Cloud computing is revolutionizing the ICT landscape by providing scalable and efficient computing resources on demand. The ICT industry --- especially data centers, are responsible for considerable amounts of CO2 emissions and will very soon be faced with legislative restrictions, such as the Kyoto protocol, defining caps at different organizational levels (country, industry branch etc.) A lot has been done around energy efficient data centers, yet there is very little work done in defining flexible models considering CO2. In this paper we present a first attempt of modeling data centers in compliance with the Kyoto protocol. We discuss a novel approach for trading credits for emission reductions across data centers to comply with their constraints. CO2 caps can be integrated with Service Level Agreements and juxtaposed to other computing commodities (e.g. computational power, storage), setting a foundation for implementing next-generation schedulers and pricing models that support Kyoto-compliant CO2 trading schemes.
A good overview of cloud computing and sustainability is given in @cite_21 , with explanations of where cloud computing stands in regard to emissions. Green policies for scheduling are proposed that, if accepted by the user, could greatly increase the efficiency of cloud computing and reduce emissions. Reducing emissions is not treated as a source of profit and a possible way to balance SLA violations, though, but more of a general guideline for running the data center to stay below a certain threshold.
@cite_21
2080019938 1983667143 2283588730 2099787804
Currently, more and more vendors such as Amazon, Google, IBM and Microsoft are dedicated to developing their cloud platforms for increasing large-scale data and more complex software systems. The cloud computing technique is rapidly changing the computing environment for various applications. However, a large number of cloud servers consume massive energy and produce huge pollution. The Smart2020 analysis shows that cloud-based computing data center and the telecommunication network will generate emission about 7 and 5 each year in 2002 and 2020, respectively. This paper aims to develop a new green algorithm that can help multiple CPUs in the cloud network not only complete the tasks before a deadline, but also greatly reduce the energy consumption. Our new green algorithm with human intelligence can effectively make task assignments via partial task shuffling and adjust the cloud servers' speeds through smart task allocation under the time constraint. Sufficient simulation results indicate that the new green algorithm with intelligent strategies is effective compared with a traditional method and another new method. In the future, we will apply both ancient and modern human's intelligent strategies to improve green optimization algorithms. Cloud computing is the new paradigm of operation in information technology. While cloud computing infrastructures have benefits, their energy consumption is becoming a growing concern. Data centers, which are used to provide the infrastructure and resource pool for cloud computing, consume a large amount of energy. Future energy consumption predictions of these data centers are even bigger concerns. To reduce this energy consumption, and hence the carbon footprint and greenhouse gas emission of cloud computing, and information technology in general, energy-efficient methods of operation have to be investigated and adopted. In addition, renewable energy usage in place of non-renewable can also reduce carbon emission. However, due to its intermittency and volatility, renewable energy cannot be used to its full potential. In this study, we introduce the renewable-energy- aware cloud service and virtual machine migration to relocate energy demand using dynamic and flexible cloud resource allocation techniques, and help overcome the challenges of renewable energy. Results from a U.S.-wide cloud network infrastructure show that, using simple migration techniques, up to 30 percent nonrenewable energy can be replaced by renewable energy, while consuming only a small amount of extra resources and energy to perform demand relocation. The aim of Green Cloud Computing is to achieve a balance between the resource consumption and quality of service. In order to achieve this objective and to maintain the flexibility of the cloud, dynamic provisioning and allocation strategies are needed to regulate the internal settings of the cloud to address oscillatory peaks of workload. In this context, we propose strategies to optimize the use of the cloud resources without decreasing the availability. This work introduces two hybrid strategies based on a distributed system management model, describes the base strategies, operation principles, tests, and presents the results. We combine existing strategies to search their benefits. To test them, we extended CloudSim to simulate the organization model upon which we were based and to implement the strategies, using this improved version to validate our solution. Achieving a consumption reduction up to 87 comparing Standard Clouds with Green Clouds, and up to 52 comparing the proposed strategy with other Green Cloud Strategy. The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. HPC users need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy cost which will reduce the profit margin of Cloud providers, but also high carbon emissions which are not environmentally sustainable. Hence, there is an urgent need for energy-efficient solutions that can address the high increase in the energy consumption from the perspective of not only the Cloud provider, but also from the environment. To address this issue, we propose near-optimal scheduling policies that exploit heterogeneity across multiple data centers for a Cloud provider. We consider a number of energy efficiency factors (such as energy cost, carbon emission rate, workload, and CPU power efficiency) which change across different data centers depending on their location, architectural design, and management system. Our carbon energy based scheduling policies are able to achieve on average up to 25 of energy savings in comparison to profit based scheduling policies leading to higher profit and less carbon emissions.