An Efficient Technique for Virtual Machine Clustering and Communications Using Task-Based Scheduling in Cloud Computing

Virtual machine cluster mobility in inter-cloud platforms

To address the virtual machine cluster deployment issues in cloud computing environment, a novel MCSA (Min-cut segmentation algorithm) of virtual machine cluster is proposed with resource and communication bandwidth constraints. In this paper, the basic idea is based on the fully consideration on the CPU, memory, hard-disk and other resource constraints between virtual machine cluster and physical host, as well as the communication bandwidth constraints between the virtual machine. We quantified the virtual machine cluster constructed an undirected graph. In the undirected graph, the nodes represent the virtual machine, so the weight of a node represents the value of resources, and the edges represent the communication bandwidth, so the weight of the edge represents the value of communication bandwidth. Base on the above transformations, the resources and bandwidth constrained optimization problem is transformed into the graph segmentation problem. Next we segment the undirected graph by minimum cut algorithm, and computing the matching degree of physical machines. Last we obtained the approximate solution. To validate the effectiveness of the new algorithm, we carried out extensive experiments based on the CloudSim platform.

Virtual Machine Cluster (VMC) is now widely used to host network applications due to its well scalability and high availability compared to physical cluster. To provide fault tolerance, VMC snapshot is one well known technique, it saves the entire VMC state into stable storage and rollbacks the VM from the latest saved state upon failures. However, due to the large snapshot size as well as numerous VM snapshots, the existing rollback approaches always utilize large amount of network resources, and further incur long rollback latency since network resource is limited. In this paper, we present Mercurial, a rollback system that finds the identical pages across the VM snapshots and then multicast a single copy of the duplicated pages to associated virtual machines, thereby saving the network traffic and reducing the rollback latency. Our approach is effective especially when the same OS and similar applications are running in the VMC. We have implemented Mercurial on QEMU/KVM and conducted several experiments to evaluate its effectiveness and efficiency. The experimental results demonstrate that our approach can save 34% network traffic introduced by transferring the identical pages repeatedly and reduce by 28% of the restore latency.

Cloud computing is the next generation of computation and is regarded as the fifth utility service. A core issue of cloud computing is the deployment of virtual machine (VM). A new method had been constructed for VM cluster based on graph theory in this study. The authors first described VM cluster by energy minimisation. Then, they changed deployment of VM cluster into maximum flow minimum cut problem, added sink and source point, and found activity node. Finally, activity tail‐nodes were connected and cut formed for VM cluster. Experimental results show that the segmentation method can effectively realise the VM deployment clusters, can reduce the overall bandwidth requirements after deployment.

Tree-based overlay multicast is an effective group communication method for media streaming applications. However, a group member’s departure causes all of its descendants to be disconnected from the multicast tree for some time, which results in poor performance. The above problem is difficult to be addressed because overlay multicast tree is intrinsically instable. In this paper, we proposed a novel stability enhancing solution, VMCast, for tree-based overlay multicast. This solution uses two types of on-demand cloud virtual machines (VMs), i.e., multicast VMs (MVMs) and compensation VMs (CVMs). MVMs are used to disseminate the multicast data, whereas CVMs are used to offer streaming compensation. The used VMs in the same cloud datacenter constitute a VM cluster. Each VM cluster is responsible for a service domain (VMSD), and each group member belongs to a specific VMSD. The data source delivers the multicast data to MVMs through a reliable path, and MVMs further disseminate the data to group members along domain overlay multicast trees. The above approach structurally improves the stability of the overlay multicast tree. We further utilized CVM-based streaming compensation to enhance the stability of the data distribution in the VMSDs. VMCast can be used as an extension to existing tree-based overlay multicast solutions, to provide better services for media streaming applications. We applied VMCast to two application instances (i.e., HMTP and HCcast). The results show that it can obviously enhance the stability of the data distribution.

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.

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.

In this work we study the I/O performance of long, sequential workloads that mimic those of Big Data applications, to understand the implications of system virtualization on data-intensive frameworks such as Apache Hadoop and Spark, which are frequently run in clusters of Virtual Machines (VMs). We do so through an experimental measurement campaign that collects low-level traces and metrics, to show the role played by important parameters such as the I/O schedulers and caching mechanisms involved in the I/O path, and the VM configuration in terms of dedicated resources. Our findings are important, especially for determining appropriate deployment strategies for today's emerging Analytics Services hosted both on public and private clouds.

Software-defined datacenters combine centralized resource management, software-defined networking, and virtualized infrastructure to meet diverse requirements of cloud computing. To fully realizing their capability in traffic engineering and flow-based bandwidth management, it is critical for the switches to measure network traffic for both individual flows between virtual machines and aggregate flows between clusters of physical or virtual machines. This paper proposes a novel hierarchical traffic measurement scheme for software-defined datacenter networks. It measures both aggregate flows and individual flows that are organized in a hierarchy with an arbitrary number of levels. The measurement is performed based on a new concept of hierarchical virtual counter arrays, which record each packet only once by updating a single counter, yet the sizes of all flows that the packet belongs to will be properly updated. We demonstrate that the new measurement scheme not only supports hierarchical traffic measurement with accuracy, but does so with memory efficiency, using a fewer number of counters than the number of flows.

