Naplus: a software distributed shared memory for virtual clusters in the cloud

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.

With the consolidation of computer services in large cloud-based data centers, almost all applications and even application development execute in virtualized systems (VS's), sometimes nested. Whether it is inside a container, a virtual machine (VM) running on a physical host, or in a nested virtual machine, every process eventually runs on a physical CPU. Consequently, multiple virtualized systems might unknowingly compete with each other for physical resources. In this paper we study the interactions between all the VS's running on a physical machine. We introduce an analysis based on kernel tracing that erases the bounds between VS's and their host, to display a multilayer system as a single layer. As a result, it becomes possible to know exactly which process is currently running on a physical CPU, even if it is launched inside multiple layers of containers, themselves enclosed into two layers of VMs. To use this analysis, we developed in Trace Compass a view that displays a time line for each host CPU, showing across time which process is running. Moreover, the full hierarchy of the VS's is retrieved from the analysis and is displayed in the view. By using a system of dynamic and permanent filters, we added the possibility to highlight in this view either traced VMs, virtual CPUs, specific processes and containers. This last feature, combined with our view, allows to thoroughly apprehend the execution flow on the physical host, although it may involve multiple nested virtualized systems.

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.

Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.

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.

In this paper, we consider virtual machine (VM) placement problem for cloud data centers. Different from previous works, the VMs considered need to be conducted within a finite duration. However, due to the limited resource at physical machines (PMs), the VM may be unable to place on a single PM within duration. To make sure the successful placement, the VM may undergo several PMs for service, e.g., it is run on one PM at this time, while migrated to another PM next time. Migrating the VM from one PM to another incurs extra cost, e.g., the bandwidth. In this paper, we formulate a joint optimization problem, which maximizes the revenue of successfully placing the VMs, and minimizes the total number of migrations for the VMs. We prove it is NP-hard, and show that the minimum number of migrations for each VM under the resource constraint of PMs is equivalent to finding a shortest path problem. We then design a heuristic algorithm, which considers both the revenue and the minimum number of migrations required for the VM. We also design a 1 over 2-approximation algorithm for a special case of the problem. Finally, we demonstrate the effectiveness of the proposed scheme through simulations.

As server consolidations based on the virtualization techniques become popular and cloud services continue to grow rapidly, more and more data centers are being built to accommodate a number of virtual clusters running various workloads. Since these virtual clusters often share the resources provided by physical machines (PMs), it is more likely that the interferences between virtual machines (VMs) affect the performance of applications running on top of the virtual clusters. While a lot of studies have proposed different virtual machine placement algorithms to investigate this issue, the problem caused by network performance variability still remains as a challenging issue. Since they usually ignore the CPU overhead to process the communications between VMs, the network bandwidth allocated to a VM cannot be fully utilized when a PM has not enough CPU resources to cover the CPU overhead for VM networking functions. This results in unpredictable application performance running on the virtual clusters. This paper proposes a virtual machine placement algorithm that considers the CPU overhead incurred to reserve network bandwidth in a virtualized data center environment. In order to decide the CPU overhead necessary to guarantee the network bandwidth allocated to a VM, a performance model based on standard linear regression using the data collected from a real environment is used. By comparing the amount of CPU resource available in the driver domain with the CPU overhead obtained from the performance model, the proposed algorithm decides whether the network bandwidth for the VM can be provided or not and selects an appropriate location for VM placement. The benchmarking results show that the proposed algorithm guarantees the network bandwidth allocated to each VM without violations when the CPU resources are shared by multiple VMs.

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

Recently, virtualization of hardware resources to run multiple instances of independent virtual machines over physical hosts has gained popularity due to an industry-wide focus on the need to reduce the cost of operation of an enterprise computing infrastructure. Xen is an open source hypervisor that provides a virtual machine abstraction layer which is very similar to the underlying physical machine. Using multiple physical hosts, each hosting multiple virtual machines over a VMM like Xen, system administrators can setup a high-availability virtual cluster to meet the ever-increasing demands of their data centers. In such an environment, the Xen hypervisor enables live migration of individual virtual machine instances from one physical node to another without significantly affecting the performance of the applications running on a target virtual machine. This paper describes a scalable Virtual Cluster Manager that provides such application agnostic cluster management capabilities to the system administrators maintaining virtual clusters over Xen powered virtual nodes.

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.

Nowadays cloud computing provides an effective way of implementing infrastructure as a service (IaaS). However virtualized data centers still face many challenges, such as low resource utilization of physical machines (PMs) and imbalanced server loads. Virtual machine (VM) consolidation based on live migration allows administrator to dynamically redeploy VMs into PMs for better resource utilization. Common VM consolidation methods usually focus on one challenge, and pay little attention to others or just ignore them, while effective VM redeployment should make tradeoffs between these challenges, and more importantly, should not let other challenges become worse. On the other hand, since VM live migration leads to performance degradation of applications, consolidation work should control migration cost. In this paper, we provide a manner to comprehensively consider power consumption, load balancing, communication delay and migration cost during VM redeployment. And we formalize VM consolidation as a multiobjective optimization problem, then solve this problem with an improved genetic algorithm. Simulation experiments based on real world workload trace show that compared with single objective optimization approaches our method effectively make tradeoffs between optimized objectives and has better overall performance, which is more practical in real data centers.

In modern data centres (DCs), energy consumption and resource management are challenging tasks. So in large virtualized DCs, performance of applications is highly dependent on energy-aware DC architecture and smooth network communication between virtual machines (VMs), while minimizing the communication burden to avoid congestion, latency etc. The communication cost of a network can be reduced by minimizing the VMs migration between physical machines (PMs). Therefore customer as well as service providers need cloud computing solutions that not only minimize operational costs, but also total network load. Minimizing the use of energy/network communication overhead with maximum resource utilization in large DCs is a challenging task as computing applications and data are growing so quickly for which increasing larger servers and disks are required to process them fast enough within the required time period. In order to minimize network load and maximize resource utilization in cloud DCs, we simulated our proposed network load-aware scheduling algorithm that ensures minimum VMs migration while delivering the negotiated Quality-of-Service (QoS). Our simulation also shows results with different bit rates.

VM consolidation steps in cloud computing: A perspective review

A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

Cloud computing is a very well known technology for all business people, software developers, end-users, and so on. Significant researches are going on to balance the cloud load. The migration of heavily loaded Virtual Machines (VMs) into lightly loaded Physical Machines (PMs) balances the Cloud load. In Resource Intensity Aware Load Balancing (RIAL) method, based on the weight of resources under utilization, it selected the VMs from heavily loaded PMs for migration and chosen the lightly loaded PMs as destination. An Improved RIAL was proposed to consider both lightly and heavily loaded PMs as destination. Later it was enhanced in the proposed Power Consumption Aware- Traffic Aware- IRIAL (PT-IRIAL) method with the consideration of power consumption, temperature and traffic measures to select the VMs for migration and select PMs for destination. From all these, in this current paper, the crossover and mutation process of GA is utilized to optimally select the migration VMs and choose the destination PMs. Thus this GA based load optimization algorithm optimally maps the migration VMs with the destination PMs efficiently.