Application-Aware Virtual Machine Placement in Data Centers

Stochastic scheduling for variation-aware virtual machine placement in a cloud computing CPS

We study the problem of virtual machine (VM) placement and migration in a data center. In the current approaches, VMs are assigned to physical servers using on-demand provisioning. Such an approach is simple but it often results in a poor performance due to resource fragmentation. Additionally, sub-optimal VM placement usually generates unneeded VM migration and unnecessary cross network traffic. The efficiency of a datacenter therefore significantly depends on how VMs are provisioned and where they are placed. A good placement scheme will not only improve the quality of service but also reduce the operation cost of the data center. In this paper, we study the problem of optimal VM placement and migration to minimize resource usage and power consumption in a data center. We formulate the optimization problem as a joint multiple objective function and solve it by leveraging the framework of convex optimization. Due to the intractable nature of the combinatorial optimization, we then propose Multi-level Join VM Placement and Migration (MJPM) algorithms based on the relaxed convex optimization framework to approximate the optimal solution. The theoretical analysis demonstrates the effectiveness of our proposed algorithms that substantially increases data center efficiency. In addition, our extensive simulation results on different practical topologies show significant performance improvement over the existing approaches.

Energy efficiency is one of the important issues in green cloud data centers (DCs). In this context, a virtual machine (VM) placement is one of the important techniques which can be used to achieve energy efficiency in such environments. Bio-inspired optimization algorithms are widely used in the literature to solve the VM placement (VMP) problem and different types of them are benefited to achieve energy efficiency while meeting Quality of Service (QoS) and user-specified constraints such as deadlines and cost. This paper presents a comprehensive survey and taxonomy of the bio-inspired VMP schemes. For this purpose, we first provide the essential concepts regarding the VMP and describe various objectives and factors which can be considered in this process. Then, we provide a taxonomy of VMP schemes regarding their applied optimization algorithms and compare their employed factors in the VMP process as well as simulator environments and the metrics which have been utilized in the verification of the investigated VMP frameworks. Finally, the concluding remarks and future researches directions are provided.

An overview of virtual machine placement schemes in cloud computing

A hybrid energy–Aware virtual machine placement algorithm for cloud environments

This paper presents a hybrid optimization model that allows a cloud service provider to establish virtual machine (VM) placement strategies for its data centers in such a way that energy efficiency and network quality of service are jointly optimized. Usually, VM placement is an activity not fully integrated with network operations. As such, the VM placement strategy does not take into account the impact it produces on the network performance in terms of quality of service parameters such as packet losses and traffic delays. The proposed strategy allows cloud providers to reach a balance between the energy efficiency of their infrastructures and the network quality of service they offer to their customers. The proposed strategy has high potential for parallelism making it feasible for the energetic optimization of large cloud infrastructures. In addition, the strategy allows network operations practices such as constraint-based routing be incorporated into the VM placement process.

Virtualization is a key technology for mobile cloud computing (MCC) and the virtual machine (VM) is a core component of virtualization. VM provides a relatively independent running environment for different applications. Therefore, the VM placement problem focuses on how to place VMs on optimal physical machines, which ensures efficient use of resources and the quality of service, etc. Most previous work focuses on energy consumption, network traffic between VMs and so on and rarely consider the delay for end users’ requests. In contrast, the latency between requests and VMs is considered in this paper for the scenario of optimal VM placement in MCC. In order to minimize average RTT for all requests, the round-trip time (RTT) is first used as the metric for the latency of requests. Based on our proposed RTT metric, an RTT-Aware VM placement algorithm is then proposed to minimize the average RTT. Furthermore, the case in which one of the core switches does not work is considered. A VM rescheduling algorithm is proposed to keep the average RTT lower and reduce the fluctuation of the average RTT. Finally, in the simulation study, our algorithm shows its advantage over existing methods, including random placement, the traffic-aware VM placement algorithm and the remaining utilization-aware algorithm.

Virtual Machine (VM) placement is considered as one of the crucial problems in Cloud Computing environments. From the perspective of Cloud Service Providers (CSPs), finding the optimal VM placement strategy is often related to optimal resource utilization, revenue maximization, and energy efficiency. However, to ensure the continuity of customer services, CSPs should also consider the reliability of deployed applications when placing VMs on their infrastructures. Existing research in this area either do not focus on the Cloud reliability evaluation aspect or do not account for the trade-off between reliability and performance in the VM placement process. In this paper, we propose a multi-objective placement model for interdependent VMs in the Cloud that considers both reliability and workload. Reliability in our model is quantitatively evaluated through a set of metrics that we propose. The model involves an Integer Linear Programming problem that aims at maximizing the reliability of the Cloud while minimizing network delay. A multi-objective genetic algorithm is then used to solve the problem heuristically. The proposed model introduces a level of flexibility and its parameters could be adjusted depending on the requirements of the infrastructure and services. The results show that our model achieves high Cloud reliability and allows to effectively control the trade-off between reliability and Quality of Service.

