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Live migration of multiple virtual machines with resource reservation in cloud computing environments
- in IEEE CLOUD
, 2011
"... Abstract—Virtualization technology is currently becoming increasingly popular and valuable in cloud computing en-vironments due to the benefits of server consolidation, live migration, and resource isolation. Live migration of virtual machines can be used to implement energy saving and load balancin ..."
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Cited by 13 (2 self)
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Abstract—Virtualization technology is currently becoming increasingly popular and valuable in cloud computing en-vironments due to the benefits of server consolidation, live migration, and resource isolation. Live migration of virtual machines can be used to implement energy saving and load balancing in cloud data center. However, to our knowledge, most of the previous work concentrated on the implementation of migration technology itself while didn’t consider the impact of resource reservation strategy on migration efficiency. This paper focuses on the live migration strategy of multiple virtual machines with different resource reservation methods. We first describe the live migration framework of multiple virtual machines with resource reservation technology. Then we perform a series of experiments to investigate the impacts of different resource reservation methods on the performance of live migration in both source machine and target machine. Additionally, we analyze the efficiency of parallel migration strategy and workload-aware migration strategy. The metrics such as downtime, total migration time, and workload perfor-mance overheads are measured. Experiments reveal some new discovery of live migration of multiple virtual machines. Based on the observed results, we present corresponding optimization methods to improve the migration efficiency. Keywords-virtual machine; live migration; resource reserva-tion; performance; I.
VC-Migration: Live Migration of Virtual Clusters
- in the Cloud. In ACM/IEEE 13th International Conference on Grid Computing (GRID
, 2012
"... Abstract—Live migration of virtual machines (VM) has recently become a key ingredient behind the management activities of cloud computing system to achieve the goals of load balancing, energy saving, failure recovery, and system maintenance. However, to our knowledge, most of the previous live VM mi ..."
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Cited by 2 (0 self)
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Abstract—Live migration of virtual machines (VM) has recently become a key ingredient behind the management activities of cloud computing system to achieve the goals of load balancing, energy saving, failure recovery, and system maintenance. However, to our knowledge, most of the previous live VM migration techniques concentrated on the migration of a single VM which means these techniques are insufficient when the whole virtual cluster or multiple virtual clusters need to be migrated. This paper investigates various live migration strategies for virtual clusters (VC). We first describe a framework VC-Migration to control the migration of virtual clusters. Then we perform a series of experiments to study the performance and overheads of different migration strategies for virtual clusters, including concurrent migration, mutual migration, homogeneous VC migration, and heterogeneous VC migration. After that, we present several optimization principles to improve the migration performance of virtual clusters. The HPCC benchmark is selected to represent the virtual cluster workloads, and the metrics such as downtime, total migration time, and workload performance are measured. Experimental results reveal some new discoveries which are useful to the future development of new migration mechanisms and algorithms to optimize the migration of virtual clusters. Keywords-virtual machine; virtual cluster; live migration; performance; cloud computing; I.
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"... Abstract—Minimizing the energy consumption in cloud computing environment is one of the key research issues. Power consumed by computing resources and storage in cloud can be optimized through energy aware resource allocation. As the resource utilization by the tasks are directly relates to energy c ..."
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Abstract—Minimizing the energy consumption in cloud computing environment is one of the key research issues. Power consumed by computing resources and storage in cloud can be optimized through energy aware resource allocation. As the resource utilization by the tasks are directly relates to energy consumption, the task consolidation are being used to optimize the energy consumption. An energy efficient heuristic algorithm has been proposed and compared with three energy-aware task consolidation heuristics by varying number of tasks. The proposed task consolidation algorithm minimizes total energy consumed by the cloud computing system. Keywords—Cloud Computing, task consolidation, energy aware, virtual machine, energy-efficient resource allocation, resource utilization. I.
CloudScope: Diagnosing and Managing Performance Interference in Multi-Tenant Clouds
"... Abstract-Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service lev ..."
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Abstract-Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%.
Towards Security-Aware Virtual Server Migration Optimization to the Cloud
"... Abstract—Cloud computing, featured by shared servers and location independent services, has been widely adopted by various businesses to increase computing efficiency, and reduce opera-tional costs. Despite significant benefits and interests, enterprises have a hard time to decide whether or not to ..."
