Profiling: An application assignment approach for green data centers

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution

Profile-based application management for green data centres

This thesis presents a profile-based application management framework for energy-efficient data centres. The framework is based on a concept of using Profiles that provide prior knowledge of the run-time workload characteristics to assign applications to virtual machines. The thesis explores the building of profiles for applications, virtual machines and servers from real data centre workload logs. This is then used to inform static and dynamic application assignment, and consolidation of applications

Use of Soil-Steel Slag-Class-C Fly Ash Mixtures in Subgrade Applications

In Indiana, the steelmaking industries and power plants generate large quantities of steel slag, blast furnace slag and fly ash every year. The excess of these underutilized industrial by-products are stockpiled and eventually landfilled at disposal sites. Use of steel slag, fly ash and blast furnace slag in road applications, such as in subgrade stabilization projects, can be a cost-effective alternative to lime stabilization in some cases. In addition, use of large quantities of these underutilized industrial by-products in these types of applications helps to reduce the need for new disposal sites and to conserve natural resources. The main objectives of this research were to evaluate the feasibility of using soil-steel slag-Class-C fly ash and soil-steel slag-blast furnace slag mixtures in subgrade applications and to implement the selected mixture as a subgrade material in a road construction project of INDOT. In order to achieve these goals, in situ clayey soils, collected from a prospective implementation site, were characterized through a series of laboratory tests which included specific gravity, grain size distribution, Atterberg limits, compaction and unconfined compressive strength. Two types of steel slag mixtures were evaluated for use in subgrade stabilization applications: i) steel slag-Class-C fly ash mixtures and ii) steel slag-blast furnace slag mixtures. The mechanical properties of soil-5% steel slag-5% Class-C fly ash, soil-7% steel slag-3% Class-C fly ash, soil-8% steel slag-2% Class-C fly ash, and soil-7% steel slag-3% blast furnace slag mixtures were determined through compaction and unconfined compression tests. CBR swelling tests were also performed to assess the swelling potential of the mixtures. The optimum moisture content and maximum dry unit weight of the in situ clayey soil samples were 13% and 18.56 kN/m3 (118.2 pcf), respectively. Based on the results of the long-term CBR swelling tests, the maximum swelling strain of the compacted soil samples was approximately 0.41 %. The average unconfined compressive strength of the in situ soil samples was 282.9 kPa (41 psi). Unconfined compressive strength tests performed on various mixtures at different times indicated the occurrence of stronger cementitious reactions in the soil-steel slag-Class-C fly ash mixtures than in the soil-steel slag-blast furnace slag mixtures. The two-day and seven-day unconfined compressive strength of the compacted soil-7% steel slag-3% Class-C fly ash mixture were 820 kPa (119 psi) and 886 kPa (128 psi), respectively. The maximum 1-D swelling strain of the soil-7% steel slag-3% Class-C fly ash mixture was 0.13 %. The soil-7% steel slag-3% Class-C fly ash mixture was selected as the most suitable and cost-effective subgrade material for the implementation project. The implementation project for the soil-steel slag-Class-C fly ash mixture was located at the intersection of 109th Avenue and I-65, near Crown Point, Indiana. The pre-mixed 7% steel slag-3% Class-C fly ash mixture was used to stabilize the in situ subgrade soils of some sections of the I-65 ramps located in the SW and NW quadrants of the intersection of 109th Avenue and I-65. Field compaction quality control was done by performing DCPTs and nuclear gauge tests. Cracks or signs of distress were not observed on the subgrade before base course and concrete placement. The soil-steel slag-Class-C fly ash stabilized subgrade performed satisfactorily

Profile-based application assignment for greener and more energy-efficient data centers

The cloud computing era has brought significant challenges in energy and operational costs of data centers. As a result, green initiatives with regard to energy-efficient management of data center infrastructure for cloud computing have become essential. Addressing a big class of widely deployed data centers with relatively consistent workload and applications, this paper presents a new profile-based application assignment approach for greener and more energy-efficient data centers. It builds realistic profiles from the raw data measured from data centers and then establishes a theoretical framework for profile-based application assignment. A penalty-based profile matching algorithm (PPMA) is further developed to obtain an assignment solution, which gives near-optimal allocations whilst satisfying energy-efficiency, resource utilization efficiency and application completion time constraints. Through experimental studies, the profiling approach is demonstrated to be feasible, scalable and energy-efficient when compared to the commonly used general and workload history based application management approaches

Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm

Energy-efficient application assignment in profile-based data center management through a Repairing Genetic Algorithm

The massive deployment of data center services and cloud computing comes with exorbitant energy costs and excessive carbon footprint. This demands green initiatives and energy-efficient strategies for greener data centers. Assignment of an application to different virtual machines has a significant impact on both energy consumption and resource utilization in virtual resource management of a data centre. However, energy efficiency and resource utilization are conflicting in general. Thus, it is imperative to develop a scalable application assignment strategy that maintains a trade-off between energy efficiency and resource utilization. To address this problem, this paper formulates application assignment to virtual machines as a profile-driven optimization problem under constraints. Then, a Repairing Genetic Algorithm (RGA) is presented to solve the large-scale optimization problem. It enhances penalty-based genetic algorithm by incorporating the Longest Cloudlet Fastest Processor (LCFP), from which an initial population is generated, and an infeasible-solution repairing procedure (ISRP). The application assignment with RGA is integrated into a three-layer energy management framework for data centres. Experiments are conducted to demonstrate the effectiveness of the presented approach, e.g., 23% less energy consumption and 43% more resource utilization in comparison with the steady-state Genetic Algorithm (GA) under investigated scenarios

Profiling : an application assignment approach for green data centers

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures\ud have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application\ud assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that\ud of the Hungarian algorithm, which does not scale well though\ud giving the optimal solution

Profile-based dynamic application assignment with a repairing genetic algorithm for greener data centers

Data centers have become essential to modern society by catering to increasing number of internet users and technologies. This results in significant challenges in terms of escalating energy consumption. Research on green initiatives that reduce energy consumption whilst maintaining performance levels is exigent for data centers. However, energy efficiency and resource utilization are conflicting in general. Thus, it is imperative to develop an application assignment strategy that maintains a trade-off between energy and Quality of Service (QoS). To address this problem, a profile-based dynamic energy management framework is presented in this paper for dynamic application assignment to virtual machines (VMs). It estimates application finishing times and addresses real-time issues in application resource provisioning. The framework implements a dynamic assignment strategy by a repairing genetic algorithm (RGA), which employs realistic profiles of applications, virtual machines and physical servers. The RGA is integrated into a three-layer energy management system incorporating VM placement to derive actual energy savings. Experiments are conducted to demonstrate the effectiveness of the dynamic approach to application management. The dynamic approach produces up to 48% better energy savings than existing application assignment approaches under investigated scenarios. It also performs better than the static application management approach with 10% higher resource utilization efficiency and lower degree of imbalance