Energy Efficient Data-Intensive Computing With Mapreduce

Power and energy consumption are critical constraints in data center design and operation. In data centers, MapReduce data-intensive applications demand significant resources and energy. Recognizing the importance and urgency of optimizing energy usage of MapReduce applications, this work aims to provide instrumental tools to measure and evaluate MapReduce energy efficiency and techniques to conserve energy without impacting performance. Energy conservation for data-intensive computing requires enabling technology to provide detailed and systemic energy information and to identify in the underlying system hardware and software. To address this need, we present eTune, a fine-grained, scalable energy profiling framework for data-intensive computing on large-scale distributed systems. eTune leverages performance monitoring counters (PMCs) on modern computer components and statistically builds power-performance correlation models. Using learned models, eTune augments direct measurement with a software-based power estimator that runs on compute nodes and reports power at multiple levels including node, core, memory, and disks with high accuracy. Data-intensive computing differs from traditional high performance computing as most execution time is spent in moving data between storage devices, nodes, and components. Since data movements are potential performance and energy bottlenecks, we propose an analysis framework with methods and metrics for evaluating and characterizing costly built-in MapReduce data movements. The revealed data movement energy characteristics can be exploited in system design and resource allocation to improve data-intensive computing energy efficiency. Finally, we present an optimization technique that targets inefficient built-in MapReduce data movements to conserve energy without impacting performance. The optimization technique allocates the optimal number of compute nodes to applications and dynamically schedules processor frequency during its execution based on data movement characteristics. Experimental results show significant energy savings, though improvements depend on both workload characteristics and policies of resource and dynamic voltage and frequency scheduling. As data volume doubles every two years and more data centers are put into production, energy consumption is expected to grow further. We expect these studies provide direction and insight in building more energy efficient data-intensive systems and applications, and the tools and techniques are adopted by other researchers for their energy efficient studies

Allocating MapReduce workflows with deadlines to heterogeneous servers in a cloud data center