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Abstract—Cloud computing, featured by shared servers and location independent services, has been widely adopted by various businesses to increase computing efficiency, and reduce opera-tional costs. Despite significant benefits and interests, enterprises have a hard time to decide whether or not to migrate thousands of servers into the cloud because of various reasons such as lack of holistic migration (planning) tools, concerns on data security and cloud vendor lock-in. In particular, cloud security has become the major concern for decision makers, due to the nature weakness of virtualization – the fact that the cloud allows multiple users to share resources through Internet-facing interfaces can be easily taken advantage of by hackers. Therefore, setting up a secure environment for resource migration becomes the top priority for both enterprises and cloud providers. To achieve the goal of security, security policies such as firewalls and access control have been widely adopted, leading to significant cost as additional resources need to employed. In this paper, we address the challenge of the security-aware virtual server migration, and propose a migration strategy that minimizes the migration cost while promising the security needs of enterprises. We prove that the proposed security-aware cost minimization problem is NP-hard and our solution can achieve an approximate factor of 2. We perform an extensive simulation study to evaluate the performance of the proposed solution under various settings. Our simulation results demonstrate that our approach can save 53% moving cost for a single enterprise case, and 66 % for multiple enterprises case comparing to a random migration strategy.
DOI 10.1007/s11036-011-0316-4 A Survey of Green Mobile Networks: Opportunities and Challenges
"... Abstract The explosive development of Informa-tion and Communication Technology (ICT) has sig-nificantly enlarged both the energy demands and the CO2 emissions, and consequently contributes to make the energy crisis and global warming problems worse. However, as the main force of the ICT field, the ..."
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Abstract The explosive development of Informa-tion and Communication Technology (ICT) has sig-nificantly enlarged both the energy demands and the CO2 emissions, and consequently contributes to make the energy crisis and global warming problems worse. However, as the main force of the ICT field, the mo-bile networks, are currently focusing on the capacity, variety and stability of the communication services, without paying too much severe concerns on the energy
An Architectural Framework for Enforcing Energy Management Policies in Cloud
"... Abstract—Management of energy consumption in Cloud has recently received considerable attention. Most existing research focuses on designing algorithms for dynamically managing the running of virtual machines in Cloud, such as placement and migration algorithms. Despite the use such on-line algorith ..."
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Abstract—Management of energy consumption in Cloud has recently received considerable attention. Most existing research focuses on designing algorithms for dynamically managing the running of virtual machines in Cloud, such as placement and migration algorithms. Despite the use such on-line algorithms being essential, another equally important dimension that is related to High-level policies and guidelines that are set by Cloud Mangers or Administrators for management energy consumption. Such policies often stem from business, legal and financial requirements. Currently, most implementations of High-level policies such as Management Energy Consumption Policies are done manually via the use of low-level programming languages and APIs for accessing Cloud interfaces. Since High-level policies can change frequently, the manual implementation for such policies increases the cost and the time of the development and maintainability. Thus, there is a clear need for a methodical way of executing High-level policies automatically in Cloud. In this paper, we propose a generic architectural framework for enforcing High-level policies particularly Management Energy Consumption Policy in Cloud via using a Business Rule Engine. The generic architecture is implemented to execute Energy Management Business Rules to fire management actions in Open-Nebula cloud environment.
Computing and Information PERFORMANCE COMBINATIVE EVALUATION FROM SINGLE VIRTUAL MACHINE TO MULTIPLE VIRTUAL MACHINE SYSTEMS
"... Abstract. Virtualization technology is widely used in server consolidation, high performance computing, and cloud data center due to its benefits on high resource utilization, flexible manageability, and dynamically scalability. However, it also introduces additional performance overheads. It’sworth ..."
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Abstract. Virtualization technology is widely used in server consolidation, high performance computing, and cloud data center due to its benefits on high resource utilization, flexible manageability, and dynamically scalability. However, it also introduces additional performance overheads. It’sworthy to evaluate the overheads and to find the bottleneck ofvirtualization in differentscenarios. In this paper, we propose a combinative evaluation method to analyze the performance from single virtual machine to multiple virtual machine systems that measures and analyzes both the macro-performance and micro-performance. By correlating the analysis results of two-granularity performance data, some potential performance bottlenecks come out. Key words. Virtualization, Performance, VMM, HPC, Virtual cluster. 1.
