VirtFlow: Guest Independent Execution Flow Analysis Across Virtualized Environments

The rapid growth in cloud computing encourages the cloud provider to create more reliable, scalable and efficient cloud environment. For providing such a scalable environment, cloud provider develops large-scale Cloud Data Centers (CDC). Such CDCs consume a large amount of electrical energy for operational purpose and cooling purpose to keep the machines operating at a right temperature, but it also emits a large amount of carbon-dioxide. Thus to decrease the energy consumption, Virtual Machines (VMs) are consolidated to fewer numbers of active servers through VM migration techniques, this leads to Service Level Agreement (SLA) violation and performance degradation. Therefore to retain the energy-performance tradeoff, we should perform an optimum number of VM migrations. There are two cases when the VM migration performed: First when a hot spot is found and Second when a cold spot is found. But in both the cases we do not directly migrate the VM instead first we check whether it is required to migrate the VM. After that, the algorithm finds an appropriate destination host on which the VM will be migrated. In this paper, we implement three approaches to select destination host: First-fit, Best-fit, and Worst-fit. Finally compare the result of a number of VM migration performed, energy consumption and SLA violations with existing one.

The rapid development of cloud computing expands the scale of modern cloud data centers, resulting in growing challenges of energy consumption. Load balancing of virtual machines(VMs), for the purposes of improving the utilization of physical resources and reducing energy consumption, has become a research focus in recent years. However, the existing researches mostly focus on how to maximize resource utilization and reduce energy consumption. Security issues in the context of load balancing of VMs were rarely addressed. In this paper, we research the key procedures of load balancing, VM selection and VM placement, we find that the existing schemes introduced several security problems. In consideration of some conclusions of recent researches and inevitable live migrations during load balancing, common tenants have sufficient reasons to worry about their VMs' security when they are migrated to strange hosts and/or co-reside with the VMs owned by strange tenants. In short, VMs' mobility introduced by load balance expands the attack surface. In this work, we classify and analyze related security threats and create an information leakage model for load balancing. We present a new security policy, SeLance, to secure the load balancing via avoiding above threats as far as possible. We develop exact implementations in CloudSim and OpenStack. We show that SeLance can effectively alleviate the threats introduced by load balancing, the security score can improve 46.90%-81.15%, while keeping the load balancer's original function to a great extent (± 2.5%).

Cloud computing is the latest trend in the world of computers. Virtualisation is the building block for cloud. In IAAS, cloud resources are allocated to the user in the form of virtual machines (VMs). VMs generally have lower resources and have a good chance of getting overloaded. Overload can happen in terms of CPU, memory or IO. All these three resources are divided among the different VMs running on a physical machine. So even if a physical machine is not overloaded, a VM running on it may get overloaded. Considering the nature of the loads, this paper proposes a load balancing solution on basis of CPU, memory and IO using process migration between the VMs of the cloud. The approach balances the load in such a way that the use of energy efficiency techniques like DVFS can be maximised. Due to the multi-tenant model of the cloud, a lot of new security issues have come up. Process migration also presents new security issues. This paper identifies such security issues and proposes solution to them. A proof of concept is provided which demonstrates the feasibility of the approach by proving that improved response times can be achieved even at lower energy consumptions.

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

In cloud computing environment, the load balancing of non pre-emptive independent tasks is an important aspect of task scheduling. The tasks are executed on Virtual Machines (VMs) and these VMs are run in parallel so that the load has to be well balanced across all VMs. Since the cloud user pay for the service he/she is interested in reducing the execution time of task as well as the cost of using VM instances (CPU, Memory, Bandwidth etc). The proposed algorithm balance the load across VMs effectively by mapping tasks to under loaded or idle VMs based on the foraging behaviour of honey bees and if there is more than one under loaded VMs, it also selects cost efficient one using Pareto dominance strategy. The experimental results show that the algorithm is effective when compared with existing algorithms and it also reduces the cost of using VM instances.

One of the major challenges that cloud providers face is minimizing power consumption of their data centers. To this point, majority of current research focuses on energy efficient management of resources in the Infrastructure as a Service model and through virtual machine consolidation. However, containers are increasingly gaining popularity and going to be major deployment model in cloud environment and specifically in Platform as a Service. This paper focuses on improving the energy efficiency of servers for this new deployment model by proposing a framework that consolidates containers on virtual machines. We first formally present the container consolidation problem and then we compare a number of algorithms and evaluate their performance against metrics such as energy consumption, Service Level Agreement violations, average container migrations rate, and average number of created virtual machines. Our proposed framework and algorithms can be utilized in a private cloud to minimize energy consumption, or alternatively in a public cloud to minimize the total number of hours the virtual machines leased.

This paper addresses the impact of Virtual Memory Streaming (VMS) technique in provisioning virtual machines (VMs) in cloud environment. VMS is a scaling virtualization technology that allows different virtual machines rapid scale, high performance, and increase hardware utilization. Traditional hypervisors do not support true no-downtime live migration, and its lack of memory oversubscription can hurt the economics of a private cloud deployment by limiting the number of VMs on each host. VMS brings together several advanced hypervisor memory management techniques including granular page sharing, dynamic memory footprint management, live migration, read caching, and a unique virtual machine cloning capability. An architecture model is described, together with a proof-of-concept implementation, that VMS dynamically scaling of virtualized infrastructure with true live migration and cloning of VMs. This paper argues that VMS for Cloud allows requiring significantly reduced server memory and reducing the time for virtualized resource scaling by instantly adding more virtual machines.

