Cost Of Data Centers Research Articles

Efficient Market Strategy Based Optimal Scheduling in Hybrid Cloud Environments

Hybrid cloud is a combination of a private cloud combined with the use of public cloud services where one or several touch points exist between the environments. Depending on utilization, data center cost and the costs of the cloud provider, an efficient scheduling policy has to decide whether or not moving from private cloud to public cloud is profitable. The paper proposes a market based hybrid cloud optimal scheduling optimization in hybrid cloud. The hybrid cloud marketplace is a virtual place where one or more public cloud providers and private cloud users meet to negotiate simultaneously. The scheduling optimization is conducted by hybrid cloud local scheduling and hybrid cloud global scheduling. For the global scheduling, the hybrid cloud system implements the allocation of public cloud resources to the private cloud application groups; the private cloud application group coordinates the deployments of all private cloud applications that consume the allocation of public cloud resources. For the local scheduling, the private cloud local level adjusts the cloud resource usages to optimize the utility of single private cloud application. In the simulations, compared with other related algorithm, our proposed market based hybrid cloud optimal scheduling algorithms achieve the better performance in terms of QoS satisfaction rate and allocation efficiency.

Energy saving for OpenFlow switch on the NetFPGA platform based on queue engineering.

Data centers play an important role in our daily activities. The increasing demand on data centers in both scale and size has led to huge energy consumption that rises the cost of data centers. Besides, environmental impacts also increase considerably due to a large amount of carbon emissions. In this paper, we present a design aimed at green networking by reducing the power consumption for routers and switches. Firstly, we design the Balance Switch on the NetFPGA platform to save consumed energy based on Queue Engineering. Secondly, we design the test-bed system to precisely measure the consumed energy of our switches. Experimental results show that energy saving of our switches is about 30% - 35% of power consumption according to variation of input traffic compared with normal Openflow Switch. Finally, we describe performance evaluations.

Dynamic Energy Storage Control for Reducing Electricity Cost in Data Centers

As the scale of the data centers increases, electricity cost is becoming the fastest-growing element in their operation costs. In this paper, we investigate the electricity cost reduction opportunities utilizing energy storage facilities in data centers used as uninterrupted power supply units (UPS). Its basic idea is to combine the temporal diversity of electricity price and the energy storage to conceive a strategy for reducing the electricity cost. The electricity cost minimization is formulated in the framework of finite state-action discounted cost Markov decision process (MDP). We applyQ-Learning algorithm to solve the MDP optimization problem and derive a dynamic energy storage control strategy, which does not require any priori information on the Markov process. In order to address the slow-convergence problem of theQ-Learning based algorithm, we introduce a SpeedyQ-Learning algorithm. We further discuss the offline optimization problem and obtain the optimal offline solution as the lower bound on the performance of the online and learning theoretic problem. Finally, we evaluate the performance of the proposed scheme by using real workload traces and electricity price data sets. The experimental results show the effectiveness of the proposed scheme.

Part II. Data Center Facilities and Cost

Part II. Data Center Facilities and Cost

On Security Management: Improving Energy Efficiency, Decreasing Negative Environmental Impact, and Reducing Financial Costs for Data Centers

Security management is one of the most significant issues in nowadays data centers. Selection of appropriate security mechanisms and effective energy consumption management together with caring for the environment enforces a profound analysis of the considered system. In this paper, we propose a specialized decision support system with a multilevel, comprehensive analysis scheme. As a result of the extensive use of mathematical methods and statistics, guidelines and indicators returned by the proposed approach facilitate the decision-making process and conserve decision-maker’s time and attention. In the paper we utilized proposed multilevel analysis scheme to manage security-based data flow in the example data center. Determining the most secure, energy-efficient, environmental friendly security mechanisms, we implemented the role-based access control method in Quality of Protection Modeling Language (QoP-ML) and evaluated its performance in terms of mentioned factors.

ORT Broker Policy: Reduce Cost and Response Time Using Throttled Load Balancing Algorithm

Abstract Cloud computing is simply define as “cloud” is delivery of demand services resources everything from application to data centers over the internet on a pay for use basis. In cloud computing load balancing is a technique to distribute the workload for balancing between two or more cloud server. Load balancing technique is defined some cases which are defining as guarantee continuity of the service provided manage, high traffic levels and always be ready for sudden peaks in traffic. Load balancing aims to optimize resource use, maintain the cost of data center and virtual machines, maximize throughput, minimize response time and avoid overload of any single resource. The main objective of this research paper is to reduce cost and response times using throttled load balancing policy across VM's in multi data center and optimise response time service broker policy. This study has evaluated throttled load balancing algorithm and their scheduling criteria like overall response time, Data center processing time and total cost of Virtual Machine and data transfer cost.

