V-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers

As cloud services become mature and provide capabilities for elastic computation and data management, hosting services become increasingly prevalent such as Amazon Web Services, Microsoft Azure, or RedHat OpenStack. Workloads are hosted in these platforms using hypervisors running on physical machines which host virtual machines (VMs) presented to the customer. Software-defined network (SDN) technologies move many of the network functions into the hosts. Accordingly SDN-based VM placements have received significant attention as a way to minimize network traffic between VMs and hosts. Careful placement of VMs can generate a significantly lower amount of network traffic to the hosting provider. Further, it may impact the level of security risk exposed by the VM placement as each VM may have a different level of security risk. In this work, we consider these two conflicting goals by presenting a Software-Defined Network based VM Placement, called SDNVMP, to minimize both network traffic between VMs (and hosts) and security risk by grouping VMs with similar security scrutiny needs. Our results show that the proposed placement algorithm outperforms the baseline counterparts in terms of network traffic overhead and security risk in each VM introduced by such algorithms.

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.

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

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.

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 (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.

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.

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

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.

Software Defined Networking (SDN) is widely used in many practical contexts and provides a method for managing networks by separating the data plane from the control plane. However, because of its centralized control logic, SDN is particularly susceptible to Distributed Denial of Service (DDoS) attacks. In numerous studies, methods based on Machine Learning (ML) have been proposed to identify DDoS attacks in the context of SDN. However, the proposed methods are resource intensive and unreliable in a large-scale SDN where a huge amount of traffic data is produced from control and data planes. This may exhaust computational resources, deteriorate the performance of the network, or even result in the shutdown of network systems as a result of resource exhaustion. To deal with the above issues, this paper presents a real-time DDoS detection and mitigation method in a multi-controller SDN environment using cloud computing. We propose to run the Machine Learning development cycle on a secured server in a virtual machine. Then we deploy back the trained model into the SDN controllers. All the exchanges between the virtual machine and the controllers are secured. In this way, we configure these controllers to detect and mitigate DDoS in SDN in real-time. Through useful experiments, we demonstrate that our method is secure, reliable, and efficient.

Based on the convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel application-aware virtual machine (VM) placement algorithm is proposed to cope with these problems. Firstly, the definition of VM placement fairness and utility function are given, and the basic algorithm of VM placement which fulfills server constraints of physical machines is con-ducted. Secondly, we formulate the VM placement as an optimization problem. A two-tier algorithm that efficiently solves the VM placement problem in large scale was designed. Comparing to existing methods, we illustrate the performance improvement such as reducing the number of physical machines efficiently, decreasing communication distance between VMs, improving energy-efficient and scalability of data centers.

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.

Infrastructure as a Service (IaaS) Cloud is emerging as a popular utility computing model in which computational resources like processor, memory, storage etc. are provided on demand. Virtual Machines (VMs) are used to provide on demand virtualized resources. Several VMs may run parallely on a physical machine depending on the demand. VM placement and VM resource allocation are two important problems in an infrastructure cloud. In such systems, performance interference may become a mutual problem between a VM which is booting and a VM which is coexisting, thus affecting the boot-time of a VM as well as response time of the coexisting VMs. The work presented here studies the impact of computational resources available on boot-time of a VM. Our experiments suggest to add a special server known as VMBooter inside an infrastructure cloud. Experimental results have shown considerable improvement in VM boot-time with lowering down the impact of performance interference on coexisting VMs with the help of VM migration.

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.