Virtual Machine Placement Research Articles

A NEURO-GAME MODEL OF STRATEGY ANALYSIS DURING THE DYNAMIC INTERACTION OF PHISHING ATTACK PARTICIPANTS

Cloud computing is a dynamically evolving computing paradigm. The demand for cloud applications and technologies has especially increased during the CoVID-19 pandemic and martial law in Ukraine. The main purpose of using cloud applications and technologies is to free users of cloud resources from managing hardware and software. One of the challenges in designing a private university cloud is estimating the required number of virtualization cluster nodes. These hosts host virtual machines (VMs) of users. These VMs can be used by students and teachers to complete academic assignments as well as scientific work. The second task is to optimize the placement of VMs in the computer network (CN) of the university, which makes it possible to reduce the number of CN nodes without affecting functionality. And this ultimately helps to reduce the cost of such a solution to deploy a private university cloud, which is not unimportant for Ukrainian universities under martial law. The article proposes a model for estimating the required number of virtualization cluster nodes for a private university cloud. The model is based on a combined approach that involves jointly solving the problem of optimal packing and finding, using a genetic algorithm, the configuration of server platforms of a private university cloud.

Bi-Objective Optimization of Bandwidth Resources and Energy Consumption for Efficient Virtual Machine Placement in Cloud Computing

Bi-Objective Optimization of Bandwidth Resources and Energy Consumption for Efficient Virtual Machine Placement in Cloud Computing

Energy efficient cloud computing using secure virtual machine migration: A taxonomy

Numerous cloud service enterprises have launched a broad variety of cloud services. Several businesses and people use these programs on a daily basis. The efficiency of cloud service depends on efficiency of the service providers. It is also important for cloud customers to analyses and review the available cloud resources thoroughly to pick the best cloud service. Secure virtual machine placement is a big issue during the migration of virtual machines in cloud computing. Secure virtual machine migration maximizes the throughput and reliability of the system and reduces Service Level Agreement (SLA) violations. This article focuses on secure virtual machine migration with energy-efficient techniques

Multi criteria optimize secure VM placement strategy against cross VM attack

Cloud computing is becoming more and more popular, but it also presents several security challenges. VMs are generally rented out on demand to users on large scale. Cloud service providers have numerous issues as a result of the increasing user demand for lowcost and high-performance cloud computing, both in terms of security and resource. One of these difficulties is the “co-residence Cross VM attack,” in which attackers use the shared cloud’s resource pooling to launch various attacks against their co-residents on shared physical machine (PM). Co-resident attacks, in which malicious users create side channels and steal sensitive data from VMs, are often used by attackers. The goal is to assure secure VM placement while optimizing physical resource in cloud data centers. In this study, we concentrate on safe virtual machine placement techniques that provide security against the co-location-based Cross VM attacks. To find the optimum allocation technique, we further apply the Particle swarm optimization (PSO algorithm motivated by particle swarm and their behavior. The proposed approach may help to make the cloud energy-efficient and secure, according to the findings of the experiments.

A multi-objective cloud energy optimizer algorithm for federated environments

Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using real-world datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment.

Fractional Artificial Bee Chicken Swarm Optimization technique for QoS aware virtual machine placement in cloud

SummaryCloud computing provides different constructive services in order to share huge scale information, computing resources, storage resources, and offer research knowledge. Cloud Service Providers (CSPs) afforded its services to cloud customers, usually in structure of Virtual Machines (VMs). In this paper, Fractional Artificial Bee Chicken Swarm Optimization (Fractional ABCSO) is introduced for VM placement in the cloud. The Fractional ABCSO is obtained by integrating the Fractional concept (FC), Chicken Swarm Optimization (CSO), and Artificial Bee Colony (ABC). Here, the cloud simulation is performed by means of VM and physical machine (PM). At first, VM placement is carried out using different system factors, such as Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, migration cost, memory, frequency, power, along with Quality of Service (QoS). The developed Fractional ABCSO algorithm outperformed other existing techniques with regard to load, migration cost, and power consumption of 0.1614, 0.0535, and 0.0408.

Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers

VM consolidation has been proposed as an effective solution to improve resource utilization and energy efficiency through VM migration. Improper VM placement during consolidation may cause frequent VM migrations and constant on–off switching of PMs, which can significantly hurt QoS and increase energy consumption. Most existing algorithms on efficient VM placement suffer the problem of easily falling into a sub-optimum prematurely since they are heuristic. Also, they do not achieve a good balance between multiple different goals, such as resource utilization, QoS, and energy efficiency. To address this problem, we propose an effective and efficient VM placement approach called MOEA/D-based VM placement, with the goal of optimizing energy efficiency and resource utilization. We develop an improved MOEA/D algorithm to search for a Pareto-compromise solution for VM placement. Our experiment results demonstrate that the proposed multi-objective optimization (MOO) model and VM placement solution have immense potential as it offers significant cost savings and a significant improvement in energy efficiency and resource utilization under dynamic workload scenarios.

A hierarchical decentralized architecture to enable adaptive scalable virtual machine migration

AbstractCloud computing is an established paradigm for end users to access resources. Cloud infrastructure providers seek to maximize accepted requests, meet Service Level Agreements (SLAs), and reduce operational costs by dynamically allocating Virtual Machines (VMs) to physical nodes. Many solutions have been presented to manage cloud infrastructure, however, these tend to be centralized and suffer in their ability to maintain Quality of Service (QOS) and support data centers with thousands of nodes. Decentralized approaches, with no central management, can manage large data centers. However, these tend to reduce the ability to obtain an optimal resource allocation across the data center. To address this, we propose a hybrid hierarchical decentralized architecture that achieves lower SLA violations and lowers network traffic. We used simulation to evaluate our proposal in practice with a variety of existing VM placement policies.

Improving Virtual Machine Migration Effects in Cloud Computing Environments Using Depth First Inspired Opportunity Exploration

The cloud platform has established itself as the de-facto standard in IT outsourcing. This is resulting in large-scale migration of infrastructure and development platforms from in-house to cloud service providers. Many recent proposals on cloud platforms have addressed several issues that appeared on the cloud horizon. VM placement (VMP) has been a serious concern when it comes to placement of VMs after migration or VM reallocation. Most of the recent works have lacked multiple VM placement (MVMP) problem instances. A recently researched idea of MVMP through depth first opportunistic exploration (DFOE) is proposed in this paper. The performance of MVMP is compared with existing single VM placement benchmark algorithm. Improvement in terms of number of VM migrations, energy consumption, and VM reallocation is reported through simulation of real-time load scenario. Cloud environments can benefit from MVMP and improve operating margins in terms of power saving and load balancing.

Dynamic Resource Allocation Based on Priority in Various Data Centers Custom-Based Waiting Queue Technique in Cloud Computing

For delivering the cloud services over the internet, cloud computing has become proficient infrastructural model for hosting cloud services. Server virtualization is key technology that enables cloud computing as a service, which authorizes dynamic sharing of physical resources. Virtualization introduces the problem of virtual machine placement that increases the overheads in load balancing. Existing infrastructure needs the strategy for the VM Placement as it may create poor allocation and load balancing issues. In most of the cases, due to lack of input parameters physical machines are partially loaded that creates issue of fragmentation which leads to in sufficient resources that causes more utilization of physical machines in any infrastructure. We did extensive survey in the said domain and found that, In load balancing approach, when VM place mentis done without measuring its lifetime, that creates fragments on PM. So we propose dynamic priority based spill over technique and add the concept of short life/long life container for solving the fragmentation issue.

