Scheduling in Mapreduce Clusters

MapReduce is a framework proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and the fault-tolerance feature of the framework make it very popular in Big Data processing. As MapReduce clusters get popular, their scheduling becomes increasingly important. On one hand, many MapReduce applications have high performance requirements, for example, on response time and/or throughput. On the other hand, with the increasing size of MapReduce clusters, the energy-efficient scheduling of MapReduce clusters becomes inevitable. These scheduling challenges, however, have not been systematically studied. The objective of this dissertation is to provide MapReduce applications with low cost and energy consumption through the development of scheduling theory and algorithms, energy models, and energy-aware resource management. In particular, we will investigate energy-efficient scheduling in hybrid CPU-GPU MapReduce clusters. This research work is expected to have a breakthrough in Big Data processing, particularly in providing green computing to Big Data applications such as social network analysis, medical care data mining, and financial fraud detection. The tools we propose to develop are expected to increase utilization and reduce energy consumption for MapReduce clusters. In this PhD dissertation, we propose to address the aforementioned challenges by investigating and developing 1) a match-making scheduling algorithm for improving the data locality of Map- Reduce applications, 2) a real-time scheduling algorithm for heterogeneous Map- Reduce clusters, and 3) an energy-efficient scheduler for hybrid CPU-GPU Map- Reduce cluster. Advisers: Ying Lu and David Swanso

Power Management in Heterogeneous MapReduce Cluster

The growing expenses of power in data centers as compared to the operation costs has been a concern for the past several decades. It has been predicted that without an intervention, the energy cost will soon outgrow the infrastructure and operation cost. Therefore, it is of great importance to make data center clusters more energy efficient which is critical for avoiding system overheating and failures. In addition, energy inefficiency causes not only the loss of capital but also environmental pollution. Various Power Management(PM) strategies have been developed over the years to make system more energy efficient and to counteract the sharply rising cost of electricity. However, it is still a challenge to make the system both power efficient and computation efficient due to many underlying system constraints. In this thesis, we investigate the Power Management technique in heterogeneous MapReduce clusters while also maintaining the required system QoS (Quality of Service). For a cluster that supports MapReduce jobs, it is necessary to develop a PM technique that also considers the data availability. We develop our PM strategy by exploiting the fact that the servers in the system are underutilized most of the time. Hence, we first develop a model of our testbed and study how the server utilization levels affect the power consumption and the system throughput. With the established models, we form and solve the power optimization problem for heterogeneous MadReduce clusters where we control the server utilization levels intelligently to minimize the total power consumption. We have conducted simulations and shown the power savings achieved using our PM technique. Then we validate some of our simulation results by running experiments in a real testbed. Our simulation and experimental data have shown that our PM strategy works well for heterogeneous MapReduce clusters which consists of different power efficient and inefficient servers. Adviser: Ying L

Interconnect Energy Savings and Lower Latency Networks in Hadoop Clusters: The Missing Link

An important challenge of modern data centres running Hadoop workloads is to minimise energy consumption, a significant proportion of which is due to the network. Significant network savings are already possible using Energy Efficient Ethernet, supported by a large number of NICs and switches, but recent work has demonstrated that the packet coalescing settings must be carefully configured to avoid a substantial loss in performance. Meanwhile, Hadoop is evolving from its original batch concept to become a more iterative type of framework. Other recent work attempts to reduce Hadoop's network latency using Explicit Congestion Notifications. Linking these studies reveals that, surprisingly, even when packet coalescing does not hurt performance, it can degrade network latency much more than previously thought. This paper is the first to analyze the impact of packet coalescing in the context of network latency. We investigate how to design and configure interconnects to provide the maximum energy savings without degrading cluster throughput performance or network latency.The research leading to these results has received funding from the European Unions Seventh Framework Programme (FP7/2007–2013) under grant agreement number 610456 (Euroserver). The research was also supported by the Ministry of Economy and Competitiveness of Spain under the contracts TIN2012-34557 and TIN2015-65316-P, Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), HiPEAC-3 Network of Excellence (ICT- 287759), and the Severo Ochoa Program (SEV-2011-00067) of the Spanish Government.Peer ReviewedPostprint (author's final draft

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