Cloud computing has overtaken the traditional computing technologies by providing virtualized resources on demand. Cloud data centers consume a huge amount of power and emit much carbon dioxide, resulting in two challenging problems: high energy consumption and global warming. Our prior work has derived an enhanced energy model that considered energy consumed in computing, migration and host reactivating, and proposed three highly efficient virtual machine (VM) migration schemes. In this work, the three schemes are further enhanced, through considering the traffic factor in VM migration and adopting VM clustering. The resulting schemes have significantly reduced the number of VM migration and their migration costs. The time complexities of these schemes have been analyzed, their performance evaluated through simulation. Results showed that, comparing with the ones without VM clustering, the migration energy usage is dropped to 32%, resulting in the total energy saving of 23%, with a small increase in SLA violation. The proposed schemes would have significant impact when applying to virtualization of wireless mobile networks, in which the communication cost, including bandwidth and delay factors, is significant.

Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs, and obtains the stage demand of resources in the future period through multi-step prediction, which is realized by iterative prediction. Then, based on the multi-step prediction, an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance. During the VM consolidation, the affinity value is used to select the migration VM and placement host. The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform. Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption, VM migration costs, and service level agreement (SLA) violations.

Scheduling in workflows plays a crucial role in optimizing task execution and resource utilization in cloud computing systems, particularly when Quality of Service (QoS) requirements, such as deadlines and task priorities, have to be fulfilled. This paper presents a novel, lightweight heuristic scheduling algorithm that incorporates QoS metrics into the heuristic scheduling process using the WorkflowSim simulation framework. The algorithm filters tasks based on the feasibility of deadlines, computes dynamic priority scores based on a blend of urgency and task weight, and employs an earliest-finish-time (EFT) policy for task to VM allocation. To test the proposed approach, simulation experiments were performed with synthetic Directed Acyclic Graph (DAG) workflows in a heterogeneous VM (Virtual Machine) cloud environment. The performance of the algorithm was evaluated relative to traditional baseline approaches, including First-Come-FirstServe (FCFS), Round Robin (RR), and Min-Min, using the metrics makespan, deadline miss rate, workflow success rate, and VM utilization. The results demonstrate the effectiveness of the proposed method in reducing makespan by up to 31.7% and improving workflow success rate to $\mathbf{7 2 \%}$ while achieving $\mathbf{7 9 \%}$ VM utilization, outperforming traditional schedulers. These findings suggest that lightweight heuristics can graciously meet the objectives of QoS without suffering the computational cost of metaheuristics. Enhancements in the future may include energy- and cost-conscious goals, re- scheduling, and predictive models to make it applicable across the various dynamics of a production field.

With the rapid development of the Internet of Things (IoT) technology, diversified applications deploy extensive sensors to monitor objects, such as PM2.5 air quality monitoring. The sensors transmit data to the server periodically and continuously. However, a single server cannot provide efficient services for the ever-growing IoT devices and the data they generate. This study bases on the concept of symmetry of architecture and quantities in system design and explores the load balancing issue to improve performance. This study uses the Linux Virtual Server (LVS) and virtualization technology to deploy a virtual machine (VM) cluster. It consists of a front-end server, also a load balancer, to dispatch requests, and several back-end servers to provide services. These receive data from sensors and provide Web services for browsing real-time sensor data. The Hadoop Distributed File System (HDFS) and HBase are used to store the massive amount of received sensor data. Because load-balancing is critical for resource utilization, this study also proposes a new load distribution algorithm for VM-based server clusters that simultaneously provide multiple services, such as sensor services and Web service. It considers the aggregate load of all back-end servers on the same physical server that provides multiple services. It also considers the difference between physical machines and VMs. Algorithms such as those for LVS, which do not consider these factors, can cause load imbalance between physical servers. The experimental results demonstrate that the proposed system is fault tolerant, highly scalable, and offers high availability and high performance.

Load balancing is the core of virtual resource management and scheduling in cloud computing. For network applications, the cost of user would be greatly saved if load balancer could dynamically adjust cluster resources in accordance with the current applied load. The current load balancing products of cloud, such as Amazon's ELB, can be used to manage virtual machines in the cloud. The main drawbacks are still only supporting template-based deployment of new virtual machines, not supporting the trend prediction, failing to gain resources dynamically, and not sufficiently providing the elastic management of resources. Since the virtual machine for load balancing management in cloud computing can be dynamically applied and released, an algorithm of prediction-based elastic load balancing resource management (TeraScaler ELB) is presented to overcome the drawbacks. Experiments have shown that the required number of virtual machines change in compliance with the change of network load, thus TeraScaler ELB is able to dynamically adjust the processing capacity of back-end server cluster with the applied load. Besides it could make full use of the 'use on demand' feature of cloud computing, TeraScaler ELB leads to a better application of prediction based load balancing in cloud computing. It concludes that compared with the traditional elastic resource management algorithm, TeraScaler ELB is more reasonable for providing scalability and high availability.

Resource Utilization Prediction with Multipath Traffic Routing for Congestion-aware VM Migration in Cloud Computing