Green Cloud Research Articles

Energy-Efficient Coordinated Multipoint Scheduling in Green Cloud Radio Access Network

The fast development of mobile computing has raised ever-increasing diverse communication needs in wireless networks. To catch up with such needs, cloud radio access networks (CRAN) is proposed to enable efficient radio resource sharing and management. At the same time, the massive deployment of radio access networks has caused huge energy consumption. Incorporating renewable green energy to lower the brown energy consumption also has become a widely concerned topic. In this paper, we are motivated to investigate a green energy aware remote radio head activation problem for coordinated multipoint communications in green energy powered CRAN, aiming at minimizing the network brown energy power consumption. The problem is first formulated into a nonconvex optimization form. By analyzing the characteristics of the formulation, we further propose a heuristic algorithm based on an ordered selection method. Extensive simulation based experiment results show that the proposed green energy aware algorithm provides an effective way to reduce brown energy power consumption, well fitting the goal of developing green communications.

Smart Configuration and Auto Allocation of Resource in Cloud Data Centers

Cloud computing is one of emerging computing models that has many advantages. The IT industry is keenly aware of the need for Green Cloud computing solutions that save energy for the environment as well as reduce operational costs. This article presents a new green Cloud Computing framework based on multi agent systems for optimizing resource allocation in data centers (DCs). Our framework based on a new cloud computing architecture that benefits from the combination of the Cloud and agent technologies. DCs hosting Cloud applications need energy-aware resource allocation mechanisms that minimize energy costs and other operational costs. This article offers a logical solution to manage physical and virtual resources in smarter data center.

A Review on Fault Tolerance Techniques and Algorithms in Green Cloud Computing

A Review on Fault Tolerance Techniques and Algorithms in Green Cloud Computing

Energy Efficient Dynamic Tasks Oriented Scheduling in Green Control Cloud *

Energy Efficient Dynamic Tasks Oriented Scheduling in Green Control Cloud *

An incentive to share resource-based crowdfunding and repeated game in green cloud computing

An incentive to share resource-based crowdfunding and repeated game in green cloud computing

Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment

Energy consumption in cloud data centers is increasing as the use of such services increases. It is necessary to propose new methods of decreasing energy consumption. Green cloud computing helps to reduce energy consumption and significantly decreases both operating costs and greenhouse gas emissions. Scheduling the enormous number of user-submitted workflow tasks is an important aspect of cloud computing. Resources in cloud data centers should compute these tasks using energy efficient techniques. This paper proposed a new energy-aware scheduling algorithm for time-constrained workflow tasks using the DVFS method in which the host reduces the operating frequency using different voltage levels. The goal of this research is to reduce energy consumption and SLA violations and improve resource utilization. The simulation results show that the proposed method performs more efficiently when evaluating metrics such as energy utilization, average execution time, average resource utilization and average SLA violation.

Energy-Aware Cloud Computing Simulators: A State of the Art Survey

Cloud computing is an emerging technology that offers pay-per-use IT services around the world. The daily increase in Cloud infrastructures and modern IT demands (such as web applications, scientific, and business) results in large-scale data centers that lead to additional electricity consumption. The high energy consumption causes a high operating cost and involves a large amount of carbon dioxide emissions that are harmful to the atmosphere. Many academic, governmental, and industrial studies show that energy consumed by computers and different communication units in a data center plays a significant role in the increase of data center operating costs. It is difficult to calculate the performance, security problems, and energy consumption in a true Cloud computing infrastructure. Therefore, in recent years, several simulation tools have been introduced to help researchers for the analysis of a Cloud computing environment. Cloud simulators need to perform simulation tests to reduce the various complexities in Cloud computing environments. Some Cloud simulators have been specially created to test the performance of Cloud computing environments. This study presents the comparative analysis of the state-of-the-art energy aware Cloud simulators. For comparison, several characteristics, functions, and models are employed for most famous energy aware Cloud simulators such as Green Cloud, CloudSim, iCanCloud, Cloud Analyst, Network CloudSim, and CloudReport.

A Review on Energy Efficient techniques in Green cloud Open Research Challenges and Issues

A Review on Energy Efficient techniques in Green cloud Open Research Challenges and Issues

A multi-resource task scheduling algorithm for energy-performance trade-offs in green clouds

Previous studies on task scheduling for cloud computing mostly focused on either energy saving or upgrading performance only. However, with the development of cloud computing, users’ demand becomes more and more diversified and difficult to meet with some previous studies’ ideas. In this paper, we first introduce two good-quality algorithms: a time-aware algorithm and an energy-aware algorithm, which are designed for task scheduling in a heterogeneous environment. Secondly, based on the two algorithms, we propose to combine them and design a new algorithm named Energy-Performance Trade-Off Multi-Resource Cloud Task Scheduling Algorithm (namely ETMCTSA). Users can flexibly manage and control the energy and performance of a cloud system via tuning the probability parameterαof the algorithm. Thus, it realizes the goal of focusing on reducing energy consumption or saving time according to users’ will. We conducted the simulated experiments based on MultiRECloudSim and evaluated ETMCTSA with several other task scheduling algorithms. The experimental results indicate that with a pre-specified α, users can tune ETMCTSA inclining to be more energy efficient or more time efficient.