Characterizing the impact of the workload on the value of dynamic resizing in data centers

Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time-scale. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits.

Cooling aware job migration for reducing cost in cloud environment

With the growth in computing needs, energy cost includes a large portion of operating cost of cloud data centers. Electricity prices vary in different times and geographical places. Such diversity provides opportunity for diminishing total cost via migration of jobs to places with lower energy prices. Most of the previous studies only focus on computing cost of data centers and disregard other significant parameters such as cooling cost of data centers. These approaches prefer data centers which are located in states with cheaper computing cost. Nonetheless, inappropriate workload migration may lead to a remarkable increase in the total cost because of ignoring the cooling cost of data centers. To address this challenge, we show that minimization of the total cost must cover both the computing and cooling cost while considering delay requirements of jobs. Moreover, we propose an analytical approach which captures the interaction between migration decisions and cooling cost in cloud data centers. Features that make our approach distinct from other similar approaches are the following: first, we consider that cooling cost increases in a nonlinear way with respect to the data center utilization; second, we model cooling cost without any assumption about how the data center cooling system works. In order to achieve energy saving, we determine how much workload should be migrated to other data centers and also the number of servers allocated to each data center for executing the workload. We accomplish migration of workload between data centers by utilizing variety in electricity prices in different locations and times and achieve lower total cost compared with previous schemes. Eventually, using MapReduce traces, we validate our method and indicate that remarkable cost saving, around 37 % can be obtained by cooling-aware job migration.

Retraction Note: Sustainable cost and energy consumption analysis for cloud data centers

The article titled “Sustainable cost and energy consumption analysis for cloud data centers” published in Frontiers of Computer Science is retracted at the request of the author and Editor-in-Chief as it constitutes plagiarism. Part of its content, mainly in Sections 3–8 of the paper including text, tables and figures, is similar to another published article: “An economic and energyaware analysis of the viability of outsourcing cluster computing to a cloud” by Carlos de Alfonso, Miguel Caballer, Fernando Alvarruiz, German Molto, Future Generation Computer Systems, 29 (2013) 704–712, Elsevier.

Analyzing the Cooling Efficiency of the Thermal Environment in Data Centers

Given the explosive growth of data, scalability and fault tolerance have become a fundamental challenge for data center network structures. Temperature in data centers significantly affects the failure ratio of high-speed network devices. Various types of air distribution schemes influence the temperature of network equipment differently, and the cooling cost in data centers dominates the overall energy cost. On the basis of the energy efficiency of cooling systems, this study analyzes and compares the thermal load distribution in the enclosure of standard and non-standard data centers by considering the effects of the external environment. Analysis results demonstrate that the external environment significantly affects the thermal load of non-standard data centers. By leveraging on the air temperature outside data centers and on the inlet/outlet of IT equipment, the air temperature and return air temperature of air conditioning are calculated when performing hot and cold aisle containment. The calculations indicate that sealing an appropriate aisle (hot or cold aisle) can significantly reduce the energy consumption of cooling systems in terms of the external air temperature outside data centers. Furthermore, if the air temperature outside data centers is higher than the temperature at the inlet of IT equipment, sealing the cold aisle outperforms sealing the hot aisle. By contrast, the aisle to be sealed depends on the energy efficiency ratio of the air conditioning.

Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets

Along with the rapid increasing energy consumption, the energy cost of Internet data centers (IDCs) has been skyrocketing. A novel scheme of geographical load balancing was proposed to reduce electricity bills for service providers. However, one important challenge faced by service providers has not been considered properly. In service systems, the service delay faced by consumers includes the queuing delay and the transmission delay. While existing work only consider the queuing delay, the transmission delay introduced by geographical load balancing has been overlooked. It is one of the most important factors affecting the quality of service for real-time service systems. In this paper, we take the transmission delay into our design consideration and formulate a mixed-integer nonlinear programming (MINLP) problem with coupled constraint to achieve the optimal load balancing and energy cost management for IDCs while meeting the service-level agreements (SLA) of consumers. A novel heuristic based branch and bound with feedback (HBBF) algorithm is proposed to decouple the MINLP problem with coupled constraint efficiently. Extensive performance evaluations based on real electricity price data and site-to-site transmission delay data demonstrate the effectiveness of our proposed algorithm.

