CloudMon: a resource-efficient IaaS cloud monitoring system based on networked intrusion detection system virtual appliances

The networked intrusion detection system virtual appliance (NIDS-VA), also known as virtualized NIDS, plays an important role in the protection and safeguard of IaaS cloud environments. However, it is nontrivial to guarantee both of the performance of NIDS-VA and the resource efficiency of cloud applications because both are sharing computing resources in the same cloud environment. To overcome this challenge and trade-off, we propose a novel system, named CloudMon, which enables dynamic resource provision and live placement for NIDS-VAs in IaaS cloud environments. CloudMon provides two techniques to maintain high resource efficiency of IaaS cloud environments without degrading the performance of NIDS-VAs and other virtual machines (VMs). The first technique is a virtual machine monitor based resource provision mechanism, which can minimize the resource usage of a NIDS-VA with given performance guarantee. It uses a fuzzy model to characterize the complex relationship between performance and resource demands of a NIDS-VA and develops an online fuzzy controller to adaptively control the resource allocation for NIDS-VAs under varying network traffic. The second one is a global resource scheduling approach for optimizing the resource efficiency of the entire cloud environments. It leverages VM migration to dynamically place NIDS-VAs and VMs. An online VM mapping algorithm is designed to maximize the resource utilization of the entire cloud environment. Our virtual machine monitor based resource provision mechanism has been evaluated by conducting comprehensive experiments based on Xen hypervisor and Snort NIDS in a real cloud environment. The results show that the proposed mechanism can allocate resources for a NIDS-VA on demand while still satisfying its performance requirements. We also verify the effectiveness of our global resource scheduling approach by comparing it with two classic vector packing algorithms, and the results show that our approach improved the resource utilization of cloud environments and reduced the number of in-use NIDS-VAs and physical hosts.The authors gratefully acknowledge the anonymous reviewers for their helpful suggestions and insightful comments to improve the quality of the paper. The work reported in this paper has been partially supported by National Nature Science Foundation of China (No. 61202424, 61272165, 91118008), China 863 program (No. 2011AA01A202), Natural Science Foundation of Jiangsu Province of China (BK20130528) and China 973 Fundamental R&D Program (2011CB302600)

A Fuzzy Predictable Load Balancing Approach in Cloud Computing

Cloud computing is a new paradigm for hosting and delivering services on demand over the internet where users access services. It is an example of an ultimately virtualized system, and a natural evolution for data centers that employ automated systems management, workload balancing, and virtualization technologies. Live virtual machine (VM) migration is a technique to achieve load balancing in cloud environment by transferring an active overload VM from one physical host to another one without disrupting the VM. In this study, to eliminate whole VM migration in load balancing process, we propose a Fuzzy Predictable Load Balancing (FPLB) approach which confronts with the problem of overload VM, by assigning the extra tasks from overloaded VM to another similar VM instead of whole VM migration. In addition, we propose a Fuzzy Prediction Method (FPM) to predict VMs migration time. This approach also contains a multi-objective optimization model to migrate these tasks to a new VM host. In proposed FPLB approach there is no need to pause VM during migration time. Furthermore, considering this fact that VM live migration contrast to tasks migration takes longer to complete and needs more idle capacity in host physical machine (PM), the proposed approach will significantly reduce time, idle memory and cost consumption

A Multi-objective Optimization Model for Virtual Machine Mapping in Cloud Data Centres

© 2016 IEEE. Modern cloud computing environments exploit virtualization for efficient resource management to reduce computational cost and energy budget. Virtual machine (VM) migration is a technique that enables flexible resource allocation and increases the computation power and communication capability within cloud data centers. VM migration helps cloud providers to successfully achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, the VM migration process can affect the performance of applications unless it is supported by smart optimization methods. This paper presents a multi-objective optimization model to address this issue. The objectives are to minimize power consumption, maximize resource utilization (or minimize idle resources), and minimize VM transfer time. Fuzzy particle swarm optimization (PSO), which improves the efficiency of conventional PSO by using fuzzy logic systems, is relied upon to solve the optimization problem. The model is implemented in a cloud simulator to investigate its performance, and the results verify the performance improvement of the proposed model

Geo-distributed Multi-tier Workload Migration Over Multi-timescale Electricity Markets

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications\u27 power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is $\mathcal {NP}$-Hard, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by $25\%$–$30\%$ and electricity cost by approximately $25\%$ compared to the baseline

