BackFillMe: An Energy and Performance Efficient Virtual Machine Scheduler for IaaS Datacenters

An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab's real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

Cloud data centers require enormous amounts of energy to run their clusters of computers. There are huge financial and environmental incentives for cloud service providers to increase their energy efficiency without causing significant negative impacts on their customers' qualities of experience. Increasing resource utilization reduces energy consumption by consolidating workloads on fewer machines and allows cloud service providers to turn off inactive devices. While traditional architectures only allow virtual machines (VMs) to use the memory and CPU resources of a single device, VMs in a disaggregated cloud can utilize the small residual capacities of multiple separate devices. Separating VM resources across multiple devices leads to severe fragmentation that eventually negates any positive impact disaggregation has on utilization. To address the fragmentation problem, we present a method of ensuring a cloud operates using the minimal number of devices over time. Here we introduce an Energy-Aware Learning Agent (EALA) that uses reinforcement learning to guarantee the system can meet minimal quality of service requirements and provide energy savings without the need for VM migration. We evaluate the use of EALA guiding the decisions of Best-Fit compared to vanilla Best-Fit using the Google cluster trace. We show that EALA improves utilization by 2% and reduces the number of times that compute nodes switch on and off by 11% compared to vanilla Best-Fit.

Efficient utilization of Cloud resources is of utmost importance for Cloud Data Centers. The overall objective is to reduce energy consumption without compromising over quality of service. In order to achieve this goal different Virtual Machine (VM) Consolidation techniques have been proposed. Threshold detection for over utilized and underutilized hosts is an important phase of these techniques. In this paper we have proposed an adaptive model based on current load to optimize thresholds for detection of over and underutilized hosts. The proposed model uses Mean utilization of hosts and Standard deviation of VM utilization to calculate an acceptable range of utilization thresholds. An algorithm based on the proposed model is devised and tested through simulation experiments using Cloudsim. Four threshold detection policies namely, Static Threshold (ST), Inter-Quartile Range (IQR), Local Regression (LR), and Median Absolute Deviation (MAD) with Minimum Migration Time (MMT) as VM selection policy are compared with our proposed algorithm. Experiment results validate that the proposed algorithm reduces energy consumption, SLA violations, number of VM migrations and standard deviation of VM utilization in order to optimize resource utilization considerably.

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.

DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

In recent years, data center network (DCN) architectures (e.g., DCell [5], FiConn [6], BCube [4], FatTree [1], and VL2 [2]) received a surge of interest from both the industry and academia. However, none of existing efforts provide an in-depth understanding of the impact of these architectures on application performance in practical multi-tier systems under realistic workload. Moreover, it is also unclear how these architectures are affected in virtualized environments. In this paper, we fill this void by conducting an experimental evaluation of FiConn and FatTree, each respectively as a representative of hierarchical and flat architectures, in a three-tier transaction system using virtual machine (VM) based implementation. We observe several fundamental characteristics that are embedded in both classes of network topologies and cast a new light on the implication of virtualization in DCN architectures. Issues observed in this paper are generic and should be properly addressed by any DCN architectures before being considered for actual deployment, especially in mission-critical real-time transaction systems.

This paper addresses the problem of simultaneously migrating a group of co-located and virtual machines (VMs), i.e, VMs executing on the same physical machine. We refer to such a mass simultaneous migration of active VMs as live gang migration. Cluster administrators may often need to perform gang migration for load balancing, system maintenance, or power savings. Application performance requirements may dictate that the total migration time, network traffic overhead, and service downtime, be kept minimal when migrating multiple VMs. State-of-the-art migration techniques optimize the migration of a single VM. In this paper, we optimize the simultaneous migration of multiple co-located VMs. We present the design, implementation, and evaluation of a de-duplication based approach to perform concurrent migration of co-located VMs. Our approach transmits memory content that is identical across VMs only once during migration to significantly reduce both the total migration time and network traffic. Using the QEMU/KVM platform, we detail a proof-of-concept prototype implementation of two types of de-duplication strategies (at page level and sub-page level) and a differential compression approach to exploit content similarity across VMs. Evaluations over Gigabit Ethernet with various types of VM workloads demonstrate that our prototype for gang migration can achieve significant reductions in both network traffic and total migration time.

Group technology-based model and cuckoo optimization algorithm for resource allocation in cloud computing

Efficient cloud resource allocation through timeseries forecasting is critical in cloud data centers, as accurate demand prediction enables optimal utilization of computing resources and cost reduction. Traditional methods based on conventional machine learning or statistical analysis often fall short in capturing complex temporal patterns, resulting in low prediction accuracy and inefficient resource use. To address these challenges, this study introduces a novel Cycle-Consistent Adversarial Adaptation Network with Geyser Inspired Algorithm (CCAAN-GIA), leveraging historical workload data from Bitbrain's cloud data center, specifically from virtual machines in the fast Storage and Rnd datasets. The raw data undergoes pre-processing; including handling of missing values, feature averaging, and min-max normalization to prepare it for time-series forecasting using the CCAAN framework. The model's predictive performance is further enhanced by tuning hyper parameters with the Geyser Inspired Algorithm (GIA). Additionally, to optimize resource distribution and minimize operational costs, the Cat Optimization Algorithm (COA) is employed, ensuring effective resource management. Experimental results demonstrate the superiority of the proposed approach, achieving 99.5% accuracy with significantly lower error metrics-MAE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 0 2}$</tex>, RMSE of 11.2, and MAPE of 0.46-outperforming traditional forecasting models. This confirms the method's potential as a robust solution for intelligent and cost-efficient cloud resource allocation.