Towards an immortal operating system in virtual environments

We show how a commercial OS can be successfully recovered from a crash.Support from the virtualization layer (Hypervisor) can significantly help in diagnosis and recovery of the OS.We evaluate the time taken to automatically recover from an OS crash for different workloads.This technology can significantly reduce the downtime and maintenance costs in data centers.This technology can be easily integrated into the support operations of OS vendors. Many OS crashes are caused by bugs in kernel extensions or device drivers while the OS itself may have been tested rigorously. To make an OS immortal we must resurrect the OS from these crashes. We present a novel OS-hypervisor infrastructure that allows automated and transparent OS crash diagnosis and recovery in a virtual environment. This infrastructure eliminates the need for reboots or checkpoint-restart mechanisms, which require preserving the states of critical applications before the crash happens and also require extensive modifications to those applications. At the core of our approach is a small hidden OS-repair-image that is dynamically created from the healthy running OS instance. When an OS crashes, the hypervisor dynamically loads this repair-image to perform diagnosis and repair. One way of repair we have experimented with, is to quarantine the offending process and resume the running of the fixed OS automatically without a reboot. Experimental evaluations demonstrated that it takes less than 3s to recover from an OS crash. This approach can significantly reduce the downtime and maintenance costs in data centers, and is the first design and implementation of an OS-hypervisor combo capable of automatically resurrecting a crashed commercial server-OS. In addition to online diagnosis and recovery, this infrastructure can also be used for offline diagnosis and can be incorporated into the technical support tools of the OS vendor. Additionally, we have used parts of this infrastructure to speed-up the diagnosis of AIX OS-crashes for the IBM technical support teams.

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical host

Design and Implementation of Next-Generation Data Center Infrastructure

A typical data center includes infrastructure, system architecture, applications and data, business processes, organization and operation comprehensive. With the application of information technology and the rapid development of information technology, the traditional data center faces tremendous challenges. Next-generation data center built adaptability and high availability computing environments through resource integration and management, virtualization, security and energy management technologies. It can solve data center costs of rapidly increasing prevalence, resource management increasingly complex information security challenges, and the energy crisis and other acute problems. Design and implementation of next-generation data center must follow from the overall situation, service-oriented, overall planning and construction of new principles.

Exploring smart grid and data center interactions for electric power load balancing

The operation of a data center consumes a tremendous amount of electricity, and the energy cost accounts for a large portion of the data center's operation cost. This leads to a growing interest towards reducing the energy cost of data centers. One approach advocated in recent studies is to distribute the computation workload among multiple geographically dispersed data centers by exploiting the electricity price differences. However, the impact of load redistributions on the power grid is not well understood yet. This paper takes the first step towards tackling this important issue, by studying how the power grid can take advantage of the data center's load distribution proactively for the purpose of power load balancing. We model the interactions between power grid and data centers as a two-stage problem, where the power grid operator aims to balance the electric power load in the first stage, and the data centers seek to minimize their total energy cost in the second stage. We show that this two-stage problem is a bilevel program with an indefinite quadratic objective function, which cannot be solved efficiently using standard convex optimization algorithms. Therefore, we reformulate this bilevel optimization problem as a linear program with additional finite complementarity slackness conditions, and propose a branch and bound algorithm to attain the globally optimal solution. The simulation results demonstrate that our proposed scheme can improve the load balancing performance by around 12% in terms of the electric load index and reduce the energy cost of data centers by 46% on average.

An Integrated Modeling Approach to Evaluate and Optimize Data Center Sustainability, Dependability and Cost

Data centers have evolved dramatically in recent years, due to the advent of social networking services, e-commerce and cloud computing. The conflicting requirements are the high availability levels demanded against the low sustainability impact and cost values. The approaches that evaluate and optimize these requirements are essential to support designers of data center architectures. Our work aims to propose an integrated approach to estimate and optimize these issues with the support of the developed environment, Mercury. Mercury is a tool for dependability, performance and energy flow evaluation. The tool supports reliability block diagrams (RBD), stochastic Petri nets (SPNs), continuous-time Markov chains (CTMC) and energy flow (EFM) models. The EFM verifies the energy flow on data center architectures, taking into account the energy efficiency and power capacity that each device can provide (assuming power systems) or extract (considering cooling components). The EFM also estimates the sustainability impact and cost issues of data center architectures. Additionally, a methodology is also considered to support the modeling, evaluation and optimization processes. Two case studies are presented to illustrate the adopted methodology on data center power systems.

