Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms

The demand for cloud computing is increasing dramatically due to the high computational requirements of business, social, web and scientific applications. Nowadays, applications and services are hosted on the cloud in order to reduce the costs of hardware, software and maintenance. To satisfy this high demand, the number of large-scale data centers has increased, which consumes a high volume of electrical power, has a negative impact on the environment, and comes with high operational costs. In this paper, we discuss many ongoing or implemented energy aware resource allocation techniques for cloud environments. We also present a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud. Finally, we come up with further research issues and challenges for future cloud environments.

Energy aware resource allocation of cloud data center: review and open issues

Multi-capacity combinatorial ordering GA in application to cloud resources allocation and efficient virtual machines consolidation

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines (VM). Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, the overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account the effect on temperature and it is challenging to simulate the thermal behavior of CDCs. There is a need for a thermal-aware framework to simulate and model the behavior of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modeling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyze the performance of various key parameters such as energy consumption, SLA violation rate, number of VM migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework. The experimental results demonstrate the proposed framework is capable of modeling and simulating the thermal behavior of a CDC and the ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage & prediction accuracy.

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ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments

At the virtualized data centers, services are presented by active virtual machines (VMs) in physical machines (PMs). The manner in which VMs are mapped to PMs affects the performance of data centers and the energy efficiency. By employing the server consolidation technique, it is possible to configure the VMs on a smaller number of PMs, while the quality of service is guaranteed. In this way, the rate of active PM utilization increases and fewer active PMs would be required. Moreover, the server consolidation technique reacts to the management of underloaded and overloaded PMs by using the VM migration technology. Considering the capabilities of the server consolidation technique and its role in developing the cloud computing infrastructure, many researches have been conducted in this context. Still, a comprehensive and systematic study has not yet been performed on various consolidation techniques to check the capabilities, advantages, and disadvantages of current approaches. In this paper, a systematic study is conducted on a number of credible researches related to server consolidation techniques. In order to do so and by studying the selected works, proposed solutions are categorized based on the type of decision for running the consolidation algorithm in 4 groups of static method, dynamic method, prediction‐based dynamic method, and hybrid method. Thereafter, the advantages and disadvantages of suggested approaches are studied and compared in each research by specifying the technique and idea applied therein. In addition, by categorizing aims of researches and specifying assessment parameters, optimization approaches and type of architecture, a possibility has been provided to get familiarized with the views of the researchers.

Server consolidation techniques in virtualized data centers of cloud environments: A systematic literature review

Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

Reducing power consumption and maintaining user or application performance criteria is an important goal in virtualized server cluster system design. Achieving this multiple objective requirement in a virtualized environment is a challenge. One of the techniques widely explored in the literature to achieve this goal is the dynamic voltage frequency scaling (DVFS) approach. However, power consumption reduction due to DVFS is far less than what can be achieved with the dynamic power management (DPM) control approach to either switch OFF the server or to transition the server to a low power SLEEP state when not processing application requests. In our work, we have formulated a power optimization problem that meets the dened performance criteria for the workload. We have considered application request response time as the performance metric. The adaptive controller is designed to track application performance in therst step, with dynamic control and batch requests to the best server in order to minimize the power consumption as the second step. In this paper, our aim is to reduce the power consumption of a virtualized server cluster by making use of an adaptive hybrid approach that identies the right server with the best performance for power metric (PPM), DPM with server processor sleep state(s), and DVFS controls. Simulation results show that our hybrid approach using PPM, DPM with sleep state, and DVFS controls is effective, achieves better energy savings, and meets the performance criteria constraint.

https://journals.tubitak.gov.tr/elektrik/issues/elk-16-24-4/elk-24-4-21-1403-264.pdf

Power management using dynamic power state transitions and dynamic voltage frequency scaling controls in virtualized server clusters

IT service providers, employ server virtualization as a main building block to improve system utilization, improve system manageability, reduce operational costs which includes energy consumption driving economies of scale with shared resources. Virtualization enables co-locating and efficient assignments of virtual servers within the bounds of limited number of heterogeneous physical servers, with Virtual Machines (VM) sharing the limited physical server resources between them. Though virtualization technologies point to the fact that each virtual server has its very own isolated environment, but in reality, perfect isolation is not possible. Primary measure to achieve assignment efficiency is to ensure that system resources are utilized effectively and performance of VM (and application workloads) is consistent within the desired bounds. Interference or contention on the limited shared resources among VMs leads to performance degradation and is referred to as performance interference. This affects (a) application Quality of Service (QOS) and (b) server cluster or data centers’ energy-efficiency. In this work, we analyze the performance degradation using (a) energy efficiency heterogeneity measure and (b) interference aware measure, with the aim to reduce energy consumption in our environment. Experimental results on different scenarios with our energy efficiency and interference aware approach shows a reduction in energy consumption to the tune of 8 to 58% and 10× improvement in per request average response time in contrast to a default energy efficiency and interference oblivious approach.

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Mohan Raj Velayudhan Kumar

Papers

Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

Power management using dynamic power state transitions and dynamic voltage frequency scaling controls in virtualized server clusters

Energy efficient and interference aware provisioning in virtualized server cluster environment