Energy Consumption In Cloud Environment Research Articles

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.

Genetic-Based Virtual Machines Consolidation Strategy With Efficient Energy Consumption in Cloud Environment

In cloud computing environments, virtualization is used to share physical machine (PM) resources among multiple users by creating virtual machines (VMs). Running the PM consumes a large amount of energy. Additionally, the PM will be overloaded when the demand for resources exceeds the PM capacity. This overload on the PM leads to violations of Service Level Agreements (SLAs). Dynamic VM consolidation techniques use live migration of VMs to optimize resource utilization and minimize energy consumption. However, excessive migration of VMs impacts negatively the application performance due to the incurred overhead at the runtime. This paper presents a modified genetic-based VM consolidation (MGVMC) strategy that aims to replace VMs in an online manner taking into account energy consumption, SLA violations, and the number of VM migrations. The MGVMC strategy utilizes the genetic algorithm to migrate VMs to the appropriate PM in a way that minimizes the number of over-utilized and under-utilized physical machines (PMs) as low as possible. The performance of the MGVMC strategy was evaluated using the CloudSim Plus framework with a large number of VMs and workload traces from the PlanetLab platform. The experimental results revealed that the MGVMC strategy achieved a significant improvement in energy consumption, SLA violations, and the number of VM migrations compared to other recent approaches. These results demonstrate the effectiveness of the MGVMC strategy in optimizing VM consolidation in the cloud environment.

Geographic Routing In WSN for Measuring Coverage Constraints and Energy Consumption in Cloud Environments

This paper investigates the problem of energy efficient and consumption process in wireless sensor networks. The fundamental problem in WSN has handled the energy constraints and avoiding end-to-end delay networks. We introduce encoding technique for handling transmission data, network delays, content aware service and energy expenditure problems. The end-to-end delay varies depending up on nodes and sensor life time. We apply hamming encoding technique for estimating energy constraints with life time of buffer capacity factor. This paper proposes the novel based approach to handle group communication, senor node status, network transmission and optimal encoded behaviors. Each cluster group values are sensed by node reference time and calculate hamming code weight for each node counts. The quality of services can be achieved by energy saving measurements and data loss can be varies up on incoming packet request. We compare the encoded result with code word situation and hazard environment. We reduce the amount of data transmission factor and life time of sensor values and longer network delays. We show the simulating results with encoding scheme and reduced energy consumption. The performance can be monitored by coverage constraints and optimal transmission behaviors.

Power Management as a Service (Pmaas) – An Energy Saving Service by “Auto-Fit VM Placement” Algorithm in Cloud Computing for Maximum Resource Utilization at Minimum Energy Consumption Without Live-Migrating the Virtual Machines by using the Concept of Virtual-Servers

Now a day Energy Consumption is one of the most promising fields amongst several computing services of cloud computing. A maximum amount of Power resources are absorbed by the data centre because of huge amount of data processing which is increased abnormally. So it’s the time to think about the energy consumption in cloud environment. Existing Energy Consumption systems are limited in terms of virtualization because improper virtualization leads to loads imbalance and excessive power consumption and inefficiency in terms of computational power. Billing[1,2 ] is another exciting feature that is closely related to energy consumption, because higher or lesser billing depends on energy consumption somehow-as we know that cloud providers allow cloud users to access resources as pay-per-use, so these resources need to be optimally selected to process the user request to maximize user satisfaction in the distributed virtualized environment. There may be an inequity between the actual power consumption by the users and the provided billing records by the providers, So any false accusation that may claimed by each other to get illegal compensations. To avoid such accusation, we propose a work to consolidate the VMs using the Power Management as a Service (PMaaS) model in such a way, to reduce power consumption by maximum resource utilization without live-migration of the virtual machines by using the concept of Virtual Servers. The proposed PMaaS model uses a new “Auto-fit VM placement algorithm”, which computes tasks resource demands, models a Virtual Machine that fits those demands, and places the Virtual Machines on a Virtual server made by the collective resources (CPU, Memory, Storage and Bandwidth) from the respective schedulers directly connected to the actual physical servers and that has the minimum remaining resources which is large enough to accommodate such a Virtual Machine.

An Approach for Minimizing Energy Consumption in Cloud Environment

Background/Objectives: Cloud computing could be considered of vital paradigms in IT which allows services to be delivered to the users via the internet on demand and on pay as you go basis. The growing demand on cloud computing environments increasing the number of datacenters which in turn increase the amount of power consumption in datacenters along with cooling equipment. Load balancing is considered a major challenge affecting in cloud performance Methods: An existing problem is how to allocate Virtual Machines (VMs) to Physical Machines (PMs) or hosts. This process is called VM placement. An algorithm is proposed that can reduce power consumption. Findings: The proposed algorithm assigns VMs onto PMs based on first fit decreasing algorithm and improves an existed one through reducing power consumption by turning-off some under load hosts if available and migrating their VMs to other active hosts. Application: The presented approach could decrease significantly energy consumption in comparison with the existing one through migrating VMs from underload hosts and turns them off. Keywords: Cloud Computing, Energy Consumption, Load Balancing, Migration, VM Placement