[EN] Total profit is one of the most important factors to be considered from the perspective of resource providers. In this paper, an original MapReduce workflow scheduling with deadline and data locality is proposed to maximize total profit of resource providers. A new workflow conversion based on dynamic programming and ChainMap/ChainReduce is designed to decrease transmission times among MapReduce jobs of workflows. A new deadline division considering execution time, float time and job level is proposed to obtain better deadlines of MapReduce jobs in workflows. With the adapted replica strategy in MapReduce workflow, a new task scheduling is proposed to improve data locality which assigns tasks to servers with the earliest completion time in order to ensure resource providers obtain more profit. Experimental results show that the proposed heuristic results in larger total profit than other adopted algorithms.This work is supported by the National Key Research and Development Program of China (No. 2017YFB1400801), the National Natural Science Foundation of China (Nos. 61872077, 61832004) and Collaborative Innovation Center of Wireless Communications Technology. Rubén Ruiz is partly supported by the Spanish Ministry of Science, Innovation, and Universities, under the project ¿OPTEP-Port Terminal Operations Optimization¿ (No. RTI2018-094940-B-I00) financed with FEDER funds¿.Wang, J.; Li, X.; Ruiz García, R.; Xu, H.; Chu, D. (2020). Allocating MapReduce workflows with deadlines to heterogeneous servers in a cloud data center. Service Oriented Computing and Applications. 14(2):101-118. https://doi.org/10.1007/s11761-020-00290-1S101118142Zaharia M, Chowdhury M, Franklin M et al (2010) Spark: cluster computing with working sets. In: Usenix conference on hot topics in cloud computing, pp 1765–1773Li L, Ma Z, Liu L et al (2013) Hadoop-based ARIMA algorithm and its application in weather forecast. Int J Database Theory Appl 6(5):119–132Xun Y, Zhang J, Qin X (2017) FiDoop: parallel mining of frequent itemsets using MapReduce. IEEE Trans Syst Man Cybern Syst 46(3):313–325Wang Y, Shi W (2014) Budget-driven scheduling algorithms for batches of MapReduce jobs in heterogeneous clouds. IEEE Trans Cloud Comput 2(3):306–319Tiwari N, Sarkar S, Bellur U et al (2015) Classification framework of MapReduce scheduling algorithms. ACM Comput Surv 47(3):1–49Bu Y, Howe B, Balazinska M et al (2012) The HaLoop approach to large-scale iterative data analysis. VLDB J 21(2):169–190Gunarathne T, Zhang B, Wu T et al (2013) Scalable parallel computing on clouds using Twister4Azure iterative MapReduce. Future Gener Comput Syst 29(4):1035–1048Zhang Y, Gao Q, Gao L et al (2012) iMapReduce: a distributed computing framework for iterative computation. J Grid Comput 10(1):47–68Dong X, Wang Y, Liao H (2011) Scheduling mixed real-time and non-real-time applications in MapReduce environment. In: International conference on parallel and distributed systems, pp 9–16Tang Z, Zhou J, Li K et al (2013) A MapReduce task scheduling algorithm for deadline constraints. Clust Comput 16(4):651–662Zhang W, Rajasekaran S, Wood T et al (2014) MIMP: deadline and interference aware scheduling of Hadoop virtual machines. In: International symposium on cluster, cloud and grid computing, pp 394–403Teng F, Magoulès F, Yu L et al (2014) A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud. J Supercomput 69(2):739–765Palanisamy B, Singh A, Liu L (2015) Cost-effective resource provisioning for MapReduce in a cloud. IEEE Trans Parallel Distrib Syst 26(5):1265–1279Hashem I, Anuar N, Marjani M et al (2018) Multi-objective scheduling of MapReduce jobs in big data processing. Multimed Tools Appl 77(8):9979–9994Xu X, Tang M, Tian Y (2017) QoS-guaranteed resource provisioning for cloud-based MapReduce in dynamical environments. Future Gener Comput Syst 78(1):18–30Li H, Wei X, Fu Q et al (2014) MapReduce delay scheduling with deadline constraint. Concurr Comput Pract Exp 26(3):766–778Polo J, Becerra Y, Carrera D et al (2013) Deadline-based MapReduce workload management. IEEE Trans Netw Serv Manag 10(2):231–244Chen C, Lin J, Kuo S (2018) MapReduce scheduling for deadline-constrained jobs in heterogeneous cloud computing systems. IEEE Trans Cloud Comput 6(1):127–140Kao Y, Chen Y (2016) Data-locality-aware MapReduce real-time scheduling framework. J Syst Softw 112:65–77Bok K, Hwang J, Lim J et al (2017) An efficient MapReduce scheduling scheme for processing large multimedia data. Multimed Tools Appl 76(16):1–24Chen Y, Borthakur D, Borthakur D et al (2012) Energy efficiency for large-scale MapReduce workloads with significant interactive analysis. In: ACM european conference on computer systems, pp 