Modified Packing Algorithm for Dynamic Energy-Saving in Cloud Computing Servers

Since the raising of the cloud computing, the applications of web service have been extended rapidly. However, the data centers of cloud computing also cause the problem of power consumption and the resources usually have not been used effectively. Decreasing the power consumption and enhancing resource utilization become main issues in cloud computing environment. In this paper, we propose a method, called MBFDP (modified best fit decreasing packing), to decrease power consumption and enhance resource utilization of cloud computing servers. From the results of experiments, the proposed solution can reduce power consumption effectively and enhance the utilization of resources of servers.     

Correlation-based virtual machine migration in dynamic cloud environments

Virtual machine (VM) migration enables flexible and efficient resource management in modern data centers. Although various VM migration algorithms have been proposed to improve the utilization of physical resources in data centers, they generally focus on how to select VMs to be migrated only according to their resource requirements and ignore the relationship between the VMs and servers with respect to their varying resource usage as well as the time at which the VMs should be migrated. This may dramatically degrade the algorithm performance and increase the operating and the capital cost when the resource requirements of the VMs change dynamically over time. In this paper, we propose an integrated VM migration strategy to jointly consider and address these issues. First, we establish a service level agreement-based soft migration mechanism to significantly reduce the number of VM migrations. Then, we develop two algorithms to solve the VM and server selection issues, in which the correlation between the VMs and the servers is used to identify the appropriate VMs to be migrated and the destination servers for them. The experimental results obtained from extensive simulations show the effectiveness of the proposed algorithms compared to traditional schemes in terms of the rate of resource usage, the operating cost and the capital cost.     

IaaS 环境下改进能源效率和网络性能的虚拟机放置方法简

现在的虚拟机放置研究大多集中在物理服务器能源能耗或网络设备能耗的优化,然而随着这些资源的过度聚合,有可能会带来应用性能的下降。提出了一种虚拟机放置方案,主要有2个目的：最小化激活物理机和网络设备的个数来减少数据中心能源消耗;最小化最大链路利用率来改善网络性能。此方案在优化网络性能的同时,减少物理服务器和网络设备的能耗,使得能源效率与网络性能达到平衡。设计了一种新的二阶段启发式算法来求解,首先,利用基于最小割的层次聚类算法与最佳适应算法相结合来优化能源效率,然后,利用局部搜索算法再次优化虚拟机位置来最小化最大链路利用率。仿真实验结果表明,所提方案取得了良好的效果。     

VM migration algorithm for the balance of energy resource across data centers in cloud computing

Cloud computing makes it possible for users to share computing power. The framework of multiple data centers gains a greater popularity in modern cloud computing. Due to the uncertainty of the requests from users, the loads of CPU(Center Processing Unit) of different data centers differ. High CPU utilization rate of a data center affects the service provided for users, while low CPU utilization rate of a data center causes high energy consumption. Therefore, it is important to balance the CPU resource across data centers in modern cloud computing framework. A virtual machine(VM)migration algorithm was proposed to balance the CPU resource across data centers. The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.     

Secure VM Migration in Cloud: Multi-Criteria Perspective with Improved Optimization Model

Distributed computing has risen as a well-known worldview for facilitating an assortment of online applications and services. The present business distributed computing stages utilize a semi concentrated design, where cloud resources, such as servers and storage are hosted in a few large global data centers. Virtualization in computing is a creation of virtual (not real) of something such as hardware, software, platform or an operating system or storage, or a network device. Further, Virtual Machine (VM) technology has recently emerged as an essential building block for data centers and cluster systems, mainly due to its capabilities of isolating, consolidating, and migrating workload. Migration of VM seeks to improve the manageability, performance, and fault tolerance of systems. In a virtual cloud computing environment, a set of submitted tasks from different users are scheduled on a set of Virtual Machines (VMs), and load balancing has become a critical issue for achieving energy efficiency. Thus to solve this issue and to achieve a good load balance, a new improved optimization algorithm is introduced namely Dual Conditional Moth Flame Algorithm (DC-MFA) that takes into account of proposed multi-objective functions defining the multi-constraints like CPU utilization, energy consumption, security, make span, migration cost, and resource cost. The performance of the proposed model will be analyzed by determining migration cost, energy consumption, and response time, and security analysis as well.

