Operational Cost Of Data Centers Research Articles

Coordinated Optimization Scheduling of Data Center and Electricity Retailer Based on Cooperative Game Theory

As the number of internet data centers (IDC) increases, the energy consumption of modern data centers is growing rapidly, which takes an increasing part of the global electricity consumption. In implementation, to cover the electricity consumption and reduce the electricity bill, data centers are often deployed with renewable energy, energy storage system (ESS) and conventional generators, which has brought the characteristics of both flexible electricity load and micro grid to the data centers. Therefore, the data center micro grids can adjust its operations according to the real time electricity price and cut down the operational cost. However, the rising of electricity will force the data center to start up the conventional generators, which leads to the incensement of operational cost and carbon tax. In view of this, we propose a coordinated operation scheduling method of data centers and electricity retailers based on cooperative game theory. In this method, a cooperative game union comprised of the data center and the electricity retailer is established to minimize the total cost. And the extra income of the alliance is distributed using the Shapley value method. A simulation is launched on Gurobi software, and the results have illustrated that the proposed method can reduce the data center operational cost by 5%.

Energy and cost aware workflow scheduling in clouds with deadline constraint

AbstractCloud computing is a promising platform for executing scientific workflow applications. Commonly, the scientific workflow applications are complex in size and computation intensive. Task scheduling in clouds is a familiar NP‐complete problem. Efficient workflow application scheduling is critical for meeting multi‐objectives in cloud environments. By reason of its pivotal role, this problem has been widely studied, and many approaches have been developed. Most of the algorithms focus on schedule makespan and execution cost. The cloud data centers consume a large amount of energy while running workflow applications due to a lack of efficient scheduling algorithms to perform the task to a virtual machine (VM) mapping. Hence, there is a significant need to address energy utilization in the cloud data center (CDC) for two reasons such as data center operational cost optimization and to improve the environment. This article presents an energy and cost‐aware scheduling (ECWS) approach with the objectives of decreasing energy utilization, execution cost, and maximizing utilization of the resources. The ECWS algorithm is a heterogeneous earliest finish time (HEFT) based energy‐efficient heuristic for cloud scheduler. The ECWS algorithm includes three sub‐algorithms like RE calculation, RE Threshold selection, and slack algorithm. The effectiveness of the proposed algorithm is evaluated on the WorkflowSim tool using distinct workloads from various scientific areas. The experimental outcome exhibit that the proposed algorithm achieved significant energy conservation, maximized resource utilization, and cost‐saving when compared with related well‐known algorithms.

A Robust Optimization Technique for Energy Cost Minimization of Cloud Data Centers

Power consumption cost constitutes significant portion of a data center's total operational cost. Variations in parameters, such as power demand, electricity price, and renewable power generation effects the data center's power consumption cost. Therefore, in this paper, a smart power management system based on a robust energy cost optimization algorithm is proposed for the data center. The designed robust optimization algorithm coordinates data center workload, battery bank, diesel generators, renewable power, and trade electricity price in real-time and day-ahead power market to reduce expected energy consumption cost. The uncertain parameters, such as data center workload and renewable power are computed using forecasting algorithms. The optimization algorithm is used to limit unbalance power purchase from real-time power market due to uncertainty of real-time electricity price. The algorithm formulation is obtained using mixed-integer linear programming. Moreover, a model to calculate prices to be used in service level agreements with data centers' clients for on-demand cloud services is designed considering operational cost of the data center. Simulations are performed on actual data center workload, weather parameters, and electricity price. The results have shown that the proposed methodology is an effective tool to minimize data center operational cost.

Data placement in distributed data centers for improved SLA and network cost

Large-scale data-intensive applications provide services to users by routing service requests to geographically distributed data centers interconnected by Internet links. In order to achieve good reliability and data access latency performance, cloud service providers often simultaneously place multiple copies of the data in different data centers. The network communication required for updating the multiple data copies incurs an operational cost. At the same time, the penalty incurred by the Service Level Agreement (SLA) violation for data access from the data centers also imposes an operational cost on the service providers. In this paper, we tackle the problem of data placement in distributed data centers with the aim to minimize the operational cost incurred by delay SLA violation penalty and inter-data center network communication, assuming each data has K data replicas. We propose a K-level Cluster-based Data Placement algorithm (K-CDP) for the problem. The algorithm solves the linear programming relaxation and dual programming problems corresponding to the problem of minimizing SLA violation penalty cost caused by placing a replica of each data in a data center. Based on the obtained solutions, the algorithm clusters the data so that the data with similar placeable data centers form a data cluster. For the data in each cluster, the algorithm selects K data centers to minimize the operational cost. We prove that algorithm K-CDP is 2-approximation to the data placement problem. Our simulation results demonstrate that the proposed algorithm can effectively reduce the penalty cost incurred by delay SLA violation, the network communication cost, and the operational cost of data centers.