Efficient utilization of energy consumption in cloud environment

In the tremendous growth of the cloud computing, server consolidation plays a vital role. It gives more benefits also it gives the pollution towards the nature. Inspite of avoiding this, saving of energy in the data center or the cluster is more important. In this regard this paper proposes a method of which reduces the energy consumption. This is done by identifying the active physical machines and the remaining machines were kept in the sleep or off mode according to the user’s availability. Here Virtual Machines (VMs) are grouped under the particular types. Based on this type the customer’s resource request can be maintained.

A heuristic task scheduling algorithm based on server power efficiency model in cloud environments

As the ever-growing energy consumption rises as a global concern, energy conservation in cloud data centers has become a topic of interest. Based on extensive research and experimental analysis of the energy consumption characteristics and performance records from SPEC data set, this paper presents a power efficiency model for cloud servers. With the model, we use server power efficiency to guide task scheduling and propose a heuristic task scheduling algorithm (ECOTS) for optimizing energy consumption in cloud environments. ECOTS takes into account multiple key factors such as task resource requirements, server power efficiency model and performance degradation in order to reduce system energy consumption at a minimal cost of performance. ECOTS algorithm has low time and space complexity with a better global searching ability to approach the optimal scheduling plan. We simulated a heterogeneous cluster environment and conducted experiments to evaluate the effectiveness of ECOTS. Simulation results show that ECOTS algorithm is the most energy-efficient without violating all the cloud tasks’ resources requirement. Moreover, ECOTS algorithm effectively reduces total energy consumption by more than 20% compared with the Improvement of Min-Min algorithm.

Erratum to: A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

Erratum to: A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

Cloud-based data centers consume a significant amount of energy which is a costly procedure. Virtualization technology, which can be regarded as the first step in the cloud by offering benefits like the virtual machine and live migration, is trying to overcome this problem. Virtual machines host workload, and because of the variability of workload, virtual machines consolidation is an effective technique to minimize the total number of active servers and unnecessary migrations and consequently improves energy consumption. Effective virtual machine placement and migration techniques act as a key issue to optimize the consolidation process. In this paper, we present a novel virtual machine consolidation technique to achieve energy–QoS–temperature balance in the cloud data center. We simulated our proposed technique in CloudSim simulation. Results of evaluation certify that physical machine temperature, SLA, and migration technique together control the energy consumption and QoS in a cloud data center.

Autonomic Resource Management and Energy Consumption in Cloud Environment

computing clouds, it is desirable to pass up slaying resources as a result of under-utilization and to avoid long response period as a result of over-utilization. This paper, proposes a latest approach for dynamic autonomous resource managing in computing clouds. The core involvement of this work is two-fold. First, it adopts a distributed design where resource management is decaying into independent tasks. The results show that the total energy consumption and decision- making time were compacting additional in the proposed algorithm than Sequential Executive Algorithm (SEA). In adding, the advantage would be increasingly apparent with the growing in assignments. KeywordsComputing, Task Tolerance, Resource consumption,

Performance bounded energy efficient virtual machine allocation in the global cloud

Reducing energy consumption is a critical step in lowering data center operating costs for various institutions. As such, with the growing popularity of cloud computing, it is necessary to examine various methods by which energy consumption in cloud environments can be reduced. We analyze the effects of global virtual machine allocation on energy consumption, using a variety of real-world policies and a realistic testing scenario. We found that by using an allocation policy designed to minimize energy, total energy consumption could be reduced by up to 14%, and total monetary energy costs could be reduced by up to 26%. Further, we have begun performance qualification of our energy cost driven allocation policies through network capability tests. Our results indicate that performance and IaaS provider implementation costs have a significant influence on selection of optimal virtual machine allocation policies.

E-mc2: A formal framework for energy modelling in cloud computing

Due to energy crisis of the last years, energy waste and sustainability have been brought both into public attention, and under industry and scientific scrutiny. Thus, obtaining high-performance at a reduced cost in cloud environments as reached a turning point where computing power is no longer the most important concern. However, the emphasis is shifting to manage energy efficiently, whereas providing techniques for measuring energy requirements in cloud systems becomes of capital importance.Currently there are different methods for measuring energy consumption in computer systems. The first consists in using power meter devices, which measure the aggregated power use of a machine. Another method involves directly instrumenting the motherboard with multimeters in order to obtain each power connector’s voltage and current, thus obtaining real-time power consumption. These techniques provide a very accurate results, but they are not suitable for large-scale environments. On the contrary, simulation techniques provide good scalability for performing experiments of energy consumption in cloud environments. In this paper we propose E-mc2, a formal framework integrated into the iCanCloud simulation platform for modelling the energy requirements in cloud computing systems.