Virtual machine consolidation aims at reducing the number of active physical servers in a data center so as to decrease the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. Besides, virtual machine consolidation should take into account multiple resource types at the same time, since CPU is not the only critical resource in cloud data centers. In fact, also memory and network bandwidth can become a bottleneck, possibly causing violations in the service level agreement. This article presents a virtual machine consolidation algorithm with multiple usage prediction (VMCUP-M) to improve the energy efficiency of cloud data centers. In this context, multiple usage refers to both resource types and the horizon employed to predict future utilization. Our algorithm is executed during the virtual machine consolidation process to estimate the long-term utilization of multiple resource types based on the local history of the considered servers. The joint use of current and predicted resource utilization allows for a reliable characterization of overloaded and underloaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our solution through simulations on both synthetic and real-world workloads. The obtained results show that consolidation with multiple usage prediction reduces the number of migrations and the power consumption of the servers while complying with the service level agreement.

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in data centers. Many heuristic algorithms have been proposed to tackle the VM consolidation as a vector bin-packing problem. However, the existing algorithms have focused mostly on the number of active Physical Machines (PMs) minimization according to their current resource requirements and neglected the future resource demands. Therefore, they generate unnecessary VM migrations and increase the rate of Service Level Agreement (SLA) violations in data centers. To address this problem, we propose a VM consolidation approach that takes into account both the current and future utilization of resources. Our approach uses a regression-based model to approximate the future CPU and memory utilization of VMs and PMs. We investigate the effectiveness of virtual and physical resource utilization prediction in VM consolidation performance using Google cluster and PlanetLab real workload traces. The experimental results show, our approach provides substantial improvement over other heuristic and meta-heuristic algorithms in reducing the energy consumption, the number of VM migrations and the number of SLA violations.

Virtual machine consolidation aims at reducing the number of active physical servers in a data center, with the goal to reduce the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. This article presents a virtual machine consolidation algorithm with usage prediction (VMCUP) for improving the energy efficiency of cloud data centers. Our algorithm is executed during the virtual machine consolidation process to estimate the short-term future CPU utilization based on the local history of the considered servers. The joint use of current and predicted CPU utilization metrics allows a reliable characterization of overloaded and under loaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our proposed solution through simulations on real workloads from the Planet Lab and the Google Cluster Data datasets. In comparison with the state of the art, the obtained results show that consolidation with usage prediction reduces the total migrations and the power consumption of the servers while complying with the service level agreement.

Virtual machine (VM) placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Several approaches have been proposed to find a solution to this problem. However, most of the existing solutions only consider a limited number of resource types, thus resulting in unbalanced load or in the unnecessary activation of physical servers. In this article, we propose an algorithm, called Max-BRU, that maximizes the resource utilization and balances the usage of resources across multiple dimensions. Our algorithm is based on multiple resource-constraint metrics that help to find the most suitable server for deploying VMs in large cloud data centers. The proposed Max-BRU algorithm is evaluated by simulations based on synthetic datasets. Experimental results show two major improvements over the existing approaches for VM placement. First, Max-BRU increases the resource utilization by minimizing the amount of physical servers used. Second, Max-BRU effectively balances the utilization of multiple types of resources.

Resources used in a cloud data center could be spread across a large number of servers that are not fully utilized. This situation results in significant operational costs which are directly related to the power consumption of active servers. Virtual machine migration enables reducing the number of active servers by consolidating the load on a limited amount of nodes. Several schemes have actually been proposed to consolidate virtual machines on the minimum number of physical servers in order to reduce power consumption. However, most of the existing solutions only consider a limited trade off among multiple types of resources, thus resulting in unnecessarily activated physical servers. This article proposes a multi-resource selection (MRS) scheme for consolidating virtual machines in cloud data centers. With MRS, each physical server is first characterized in terms of multiple types of resources and then classified through its overall resource utilization. Based on the MRS scheme, a balanced multiple-resource utilization algorithm is also used to spread the load across different types of resources while consolidating virtual machines. The proposed solution is evaluated through simulations on both synthetic and real-world workloads. Experimental results show that the proposed approach outperforms several existing schemes in terms of the number of active physical servers and the utilization of multiple resources.

This paper addresses the design and implementation of an Adaptive Fading Extended Kaiman Filter (AF-EKF) for the sensorless Permanent Magnet Synchronous Motor (PMSM) on a Field Programmable Gate Array (FPGA) chip. The rotor position and speed of the motor are estimated by the implemented AF-EKF and their estimates are then used in vector control of the PMSM. In conventional Kaiman filtering, abrupt state changes may not be tracked adequately since sudden variations may seriously affect the auto-correlation Gaussian property of white noise in the filter residuals. For this, the AF-EKF has been developed to recover the estimation results in events of frequent and sharp state jumps. The AF-EKF is, therefore, a promising estimator for PMSM drives that are subject to frequently-varying loads speed commands. Here, for realization of the PMSM sensorless control using the system-on-programmable-chip technology, high-speed arithmetic functions and pipelining are employed in the FPGA implementation. The finite state machine method is also used to facilitate the execution timing and chip design. The co-simulation of Modelsim/Simulink shows effectiveness of the proposed chip-based AF-EKF PMSM speed estimation.

It is well known that Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing Eigenbeam-Space Division Multiplexing systems, namely MIMO-OFDM E-SDM systems, are considered a promising candidate for future high-speed wireless communication networks because of having the maximal channel capacity and good communications reliability. In the systems, orthogonal transmission beams are formed between transmit and receive sides; and also optimal transmit input data are adaptively allocated. In addition, a simple detection can be used at a receiver to totally eliminate sub-stream interference. Although many papers have studied and evaluated the systems based on theoretical analyses and/or computer-based simulations, there have not been papers that investigated the systems based on their design and implementation on FPGA hardware. The main contribution of this paper is to present our own design and implementation of 2×2 and 2×3 MIMO-OFDM E-SDM systems on FPGA Altera Stratix DSP Development KIT using Verilog HDL. The bit-error rate performance of these systems is evaluated and shown that our design is successful. In the paper, we also show the consumption of FPGA elements for our design of the systems.

This paper presents an approach for scheduling U-Life care applications in the cloud computing environment based on virtual resources to support real-time services and to improve user Quality of Service (QoS) requirements. We design and develop an architecture called ULC3 (Ubiquitous Life Care Cloud Computing) that uses virtual resources provided by cloud computing to schedule U-Life care applications. The ULC3 is based on the concepts of cloud computing and wireless sensor networks. The architecture is very important and necessary to support create virtual clusters dynamically, deploys the required number of virtual machines (VMs) in potential computing resources to meet the application requirements, and to configure with the required software execution environment. Thus, the system can improve computation time, guarantee the QoS, and support real-time services. Finally, the results from the execute applications are provided to the end-users as a service.