Resource‐efficient load‐balancing framework for cloud data center networks

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

Stackelberg Game for Energy-Aware Resource Allocation to Sustain Data Centers Using RES

Smart Grid (SG) has emerged as one of the most powerful technologies of the modern era for an efficient energy management by integrating information and communication technologies (ICT) in the existing infrastructure. Among various ICT, cloud computing (CC) has emerged as one of the leading service providers which uses geo-distributed data centers (DCs) to serve the requests of users in SG. In recent times, with an increase in service requests by end users for various resources, there has been an exponential increase in the number of servers deployed at various DCs. With an increase in the size, the energy consumption of DCs has increased many folds which leads to an increase in overall operational cost of DCs. However, efficient resource allocation among these geo-distributed DCs may play a vital role in reducing the energy consumption of DCs. Moreover, with an increase in harmful emissions, the use of renewable energy sources (RES) can benefit DCs, SG, and society at large. Keeping focus on these points, in this paper, an energy-aware resource allocation scheme is proposed using a Stackelberg game for energy management in cloud-based DCs. For this purpose, a cloud controller is used to receive the requests of users which then distributes these requests among geo-distributed DCs in such a way that the energy consumption of DCs is sustained by RES. However, if energy consumption of DCs is not sustained by RES then the energy is drawn from the grid. The requests of users are routed to the DC which is offered lowest energy tariff from the grid. For this purpose, a Stackelberg game for energy trading is also proposed to select the grid offering lowest energy tariff to DCs. The proposed scheme is evaluated using various performance metrics using Google workload traces. The results obtained show the effectiveness of the proposed scheme.

Online Energy Budgeting for Cost Minimization in Virtualized Data Center

The growing environmental and sustainability concerns have made energy efficiency a pressing issue for data center operation. Governments, as well as various organizations, are urging data centers to cap the increasing energy consumption. Naturally, achieving long term energy capping involves deciding energy usage over a long timescale (without accurately foreseeing the far future) and hence, we call this process “energy budgeting”. In this paper, we introduce an online resource management solution, called eBud (energy Budgeting), for a virtualized data center. eBud determines the number of servers, resource allocation to virtual machines and corresponding workload distribution to minimize data center operational cost while satisfying a long term energy cap. We prove that eBud achieves a close-to-minimum cost compared to the optimal offline algorithm with future information, while bounding the potential violation of energy budget constraint, in an almost arbitrarily random environment. We also perform a trace-based simulation study to complement the performance analysis. The simulation results show that eBud reduces the cost by more than $\mathrm{16}$ percent (compared to state-of-the-art prediction-based algorithm) while resulting in a zero energy budget deficit. We also perform an experimental study based on RUBiS, demonstrating that in a real life scenario, eBud can achieve energy capping with a negligible increase in operational cost.

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.

Online capacity provisioning for carbon-neutral data center with demand-responsive electricity prices

Due to the huge electricity consumption and carbon emissions, data center operators have been increasingly pressured to reduce their net carbon footprints to zero, i.e., carbon neutrality. In this paper, we propose an efficient online algorithm, called CNDC (optimization for Carbon-Neutral Data Center), to control the number of active servers for minimizing the data center operational cost (defined as a weighted sum of electricity cost and delay cost) while satisfying carbon neutrality without requiring long-term future information. Unlike prior research on carbon neutrality, we explore demand-responsive electricity price enabled by the emerging smart grid technology and demonstrate that it can be incorporated in data center operation to reduce the operational cost. Leveraging the Lyapunov optimization technique, we prove that CNDC achieves a close-to-minimum operational cost compared to the optimal algorithm with future information, while bounding the potential violation of carbon neutrality, in an almost arbitrarily random environment. We also perform trace-based simulation as well as experiment studies to complement the analysis. The results show that CNDC reduces the cost by more than 20% (compared to state-of-the-art prediction-based algorithm) while resulting in a smaller carbon footprint. Moreover, by incorporating demand-response electricity prices, CNDC can further decrease the average cost by approximately 2.5%, translating into hundreds of thousands of dollars per year.

Cloud3DView

The emergence of cloud computing has promoted growing demand and rapid deployment of data centers. However, data center operations require a set of sophisticated skills (e.g., command-line-interface), resulting in a high operational cost. In this demo, to reduce the data center operational cost, we design and build a novel cloud data center management system, based on the concept of 3D gamification. In particular, we apply data visualization techniques to overlay operational status upon a data center 3D model, allowing the operators to monitor the real-time situation and control the data center from a friendly user interface. This demo highlights: (1)a data center 3D view from a First Person Shooter (FPS) camera, (2)a run-time presentation of visualized infrastructures information. Moreover, to improve the user experience, we employ cutting-edge HCI technologies from multi-touch, for remote access to Cloud3DView.

Web Applications & Operating Cost of Data Center

Web Applications & Operating Cost of Data Center