Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 201

JUMMP: Job Uninterrupted Maneuverable MapReduce Platform

In this paper, we present JUMMP, the Job Uninterrupted Maneuverable MapReduce Platform, an automated scheduling platform that provides a customized Hadoop environment within a batch-scheduled cluster environment. JUMMP enables an interactive pseudo-persistent MapReduce platform within the existing administrative structure of an academic high performance computing center by “jumping” between nodes with minimal administrative effort. Jumping is implemented by the synchronization of stopping and starting daemon processes on different nodes in the cluster. Our experimental evaluation shows that JUMMP can be as efficient as a persistent Hadoop cluster on dedicated computing resources, depending on the jump time. Additionally, we show that the cluster remains stable, with good performance, in the presence of jumps that occur as frequently as the average length of reduce tasks of the currently executing MapReduce job. JUMMP provides an attractive solution to academic institutions that desire to integrate Hadoop into their current computing environment within their financial, technical, and administrative constraints

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Dynamic server selection in a multithreaded network computing environment

Research has been conducted at the Iowa State University Center for Nondestructive Evaluation (CNDE) to create a structure in which existing numerical modeling programs can be converted to execute in a network computing environment. This research task is to include the development of an extensible architecture which accommodates the timely integration of new processing capabilities and requirements. The research was motivated by many needs within the CNDE to reduce the predicted run times associated with the current and future modeling programs

EXA2PRO programming environment:Architecture and applications

The EXA2PRO programming environment will integrate a set of tools and methodologies that will allow to systematically address many exascale computing challenges, including performance, performance portability, programmability, abstraction and reusability, fault tolerance and technical debt. The EXA2PRO tool-chain will enable the efficient deployment of applications in exascale computing systems, by integrating high-level software abstractions that offer performance portability and efficient exploitation of exascale systems' heterogeneity, tools for efficient memory management, optimizations based on trade-offs between various metrics and fault-tolerance support. Hence, by addressing various aspects of productivity challenges, EXA2PRO is expected to have significant impact in the transition to exascale computing, as well as impact from the perspective of applications. The evaluation will be based on 4 applications from 4 different domains that will be deployed in JUELICH supercomputing center. The EXA2PRO will generate exploitable results in the form of a tool-chain that support diverse exascale heterogeneous supercomputing centers and concrete improvements in various exascale computing challenges

Modeling Natural Hazards Engineering Data to Cyberinfrastructure

DesignSafe-CI is an end-to-end data lifecycle management, analysis, and publication cloud platform for natural hazards engineering. To facilitate ongoing data curation and sharing in a cloud environment that is intuitive to the end users, developers and curators teamed with experts in the different hazards to design data models and vocabularies that map their research workflows and domain terminology. The experimental data models - six - emphasize provenance through relationships between research processes, data and their documentation, and highlight commonalities between experiment types. They mediate between the user interface and the repository layers of the cyberinfrastructure to automate tasks such as organizing data and facilitating its description. Using data from triaxial experiments, we conducted a user evaluation of the geotechnical data model, both for its fitness to real data and for purposes of data understandability during reuse. The results of the evaluation guided testing and selection of the Fedora 4 repository backend to enhance data discovery and reuse.National Science FoundationTexas Advanced Computing Center (TACC

Modelling the Shifts in Activity Centres along the Subway Stations. The Case Study of Metropolitan Tehran

Activity centers are areas of strong development of a particular activity, such as residence, employment or services. Understanding the subway system impacts on the type, combination, distribution and the development of basic activities in such centers plays an important role in managing development opportunities created along the Tehran subway lines. The multi criteria and fuzzy nature of evaluating the development of activity centers makes the issue so complex that it cannot be addressed with conventional logical systems. One of the most important methods of multi criteria evaluation is Fuzzy Inference System. Fuzzy inference system is a popular computing framework based on the concepts of Fuzzy Sets Theory, which is capable of accommodating inherent uncertainty in the multi-criteria evaluation process. This paper analyses shifts in activity centers along two lines of the Tehran subway system based on three major criteria by designing a comprehensive fuzzy inference system. The data for the present study were collected through documentary analysis, questionnaires and semi-structured interviews. The result revealed that the level of the subway system influence on the pattern and process of the development of activities varied with the location, physical environment and entity of each station. Furthermore, empirical findings indicated that the subway line might weaken residential activities while attracting employment and service activities to the city center. Specifically, residential estates have moved away from the city center to the suburbs whereas employment and service activities have expanded from the existing central business district (CBD). The results can be applied to suggest planning policies aimed at improving the effects of public transit on property development and land use change in a developing country

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper

Towards green computing in wireless sensor networks: controlled mobility-aided balanced tree approach

Virtualization technology has revolutionized the mobile network and widely used in 5G innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like SaaS, PaaS, and IaaS. The proliferation of these services among the users led to the establishment of large-scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models namely Median Migration Time (MeMT), Smallest Void Detection (SVD) and Maximum Fill (MF) that can reduce energy consumption with minimal variation in SLAs negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on liner relationship between power consumption and resource utilization. A virtual machine migration technique is considered focusing on synchronization oriented shorter stop-and-copy phase. The complete operational steps as algorithms are developed for energy aware heuristic models including MeMT, SVD and MF. To evaluate proposed heuristic models, we conduct experimentations using PlanetLab server data often ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab Servers. Through evaluation process, we deduce that proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining the high system performance