Planning vs. Dynamic Control: Resource Allocation in Corporate Clouds

Abstract

Abstract-Nowadays corporate data centers leverage virtualization technology to cut operational and management costs. Virtualization allows splitting and assigning physical servers to virtual machines (VM) that run particular business applications. This has led to a new stream in the capacity planning literature dealing with the problem of assigning VMs with volatile demands to physical servers in a static way such that energy costs are minimized. Live migration technology allows for dynamic resource allocation, where a controller responds to overload or underload on a server during runtime and reallocates VMs in order to maximize energy efficiency. Dynamic resource allocation is often seen as the most efficient means to allocate hardware resources in a data center. Unfortunately, there is hardly any experimental evidence for this claim. In this paper, we provide the results of an extensive experimental analysis of both capacity management approaches on a data center infrastructure. We show that with typical workloads of transactional business applications dynamic resource allocation does not increase energy efficiency over the static allocation of VMs to servers and can even come at a cost, because migrations lead to overheads and service disruptions.

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

“…We use the workload patterns from the three distinct sets MIX0, MIX1, and MIX2 provided in [21]. In each set of MIX0, MIX1, and MIX2, there are 20 traces selected from the 481 raw workload traces from a large European data center [21].…”

“…The demand patterns in MIX0, MIX1, and MIX2 are illustrated in Figure 2. Most of the workload traces in MIX0 present small variance during the execution time, while the workload traces in MIX1 show many short-term bursts [21]. MIX3 mixes the samples in MIX0 and MIX1 simultaneously.…”

“…The threshold-based method is simple and commonly used. In commercial and open-source approaches such as VMware's Distributed Resource Management [21], the management performs VM migrations when the resource utilization violates the predefined threshold. However, setting static thresholds is not efficient for non-stationary resource usage patterns.…”

An Adaptive Control Method for Resource Provisioning with Resource Utilization Constraints in Cloud Computing

“…Recent studies have focused on dynamic resource allocation by maximizing resource utilization in cloud computing systems [3][4][5]. Maximizing resource utilization provides many benefits for both users and service providers; for example, it can greatly reduce users' costs [6].…”

“…Moreover, the feedback mechanism of MIMO control enables to react to the workload fluctuations to provide roughly the same QoS with the requirements maximum allowed QoS according to the dynamic workload at run time. In contrast to traditional MIMO control approaches [4,[12][13][14] that rely on empirical evaluations and manual adjustment, we design an adaptive module for the controller to adjust the parameters of the controller in real time. As a local approximation network, radial basis function (RBF) neural network can highly improve the convergence speed and avoids local minimum [15].…”