Improving Energy Efficiency of Virtual Resource Allocation In Cloud Datacenter

Objective: To minimize the energy frenzied around cloud datacenters. The proposed system provides a dynamic and continuous load prediction along with existing heuristic load prediction to improve the adaptation performance for green computing through Markov chaining of resources. Methodology: The trouble of energy competence through resource management for a large-scale cloud environment to facilitate hosts sites is resolute here. In apply there will be number of underutilized servers in the datacenter which guzzle bulky part of established energy. To minimize the energy frenzied around datacenters the existing system outlines a distributed middleware architecture and presenting one of its key elements, a gossip protocol, Skewness algorithm and load balancing that meets our design goals: justice of resource allocation with respect to hosted sites through overload escaping and efficient edition to load changes and scalability in terms of together the numeral of machines and sites thereby turning off the unused servers for green computing through Markov Chaining Model. This model is scalable enough to signify systems composed of thousands of resources and it makes potential to represent both physical and virtual wealth exploiting cloud explicit concepts such as the infrastructure elasticity. Results: To certify the model, simulation is conducted within the Network Simulator (NS) 2.28 have platform with GCC 4.3 and Fedora 13. The load prediction algorithm and Markov chain model achieves both overload avoidance and green computing for systems with multiresource limitations. Application: Green Wireless Network: To improve the energy efficient, data retrieval and resource service based on coaching, computing and networking in the area of Green Wireless. Green Big Data: To reduce the power consumption, big data merge the concept with green computing. Green job Scheduling: Each Server has different jobs, to save the energy server merged with green computing. Green Cloud data center: To use the green computing in cloud data center to increases the energy efficiency. Keywords: Green Computing, Load Prediction, Markov Chaining, Virtualization

CCN: A novel energy efficient greedy routing protocol for green computing

SummaryWe proposed a novel Energy Efficient Greedy Routing Protocol (EEGRP) in CCN for MANETs to achieve the gaps in the existing infrastructure of Green Cloud Computing. Initially, the consumer node broadcasts the interest packet; each relay node verifies if it is already satisfied, and then it drops the packet. Otherwise, the provider node selects shortest path to unicast data packet within minimum delay. Each relay node receives a data packet, stores its content in content store, and forwards it to next node according to path mention in the hop‐count field. If the data packet custodian node does not find path mention in the hop‐count field, then it searches the routing table entries to choose the second best path to unicast the data packet. EEGRP is implemented in three scenarios using ns2 and compare its performance with AIRDrop routing protocol on bases of three performance parameters like packet delivery ratio, delay, and energy consumption. EEGRP achieves high PDR with minimum delay and less energy consumption as compared to AIRDrop. Therefore, EEGRP is feasible to use on green computing and cloud technology. This can save energy issue to secure energy for Green Computing and Cloud before moving to full‐scale virtualization and Cloud services.

Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing

Cloud computing offers utility-based IT services on-demand to the users on a pay-per-use-basis. The cloud centers consist of physical machines (PMs) with virtual machines (VMs). These data centers consume a large amount of energy due to the improper resource utilization and lack of efficient schedu ling algorithms to perform the task-resource mapping. These issues lead to huge energy consumption along with high maintenance costs and carbon emissions. In this paper, a Power Efficient Scheduling and VM Consolidation (PESVMC) algorithm is proposed to address these issues and the associated challenges. The numerous existing research works concentrated on the application of energy management techniques to hardware level support for the reduction of energy consumption. The proposed algorithm emphasizes on the software level by taking the flexibility of the virtualization technology and it consists of two phases, VM Scheduling phase, and VM Consolidation phase. In the Scheduling phase, the tasks with maximum runtime are allocated to VM, which is expected to consume minimal energy. In the VM Consolidation phase, overloaded and underloaded hosts are determined based on the double-threshold scheme. Further, Live Migration technique is applied for migrating the VMs from over-utilized or underutilized hosts to other hosts with the normal utilization. A power efficient utilization factor is introduced to determine the underloaded hosts. This utilization factor is proven to reduce the number of migrations, which can cause additional energy consumption. Energy efficient Scheduling combined with VM Consolidation is successful in maximizing the resource utilization and minimizing the energy consumption. The experimental evaluation is performed using WorkflowSim and the proposed algorithm achieves significant energy conservation and resource utilization.

