Markov Prediction Model for Host Load Detection and VM Placement in Live Migration

The design of good host overload/underload detection and virtual machine (VM) placement algorithms plays a vital role in assuring the smoothness of VM live migration. The presence of the dynamic environment that leads to a changing load on the VMs motivates us to propose a Markov prediction model to forecast the future load state of the host. We propose a host load detection algorithm to find the future overutilized/underutilized hosts state to avoid immediate VMs migration. Moreover, we propose a VM placement algorithm to determine the set of candidates hosts to receive the migrated VMs in a way to reduce their VM migrations in near future. We evaluate our proposed algorithms through CloudSim simulation on different types of PlanetLab real and random workloads. The experimental results show that our proposed algorithms have a significant reduction in terms of service-level agreement violation, the number of VM migrations, and other metrics than the other competitive algorithms

VM Selection Process Management for Live Migration in Cloud Data Centers

With immense success and fast growth within the past few years, cloud computing has been established as the dominant computing paradigm in information technology (IT) industry, wherein it utilizes dissipated resource benefits and supports resource sharing and time access flexibility. The proliferation of cloud computing has resulted in the establishment of large-scale data centers across the world, consisting of hundreds of thousands, even millions of servers. The emerging cloud computing paradigm provides administrators and IT organizations with considerable freedom to dynamically migrate virtualized computing services among physical servers in cloud data centers. Normally, these data centers incur very high investment and operating costs for the computing and network devices as well as for the energy consumption. Virtualization and virtual machine (VM) migration offers significant benefits such as load balancing, server consolidation, online maintenance and proactive fault tolerance along data centers. VM migration relies on how to determine the trigger condition of VM migration, select the target virtual machine, and choose the destination node. As a result, dynamic VM migration in the scope of resource management is becoming a crucial issue to emphasize on optimal resource utilization, maximum throughput, minimum response time, enhancing scalability, avoiding over-provisioning of resources and prevention of overload to make cloud computing successful. Intelligent host underload/overload detection, VM selection, and VM placement are the primary means to address VM migration issue. Therefore, these three problems are considered to be the most common tasks in VM migration. This thesis presents novel techniques, models, and algorithms, for distributed dynamic consolidation of virtual machines in cloud data centers. The goal is to improve the utilization of computing resources and reduce energy consumption under workload independent quality of service constraints. The proposed approaches are distributed and efficient in managing the energy-performance trade-off

Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

In cloud data center, without efficient virtual machine placement, the overload of any types of resources on physical machines (PM) can easily cause the waste of other types of resources, and frequent costly virtual machine (VM) migration, which further negatively affects quality of service (QoS). To address this problem, in this paper we propose an evidence-efficient affinity propagation scheme for VM placement (EEAP-VMP), which is capable of balancing the workload across various types of resources on the running PMs. Our approach models the problem of searching the desirable destination hosts for the liveVMmigration as the propagation of responsibility and availability. The sum of responsibility and availability represent the accumulated evidence for the selection of candidate destination hosts for the VMs to be migrated. Further, in combination with the presented selection criteria for destination hosts. Extensive experiments are conducted to compare our EEAP-VMP method with the previousVMplacement methods. The experimental results demonstrate that the EEAP-VMP method is highly effective on reducing VM migrations and energy consumption of data centers and in balancing the workload of PMs

CloudBench: an integrated evaluation of VM placement algorithms in clouds

A complex and important task in the cloud resource management is the efficient allocation of virtual machines (VMs), or containers, in physical machines (PMs). The evaluation of VM placement techniques in real-world clouds can be tedious, complex and time-consuming. This situation has motivated an increasing use of cloud simulators that facilitate this type of evaluations. However, most of the reported VM placement techniques based on simulations have been evaluated taking into account one specific cloud resource (e.g., CPU), whereas values often unrealistic are assumed for other resources (e.g., RAM, awaiting times, application workloads, etc.). This situation generates uncertainty, discouraging their implementations in real-world clouds. This paper introduces CloudBench, a methodology to facilitate the evaluation and deployment of VM placement strategies in private clouds. CloudBench considers the integration of a cloud simulator with a real-world private cloud. Two main tools were developed to support this methodology, a specialized multi-resource cloud simulator (CloudBalanSim), which is in charge of evaluating VM placement techniques, and a distributed resource manager (Balancer), which deploys and tests in a real-world private cloud the best VM placement configurations that satisfied user requirements defined in the simulator. Both tools generate feedback information, from the evaluation scenarios and their obtained results, which is used as a learning asset to carry out intelligent and faster evaluations. The experiments implemented with the CloudBench methodology showed encouraging results as a new strategy to evaluate and deploy VM placement algorithms in the cloud.This work was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness under the Grant TIN2016-79637-P “Towards Unifcation of HPC and Big Data Paradigms” and by the Mexican Council of Science and Technology (CONACYT) through a Ph.D. Grant (No. 212677)

