Deadline-constrained energy-aware workflow scheduling in geographically distributed cloud data centers

Cloud computing is the combination of grid computing, distributed computing and utility computing. Cloud computing provides various types of services (servers and storage facility) in the on demand basis. The main goal of the cloud computing is to preserve and organize the very huge data center or data forms. The data forms are composed of thousands of servers that absorb the giant (ample) amount of electricity in the word of energy. The decreasing the energy consumption in datacenter is the major challenge in cloud computing now a day. This research article is going to address the problem of high energy consumption at datacenter. Concentrate on virtual machine scheduling in cloud datacenter with Dynamic Voltage Frequency Scaling (DVFS) approach. We have combined shortest job first and Round Robin algorithms with Vibrant Quantum. This combination of algorithm is considered as shortest round vibrant queue (SRVQ) algorithm. SRVQ reduces the waiting time of the scheduling process and minimize the starvation. The DVFS and SRVQ worked together and produced fruitful results in the final experiments. This work reduced the server’s energy consumption in the cloud data center. In the final results, our proposed framework exhibits 45% of energy efficiency compare to other previously proposed algorithms. 33% of QoS performance were enhanced by our framework.

Modern information and communication technology along with the broad adaptation of computing paradigms, namely cloud computing has significant impact on environment due to huge amount of CO 2 emission. In the recent decade, a significant growth in the research domain of “green” and low power consumption network has been observed. With the recent advancements of compute intensive machine learning frameworks like deep learning, the energy consumption by GPUs is a challenging issue. In all of these cases, Dynamic Voltage Frequency Scaling (DVFS) has gained significant research interests and shown encouraging outcome. Dynamic Voltage and Frequency Scaling (DVFS) is utilized to reduce the energy consumption in data centers without impacting the Quality of Service. On the other side, spatio-temporal dataset is huge in size and requires compute and time intensive algorithms and techniques for different spatio-temporal query processing. In all aspects, the correlations among energy, DVFS, consolidation, and performance are the key-enablers of energy-efficient management. In this chapter, we explore different existing DVFS techniques and challenges to analyse and process spatio-temporal query in cloud based paradigm. We have considered movement datasets for demonstrating energy consumption and utility of dynamic voltage and frequency scaling.KeywordsDynamic voltage and frequency scalingSpatio-temporal QueryCloud datacenterScheduling algorithmPowerEnergy

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in cloud data centers. However, aggressive consolidation of VMs may lead to performance degradation of data centers and even Service Level Agreement (SLA) violation. Therefore, it is very important to solve the tradeoff between reduction of SLA violation level and energy costs. In this paper, we propose a Host State 3-order Markov Chain prediction model for SLA-aware and energy efficient consolidation of VMs in cloud data centers. We propose two adaptive utilization thresholds based on a robust statistical method for host state estimate, and our model uses them to build a 3-order Markov Chain Transition Matrix manually for host state detection. We implement our algorithms on Cloudsim simulator for comparative study. The experimental results show that our model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLA Violation by at most 98.69% and the metric of Energy by at most 21.88% for real world workload.

Nowadays, the expanding computational capabilities of the cloud system rely on the minimisation of the consumed power to make them sustainable and economically productive. Power management of cloud data centres received a great attention from industry and academia as it consumes high energy and thus increases the operational cost. One of the core approaches for the conservation of energy in the cloud data centre is the task scheduling. This task allocation in a heterogeneous environment is a well known NP-hard problem due to which researchers pay attention for proposing various heuristic techniques for the problem. In this paper, a technique is proposed based on dynamic voltage frequency scaling (DVFS) for optimising the energy consumption in the cloud environment. The basic idea is to address the trade-off between energy consumption and makespan of the system. Here, we formally introduce a model that includes various subsystems and assess the implementation of the algorithm in the heterogeneous environment.

When the holistic containerized edge cloud data center is overloaded, VM consolidation and Dynamic Voltage Frequency Scaling cannot function well. Brownout has shown to be a promising technique to handle overloads through dynamically deactivating optimal containers. However, it cannot guarantee the efficiency of handling data center overload and the profit of edge service provider. Therefore, we propose a container selection policy based on penalty and resource utilization equilibrium PU-CSP. Considering the profit of edge service provider, it also can minimize the overload time of data center. The results show that our approach reduces about 26.14% penalty and 33.33% overload time than the approach with lowest utilization container first selection policy LUCF.