A Survey on Geographic Load Balancing Based Data Center Power Management in the Smart Grid Environment

Power management is becoming an increasingly important issue for Internet services supported by multiple geo-distributed data centers. These data center's energy consumptions and costs are becoming unacceptably high, and placing a heavy burden on both energy resources and the environment. Emerging smart grid provides a feasible way for dynamic and efficient power management of data centers. Various power management methodologies based on geographic load balancing (GLB) have recently been proposed to effectively utilize several features of smart grid. In this paper, we summarize the motivations, current state of the art, approaches and techniques proposed in the recent research works in this discipline. In all of these works, many perspectives of power management have been addressed using various computer science principles. We specifically elaborate on how researchers are exploiting mathematical tools to address these perspectives. Finally, we point out subject matters that need more attentions from the research community and provide our vision on possible future works along this direction.

Thermal Modeling and Analysis of Cloud Data Storage Systems

An explosive increment of data and a variety of data analysis make it indispensable to lower power and cooling costs of cloud datacenters. To address this issue, we investigate the thermal impact of I/O access patterns on data storage systems. Firstly, we conduct some preliminary experiments to study the thermal behavior of a data storage node. The experimental results show that disks have ignorable thermal impacts as processors to outlet temperatures of storage nodes. We raise an approach to model the outlet temperature of a storage node. The thermal models generated by our approach gains a precision error less than 6%. Next, we investigate the thermal impact of data placement strategies on storage systems. We compare the cooling cost of storage systems governed by different data placement schemes. Our study shows that evenly distributing the data leads to highest outlet temperature for the sake of shortest execution time and energy efficiency. According to the energy consumption of various data placement schemes, we propose a thermal-ware energy-efficient data placement strategy. We further show that this work can be extended to analyze the cooling cost of data centers with massive storage capacity. Big data, which is composed of a collection of huge and complex data sets, has been positioned as must have commodity and resource in industry, government, and academia. Processing big data requires a large-scale storage system, which increases both power and cooling costs. In this study, we investigate the thermal behavior of real storage systems and their I/O access patterns, which offer a guideline of building energy-efficient cloud storage systems. The cooling consumption of data centers can be considerably reduced by using an efficient thermal management for storage systems. However, disk is not considered in traditional thermal models for data centers. In this paper, we investigate the thermal impact of hard disks and propose a thermal modeling approach for storage systems. In addition, we estimate the outlet temperature of a storage server by applying the proposed

Cost-Aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers

Recent cost analysis shows that the server cost still dominates the total cost of high-scale data centers or cloud systems. In this paper, we argue for a new twist on the classical resource provisioning problem: heterogeneous workloads are a fact of life in large-scale data centers, and current resource provisioning solutions do not act upon this heterogeneity. Our contributions are threefold: first, we propose a cooperative resource provisioning solution, and take advantage of differences of heterogeneous workloads so as to decrease their peak resources consumption under competitive conditions; second, for four typical heterogeneous workloads: parallel batch jobs, web servers, search engines, and MapReduce jobs, we build an agile system PhoenixCloud that enables cooperative resource provisioning; and third, we perform a comprehensive evaluation for both real and synthetic workload traces. Our experiments show that our solution could save the server cost aggressively with respect to the noncooperative solutions that are widely used in state-of-the-practice hosting data centers or cloud systems: for example, EC2, which leverages the statistical multiplexing technique, or RightScale, which roughly implements the elastic resource provisioning technique proposed in related state-of-the-art work.

Cost-aware Workload Dispatching and Server Provisioning for Distributed Cloud Data Centers

As the demand on online services and cloud computing has kept increasing in recent years, the power usage and cost associated with cloud data centers’ operation have been uprising significantly. Most existing research focuses on reducing power consumption of data centers. However, the ultimate goal of cloud service operators is to reduce the total operating cost of data centers while guaranteeing the quality of service such as service delay to the end users. This paper exploits both the workload dispatching and the service provisioning to address the total electricity cost minimization problem. This problem is formulated as a hierarchical capacitated median model based on mixed integer linear programming (MILP) technique. Extensive evaluations based on real-life electricity price data for multiple data centers show the efficiency and efficacy of our approach.