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

Power-Efficient Virtual Machine Placement in Cloud Datacenters using Heuristic Assisted Enhanced Discrete Particle Swarm Optimization

The increase in cloud computing services and the large-scale construction of data centers led to excessive power consumption. Datacenters contain a large number of servers where the major power consumption takes place. An efficient virtual machine placement algorithm is substantial to attain energy consumption minimization and improve resource utilization through reducing the number of operating servers. In this paper, an enhanced discrete particle swarm optimization (EDPSO) is proposed. The enhancement of the discrete PSO algorithm is achieved through modifying the velocity update equation to bound the resultant particles and ensuring feasibility. Furthermore, EDPSO is assisted by two heuristic algorithms random first fit (RFF) and random best fit (RBF) to produce hydride algorithms termed RFF-EDPSO and RBF-EDPSO. The proposed algorithms are evaluated and compared with recent algorithms to minimize power consumption. Simulation results showed the effective performance of RFF-EDPSO and RBF-EDPSO in minimizing the number of operating servers.

GVMP: A multi-objective joint VM and vGPU placement heuristic for API remoting-based GPU virtualization and disaggregation in cloud data centers

The diverse needs of customers drive cloud providers to incorporate more GPU-enabled services. It is known that users barely utilize GPUs. Hence, GPU virtualization techniques are employed to enable GPU sharing and increase resource utilization. Among GPU virtualization techniques, API remoting leverages disaggregation for GPU provisioning, allowing GPUs to be accessed remotely by non-resident VMs. It enables a VM to access any available GPU in the data center which, in turn, provides more flexibility to reach a better placement. It also enables users to choose VM and vGPU instances separately based on computational needs as GPU-enabled VM instances with fixed configurations may not proportionally utilize allocated resources. However, an inefficient VM or vGPU placement may result in poor performance and increase in energy consumption and rejection ratio. There is also a network communication overhead for remotely allocated vGPUs. The main challenge is how to place VMs and vGPUs such that rejection ratio is decreased and the negative performance impacts caused by GPU sharing and remote access are kept at a minimum. In this work, we formally define the joint problem of VM and vGPU placement based on API remoting as a multi-objective ILP model and introduce gVMP as a heuristic to solve it. The proposed method is compared against state-of-the-art heuristics and commercial solutions that are based on API remoting and GRID technologies for the placement of GPU-enabled VMs. We study the effectiveness of the proposed gVMP algorithm for low and high arrival rates in slow and fast networks. Our results show that under different scenarios and in comparison to state-of-the-art policies, our heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively.

A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment

A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment

Review and analysis of secure energy efficient resource optimization approaches for virtual machine migration in cloud computing

Computing operations such as databases, networks, hardware, programs, analytics, and so on are all part of the cloud computing service. As such, it may serve as an option to in-house hardware and software. However, dynamic consolidation of VMs is required to improve power consumption, load balance, the frequency of migrations, Quality of Service (QoS), and the rate at which SLA violations are addressed, all of which contribute to better resource use. VM technology has quickly become a pillar of data centers and cluster systems because to its utility in partitioning, consolidating, and moving workloads. VM placement is another thing that affects the quality of consolidation. It is important to design a system that improves energy efficiency by allocating resources to applications in a smart way while still meeting QoS requirements for applications. Moreover, the security of information that is being processed during the migration process is also one of the important tasks. Hence, in this paper, review and analysis based on parameters and metrics for the VM migration under the influence of energy optimization, consolidation, and security have been investigated in a wider manner. Moreover, some open issues that are still prevalent in the current field have also been highlighted.

Evaluating the Security and Economic Effects of Moving Target Defense Techniques on the Cloud

Moving Target Defense (MTD) is a proactive security mechanism that changes the attack surface with the aim of confusing attackers. Cloud computing leverages MTD techniques to enhance the cloud security posture against cyber threats. While many MTD techniques have been applied to cloud computing, there has so far been no joint evaluation of the effectiveness of MTD techniques with respect to security and economic metrics. In this paper, we first introduce mathematical definitions for the combination of three MTD techniques: Shuffle, Diversity, and Redundancy. Then, we utilize four security metrics – namely, system risk, attack cost, return on attack, and reliability – to assess the effectiveness of the combined MTD techniques applied to large-scale cloud models. Second, we focus on a specific context based on a cloud model for e-health applications to evaluate the effectiveness of the MTD techniques using security and economic metrics. We introduce (1) a strategy to effectively deploy the Shuffle MTD technique using a virtual machine placement technique, and (2) two strategies to deploy the Diversity MTD technique through operating system diversification. As deploying the Diversity technique incurs costs, we formulate the optimal diversity assignment problem (O-DAP), and solve it as a binary linear programming model to obtain the assignment that maximizes the expected net benefit.

Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

SummaryData sharing in cloud computing happens with multiple participants to freely distribute the group data, which focuses on advancing the effectiveness of work in cooperative backgrounds and has attained widespread benefits. The main intent of this article is to accomplish a virtual machines (VMs) placement and migration model using a hybrid meta‐heuristic concept. A new meta‐heuristic algorithm named DJ‐HA is developed for optimal VM placement and migration to reduce the count of active servers, and minimization of makespan, and energy consumption with a faster convergence rate in a cloud background. Then, the VM migration is done based on the multi‐objective function concerning energy consumption and makespan using the same hybrid DJ‐HA. From the result analysis, the energy consumption of the DJ‐HA is correspondingly secured at 4.3%, 3.5%, 31%, and 33% more advanced than PSO, GWO, DHOA, and JA, at the 100th iteration for Experiment 1. Accordingly, the cost function of the suggested DJ‐HA is secured at 88.8%, 89.4%, 33.3%, and 50% increased than PSO, GWO, DHOA, and JA at the 100th iteration for Experiment 4. Hence, it is proved that the suggested VM migration using DJ‐HA is enriched than the other conventional algorithms.

Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform

In the era of information explosion, the energy consumption of cloud data centers is significant. It’s critical to reduce the energy consumption of large-scale data centers while guaranteeing quality of service (QoS), especially the energy consumption of video cloud computing platforms. The application of virtual machine (VM) consolidation has been regarded as a promising approach to improve resource utilization and save energy of the data centers. In this paper, an energy efficient and QoS-aware VM consolidation method is proposed to address the issues. A combined prediction model based on grey model and ARIMA is applied to host status detection, and we provide a new scheme that VM placement policy based on resource utilization and varying energy consumption to search most suitable host and VM selection policy called AUMT selecting VM with low average CPU utilization and migration time. Extensive experimental results based on the cloudsim simulator demonstrate that proposed approach enables to achieve the objectives reducing energy consumption, number of migrations, SLAV and ESV by an average of 56.07%, 79.21%, 91.01% and 84.34% compared with the benchmark methods and the AUMT can reduce energy consumption, the number of migrations and ESV by an average of 15.46%, 28.11% and 3.96% compared with the state-of-the-art method.

Learning-Based Virtual Machine Selection in Cloud Server Consolidation

In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection, and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects appropriate VMs for migration without direct host overloading detection, thereby reducing the generation of SLAV, ensuring the performance, and reducing the energy consumption of CDC. Simulations driven by real VM workload traces show that our method outperforms the existing methods in reducing SLAV generation and CDC energy consumption.

Resource scheduling methods for cloud computing environment: The role of meta-heuristics and artificial intelligence

The growth and development of scientific applications have demanded the creation of efficient resource management systems. Resource provisioning and scheduling are two core components of cloud resource management systems. Cloud resource scheduling is the most critical problem to solve efficiently due to the heterogeneity of resources, their inter-dependencies, and unpredictability of load in the cloud environment. In this paper, we review the background of scheduling and state-of-the-art scheduling techniques in cloud computing. We first introduce the general background, and phases of scheduling. A comprehensive survey of existing resource scheduling problems proposed so far is presented considering high-level taxonomy. This high-level taxonomy considers Virtual Machine (VM) placement, Quality of Service (QoS) parameters, heuristic methods, and other miscellaneous techniques for resource scheduling. This study also discusses scheduling in Infrastructure as a Service (IaaS) clouds and comparison based on important parameters is also investigated. The importance of meta-heuristic methods and artificial intelligence for resource scheduling methods in cloud computing is discussed thoroughly. The objective of this work is to help the researchers to understand the basic concepts related to scheduling and facilitate the process of designing new scheduling methods by addressing issues raised in the scheduling and studying the existing methodologies.