Machine Learning Centered Energy Optimization In Cloud Computing: A Review

The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing

Optimizing Virtual Resource Management in Cloud Datacenters

Datacenter clouds (e.g., Microsoft\u27s Azure, Google\u27s App Engine, and Amazon\u27s EC2) are emerging as a popular infrastructure for computing and storage due to their high scalability and elasticity. More and more companies and organizations shift their services (e.g., online social networks, Dropbox file hosting) to clouds to avoid large capital expenditures. Cloud systems employ virtualization technology to provide resources in physical machines (PMs) in the form of virtual machines (VMs). Users create VMs deployed on the cloud and each VM consumes resources (e.g., CPU, memory and bandwidth) from its host PM. Cloud providers supply services by signing Service Level Agreement (SLA) with cloud customers that serves as both the blueprint and the warranty for cloud computing. Under-provisioning of resources leads to SLA violations while over-provisioning of resources leads to resource underutilization and then revenue decrease for the cloud providers. Thus, a formidable challenge is effective management of virtual resource to maximize energy efficiency and resource utilization while satisfying the SLA. This proposal is devoted to tackle this challenge by addressing three fundamental and essential issues: i) initial VM allocation, ii) VM migration for load balance, and iii) proactive VM migration for long-term load balance. Accordingly, this proposal consists of three innovative components: (1) Initial Complementary VM Consolidation. Previous resource provisioning strategies either allocate physical resources to virtual machines (VMs) based on static VM resource demands or dynamically handle the variations in VM resource requirements through live VM migrations. However, the former fail to maximize energy efficiency and resource utilization while the latter produce high migration overhead. To handle these problems, we propose an initial VM allocation mechanism that consolidates complementary VMs with spatial/temporal-awareness. Complementary VMs are the VMs whose total demand of each resource dimension (in the spatial space) nearly reaches their host\u27s capacity during VM lifetime period (in the temporal space). Based on our observation of the existence of VM resource utilization patterns, the mechanism predicts the lifetime resource utilization patterns of short-term VMs or periodical resource utilization patterns of long-term VMs. Based on the predicted patterns, it coordinates the requirements of different resources and consolidates complementary VMs in the same physical machine (PM). This mechanism reduces the number of PMs needed to provide VM service hence increases energy efficiency and resource utilization and also reduces the number of VM migrations and SLA violations. (2) Resource Intensity Aware VM Migration for Load Balance. The unique features of clouds pose formidable challenges to achieving effective and efficient load balancing. First, VMs in clouds use different resources (e.g., CPU, bandwidth, memory) to serve a variety of services (e.g., high performance computing, web services, file services), resulting in different overutilized resources in different PMs. Also, the overutilized resources in a PM may vary over time due to the time-varying heterogenous service requests. Second, there is intensive network communication between VMs. However, previous load balancing methods statically assign equal or predefined weights to different resources, which leads to degraded performance in terms of speed and cost to achieve load balance. Also, they do not strive to minimize the VM communications between PMs. This proposed mechanism dynamically assigns different weights to different resources according to their usage intensity in the PM, which significantly reduces the time and cost to achieve load balance and avoids future load imbalance. It also tries to keep frequently communicating VMs in the same PM to reduce bandwidth cost, and migrate VMs to PMs with minimum VM performance degradation. (3) Proactive VM Migration for Long-Term Load Balance. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of SLA violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time, energy) and delay by quickly identifying VMs and destination PMs to migrate. Finally, we conducted extensive experiments to evaluate the proposed three mechanisms. i) We conducted simulation experiments based on two real traces and real-world testbed experiments to show that the initial complementary VM consolidation mechanism significantly reduces the number of PMs used, SLA violations and VM migrations of the previous resource provisioning strategies. ii) We conducted trace-driven simulation and real-world testbed experiments to show that RIAL outperforms other load balancing approaches in regards to the number of VM migrations, VM performance degradation and VM communication cost. iii) We conducted trace-driven experiments to show that the MDP-based load balancing algorithm outperforms previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency and long-term load balance maintenance

Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation

Multimedia computing (MC) is rising as a nascent computing paradigm to process multimedia applications and provide efficient multimedia cloud services with optimal Quality of Service (QoS) to the multimedia cloud users. But, the growing popularity of MC is affecting the climate. Because multimedia cloud data centers consume an enormous amount of energy to provide services, it harms the environment due to carbon dioxide emissions. Virtual machine (VM) migration can effectively address this issue; it reduces the energy consumption of multimedia cloud data centers. Due to the reduction of Energy Consumption (EC), the Service Level Agreement violation (SLAV) may increase. An efficient VM selection plays a crucial role in maintaining the stability between EC and SLAV. This work highlights a novel VM selection policy based on identifying the Maximum value among the differences of the Sum of Squares Utilization Rate (MdSSUR) parameter to reduce the EC of multimedia cloud data centers with minimal SLAV. The proposed MdSSUR VM selection policy has been evaluated using real workload traces in CloudSim. The simulation result of the proposed MdSSUR VM selection policy demonstrates the rate of improvements of the EC, the number of VM migrations, and the SLAV by 28.37%, 89.47%, and 79.14%, respectively