SAVM: A practical secure external approach for automated in‐VM management

SummaryIn‐VM management is usually needed by cloud service providers for cloud management, which includes monitoring the in‐VM application running state, reconfiguring VM system settings, etc. In‐VM management is also very useful in green cloud computing, because it provides the abilities of in‐VM monitoring, VM reconfiguration, performance measurement, etc. Leveraging a shell or an in‐VM agent to manage VMs is faced with generality and security challenges. In this paper, we propose a secure automated in‐VM management approach, ie, SAVM, which likes a hypervisor‐based shell managing the VMs in an out‐of‐box way. To bridge the semantic gap, we reuse the target VM's system calls to process the semantic information automatically. More importantly, we introduce a secure instruction fetch approach to enhance the system security. As a result, SAVM does not rely on the target VM's kernel integrity. In addition, we also present a dummy process selection and a system call injection method to further enhance the system security and transparency. After the implementation, we evaluate the prototype. The experimental results show that SAVM can achieve most of the in‐VM management operations. Furthermore, SAVM can work correctly under the target VM attacked by several popular rootkits.

TOWARDS GREEN COMPUTING, IMPORTANCE, IMPACT AND POSSIBLE SOLUTIONS-A REVIEW

Fresh environment is the key to human life, according to report [8] 25 lack people dead only in India in 2015 due to the polluted atmosphere. Major causes of air pollution are burning of fossil fuels, agricultural activities, exhaust from factories and industries, mining operations, and Indoor air pollution [9]. Unfortunately, our beloved computer is one of them. Datacentres discharge massive amount of CO2 in the air because here in datacentres thousands of servers, and other computer components are continuously in working condition 24/7. It occurs to researchers that they must search some new ways to control this problem. That's why green computing is a trending topic today. In this paper, we will tell the importance of green computing or green cloud computing, the impact of greenhouse gases on nature, also we will suggest some ideas that could be implemented in datacenters.

An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments

Cloud computing is a promising paradigm that enables a “computing-as-a-service” model, in which a dynamic pool of virtualized computational resources (e.g. CPU) can be leased and released on demand. With the increased demand for cloud computing infrastructures and the explosion in data center sizes, energy efficiency becomes an important issue to consider. Green cloud computing is an area that focuses on the design of energy efficient data centers, in order to achieve cost savings and minimize negative impacts on the environment. One of the main green cloud computing strategies used for the reduction of energy consumption consists in maximizing the utilization of a number of physical machines (PMs) and turning off or suspending unused servers. This strategy is typically achieved using two types of algorithms: Virtual Machines (VMs) placement and VMs consolidation algorithms. VMs’ placement is a process of dynamically placing VMs onto PMs while satisfying specific VM-to-PM mapping rules. VMs’ consolidation optimizes the resource utilization and groups dispersed VMs on a minimal number of active PMs, based on live migration techniques. Both approaches are time and resource consuming, and are categorized as NP-hard optimization problems. Moreover, reducing energy consumption by means of resource consolidation may reduce the system’s availability and reliability, and lead to SLA violations. Therefore, there is a need for multi-objective optimization approaches that can strike a balance between energy consumption and the system’s ability to meet QoS and SLA requirements. In this work, we propose an energy-aware and QoS-aware multi-objective Ant Colony Optimization (MACO) approach for VM placement and consolidation. Our approach aims at achieving a trade-off between energy efficiency, system performance, and SLA-compliance. The proposed approach was implemented and tested in small to mid-size data center settings, simulated using cloudSim, and was compared with a number of heuristic and meta-heuristic approaches, using eight performance metrics. The results show that our approach outperforms the other tested approaches in terms of energy savings, reduction of resource wastage in term of CPU, reduction of communication cost in term of energy induced by traffic load exchanged between VMs, and minimization of the number of VM migrations and SLA violations – thus demonstrating an ability to balance energy efficiency with system performance and QoS requirements.

Resource Allocation for Green Cloud Radio Access Networks With Hybrid Energy Supplies

In this paper, we study sustainable resource allocation for cloud radio access networks (CRANs) powered by hybrid energy supplies (HES). Specifically, the central unit (CU) in the CRANs distributes data to a set of radio units (RUs) powered by both on-grid energy and energy harvested from green sources, and allocates channels to the selected RUs for downlink transmissions. We formulate an optimization problem to maximize the net gain of the system which is the difference between the user utility gain and on-grid energy costs, taking into consideration the stochastic nature of energy harvesting process, time-varying on-grid energy price, and dynamic wireless channel conditions. A resource allocation framework is developed to decompose the formulated problem into three subproblems, i.e., the hybrid energy management, data requesting, and channel and power allocation. Based on the solutions of the subproblems, we propose a net gain-optimal resource allocation (GRA) algorithm to maximize the net gain while stabilizing the data buffers and ensuring the sustainability of batteries. Performance analysis demonstrates that the GRA algorithm can achieve close-to-optimal net gain with bounded data buffer and battery capacity. Extensive simulations validate the analysis and demonstrate that GRA algorithm outperforms other algorithms in terms of the net gain and delay performance.