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.

Virtual machine placement (VMP) technology is widely used in cloud computing systems. The existing VMP methods mainly aimed at improving the cloud resource utilization, such as load balancing among physical machines (PMs), but they may result in virtual machine (VM) performance degradation because of the great resource contention among VMs running on top of the same PM. In contract to existing VMP algorithms, this paper proposes a virtual machine (VM) Performance maximization and physical machine (PM) Load balancing Virtual Machine Placement method (PLVMP) in cloud, which tries to maximize VM performance and balance PM workload from both users’ and cloud providers’ perspectives. First, we study the relationship between PM workload and VM performance to train a new and improved VM performance model, which can predict VM performance more accurately and offer help to the following VMP. Second, we take VM performance maximization and PM workload balancing into account to formulate the VMP as an optimization problem, which tries to maximize VM performance for users and make load balancing among PMs for cloud providers. Third, we propose a greedy-based algorithm to solve the VMP problem efficiently. We then evaluate PLVMP with other VMP methods on CloudSim platform and a real OpenStack platform. The results show PLVMP can maximize the VM performance significantly and make a good load balancing among PMs.

Joint flow and virtual machine placement in hybrid cloud data centers

As more and more applications migrate into clouds, the placement of virtual machines for these applications has much impact on the performance of cloud systems. A number of virtual machine (VM) placement techniques have been proposed over recent years. However, most of the existing works on VM placement ignore the response latency of the requests from tenants. In this paper, we investigate the techniques of VM placement with stochastic requests from the tenants to minimize the total (average) response latency. We first model the requests for each application from the corresponding tenant as independent Poisson stream. Moreover, the VMs are modeled as simple M/M/1 queueing systems. Then, we define the problem of VM placement for minimizing the total response delay (VMMD) and show it is NP-hard. We propose three heuristic algorithms, namely, Greedy, Local Adjustment (LA) and Simulated Annealing (SA). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. The simulation results show that the proposed algorithms are efficient in decreasing the total response latency of the requests from tenants. Especially, the SA heuristic, which decreases the total response latency about 68% at most, shows the best performance on minimizing the total response latency in cloud systems.

In this paper, we develop a model for the competitive relationship between cloud service providers and cloud brokers during the virtual machine (VM) placement. The model adopts the idea of a strategy game, in which the cloud service provider and all VMs are participants. First, we define the strategy for the participants in the strategy game model according to the difference between the number of physical resources provided by the provider, and that requested for the VMs by the brokers. Next, payment values between the providers and brokers are obtained from each strategy in the game matrix, which corresponds to the strategy set of VM placement. In addition, the Nash equilibrium solution of the current strategy game demonstrates the trade-off between reducing energy consumption and guaranteeing QoS. On the basis of this, the optimal VM placement scheme is obtained, and the ongoing VM consolidation is examined. Finally, a strategy-game-based VM consolidation (SG-VMC) method is proposed. The extensive experimental results show that the proposed SG-VMC method can significantly reduce energy consumption while guaranteeing QoS.

In cloud computing environment there are many strategies used for virtual machine (VM) placement. Virtualization technology can create multiple virtual machines. The mapping method of virtual machines to physical machines is thought as virtual machine placement. Objectives for VM selection can be multi-objective and mono objective. MP’s main aims are to reduce the make span time and speed. Cloud computing plays a vital role in content delivery services for processing data and scheduling the task like smart cities sensors data, healthcare data processing services (HDPS). Cloud computing for HDPS can restore patients’ records, diseases diagnosing and different medical domains in less time and fewer of price. Here we proposed a methodology which is GWO algorithm based on nature inspired concepts. The algorithm will work on optimal selection of virtual machine.

A major challenge of IaaS cloud data centers is the placement of a huge number of Virtual Machines (VMs) over a physical infrastructure with a high number of nodes. The VMs placement process must strive to reduce as much as possible the number of physical nodes to improve management efficiency, reduce energy consumption and guarantee economical savings. However, since each VM is considered as a black box with independent characteristics, the VMs placement task presents scalability issues due to the amount of involved data and to the resulting number of constraints in the underlying optimization problem. For large data centers, this condition often leads to the impossibility to reach an optimal solution for VMs placement. Existing solutions typically exploit heuristics or simplified formulations to solve the placement problem, at the price of possibly sub-optimal solutions. We propose an innovative VMs placement technique, namely Class-Based, that takes advantage from existing solutions to automatically group VMs showing similar behavior. The Class-Based technique solves a placement problem that considers only some representatives for each class, and that can be replicated as a building block to solve the global VMs placement problem. Our experiments demonstrate that the proposed technique is viable and can significantly improve the scalability of the VMs placement in IaaS Cloud systems with respect to existing alternatives.