     

A learning‐based approach for virtual machine placement in cloud data centers

In recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on best‐fit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well‐familiar baseline VM allocation algorithms.     

An Energy Efficient and Adaptive Threshold VM Consolidation Framework for Cloud Environment

Cloud-based computing, in spite of its enormous benefits has ill effects on the environment also. The release of greenhouse gases and energy consumed by cloud data centers is the most important issue that needs serious attention. Virtual machine (VM) consolidation is a prominent method for energy efficient and optimal utilization of resources. However, existing VM consolidation approaches aggressively reduce energy consumption without considering quality of service (QoS) factors. In this paper, QoS-aware VM consolidation framework is presented which reduces energy consumption and tries to minimize Service Level Agreement violations at the same time. Unlike existing solutions, the framework is generic as it works for both CPU and input/output intensive tasks. The effectiveness of proposed framework is illustrated by using real dataset of Planet lab and CloudSim platform. The proposed solution can be used in cloud data centers to enable energy efficient computing.

     

IaaS云中面向能源效率优化的虚拟机调度算法(英文)

In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.     

Design of energy‐efficient cloud systems via network and resource virtualization

Data centers play a crucial role in the delivery of cloud services by enabling on‐demand access to the shared resources such as software, platform and infrastructure. Virtual machine (VM) allocation is one of the challenging tasks in data center management since user requirements, typically expressed as service‐level agreements, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors to greenhouse gas emissions of IT services. In this paper, we propose a holistic approach for a large‐scale cloud system where the cloud services are provisioned by several data centers interconnected over the backbone network. Leveraging the possibility to virtualize the backbone topology in order to bypass IP routers, which are major power consumers in the core network, we propose a mixed integer linear programming (MILP) formulation for VM placement that aims at minimizing both power consumption at the virtualized backbone network and resource usage inside data centers. Since the general holistic MILP formulation requires heavy and long‐running computations, we partition the problem into two sub‐problems, namely, intra and inter‐data center VM placement. In addition, for the inter‐data center VM placement, we also propose a heuristic to solve the virtualized backbone topology reconfiguration computation in reasonable time. We thoroughly assessed the performance of our proposed solution, comparing it with another notable MILP proposal in the literature; collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization and fairness for medium size data centers. Copyright © 2013 John Wiley & Sons, Ltd.     

Resource-efficient load-balancing framework for cloud data center networks

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.     

Workload Prediction Based Virtual Machine Migration and Optimal Switching Strategy for Cloud Power Management

Virtual Machine (VM) Migration has been popular nowadays, as it helps to balance the load effectively. Various VM migration-based approaches are modeled for better VM placement but remain the challenge because of inappropriate load balancing. Thus, workload prediction-based VM migration is introduced to improve the energy efficiency of the system. Importantly, load prediction is very important to enhance resource allocation and utilization. Chaotic Fruitfly Rider Neural Network is devised by combining Rider neural network and chaotic Fruitfly optimization algorithm to predict load. Moreover, the fitness for predicting the load is based on old-time load, resource constraint, and network parameters. Once the load is predicted, the power optimization is performed using VM migration and optimal switching strategy. When the load is found overloaded, the VM migration is performed using the proposed Harris Hawks spider monkey optimization (HHSMO). Thus, the optimal finding of VM for executing the removed task is found out using the proposed HHSMO. The fitness function utilized for the VM migration is based on power, load, and resource parameter. If the load predicted is underloaded, the optimal switch ON/OFF is done optimally by switch ON/OFF the servers using the proposed HHSMO algorithm. Through the migration and switching strategy, the power consumption is optimized. The performance of the proposed model is evaluated in terms of power consumption, load, and resource utilization. The proposed HHSMO achieves the minimal power consumption of 0.0181, the minimal load of 0.002, and the minimal resource utilization of 0.0376.