A green proactive orchestration architecture for cloud resources

ABSTRACTIntrinsically, cloud computing is the paradigm that is been envisioned to dynamically furnish endless computing services to pervasive worldwide clients. However, the massive energy consumption resulted from the underpinning data centers would drastically hinder the full potential from the dreamed paradigm. Accordingly, this work opts towards devising green proactive cloud resources orchestration architecture. Specifically, this work focuses on cloud-based web applications which considered one of the most paramount cloud service categories. The proposed framework hence geared for leveraging cloud system resources utilization, ensuring explicit guarantees on negotiated web services’ obligations, meanwhile achieving power consumption minimization objectives. The proposed framework consolidates heuristic methodologies along with control theory approaches in a green resources orchestration hierarchical framework. Firstly, an energy-aware global controller is presented. The proposed global controller employs an online graph coloring-based approach for mapping the submitted virtual machines (VMs) to the appropriate cloud resources. The proposed global controller seeks energy-efficient configurations for the submitted VMs in a constrained environment without obligating the negotiated services SLAs. Secondly, a proactive fuzzy-logic-based local controller is proposed. The proposed local controller aimed at in confronting workloads’ sustainable fluctuations via proactive amendment for the placement and provisioning schedules. Additionally, the proposed local controller oriented towards maintaining active power management policy, especially during transient peak of usage, thereby mitigating overall costs, and extending resources capacity and performance capabilities. Conducted simulations and comparisons highly demonstrate the effectiveness of the proposed architecture at outperforming counterpart approaches specifically in terms of scalability, total energy consumption, and maintaining the web applications SLAs objectives in spite of workload fluctuations.

Energy Saving from Cloud Resources for a Sustainable Green Cloud Computing Environment

Cloud computing services are proliferation. The Cloud computing resources face major pitfall in energy consumes. The prime of energy consumption in cloud computing is by means of client computational devices, server computational devices, network computational devices and power required to cool the IT load. The cloud resources contribute high operational energy cost and emit more carbon emission to the environment. Therefore the cloud services providers need green cloud environment resolution to decrease the operational energy cost along with environmental impact. The most important objective of this effort is to trim down the energy from utilized and unutilized (idle) cloud resources and save the energy in cloud resources efficiently. To achieve the sustainable green cloud environment from an Energy Saving Algorithm used to choose the appropriate virtual services so that the power at the client, server, and network recourses can be reduced.

Secure Hash Algorithm based Multiple Tenant User Security over Green Cloud Environment

This paper proposes a green cloud multi-tenant trust authentication with secure hash algorithm-3 (GreenCloud-MTASHA3) scheme to eliminate the unauthorised tenant access. GreenCloud-MTASHA3 scheme provide security over the multiple tenant requests by referring the confidentiality, integrity and availability rate. Confidentiality refers to limiting the unauthorised tenant's green cloud data access using the additive homomorphic privacy property in proposed scheme. Additive homomorphic privacy property-based encryption function is developed to improve the privacy preserving level. To attain the integrity level between the tenant requests and green cloud server machine in GreenCloud-MTASHA3 scheme an encrypted trust data management process is carried out. Trustworthiness of tenant request is measured to maintain the consistency level on security with minimal computational time. The proposed scheme attains the confidentiality, integrity and availability rate on communicating task. Experiment is conducted on factors such as secure computation confidence, authorised tenant computational time and space taken on storing encrypted data.

Research on grade evaluation of green urbanization based on cloud matter element model

In the important transition period of urbanization development in China, the concept of “green development” was integrated into the current urbanization research. This paper constructs the grade evaluation index system from six dimensions: population employment, economic development, urban construction, residents' life, social development and ecological development, and combines the cloud model with the matter element model to construct the green urbanization cloud element material model. We took Shandong Province as an example and evaluated the current level of green urbanization in 17 cities by using this model. The results showed that the green urbanization levels of the four cities such as Ji'nan and Qingdao are the highest; the green urbanization levels of the nine cities such as Zibo and Weifang are good; the green urbanization levels of the four cities such as Zaozhuang and Heze are low. Through analysis, the evaluation results of the green urbanization cloud element matter model are objective and accurate, which verifies the rationality of using this model to evaluate the level of green urbanization, and improved the theoretical research of the current green urbanization. In practice, we can provide scientific guidance for the future development of green urbanization in cities to promote the healthy development of China's green urbanization.