As the prevalence of smart city applications in the era of the digital economy is significantly growing, the Internet of Things (IoT) has evolved to be a part of our everyday lives. Most IoT applications and smart spaces are implemented in the context of a two-tier approach, with the lower tier being the IoT edge devices and the upper tier being the back-end servers in cloud data centers. However, the growth of IoT data and devices at the edge of smart applications challenges the efficiency and time-sensitivity of the two-tier approach. Hence, it is critical to comprehend the significance of integrating a middle tier between the cloud and edge tiers, known as the fog tier. The fog tier’s main purpose is to decentralize the IoT workload to reduce latency and increase efficiency. This paper provides a review and presents a 3-Tier, 3-Dimensions reference model for smart spaces. The 3-tier represent the edge, fog, and cloud tiers, where each tier is coupled with the 3-dimensions that include computing, network, and application. Each dimension within each tier is presented along with the existing technologies that deliver on each dimension. The work presents a case study within a smart healthcare application that utilizes the presented reference model.

Geographically distributed data centens (GDCs) serving as infrastructures for cloud services, are growing in both number and scale. They usually consume enormous amount of electric power, which lead to high operational costs and this has been recognized as a main challenge in cloud computing. Energy cost can be reduced by directing the requests to the favor of data center with lower electricity price by incorporating spatially and temporally price diversity, especially in the multi-region electricity markets. If the electricity prices of data centers are predicted in advance, the cloud provider can reduce energy cost. An efficient electricity price prediction is needed for minimizing electricity bill of GDCs. This paper proposes electricity price prediction for GDCs in multi-region electricity markets. Experiment is conducted on real-life electricity price data sets with machine learning algorithms. By comparatively assessing the prediction accuracy of the models, the most accurate one is selected. Experiment results show that the prediction model can provide promising accuracy.

Greening cloud data centers in an economical way by energy trading with power grid

Achieving carbon-neutral energy supplies in collective heating systems is increasingly feasible through the use of renewable energy sources and optimized heating capacities. Within apartment buildings, a combined heat distribution circuit (CHDC) that supplies both domestic hot water (DHW) and space heating through a single pipe offers significant potential.Traditional CHDCs require high distribution temperatures to deliver DHW, leading to inefficiencies. Lowering distribution temperatures enhances system efficiency, but might require decentralized booster heat pumps (BHPs) in each dwelling for DHW production. Currently, these BHPs recharge local DHW storage tanks based on temperature sensors, ignoring electricity prices [1]. As time-variable electricity pricing models are on the rise to balance production and demand in the electricity grid, there is an opportunity to control BHPs based on such variable price signals to minimize operational costs.Demand-side management strategies have been focusing on day-ahead electricity market interaction. Thermal storage can be used to shift electricity consumption and thereby reduce energy costs. However, the most optimal moments depend on future prices, current state-of-charge, and future DHW demand in the respective dwelling. This research contributes to the literature by finding the optimal trade-off between energy cost and the extra heat losses of storage tanks during demand-side management.This research aims to develop and evaluate various price-based control strategies for the optimal activation scheme of decentralized BHPs in CHDCs. These activation schemes decide when to recharge DHW storage tanks with BHPs based on future electricity prices. The objective is to minimize energy costs while maintaining thermal comfort. The methodology includes creating predefined recharging time slots during the lowest electricity price of the day-ahead market prices and comfort-based approaches that incentivize BHPs to recharge during low prices or restrict recharging during high prices. A detailed simulation model is developed, incorporating thermal dynamics, electricity price fluctuations, and various DHW consumption patterns.Simulation results show that increasing the central distribution temperature during BHP activation offers no cost savings, as the central geothermal heat pump is decisive for overall economic efficiency. While price-based (de)activation strategies with constant supply temperature can reduce operational costs, they may slightly increase DHW discomfort.To properly benefit from low-price periods, BHPs should have larger thermal heating power. Future work should incorporate demand forecasting and include potential tariff structures for space cooling to improve system efficiency and cost-effectiveness.

Cloud data centers play a significant role in providing services needed by users in a quick way. Recent studies show that, traffic patterns in data centers have a special importance to be improved, since they have significant effects on various aspects such as congestion, overall energy consumption and service response time. The traffic patterns inside a cloud data center have two categories: North-South and East-West. The former one is the outside-inside and inside-outside traffic, Whereas, the latter is the traffic among Virtual Machines (VMs) within data centers. Previous studies have shown that the East-West traffic pattern is multiple times larger than the North-South one. This leads data centers to experience congestion and packet loss in the core layer of their topology. Common cause of large traffic patterns is that, VMs of service chains are scattered within the data center in different racks, so that, it causes lots of packet injection into the data center. In this paper, we propose a heuristic algorithm to place VMs of a service chain in a closer proximity of each other to improve the East-West traffic pattern by reducing response time of services and also data centers' overall energy consumption. The simulation results compared to the state-of-the-art method demonstrate about 18% improvement in response time for users' requests and 10% of total energy consumption reduction in the data center.

