Architecting Time-Critical Big-Data Systems

Current infrastructures for developing big-data applications are able to process –via big-data analytics- huge amounts of data, using clusters of machines that collaborate to perform parallel computations. However, current infrastructures were not designed to work with the requirements of time-critical applications; they are more focused on general-purpose applications rather than time-critical ones. Addressing this issue from the perspective of the real-time systems community, this paper considers time-critical big-data. It deals with the definition of a time-critical big-data system from the point of view of requirements, analyzing the specific characteristics of some popular big-data applications. This analysis is complemented by the challenges stemmed from the infrastructures that support the applications, proposing an architecture and offering initial performance patterns that connect application costs with infrastructure performance

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

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

Model-Based Design, Analysis, and Implementations for Power and Energy-Efficient Computing Systems

Modern computing systems are becoming increasingly complex. On one end of the spectrum, personal computers now commonly support multiple processing cores, and, on the other end, Internet services routinely employ thousands of servers in distributed locations to provide the desired service to its users. In such complex systems, concerns about energy usage and power consumption are increasingly important. Moreover, growing awareness of environmental issues has added to the overall complexity by introducing new variables to the problem. In this regard, the ability to abstractly focus on the relevant details allows model-based design to help significantly in the analysis and solution of such problems. In this dissertation, we explore and analyze model-based design for energy and power considerations in computing systems. Although the presented techniques are more generally applicable, we focus their application on large-scale Internet services operating in U.S. electricity markets. Internet services are becoming increasingly popular in the ICT ecosystem of today. The physical infrastructure to support such services is commonly based on a group of cooperative data centers (DCs) operating in tandem. These DCs are geographically distributed to provide security and timing guarantees for their customers. To provide services to millions of customers, DCs employ hundreds of thousands of servers. These servers consume a large amount of energy that is traditionally produced by burning coal and employing other environmentally hazardous methods, such as nuclear and gas power generation plants. This large energy consumption results in significant and fast-growing financial and environmental costs. Consequently, for protection of local and global environments, governing bodies around the globe have begun to introduce legislation to encourage energy consumers, especially corporate entities, to increase the share of renewable energy (green energy) in their total energy consumption. However, in U.S. electricity markets, green energy is usually more expensive than energy generated from traditional sources like coal or petroleum. We model the overall problem in three sub-areas and explore different approaches aimed at reducing the environmental foot print and operating costs of multi-site Internet services, while honoring the Quality of Service (QoS) constraints as contracted in service level agreements (SLAs). Firstly, we model the load distribution among member DCs of a multi-site Internet service. The use of green energy is optimized considering different factors such as (a) geographically and temporally variable electricity prices, (b) the multitude of available energy sources to choose from at each DC, (c) the necessity to support more than one SLA, and, (d) the requirements to offer more than one service at each DC. Various approaches are presented for solving this problem and extensive simulations using Google’s setup in North America are used to evaluate the presented approaches. Secondly, we explore the area of shaving the peaks in the energy demand of large electricity consumers, such as DCs by using a battery-based energy storage system. Electrical demand of DCs is typically peaky based on the usage cycle of their customers. Resultant peaks in the electrical demand require development and maintenance of a costlier energy delivery mechanism, and are often met using expensive gas or diesel generators which often have a higher environmental impact. To shave the peak power demand, a battery can be used which is charged during low load and is discharged during the peak loads. Since the batteries are costly, we present a scheme to estimate the size of battery required for any variable electrical load. The electrical load is modeled using the concept of arrival curves from Network Calculus. Our analysis mechanism can help determine the appropriate battery size for a given load arrival curve to reduce the peak. Thirdly, we present techniques to employ intra-DC scheduling to regulate the peak power usage of each DC. The model we develop is equally applicable to an individual server with multi-/many-core chips as well as a complete DC with an intermix of homogeneous and heterogeneous servers. We evaluate these approaches on single-core and multi-core chip processors and present the results. Overall, our work demonstrates the value of model-based design for intelligent load distribution across DCs, storage integration, and per DC optimizations for efficient energy management to reduce operating costs and environmental footprint for multi-site Internet services