Performance Evaluation for Traditional Virtual Machine Placement Algorithms in the Cloud

Cloud computing is concept of computing technology in which user uses remote server for maintain their data and application. Resources in cloud computing are demand driven utilized in forms of virtual machines to facilitate the execution of complicated tasks. Virtual machine placement is the process of mapping virtual machines to physical machines. This is an active research topic and different strategies have been adopted in literature for this problem. In this paper, the problem of virtual machine placement is formulated as a multi-objective optimization problem aiming to simultaneously optimize total processing resource wastage and total memory resource wastage. After that ant colony optimization algorithm is proposed for solving the formulated problem. The main goal of the proposed algorithm is to search the solution space more efficiently and obtain a set of non-dominated solutions called the Pareto set. The proposed algorithm has been compared with the well-known algorithms for virtual machine placement problem existing in the literature. The comparison results elucidate that the proposed algorithm is more efficient and significantly outperforms the compared methods on the basis of CPU resource wastage and memory resource wastage.

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.

Cloud data centers are hosting more and more complicated applications or services. This makes the network bandwidth becoming essential and critical for the normal application conduction or service provision. Hence, it is necessary to consider the network issue appropriately when eploying virtual machines (VMs) to avoid the network bottleneck and to guarantee the quality of services. In this paper, we focus on the VM placement problem for minimizing the maximal link utilization to avoid network congestion. We formulate the VM placement problem by representing the user requests with resource topologies, and prove the problem to be NP-hard. We present a heuristic algorithm based on the graph theory, which takes the resource topologies into account. The basic idea is to divide the requested VMs into servers with low network communication cost by analyzing the resource topology. We conduct extensive simulations, and the results show that our algorithm has a significant performance improvement on reducing network occupation compared to the best-fit strategy and divide-and-conquer strategy.

In this paper, a research work is carried out to improve workload management by minimize energy consumption of node, localizing traffic in a network and reduce communication delay between virtual machines in cloud environment. Cloud computing consists numerous virtual machine to store huge volume of data. The usage of virtual machines is to provide separate guest operating system which is popular now-a-days. The workload management across different virtual machines in cloud is really a difficult process. Decentralized approaches were proposed in the literature for energy efficient management of virtual machines. But still it has some issues like failed to consider the communication between virtual machines and it leads to traffic in a network. Thus it reduces the performance of work load management.

Joint flow and virtual machine placement in hybrid cloud data centers

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.

Objectives: Hardware virtualization is an evolving technology, because it has the potential to facilitate the consolidation of multiple workloads on a single physical host directly through cloud. We introduce the First-Last algorithm to minimize energy consumption and migration. Methods: The problem is treated as an instance of bin packing algorithm which focuses on saving energy. However the overloading of active hosts and the resultant migration are the drawbacks of this approach. This algorithm was tested on a set of heterogeneous virtual machines which have demands in both single and multi-dimension and came either statically or dynamically to the server. Findings: The virtual machine placement problem can be treated as a bin packing problem, wherein the physical machines/hosts are the bins and the virtual machines are the objects. It can then be solved through various approaches such as first fit, best fit, next fit et cetera. Using the first fit algorithm, the virtual machine is placed into the first active host which can accommodate it. If none of the active hosts satisfy the VM’s demand, then a new host is made active. In the best fit method, all the active hosts are checked first and then the host which would suffer minimum resource wastage upon VM placement is chosen. Next fit algorithm places the VM in the last /most recently activated host. While first fit is best suited for optimal performance, best fit keeps the least number of hosts active, thereby saving energy but at the cost of performance since migration overhead increases. Next fit approach would fail unless the virtual machines have been arranged in the increasing order of their demands. The firstlast algorithm that we introduce balances on energy consumption as well as performance by reducing migration. It also reduces the time complexity to a certain extent. Applications: The algorithm has a comparatively lower time complexity than the existing virtual machine placement algorithms. It also reduces the energy consumption as well as migration to a certain extent.

Nowadays, review mining is very popular. People usually want to go for the reviews first rather than directly going to the market and purchase the product or paying for a service. However, it is very difficult to get the best one until making the comparison between two or more positively categorized products. This work highlights a platform where review mining become very easy and automated. The automation of crawling is done through various websites for a product or service and downloading their reviews at their own. The model is deployed in the Cloud environment for executing the multiple websites in parallel, which speed up the process to meet the requirements of real time recommendations. Experiments are performed on various product reviews datasets in order to provide and analyze the reviews in the form of star rating score scaling from 0–5.

Performance analysis of local, network and distributed file systems running inside user’s virtual machines in cloud environment

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.

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

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption.

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

The consumption of energy and carbon emission in cloud datacenters are the alarming issues in recent times, while optimizing the average response time and service level agreement (SLA) violations. Handful of researches have been conducted in these domains during virtual machine placement (VMP) in cloud milieu. Moreover it is hard to find researches on VMP considering the cloud regions and the availability zones along with the datacenters, although both of them play significant roles in VMP. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable regions, availability zones, datacenters, and servers in a cloud environment, while optimizing the mentioned quality of service parameters. Our suggested technique is compared with some of the contemporary hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that the proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy and emitting 28.8%, 32.83%, and 37.45% less carbon, whereas reducing the average response time by 11.43%, 18.57%, and 26% as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively. In case of SLA violations, SSEPC has shown its effectiveness by lessening the value of this parameter by 0.4%, 1.2%, and 2.8% as compared to SCA-SSA, GATA, and OEMACS respectively.

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.