Containerization does not only support fast development and deployment of web applications but also provides the potential to improve the energy efficiency in cloud data centers. In container-based clouds, containers are allocated to virtual machines (VMs) and VMs are allocated to physical machines (PMs). This new architecture requires consolidation algorithms to select heterogeneous VMs to host containers and consolidate VMs to PMs simultaneously. Existing server consolidation techniques in VM-based clouds can hardly be applied because of the two-level architecture of the container-based clouds. This paper proposes a novel genetic algorithm (GA) with dual chromosome representation to solve the problem. The experiments show that the proposed GA achieves significantly higher energy efficiency than the compared state-of-the-art algorithms on a wide range of test problems.

Cloud environment is usually associated with non-homogeneity and dynamicity in terms of resource usage and access at all levels. The study of this heterogeneous and non-uniform behavior is therefore an important problem. Google cluster trace which is a production trace released by Google in November 2014 serves as an example of a high scale Cloud environment. This paper deals with statistical analysis of this cluster trace. Since the size of production trace is very huge therefore, Hive which is a HadoopDistributed File System (HDFS) based platform for querying and analysis of big data, has been used. Hive was accessed through its Beeswax interface. The data was imported into HDFS through HCatalog. Apart from Hive, Pig which is a scripting language and provides abstraction on top of Hadoop was used. The method adopted deals with clustering and studying the distribution of arrival time of jobs, distribution of resource usage and also study of distribution of process runtime. To the best of our knowledge the analytical method adopted by us is novel. The findings revealed that jobs in a production trace can be classified into major, mediocre and minor resource usage types. Furthermore, it can be concluded from our study that arrival time of jobs followed weibull distribution. Usage of resources such as CPU and memory was observed to be following a zipf like distribution while study of process runtime shows that some jobs had very small values of runtime while others had very large values hence they followed heavy tailed distribution. Our analysis will help researchers in properly understanding the nonhomogenous and dynamic behavior synonymous with cloud environment. It will also help them in developing new algorithms for resource allocation and scheduling in Cloud. Keywords: Dynamicity, Hadoop,High Scale Cloud, Hive, Pig, Non-Homogenous

Cloud providers generally run one or more Virtual Machine (VM) instances on the same physical machine. Though it increases data center utilization, it exposes VM to a co-location attack. VM placement and migration are the two strategies adopted for mitigating co-locations. Current methods for VM placement or VM migration consider only security as decision criteria and do not consider other factors like Quality-of-Service degradation, data center utilization, etc. This work proposes a placement and migration strategy for mitigation of co-location attacks with joint consideration of multi objectives like QoS, data center utilization, energy consumption, and security risks. A security-driven multi-criteria optimization -based VM placement policy is proposed. A joint consideration of multiobjective performance optimization along with co-location security risk minimization is done to design a novel VM placement policy based on user categorization. The policy can reduce the likelihood of co-location target VM with attacker VM without much degradation to the performance of VM and data center utilization. The solution mitigates co-location risks without much compromise to the performance of VM and data center resource utilization. The co-residence risk is mitigated by the categorization of users into three levels i.e. unlabeled, risky, and safe, and physical machines into two groups as safe and unsafe. The PMs available in data center is grouped into three different VM placement policies, they are undecided pool, safe pool and unsafe pool.

Network Function Virtualization is based on the virtualization of the network functions and it is a new technology allowing for a more flexible allocation of cloud and bandwidth resources. In order to employ the flexibility of the technology and to adapt its use according to the traffic variation, reconfigurations of the cloud and bandwidth resources are needed by means of both migration of the Virtual Machines executing the network functions and reconfiguration of circuits interconnecting the Virtual Machines. The objective of the paper is to study the impact of the maximum number of switch reconfigurations on the cost saving that the Networking Function Virtualization technology allows us to achieve. The problem is studied in the case of a scenario with an elastic optical network interconnecting datacenters in which the Virtual Machines are executed. The problem can be formulated as an Integer Linear Programming one introducing a constraint on the maximum number of switch reconfigurations but due to its computational complexity we propose a low computational complexity heuristic allowing for results close to the optimization ones. The results show how the limitation on the number of possible reconfigurations has to be taken into account to evaluate the effectiveness in terms of cost saving that the Virtual Machine migrations in Network Function Virtualization environment allows us to achieve.

Nowadays cloud computing provides an effective way of implementing infrastructure as a service (IaaS). However virtualized data centers still face many challenges, such as low resource utilization of physical machines (PMs) and imbalanced server loads. Virtual machine (VM) consolidation based on live migration allows administrator to dynamically redeploy VMs into PMs for better resource utilization. Common VM consolidation methods usually focus on one challenge, and pay little attention to others or just ignore them, while effective VM redeployment should make tradeoffs between these challenges, and more importantly, should not let other challenges become worse. On the other hand, since VM live migration leads to performance degradation of applications, consolidation work should control migration cost. In this paper, we provide a manner to comprehensively consider power consumption, load balancing, communication delay and migration cost during VM redeployment. And we formalize VM consolidation as a multiobjective optimization problem, then solve this problem with an improved genetic algorithm. Simulation experiments based on real world workload trace show that compared with single objective optimization approaches our method effectively make tradeoffs between optimized objectives and has better overall performance, which is more practical in real data centers.