43–56Mashayekhy L, Nejad M, Grosu D et al (2015) Energy-aware scheduling of MapReduce jobs for big data applications. IEEE Trans Parallel Distrib Syst 26(10):2720–2733Lei H, Zhang T, Liu Y et al (2015) SGEESS: smart green energy-efficient scheduling strategy with dynamic electricity price for data center. J Syst Softw 108:23–38Oliveira D, Ocana K, Baiao F et al (2012) A provenance-based adaptive scheduling heuristic for parallel scientific workflows in clouds. J Grid Comput 10(3):521–552Li S, Hu S, Abdelzaher T (2015) The packing server for real-time scheduling of MapReduce workflows. In: IEEE real-time and embedded technology and applications symposium, pp 51–62Cai Z, Li X, Ruiz R et al (2017) A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds. Future Gener Comput Syst 71:57–72Cai Z, Li X, Ruiz R (2017) Resource provisioning for task-batch based workflows with deadlines in public clouds. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2017.2663426Cai Z, Li X, Gupta J (2016) Heuristics for provisioning services to workflows in XaaS clouds. IEEE Trans Serv Comput 9(2):250–263Li X, Cai Z (2017) Elastic resource provisioning for cloud workflow applications. IEEE Trans Autom Sci Eng 14(2):1195–1210Tang Z, Liu M, Ammar A et al (2014) An optimized MapReduce workflow scheduling algorithm for heterogeneous computing. J Supercomput 72(6):1–21Xu C, Yang J, Yin K et al (2017) Optimal construction of virtual networks for cloud-based MapReduce workflows. Comput Netw 112:194–207Chiara S, Danilo A, Gianpaolo C et al (2013) Optimizing service selection and allocation in situational computing applications. IEEE Trans Serv Comput 6(3):414–428Baresi L, Elisabetta D, Carlo G et al (2007) A framework for the deployment of adaptable web service compositions. Serv Oriented Comput Appl 1(1):75–91Lim H, Herodotou H, Babu S (2012) Stubby: a transformation-based optimizer for MapReduce workflows. VLDB Endow 5(11):1196–1207Ke H, Li P, Guo S et al (2016) On traffic-aware partition and aggregation in MapReduce for big data applications. IEEE Trans Parallel Distrib Syst 27(3):818–828Yu W, Wang Y, Que X et al (2015) Virtual shuffling for efficient data movement in MapReduce. IEEE Trans Comput 64(2):556–568Chowdhury M, Zaharia M, Ma J et al (2011) Managing data transfers in computer clusters with orchestra. ACM SIGCOMM Comput Commun 41(4):98–109Guo D, Xie J, Zhou X et al (2015) Exploiting efficient and scalable shuffle transfers in future data center network. IEEE Trans Parallel Distrib Syst 26(4):997–1009Li D, Yu Y, He W et al (2015) Willow: saving data center network energy for network-limited flows. IEEE Trans Parallel Distrib Syst 26(9):2610–2620Tan J, Meng X, Zhang L (2013) Coupling task progress for MapReduce resource-aware scheduling. In: IEEE INFOCOM, pp 1618–1626Hammoud M, Rehman M, Sakr M (2012) Center-of-gravity reduce task scheduling to lower MapReduce network traffic. In: International conference on cloud computing, pp 49–58Guo Z, Fox G, Zhou M et al (2012) Improving resource utilization in MapReduce. In: International conference on cluster computing, pp 402–410Fischer M, Su X, Yin Y (2010) Assigning tasks for efficiency in Hadoop. In: Proceedings of the 22nd ACM symposium on parallelism in algorithms and architectures, pp 30–39Zhu Y, Jiang Y, Wu W et al (2014) Minimizing makespan and total completion time in MapReduce-like systems. In: IEEE INFOCOM, pp 2166–2174Kavulya S, Tan J, Gandhi R et al (2010) An analysis of traces from a production MapReduce cluster. In: IEEE/ACM international conference on cluster, cloud and grid computing, pp 94–103Abrishami S, Naghibzadeh M, Epema D (2013) Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service clouds. Future Gener Comput Syst 29(1):158–169Fernando B, Edmundo R (2010) Towards the scheduling of multiple workflows on computational grids. J Grid Comput 8(3):419–441Tiwari N, Sarkar S, Bellur U et al (2015) Classification framework of MapReduce scheduling algorithms. ACM Comput Surv 47(3):1–38Verma A, Cherkasova L, Campbell R (2013) Orchestrating an ensemble of MapReduce jobs for minimizing their makespan. IEEE Trans Dependable Secur Comput 10(5):314–327Heintz B, Chandra A, Sitaraman R et al (2017) End-to-end optimization for geo-distributed MapReduce. IEEE Trans Cloud Comput 4(3):293–306Chen L, Li X (2018) Cloud workflow scheduling with hybrid resource provisioning. J Supercomput 74(12):6529–6553Li X, Jiang T, Ruiz R (2016) Heuristics for periodical batch job scheduling in a MapReduce computing framework. Inf Sci 326:119–133Vanhoucheabcd M, Maenhout B, Tavares L (2008) An evaluation of the adequacy of project network generators with systematically sampled networks. Eur J Oper Res 187(2):511–52