Data centres in contemporary times are essential as the supply of data increases. Data centres are areas where computing systems are concentrated for facilitating data processing, transfer and storage. At present, traditional data centres have moved more towards the cloud model—thereby making the processing, storage and harnessing of data more manageable and more accessible via the utility and subscription-based model of computing services. From the administrative point of view, cloud data centres are complex systems and hard to grasp and require large amounts of time to analyse different aspects of the cloud data centre such as maintenance and resource management. For a cloud data centre admin, this could be a challenging problem and a highly time-consuming task. Accordingly, there is a need to improve the useability of cloud data centre monitoring and management tools, and the digital twin could fulfil this need. This book chapter’s primary objective is to construct a digital twin—a 3D visualisation and monitoring tool—of a cloud data centre managed by OpenStack, the well-known open-source cloud computing infrastructure software. To evaluate our proposed tool, we garner feedback on the digital twin’s useability compared to the OpenStack dashboard. The input will be received from cloud data centre experts as they test the digital twin and answer various questions in an interview. The study results show that our proposed digital twin will help data centre admins better monitor and manage their data centres. It will also facilitate further research and implementation of the digital twin of data centres to improve usability.KeywordsCloud data centreDigital twinOpenStackVisualisationMonitoringResource management

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

Decreasing the soaring energy cost is imperative in large data centers. Meanwhile, limited computational resources need to be fairly allocated among different organizations. Latency is another major concern for resource management. Nevertheless, energy cost, resource allocation fairness, and latency are important but often contradicting metrics on scheduling data center workloads. Moreover, with the ever-increasing power density, data center operation must be judiciously optimized to prevent server overheating. In this paper, we explore the benefit of electricity price variations across time and locations. We study the problem of scheduling batch jobs to multiple geographically-distributed data centers. We propose a provably-efficient online scheduling algorithm - GreFar - which optimizes the energy cost and fairness among different organizations subject to queueing delay constraints, while satisfying the maximum server inlet temperature constraints. GreFar does not require any statistical information of workload arrivals or electricity prices. We prove that it can minimize the cost arbitrarily close to that of the optimal offline algorithm with future information. Moreover, we compare the performance of GreFar with ones of a similar algorithm, referred to as T-unaware, that is not able to consider the server inlet temperature in the scheduling process. We prove that GreFar is able to save up to 16 percent of energy-fairness cost with respect to T-unaware.

We propose a new optimal data placement technique to improve the performance of MapReduce in cloud data centers by considering not only the data locality but also the global data access costs. We first conducted an analytical and experimental study to identify the performance issues of MapReduce in data centers and to show that MapReduce tasks that are involved in unexpected remote data access have much greater communication costs and execution time, and can significantly deteriorate the overall performance. Next, we formulated the problem of optimal data placement and proposed a generative model to minimize global data access cost in data centers and showed that the optimal data placement problem is NP-hard. To solve the optimal data placement problem, we propose a topology-aware heuristic algorithm by first constructing a replica-balanced distribution tree for the abstract tree structure, and then building a replica-similarity distribution tree for detail tree construction, to construct an optimal replica distribution tree. The experimental results demonstrated that our optimal data placement approach can improve the performance of MapReduce with lower communication and computation costs by effectively minimizing global data access costs, more specifically reducing unexpected remote data access.

Recent work has presented hierarchical deployment of energy storage devices (ESDs) at the data center, rack, and server levels within a data center, along with a corresponding control framework for peak power shaving and energy cost reduction under (time-of-use) dynamic energy pricing policies. However, the prior work does not use a realistic power delivery architecture of the data center with hierarchical ESD structure, and fails to account for some key characteristics such as rate capacity effect of batteries and power losses in various AC/DC and DC/DC converters in the power delivery architecture. This paper aims to overcome these shortcomings by (i) adopting a realistic power delivery architecture (from Intel) for centralized ESD structure as the starting point, (ii) presenting a novel power delivery architecture for data centers with hierarchical ESD structure, borrowing the best features of the centralized ESD structure from Intel and the distributed single-level ESD structures from Google and Microsoft, (iii) providing a mathematical framework for the optimal design (i.e., ESD provisioning) and control (i.e., Scheduling the charging and discharging of various ESDs) of the hierarchical ESD structure to minimize overall energy cost under dynamic energy pricing functions. This framework accounts for constraints on ESD volume (for each level) and the overall (annually amortized) capital cost, and power losses due to the rate capacity effect and conversion circuitry. The ESD design problem is solved by using a search-based algorithm, whereas the ESD control problem is formulated and solved as a hierarchical convex optimization algorithm. Experiments have been conducted using real Google cluster workload based on realistic data center specifications, demonstrating the effectiveness of the proposed optimal design and control framework.