Due to the high growth of SNS population, service scalability is one of the critical issues to be addressed. The cloud environment provides the flexible computing and storage resources for services deployment, which fits the characteristics of scalable SNS deployment. However, if the SNS related information is not properly placed, it will cause unbalance load and heavy transmission cost on the storage virtual machine (VM) and cloud data center (CDC) network. In this paper, we characterize the SNS into a graph model based on the users associations and interest correlations. The node weight represents the degree of associations, which can be indexed by the number of friends or data sources, and the link weight denotes the correlation between users/data sources. Then, based on the SNS graph, the two-step algorithm is proposed in this paper to determine the placement of SNS related data among VMs. Two k-means based clustering schemes are proposed to allocate social data in proper VM and physical servers for pre-configured VM and dynamic VM environment, respectively. The experimental example was conducted and to illustrate and compare the performance of the proposed schemes.

SummaryIn this paper, a variety of anomaly detection strategies are provided with the application of the cloud platform center in view of the processor resources allocation according to the peak load demand, provision of the single anomaly detection strategy, and the problem of the sudden increase of the abnormal rate due to the change of the resource demand. The gray waveform detection algorithm is applied to detect the anomaly arriving at the virtual machine in the future time period, and the abnormal utility function of the virtual machine is given to balance the resource requirements and anomaly detection priority, and all the virtual machines are dynamically configured for each virtual machine with the maximization of the anomaly utility value of the virtual machine as the objective. Through the global load balancing between the same virtual machines and the redistribution of the virtual machines for multiple times, the detection quantity on the virtual machine anomaly with relatively huge increase of anomaly is further increased. Finally, the anomaly detection algorithm based on the gray waveform detection in the cloud platform center ADGWT is given. Simulation experiment results show that the proposed algorithm can effectively improve the abnormal rate of the processors in the cloud platform, and it has practical significance to improve the completion rate of the user requests.

As embedded systems take more important roles at many places, it is more important for them to be able to show the evidences of system failures. Providing such evidences makes it easier to investigate the root causes of the failures and to prove the responsible parties. This paper proposes simultaneous logging and replaying of a system that enables recording evidences of system failures. The proposed system employs two virtual machines, one for the primary execution and the other for the backup execution. The backup virtual machine maintains the past state of the primary virtual machine along with the log to make the backup the same state as the primary. When a system failure occurs on the primary virtual machine, the VMM saves the backup state and the log. The saved backup state and the log can be used as an evidence. By replaying the backup virtual machine from the saved state following the saved log, the execution path to the failure can be completely analyzed. We developed such a logging and replaying feature in a VMM. It can log and replay the execution of the Linux operating system. The experiment results show the overhead of the primary execution is only fractional.

Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service experienced by cloud customers.

We introduce a new scheduling method that reduces the time required to deploy applications by parallelizing and optimizing a schedule on multiple virtual machines (VMs) in cloud computing. Enterprise solutions typically consist of multiple servers and clients with complicated installation dependencies. If users deploy such a solution, they need to install the software sequentially in the proper installation order without violating the dependencies. Auto-deployment tools such as Chef and Puppet take a long time to install multiple applications on multiple VMs in sequential order because these tools cannot find the dependencies automatically and cannot optimize the deployment schedules. Users need to configure and schedule the deployment carefully after they understand the entire architecture of the enterprise solution. Our new automated deployment approach, Parallelized and Optimized Deployment Scheduling (PODS), can detect these application dependencies and optimize the deployment schedules based on two profiles: 1) the network connectivity between a server and clients, and 2) the time required to install applications for sequential deployment. PODS optimization can reduce the duplication timings of multiple VMs' requests to download applications from the same repository server in the parallelized deployment, which reduces network congestion of the repository server. We implemented PODS with Chef and evaluated it on a commercial public cloud environment. PODS reduced the time to deploy four VMs by 36% compared with the standard sequential deployment method.

Nowadays graphics processing units (GPUs) have become popular parallel computing platforms known as General-Purpose GPU (GPGPU) computing. GPUs thereby are chosen by some security researchers as cryptographic accelerators to secure massive volumes of transactions. However, their security issues are ignored in spite of their popularity and performance. There are some possible information leakages faced with malicious attacks or even in the normal GPU computing. Our objective is to secure the confidentiality of cryptographic keys in GPU computing environments and provide easy-to-use programming with few constraints. In this paper, we propose a prototype in Linux, a system of GPGPU computing solution empowered by GPU virtualization technology, which keeps encrypted keys in guest machine to protect secret keys from leakage even in the event of full system compromise. With the API interception and redirection of CUDA, applications in Virtual Machines (VMs) can access the GPU device in a transparent way. Besides, we use virtio, a dedicated virtual I/O device, to transfer data between virtual and host machines in high performance. In our current study, we evaluate our prototype with the GPU implementation of ECC. We show that it can protect private keys of GPU cryptographic computation and it also incurs low performance penalty compared with the native environment, therefore demonstrating the prototype is secure and requires reasonable overhead.

Evolution of virtualization in data center is fast and is being widely used in the world. Usage of virtual machine (VM) in data center (DC) cannot be aside from problems such as: Operating System (OS) problem, virtual network, memory and CPU utilizations. Besides of those problems, utilization of VM can be found in cloud computing technology to make more efficient and its performance should be good entirely. Problem in VM is very complex. It can be found in OS, application and physical server. To keep it in good performance, operational engineer should operate VM monitoring system in order to keep VM run well. This research will use several methods, such as: fuzzy Mamdani, holdout validation and naive Bayes. These methods will then create decision making for VM performance condition.

Cloud computing is distributed computing, storing, sharing and accessing data over the Internet. It provides a pool of shared resources to the users available on the basis of pay as you go service that means users pay only for those services which are used by him according to their access times. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.Â