     

Dynamic resource allocation in cloud download service

Cloud download service, as a new application which downloads the requested content offline and reserves it in cloud storage until users retrieve it, has recently become a trend attracting millions of users in China. In the face of the dilemma between the growth of download requests and the limitation of storage resource, the cloud servers have to design an efficient resource allocation scheme to enhance the utilization of storage as well as to satisfy users' needs like a short download time. When a user's churn behavior is considered as a Markov chain process, it is found that a proper allocation of download speed can optimize the storage resource utilization. Accordingly, two dynamic resource allocation schemes including a speed switching (SS) scheme and a speed increasing (SI) scheme are proposed. Both theoretical analysis and simulation results prove that our schemes can effectively reduce the consumption of storage resource and keep the download time short enough for a good user experience.     

A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.     

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization.     

Efficient Resource Management on Cloud Using Energy and Power Aware Dynamic Migration (EPADM) of VMs

In present decade, cloud computing provides utility-based IT services to the global consumers. According to pay-by-use manner, it facilitates hosting of persistent services from the user, business and technical fields. But, it is to be mentioned that the data centers hosting the cloud-based services utilize large amount of energy, power and resources. Hence, there is a need of an efficient resource management model for cloud that involves in reducing the resource consumption and computational cost. And, for managing the virtual resources with respect to the varying demands in cloud environment, dynamic virtual resource management is required. With that concern, this paper presents a model called Energy and Power Aware Dynamic Migration (EPADM). Based on the model design, the main objectives such as, efficient resource mapping and provisioning algorithms are presented. The dynamic Virtual Migration (VM) operation comprises the VM relocation and consolidation parts for achieving desirable results. Moreover, the paper also concentrates on reducing the SLA (Service Level Agreement) based violation, which is a significant factor to be considered on cloud. The proposed EPADM model is evaluated using the CloudSim toolkit. The results illustrate that the proposed model has massive potential than others, as it provides energy-power efficiency, reduced SLA violations under distinctive workload cases.

     

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Cloud computing introduced a new paradigm in IT industry by providing on‐demand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state‐of‐the‐art multiobjective VM placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.     

A Live Migration Algorithm for Containers Based on Resource Locality

With the wide application of cloud computing, the scale of cloud data center network is growing. The virtual machine (VM) live migration technology is becoming more crucial in cloud data centers for the purpose of load balance, and efficient utilization of resources. The lightweight virtualization technique has made virtual machines more portable, efficient and easier to management. Different from virtual machines, containers bring more lightweight, more flexible and more intensive service capabilities to the cloud. Researches on container migration is still in its infancy, especially live migration is still very immature. In this paper, we present the locality live migration model where we take into account the distance, available bandwidth and costs between containers. Furthermore, we conduct comprehensive experiments on a cluster. Extensive simulation results show that the proposed method improves the utilization of resources of servers, and also improves the balance of all kinds of resources on the physical machine.

     

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service‐level agreement violation (SLAV) is also improved.     

Resource allocation in cloud virtual machines based on empirical service traces

One of the key technologies in cloud computing is virtualization. Using virtualization, a system can optimize usage of resources, simplify management of infrastructure and software, and reduce hardware requirements. This research focuses on infrastructure as a service, resource allocation by providers for consumers, and explores the optimization of system utilization based on actual service traces of a real world cloud computing site. Before activating additional virtual machines (VM) for applications, the system examines CPU usage in the resource pools. The behavior of each VM can be estimated by monitoring the CPU usage for different types of services, and consequently, additional resources added or idle resources released. Based on historical observations of the required resources for each kind of service, the system can efficiently dispatch VMs. The proposed scheme can efficiently and effectively distribute resources to VMs for maximizing utilization of the cloud computing center. Copyright © 2013 John Wiley & Sons, Ltd.     

一种支持解密外包的KP-ABE方案

针对现有密钥策略基于属性加密KP-ABE（Key-Policy Attribute-Based Encryption）方案在解密时存在用户端计算开销大、解密时间长等问题,一些方案提出将解密外包给云服务器,但这些方案并未给出外包解密的并行化方法,存在解密效率低的问题.本文提出一种支持解密外包的KP-ABE方案.在该方案中,把大部分解密计算外包给Spark平台;并根据KP-ABE的解密特点设计并行化解密算法,完成对叶子节点和根节点的并行化解密.性能分析表明,用户端仅需进行一次指数运算即可解密出共享数据,同时并行化设计能有效提高云端解密速率.