values determining the importance of cloudlets, conditions for loading software applications of a user to a virtual machine are explored and an appropriate method is proposed.

Energy-Efficiency in Cloud Data Centers

2013 ◽  
pp. 241-263
Author(s):  
Burak Kantarci ◽  
Hussein T. Mouftah
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Large Scale ◽  
Data Centers ◽  
High Energy ◽  
It Services ◽  
Data Center Network ◽  
Cloud Data

Cloud computing aims to migrate IT services to distant data centers in order to reduce the dependency of the services on the limited local resources. Cloud computing provides access to distant computing resources via Web services while the end user is not aware of how the IT infrastructure is managed. Besides the novelties and advantages of cloud computing, deployment of a large number of servers and data centers introduces the challenge of high energy consumption. Additionally, transportation of IT services over the Internet backbone accumulates the energy consumption problem of the backbone infrastructure. In this chapter, the authors cover energy-efficient cloud computing studies in the data center involving various aspects such as: reduction of processing, storage, and data center network-related power consumption. They first provide a brief overview of the existing approaches on cool data centers that can be mainly grouped as studies on virtualization techniques, energy-efficient data center network design schemes, and studies that monitor the data center thermal activity by Wireless Sensor Networks (WSNs). The authors also present solutions that aim to reduce energy consumption in data centers by considering the communications aspects over the backbone of large-scale cloud systems.

Mobile Cloud Computing

2015 ◽  
pp. 1-23
Author(s):  
Jyoti Grover ◽  
Gaurav Kheterpal
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Applications ◽  
Large Scale ◽  
Mobile Cloud Computing ◽  
Research Area ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Mobile Environment

Mobile Cloud Computing (MCC) has become an important research area due to rapid growth of mobile applications and emergence of cloud computing. MCC refers to integration of cloud computing into a mobile environment. Cloud providers (e.g. Google, Amazon, and Salesforce) support mobile users by providing the required infrastructure (e.g. servers, networks, and storage), platforms, and software. Mobile devices are rapidly becoming a fundamental part of human lives and these enable users to access various mobile applications through remote servers using wireless networks. Traditional mobile device-based computing, data storage, and large-scale information processing is transferred to “cloud,” and therefore, requirement of mobile devices with high computing capability and resources are reduced. This chapter provides a survey of MCC including its definition, architecture, and applications. The authors discuss the issues in MCC, existing solutions, and approaches. They also touch upon the computation offloading mechanism for MCC.

Service Level Agreement Conformance in Virtual Machine Migration with an Improved Energy and Performance Management Method

2020 ◽  
Vol 17 (9) ◽  
pp. 3904-3906
Author(s):  
Susmita J. A. Nair ◽  
T. R. Gopalakrishnan Nair
Keyword(s):  
Energy Consumption ◽  
Performance Management ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Performance Degradation ◽  
Virtual Machine Migration ◽  
And Performance

Increasing demand of computing resources and the popularity of cloud computing have led the organizations to establish of large-scale data centers. To handle varying workloads, allocating resources to Virtual Machines, placing the VMs in the most suitable physical machine at data centers without violating the Service Level Agreement remains a big challenge for the cloud providers. The energy consumption and performance degradation are the prime focus for the data centers in providing services by strictly following the SLA. In this paper we are suggesting a model for minimizing the energy consumption and performance degradation without violating SLA. The experiments conducted have shown a reduction in SLA violation by nearly 10%.

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