Adaptive Energy-Optimized Consolidation Algorithm

We have been hearing about cloud computing for quite a long time now. This type of computing is booming and emerging as a popular computing paradigm for its scalability and flexibility in nature. Cloud computing provides the provision of service on-demand, on-demand resources supply and services to end-users. However, energy consumption and energy wastage are becoming a major concern for cloud providers due to its direct impression on costs required for operations and carbon emissions. To tackle this issue, Adaptive Energy-Optimized Consolidation Algorithm has been proposed to efficiently manage energy consumption in cloud environments. This algorithm involves sharing by dividing, in this process resource allocation is done into two different phases, those are, consolidation of tasks and consolidation of resources. Compared to single-task consolidation algorithms, the proposed two-phase Adaptive energy optimized consolidation algorithm shows improved performance in terms of energy efficiency and resource utilization. The results of experiments conducted using a cloud-sim show the effectiveness of the proposed algorithm in decreasing energy consumption while maintaining the quality-of-service requirements of computing in cloud.  The need for an hour is to automate things without human intervention. Thus, using Autonomous computing refers to a type of computing system that is capable of performing tasks and making decisions without the intervention of humans. This type of system typically relies on Artificial.Intelligence, Machine.Learning, and other futuristic technologies to study the data, identify patterns, and make decisions based on that data. Cloud computing can certainly be incorporated into an autonomous computing system. The performance of an automated computing environment depends on a various factor, considering the quality of the different algorithms used, also the amount and quality of various data available to the system, the computational resources available, and the system's ability to learn and adapt over time. However, by incorporating cloud computing, an autonomous computing system can potentially access more resources and process data more quickly, which can improve its overall performance

A review of performance and energy aware improvement methods for future green cloud computing

With the advent of increased use of computers and computing power, state of the art of cloud computing has become imperative in the present-day global scenario. It has managed to remove the constraints in many organizations in terms of physical internetworking devices and human resources, leaving room for better growth of many organizations. With all these benefits, cloud computing is still facing a number of impediments in terms of energy consumption within data centers and performance degradation to end users. This has led many industries and researchers to find feasible solutions to the current problems. In the context of realizing the problems faced by cloud data centers and end users, this paper presents a summary of the work done, experimentation setup and the need for a greener cloud computing technique/algorithm which satisfies minimum energy consumption, minimum carbon emission and maximum quality of service

Green Cloud - Load Balancing, Load Consolidation using VM Migration

Recently, cloud computing is a new trend emerging in computer technology with a massive demand from the clients. To meet all requirements, a lot of cloud data centers have been constructed since 2008 when Amazon published their cloud service. The rapidly growing data center leads to the consumption of a tremendous amount of energy even cloud computing has better improved in the performance and energy consumption, but cloud data centers still absorb an immense amount of energy. To raise company’s income annually, the cloud providers start considering green cloud concepts which gives an idea about how to optimize CPU’s usage while guaranteeing the quality of service. Many cloud providers are paying more attention to both load balancing and load consolidation which are two significant components of a cloud data center. Load balancing is taken into account as a vital part of managing income demand, improving the cloud system’s performance. Live virtual machine migration is a technique to perform the dynamic load balancing algorithm. To optimize the cloud data center, three issues are considered: First, how does the cloud cluster distribute the virtual machine (VM) requests from clients to all physical machine (PM) when each computer has a different capacity. Second, what is the solution to make CPU’s usage of all PMs to be nearly equal? Third, how to handle two extreme scenarios: rapidly rising CPU’s usage of a PM due to sudden massive workload requiring VM migration immediately and resources expansion to respond to substantial cloud cluster through VM requests. In this chapter, we provide an approach to work with those issues in the implementation and results. The results indicated that the performance of the cloud cluster was improved significantly. Load consolidation is the reverse process of load balancing which aims to provide sufficient cloud servers to handle the client requests. Based on the advance of live VM migration, cloud data center can consolidate itself without interrupting the cloud service, and superfluous PMs are turned to save mode to reduce the energy consumption. This chapter provides a solution to approach load consolidation including implementation and simulation of cloud servers

Cloud Host Selection using Iterative Particle-Swarm Optimization for Dynamic Container Consolidation

A significant portion of the energy consumption in cloud data centres can be attributed to the inefficient utilization of available resources due to the lack of dynamic resource allocation techniques such as virtual machine migration and workload consolidation strategies to better optimize the utilization of resources. We present a new method for optimizing cloud data centre management by combining virtual machine migration with workload consolidation. Our proposed Energy Efficient Particle Swarm Optimization (EE-PSO) algorithm to improve resource utilization and reduce energy consumption. We carried out experimental evaluations with the Container CloudSim toolkit to demonstrate the effectiveness of the proposed EE-PSO algorithm in terms of energy consumption, quality of service guarantees, the number of newly created VMs, and container migrations

Green IT based energy efficiency model for data centers to reduce energy consumption & CO2 emissions

Problem Statement: The advancement of Information and Communication Technologies (ICTs) based business and social practices in the last few decades has transformed many, if not most, economies into e-economy and businesses into e-businesses. For economies, ICTs are increasingly playing critical roles in transforming and generating ortunities. On the other hand, global warming and climate change coalescing with limited availability and rising cost of energy are posing serious challenges for the sustainability of the global digital (or otherwise) economy. Technology has a potential to create sustainable business and society both in grim and green economic times. Especially, the recovery from the current economic crisis is going to need and lead to more Greener and energy efficient industries. As corporations look to become more energy efficient, they are examining their operations more closely. Data centers provide capabilities of central storage, backups and networking, recovery. Data centers are found major culprits in consuming too much energy in their overall operations and generating too much CO2. In order to handle the sheer magnitude of today’s data, data centers have had to use much more power and servers have become larger, denser, hotter and significantly more costly to operate. This study determine the properties and attributes of green IT infrastructures and determines the way it will be helpful in achieving green sustainable businesses. The proposed Green IT model will be drafted using Virtualization technology for data centers to make them more energy efficient and green, hence reducing the emission of green house gases so that the overall effect on global warming can be reduced or even eliminated. Results & Conclusion: The proposed model would reveal the qualities of green IT to enhance the proper utilization of hardware and software resources available in the data center. It helps data center managers to come up with a new environment friendly and sustainable green IT strategy making environment greener and sustainable. The heart of this strategy is to reduce global warming effects by using green and energy efficient data centers