Efficient multi-tenant virtual machine allocation in cloud data centers

Live migration of virtual machine (VM) is a promising technology that helps physical machines (PMs) adapt to load changes and guarantees Quality of Service (QoS) in cloud data center. Many individual-based VM migration studies ignore the association between VMs, resulting in high communication cost. Some research on multiple VMs migration migrates the VM group as a whole, which is likely to result in ineffective migration and increase the network burden. In this paper, a VM migration algorithm based on group selection (VMMAGS) is proposed, which takes into account the migration cost, communication cost, and VM heat to optimize migration performance. The appropriate VM groups are selected as migration options, and the optimal migration scheme is obtained according to the integration cost of partitions of selected VM groups. Extensive experiments show that our algorithm can effectively reduce the migration cost and communication cost, improve the system reliability compared with other related algorithms.

A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

Security-aware dynamic VM consolidation

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

Nowadays, Cloud Service Providers (CSPs) provide flexible architectures and reliable internet accessories in cloud datacenters, where different types of applications are hosted. In order to achieve higher resource utilization and better quality of service, virtualization is widely used and implemented with virtual machines (VMs). However, most studies focus on VM management for utilization and energy, not yet considering the cost for emerging internet applications. Therefore, to further reduce network cost, we propose utilization-and-network-driven (UND) and only-network-driven (OND) strategies on top of the common minimal-migration (MM) based VM management as UND-MM and OND-MM. According to simulation results, both UND-MM and OND-MM strategies effectively reduce network cost by averagely 7% and 37%, respectively, of the original MM while retaining comparable resource utilization in CPU and memory for normally-loaded and overloaded datacenters. Moreover, the OND-MM, which is simpler, outperforms the UND-MM, and thus becomes more preferable for VM management in cloud datacenters.

The swift advancement of cloud computing and virtualization technologies has reshaped the IT infrastructure domain, rendering virtual machine (VM) allocation a pivotal fac- tor in achieving both optimal performance and cost-effectiveness. This paper presents an in-depth examination of current VM allocation methodologies, classifying them into three primary categories: static allocation, dynamic allocation, and heuristic- based approaches. We investigate the foundational principles of each strategy, analyzing their operational frameworks, benefits, and drawbacks across varying workload scenarios. Static allocation, although simple, frequently results in re- source under utilization due to its lack of adaptability to changing demands. In contrast, dynamic allocation techniques, which mod- ify resources based on real-time consumption, provide greater flexibility but introduce challenges related to overhead and latency. Heuristicbased approaches, which utilize optimization algorithms, can effectively manage resource distribution but may necessitate considerable computational resources and time for execution.To assess the effectiveness of these strategies, we adopt a simulation-based approach that replicates actual cloud environments. Our experiments concentrate on critical performance metrics, such as response time, throughput, resource utilization, and energy consumption. The results highlight notable performance variations among the different strategies, emphasizing the necessity for a more adaptive and intelligent method for VM allocation. In light of these challenges, we introduce an innovative VM allocation policy that integrates machine learning techniques to forecast workload patterns based on historical data. By utilizing predictive analytics, our policy dynamically allocates resources, ensuring that VMs receive the optimal amount of resources in real-time, thereby reducing latency and enhancing throughput. Our findings indicate that this advanced policy significantly sur- passes traditional allocation methods, achieving superior resource utilization, lower operational costs, and enhanced overall system efficiency. The implications of this research extend beyond theoretical exploration, offering practical insights for cloud service providers and organizations aiming to refine their VM allocation strategies. By adopting adaptive allocation techniques, stakeholders can more effectively respond to the demands of contemporary cloud workloads, ultimately fostering improved performance and user satisfaction.

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

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.

The article examines the importance of container migration within the context of container placement tasks in cloud data centers. Cloud computing offers organizations significant benefits such as flexibility, scalability, and reduced costs for equipment, energy, and maintenance. Cloud providers face the challenge of not only efficiently placing containers initially but also dynamically reallocating them, along with virtual machines, in response to changing demands and workloads. This necessitates the development and application of migration algorithms. The study analyzes how migration is a critical component for ensuring optimal resource use, maintaining high service availability, and reducing service level agreement (SLA) violations. The research aims to enhance the productivity of cloud data centers in the task of container placement by utilizing container mi-gration algorithms. This objective is driven by the need for cloud providers to use their resources effectively to provide higher quality service and maintain competitive pricing. The use of migrations prevents overloading or underloading of servers and virtual machines, which is especially impor-tant in the highly dynamic environment of cloud computing. The research developed a hybrid mi-gration algorithm that includes the migration of both containers and virtual machines. Mathemati-cal aspects of the container placement task are described, and the migration algorithm is detailed. Experiments showed that implementing this algorithm reduces operational costs by 24% compared to traditional methods that do not use migration, and by 9% compared to less flexible migration strategies. There was also a demonstrated reduction in SLA violation times by 40%, indicating im-provements in service quality and reliability of cloud services. The conclusions of this study confirm that container and virtual machine migration is necessary for effective management of dynamic re-sources in cloud data centers in the context of container placement tasks.