A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce

Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In this paper we consider two mathematical programming problems that model the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers

Hadoop performance modeling and job optimization for big data analytics

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonBig data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.Abdul Wali Khan University Marda

Energy Efficient Ethernet on MapReduce Clusters: Packet Coalescing To Improve 10GbE Links

An important challenge of modern data centers is to reduce energy consumption, of which a substantial proportion is due to the network. Switches and NICs supporting the recent energy efficient Ethernet (EEE) standard are now available, but current practice is to disable EEE in production use, since its effect on real world application performance is poorly understood. This paper contributes to this discussion by analyzing the impact of EEE on MapReduce workloads, in terms of performance overheads and energy savings. MapReduce is the central programming model of Apache Hadoop, one of the most widely used application frameworks in modern data centers. We find that, while 1GbE links (edge links) achieve good energy savings using the standard EEE implementation, optimum energy savings in the 10 GbE links (aggregation and core links) are only possible, if these links employ packet coalescing. Packet coalescing must, however, be carefully configured in order to avoid excessive performance degradation. With our new analysis of how the static parameters of packet coalescing perform under different cluster loads, we were able to cover both idle and heavy load periods that can exist on this type of environment. Finally, we evaluate our recommendation for packet coalescing for 10 GbE links using the energy-delay metric. This paper is an extension of our previous work [1], which was published in the Proceedings of the 40th Annual IEEE Conference on Local Computer Networks (LCN 2015).This work was supported in part by the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant 610456 (EUROSERVER), in part by the Spanish Government through the Severo Ochoa programme (SEV-2011-00067 and SEV-2015-0493), in part by the Spanish Ministry of Economy a nd Competitiveness under Contract TIN2012-34557 and Contract TIN2015-65316-P, and in part by the Generalitat de Catalunya under Contract 2014-SGR-1051 and Contract 2014-SGR-1272.Peer ReviewedPostprint (author's final draft

Evolutionary Neural Network Based Energy Consumption Forecast for Cloud Computing

The success of Hadoop, an open-source framework for massively parallel and distributed computing, is expected to drive energy consumption of cloud data centers to new highs as service providers continue to add new infrastructure, services and capabilities to meet the market demands. While current research on data center airflow management, HVAC (Heating, Ventilation and Air Conditioning) system design, workload distribution and optimization, and energy efficient computing hardware and software are all contributing to improved energy efficiency, energy forecast in cloud computing remains a challenge. This paper reports an evolutionary computation based modeling and forecasting approach to this problem. In particular, an evolutionary neural network is developed and structurally optimized to forecast the energy load of a cloud data center. The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising

