Autonomic virtual resource management for service hosting platforms

Proceedings of the Workshop on Software Engineering Challenges in Cloud Computing 01/2009; DOI: 10.1109/CLOUD.2009.5071526
Source: OAI

ABSTRACT Cloud platforms host several independent applications on a shared resource pool with the ability to allocate com- puting power to applications on a per-demand basis. The use of server virtualization techniques for such platforms provide great flexibility with the ability to consolidate sev- eral virtual machines on the same physical server, to resize a virtual machine capacity and to migrate virtual machine across physical servers. A key challenge for cloud providers is to automate the management of virtual servers while taking into account both high-level QoS requirements of hosted applications and resource management costs. This paper proposes an autonomic resource manager to con- trol the virtualized environment which decouples the provi- sioning of resources from the dynamic placement of virtual machines. This manager aims to optimize a global utility function which integrates both the degree of SLA fulfillment and the operating costs. We resort to a Constraint Pro- gramming approach to formulate and solve the optimization problem. Results obtained through simulations validate our approach.

1 Bookmark
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: With immense success and rapid growth within the last few years, cloud computing has been established as the dominant paradigm of IT industry. In order to meet the increasing demand of computing and storage resources, infra-structure cloud providers are deploying planet-scale data centers across the world, consisting of hundreds of thousands, even millions of servers. These data centers incur very high investment and operating costs for the compute and network devices as well as for the energy consumption. Moreover, because of the huge energy usage, such data centers leave large carbon footprints and thus have adverse effects on the environment. As a result, efficient computing resource utilization and energy consumption reduction are becoming crucial issues to make cloud computing successful. Intelligent workload placement and relocation is one of the primary means to address these issues. This chapter presents an overview of the infra-structure resource management systems and technologies, and detailed description of the proposed solution approaches for efficient cloud resource utilization and minimization of power consumption and resource wastages. Different types of server consolidation mechanisms are presented along with the solution approaches proposed by the researchers of both academia and industry. Various aspects of workload reconfiguration mechanisms and existing works on workload relocation techniques are described.
    Cloud Computing: Challenges, Limitations and R&D Solutions, Edited by Zaigham Mahmood, 11/2014: chapter 55: pages 33; Springer.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
    Global Communications Conference (GLOBECOM), 2012 IEEE; 01/2012
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Efficient Virtual Machine (VM) provisioning and allocation allows the cloud providers to effectively utilize their available resources and obtain higher profits. Existing combinatorial auction-based mechanisms assume that the VM instances are already provisioned, that is they assume static VM provisioning. A better solution would be to take into account the users' demand when provisioning VM instances. We design an auction-based mechanism for dynamic VM provisioning and allocation that takes into account the user demand for VMs when making VM provisioning decisions. We perform extensive simulation experiments using real workload traces and show that the proposed mechanism can improve the utilization, increase the efficiency of allocation, and yield higher revenue for the cloud provider.
    Proc. of the 3rd IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2011), Athens, Greece; 11/2011


Available from