Cloud computing(CC) delivers multiple services to users by processing complex tasks through internet connections to serve as a dynamic, dependable, and flexible computing solution. With the increased Internet speed, the user demands to perform many tasks through computing provided by cloud virtual machines (VMs) created in heterogeneous servers. The complexity of cloud computing(CC) increases through the diversity of servers and the increased demand for resources to perform more tasks, which makes the cloud data center(CDC) unbalanced in load. Therefore, future cloud systems will require more effective resource management(RM) methods to balance load and improve Quality of Service (QoS). This paper proposes KMPPVM-DRM, a dynamic clustering and scheduling approach for VM resource allocation(RA) based on the KMeans++ algorithm. VMs are clustered into three groups (high, medium, low) according to their real-time normalized CPU and RAM weights, using weighting parameters α = 0.6 and β = 0.4. Tasks are categorized into three levels based on length and mapped to appropriate VM clusters. Within each cluster, a Utilization-Level Comparator (ULC) dynamically selects the optimal VM for task execution. The experimental results were conducted using CloudSim, with 5 and 10 VMs and task counts ranging from 1000 to 10000. The proposed model was compared with PSO, ACO, and DBSCAN algorithms using execution time, average start time, and average finish time. Results show that with 10 VMs, the proposed model achieved a harmonic mean execution time of 886.72 ms, compared to 4102.99 ms (PSO), 4672.25 ms (ACO), and 3071.51 ms (DBSCAN). It also attained the lowest harmonic mean start and finish times of 166.41 ms and 167.19 ms, respectively. Relative reduction in execution time against DBSCAN ranged from 69.90% to 71.52%, with start and finish time improvements between 52.65% and 81.51%.To ensure statistical reliability, a paired t-test confirmed that the performance improvements were statistically significant (p < 0.05) across all task sizes. The findings confirm that KMPPVM-DRM enhances resource allocation(RA) efficiency and scheduling effectiveness, maintaining balanced loads even with limited resources (e.g., only 5 VMs) and outperforming PSO, ACO, and DBSCAN in all tested scenarios.

Energy consumption is significant in cloud data centres even when some virtual machines (VMs) are idle. Taking into account both the energy efficiency and the response performance, we propose a VM allocation strategy with speed switch and VM consolidation in the cloud data centre. When the traffic is lighter, the processing speed of VMs in baseline module will be switched between a low speed and a high speed. When the traffic is much heavier, the VMs in reserved module will be consolidated with the VMs in baseline module, and operates at a high speed. We establish a two-dimensional Markov chain to mathematically estimate the energy-saving degree and the response performance. Numerical experiments with analysis and simulation show that our proposed VM allocation strategy can effectively reduce the energy consumption as well as guarantee the response performance.

The rapid revolution of cloud computing model is accompanied by huge amounts of energy consumed by the cloud data centers. So, enhancing the energy efficiency of those data centers has become a major challenge. This paper tackles the problem of enhancing the energy consumption of cloud data centers by proposing a novel virtual machine placement strategy. The proposed strategy suits both static and dynamic placement process. It aims to better utilize the involved physical machines which host the virtual machines. As different types of jobs do not intensively use the compute and/or non-compute resources in the hosted physical machine, virtual machines allocated to the jobs of different types are placed on the same physical machine where possible. The paper presents a mathematical formulation of the virtual machine placement process based on the Multiple Choice Knapsack Problem which is a generalization of the classical Knapsack Problem. The performance evaluation of the proposed strategy shows that it can enhance the energy efficiency of the cloud data centers by trying to minimize the number of the involved physical machines which host the virtual machines, and by optimally utilizing the involved physical machines.

Modern cloud data centers typically exploit management strategies to reduce the overall energy consumption. While most of the solutions focus on the energy consumption due to computational elements, the optimization of network-related aspects of a data center is becoming more and more important, considering also the advent of the Software-Defined Network paradigm. However, an enabling step to implement network-aware Virtual Machine (VM) allocation is the knowledge of data exchange patterns. In this way we can place in well-connected hosts (or on the same physical host) the couples of VMs that exchange a large amount of information. Unfortunately, in Infrastructure as a Service data centers, a detailed knowledge on VMs data exchange is seldom available without the deployment of a specialized (and costly) monitoring infrastructure. In this paper, we propose a technique to infer VMs communication patterns starting from input/output network traffic time series of each VM. We discuss both the theoretical aspect of such technique and the design challenges for its implementation. A case study is used to demonstrate the viability of our idea.

Smart Cloud resource management has gained much research interest in recent years. Optimized resource allocation is necessary while moving from existing computing infrastructure to smart cloud infrastructure. This will allow cloud service providers to get more benefits out of what they already have, increasing capacity and efficiency of existing cloud infrastructure. Optimal Virtual Machine (VM) allocation is a major challenge in cloud resource management. Development of a VM placement strategy which can guarantee required bandwidth and optimizes data transfer delay between VMs is a challenging issue. This paper presents an optimization algorithm to ensure guaranteed bandwidth while minimizing data transfer delay between VMs. Simulation results show that the generated VM placement plan gives minimum inter-distance between allocated VMs while satisfying bandwidth requirements compared to the other existing algorithms.

Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.