E-EON : Energy-Efficient and Optimized Networks for Hadoop

Energy efficiency and performance improvements have been two of the major concerns of current Data Centers. With the advent of Big Data, more information is generated year after year, and even the most aggressive predictions of the largest network equipment manufacturer have been surpassed due to the non-stop growing network traffic generated by current Big Data frameworks. As, currently, one of the most famous and discussed frameworks designed to store, retrieve and process the information that is being consistently generated by users and machines, Hadoop has gained a lot of attention from the industry in recent years and presently its name describes a whole ecosystem designed to tackle the most varied requirements of today’s cloud applications. This thesis relates to Hadoop clusters, mainly focused on their interconnects, which is commonly considered to be the bottleneck of such ecosystem. We conducted research focusing on energy efficiency and also on performance optimizations as improvements on cluster throughput and network latency. Regarding the energy consumption, a significant proportion of a data center's energy consumption is caused by the network, which stands for 12% of the total system power at full load. With the non-stop growing network traffic, it is desired by industry and academic community that network energy consumption should be proportional to its utilization. Considering cluster performance, although Hadoop is a network throughput-sensitive workload with less stringent requirements for network latency, there is an increasing interest in running batch and interactive workloads concurrently on the same cluster. Doing so maximizes system utilization, to obtain the greatest benefits from the capital and operational expenditures. For this to happen, cluster throughput should not be impacted when network latency is minimized. The two biggest challenges faced during the development of this thesis were related to achieving near proportional energy consumption for the interconnects and also improving the network latency found on Hadoop clusters, while having virtually no loss on cluster throughput. Such challenges led to comparable sized opportunity: proposing new techniques that must solve such problems from the current generation of Hadoop clusters. We named E-EON the set of techniques presented in this work, which stands for Energy Efficient and Optimized Networks for Hadoop. E-EON can be used to reduce the network energy consumption and yet, to reduce network latency while cluster throughput is improved at the same time. Furthermore, such techniques are not exclusive to Hadoop and they are also expected to have similar benefits if applied to any other Big Data framework infrastructure that fits the problem characterization we presented throughout this thesis. With E-EON we were able to reduce the energy consumption by up to 80% compared to the state-of-the art technique. We were also able to reduce network latency by up to 85% and in some cases, even improve cluster throughput by 10%. Although these were the two major accomplishment from this thesis, we also present minor benefits which translate to easier configuration compared to the stat-of-the-art techniques. Finally, we enrich the discussions found in this thesis with recommendations targeting network administrators and network equipment manufacturers.La eficiencia energética y las mejoras de rendimiento han sido dos de las principales preocupaciones de los Data Centers actuales. Con el arribo del Big Data, se genera más información año con año, incluso las predicciones más agresivas de parte del mayor fabricante de dispositivos de red se han superado debido al continuo tráfico de red generado por los sistemas de Big Data. Actualmente, uno de los más famosos y discutidos frameworks desarrollado para almacenar, recuperar y procesar la información generada consistentemente por usuarios y máquinas, Hadoop acaparó la atención de la industria en los últimos años y actualmente su nombre describe a todo un ecosistema diseñado para abordar los requisitos más variados de las aplicaciones actuales de Cloud Computing. Esta tesis profundiza sobre los clusters Hadoop, principalmente enfocada a sus interconexiones, que comúnmente se consideran el cuello de botella de dicho ecosistema. Realizamos investigaciones centradas en la eficiencia energética y también en optimizaciones de rendimiento como mejoras en el throughput de la infraestructura y de latencia de la red. En cuanto al consumo de energía, una porción significativa de un Data Center es causada por la red, representada por el 12 % de la potencia total del sistema a plena carga. Con el tráfico constantemente creciente de la red, la industria y la comunidad académica busca que el consumo energético sea proporcional a su uso. Considerando las prestaciones del cluster, a pesar de que Hadoop mantiene una carga de trabajo sensible al rendimiento de red aunque con requisitos menos estrictos sobre la latencia de la misma, existe un interés creciente en ejecutar aplicaciones interactivas y secuenciales de manera simultánea sobre dicha infraestructura. Al hacerlo, se maximiza la utilización del sistema para obtener los mayores beneficios al capital y gastos operativos. Para que esto suceda, el rendimiento del sistema no puede verse afectado cuando se minimiza la latencia de la red. Los dos mayores desafíos enfrentados durante el desarrollo de esta tesis estuvieron relacionados con lograr un consumo energético cercano a la cantidad de interconexiones y también a mejorar la latencia de red encontrada en los clusters Hadoop al tiempo que la perdida del rendimiento de la infraestructura es casi nula. Dichos desafíos llevaron a una oportunidad de tamaño semejante: proponer técnicas novedosas que resuelven dichos problemas a partir de la generación actual de clusters Hadoop. Llamamos a E-EON (Energy Efficient and Optimized Networks) al conjunto de técnicas presentadas en este trabajo. E-EON se puede utilizar para reducir el consumo de energía y la latencia de la red al mismo tiempo que el rendimiento del cluster se mejora. Además tales técnicas no son exclusivas de Hadoop y también se espera que tengan beneficios similares si se aplican a cualquier otra infraestructura de Big Data que se ajuste a la caracterización del problema que presentamos a lo largo de esta tesis. Con E-EON pudimos reducir el consumo de energía hasta en un 80% en comparación con las técnicas encontradas en la literatura actual. También pudimos reducir la latencia de la red hasta en un 85% y, en algunos casos, incluso mejorar el rendimiento del cluster en un 10%. Aunque estos fueron los dos principales logros de esta tesis, también presentamos beneficios menores que se traducen en una configuración más sencilla en comparación con las técnicas más avanzadas. Finalmente, enriquecimos las discusiones encontradas en esta tesis con recomendaciones dirigidas a los administradores de red y a los fabricantes de dispositivos de red