MorphoSys: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798

A Multi-Objective Load Balancing System for Cloud Environments

© 2017 The British Computer Society. All rights reserved. Virtual machine (VM) live migration has been applied to system load balancing in cloud environments for the purpose of minimizing VM downtime and maximizing resource utilization. However, the migration process is both time-and cost-consuming as it requires the transfer of large size files or memory pages and consumes a huge amount of power and memory for the origin and destination physical machine (PM), especially for storage VM migration. This process also leads to VM downtime or slowdown. To deal with these shortcomings, we develop a Multi-objective Load Balancing (MO-LB) system that avoids VM migration and achieves system load balancing by transferring extra workload from a set of VMs allocated on an overloaded PM to other compatible VMs in the cluster with greater capacity. To reduce the time factor even more and optimize load balancing over a cloud cluster, MO-LB contains a CPU Usage Prediction (CUP) sub-system. The CUP not only predicts the performance of the VMs but also determines a set of appropriate VMs with the potential to execute the extra workload imposed on the VMs of an overloaded PM. We also design a Multi-Objective Task Scheduling optimization model using Particle Swarm Optimization to migrate the extra workload to the compatible VMs. The proposed method is evaluated using a VMware-vSphere-based private cloud in contrast to the VM migration technique applied by vMotion. The evaluation results show that the MO-LB system dramatically increases VM performance while reducing service response time, memory usage, job makespan, power consumption and the time taken for the load balancing process

A Review On Green Cloud Computing

The objective of green computing is to reap monetary growth and enhance the way the computing devices are used. In large data centers computational offloading is main problem due to increased demand for timely and response for real time application which lead to high energy consumption by data centers, so the aim of green computing is to find energy efficient solution which monopolize optimal utilization of the available resources. Green IT methods comprises of environmentally viable management, energy efficient computers and enhanced recycling procedures. By using different algorithm and energy efficient scheduling power consumption of virtual machine can be minimize, this paper provide an overview of different algorithms and techniques which are used to move towards the green computing

CoolCloud: improving energy efficiency in virtualized data centers

In recent years, cloud computing services continue to grow and has become more pervasive and indispensable in people\u27s lives. The energy consumption continues to rise as more and more data centers are being built. How to provide a more energy efficient data center infrastructure that can support today\u27s cloud computing services has become one of the most important issues in the field of cloud computing research. In this thesis, we mainly tackle three research problems: 1. how to achieve energy savings in a virtualized data center environment; 2. how to maintain service level agreements; 3. how to make our design practical for actual implementation in enterprise data centers. Combining all the studies above, we propose an optimization framework named CoolCloud to minimize energy consumption in virtualized data centers with the service level agreement taken into consideration. The proposed framework minimizes energy at two different layers: (1) minimize local server energy using dynamic voltage \& frequency scaling (DVFS) exploiting runtime program phases. (2) minimize global cluster energy using dynamic mapping between virtual machines (VMs) and servers based on each VM\u27s resource requirement. Such optimization leads to the most economical way to operate an enterprise data center. On each local server, we develop a voltage and frequency scheduler that can provide CPU energy savings under applications\u27 or virtual machines\u27 specified SLA requirements by exploiting applications\u27 run-time program phases. At the cluster level, we propose a practical solution for managing the mappings of VMs to physical servers. This framework solves the problem of finding the most energy efficient way (least resource wastage and least power consumption) of placing the VMs considering their resource requirements

Energy optimization methods for Virtual Machine Placement in Cloud Data Center

The Information Technology industry has been upheaved by the influx of cloud computing. The extension of Cloud computing has resulted in the creation of huge data centers globally containing numbers of computers that consume large amounts of energy resulting in high operating costs. To reduce energy consumption providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an efficient way. The problems of VM consolidation are host overload detection, host under-load detection, VM selection and VM placement. Each of the aforestated sub-problems must operate in an optimized manner to maintain the energy usage and performance. The process of VM placement has been focused in this work, and energy efficient, optimal virtual machine placement (E2OVMP) algorithm has been proposed. This minimizes the expenses for hosting virtual machines in a cloud provider environment in two different plans such as i) reservation and ii) on-demand plans, under future demand and price uncertainty. It also reduces energy consumption. E2OVMP algorithm makes a decision based on the gilt-edged solution of stochastic integer programming to lease resources from cloud IaaS providers. The performance of E2OVMP is evaluated by using CloudSim with inputs of planet lab workload. It minimized the user’s budget, number of VM migration resulting efficient energy consumption. It ensures a high level of constancy to the Service Level Agreements (SLA).Keywords: Cloud resource management; virtualization; dynamic consolidation; stochastic integer programming (SIP)*Cite as: Esha Barlaskar, N. Ajith Singh, Y. Jayanta Singh, “Energy optimization methods for Virtual Machine Placementin Cloud Data Center†ADBU J.Engg.Tech., 1(2014) 0011401(7pp