Human-computer cooperation platform for developing real-time robotic applications

[EN] This paper presents a human-computer cooperation platform, which permits the coordination between the user and the tool to improve the development of real-time control applications (e.g., mobile robots). These applications have functional (robot objectives) and temporal requirements to accomplish (deadlines guarantee of tasks). The simulation tool has been designed in order to permit the testing and validation of these two requirements. To this end, the tool is composed of two independent simulators interconnected through a shared memory: the robot simulator (functional level) and the real-time task scheduler simulator (task execution level). Robotic applications can be defined with the robot simulator while the real-time scheduler simulator permits to analyze the schedulability of the robotic tasks. The real-time task simulator incorporates a flexible task model where the task temporal parameters (e.g., computation time) adapt to the requirements of the application (e.g., number of objects in scenes); thus, the use of the CPU is not overestimated. A key issue of the framework is the human-computer interface, which allows the monitoring of different parameters of the application: robot objectives, task schedule, robot speed, computation time, CPU utilization, deadline misses. The usefulness of the simulation tool is shown through different robotic navigation experiments. Finally, the simulation tool has been used to evaluate the proposed flexible model of tasks compared to a traditional fixed temporal parameters task model. Results show that the robot fulfills the objectives earlier, about 32% on average, and consumes on average about 15% less CPU to accomplish the objectives.Domínguez Montagud, CP.; Martínez-Rubio, J.; Busquets Mataix, JV.; Hassan Mohamed, H. (2019). Human-computer cooperation platform for developing real-time robotic applications. The Journal of Supercomputing. 75(4):1849-1868. https://doi.org/10.1007/s11227-018-2343-4S18491868754Dominguez C, Hassan H, Crespo A (2007) Real-time embedded architecture for pervasive robots. In: The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007), pp 531–536Audsley NC, Burns A, Davis RI, Tindell KW, Wellings AJ (1995) Fixed priority pre-emptive scheduling: an historical perspective. Real Time Syst 8(2–3):173–198Stankovic JA, Lee I, Mok A, Rajkumar R (2005) Opportunities and obligations for physical computing systems. Computer 38(11):23–31Zhen Z, Qixin C, Lo C, Lei Z (2009) A CORBA-based simulation and control framework for mobile robots. Robotica 27(3):459Ferretti G, Magnani G, Porrati P, Rizzi G, Rocco P, Rusconi A (2008) Real-time simulation of a space robotic arm. In: IROSQadi A, Goddard S, Huang J, Farritor S (2005) A performance and schedulability analysis of an autonomous mobile robot. In: 17th Euromicro Conference on Real-Time Systems (ECRTS’05), pp 239–248Goud GR, Sharma N, Ramamritham K, Malewar S (2006) Efficient real-time support for automotive applications: a case study. In: 12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’06), pp 335–341Pedreiras P, Luis A (2003) The flexible time-triggered (FTT) paradigm: an approach to QoS management in distributed real-time systems. In: Proceedings International Parallel and Distributed Processing Symposium, p 9Li H, Sweeney J, Ramamritham K, Grupen R, Shenoy P (2003) Real-time support for mobile robotics. In: The 9th IEEE Real-Time and Embedded Technology and Applications Symposium. Proceedings, pp 10–18Chetto H, Chetto M (1989) Some results of the earliest deadline scheduling algorithm. IEEE Trans Softw Eng 15(10):1261–1269Liu R, Zhang X (2017) Systems of natural-language-facilitated human-robot cooperation: a review. arXiv:1701.08269v2Tsarouchi P, Makris S, Chryssolouris G (2016) Human–robot interaction review and challenges on task planning and programming. Int J Comput Integr Manuf 29(8):916–931Moniz A (2013) Organizational concepts and interaction between humans and robots in industrial environments. In: IEEE-RAS-IARP Joint Workshop on Technical Challenges for Dependable Robots in Human Environment, TokyoMayer MP, Odenthal B, Faber M, Winkelholz C, Schlick CM (2014) Cognitive engineering of automated assembly processes. Hum Factors Ergon Manuf Serv Ind 24(3):348–368Agostini A, Torras C, Wörgötter F (2011) Integrating task planning and interactive learning for robots to work in human environments. In: IJCAIKwon W, Suh I (2014) Planning of proactive behaviors for human–robot cooperative tasks under uncertainty. Knowl Based Syst 72:81–95Chen F, Sekiyama K, Sasaki H, Huang J, Sun B, Fukuda T (2011) Assembly strategy modeling and selection for human and robot coordinated cell assembly. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 4670–4675Gombolay M, Wilcox R, Diaz A, Yu F (2013) Towards successful coordination of human and robotic work using automated scheduling tools: an initial pilot study. In: Proceedings of Robotics: Science and Systems, Human–Robot Collaboration WorkshopGombolay MC, Gutierrez RA, Clarke SG, Sturla GF, Shah JA (2015) Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams. Auton Robots 39(3):293–312Frontoni E, Mancini A, Caponetti F, Zingaretti P (2006) A framework for simulations and tests of mobile robotics tasks. In: 2006 14th Mediterranean Conference on Control and Automation, pp 1–6I. Embarcadero Technologies, C++ Builder 10.2. https://www.embarcadero.com

A cluster computer performance predictor for memory scheduling

Remote Memory Access (RMA) hardware allow a given motherboard in a cluster to directly access the memory installed in a remote motherboard of the same cluster. In recent works, this characteristic has been used to extend the addressable memory space of selected motherboards, which enable a better balance of main memory resources among cluster applications. This way is much more cost-effective than than implementing a full-fledged shared memory system. In this context, the memory scheduler is in charge of finding a suitable distribution of local and remote memory that maximizes the performance and guarantees a minimum QoS among the applications. Note that since changing the memory distribution is a slow process involving several motherboards, the memory scheduler needs to make sure that the target distribution provides better performance than the current one. In this paper, a performance predictor is designed in order to find the best memory distribution for a given set of applications executing in a cluster motherboard. The predictor uses simple hardware counters to estimate the expected impact on performance of the different memory distributions. The hardware counters provide the predictor with the information about the time spent in processor, memory access and network. The performance model used by the predictor has been validated in a detailed microarchitectural simulator using real benchmarks. Results show that the prediction accuracy never deviates more than 5% compared to the real results, being less than 0.5% in most of the cases.This work was supported by Spanish CICYT under Grant TIN2009-14475-C04-01, and by Consolider-Ingenio under Grant CSD2006-00046Serrano Gómez, M.; Sahuquillo Borrás, J.; Hassan Mohamed, H.; Petit Martí, SV.; Duato Marín, JF. (2011). A cluster computer performance predictor for memory scheduling. En Algorithms and Architectures for Parallel Processing. Springer Verlag (Germany). 7017:353-362. doi:10.1007/978-3-642-24669-2_34S3533627017Meuer, H.W.: The top500 project: Looking back over 15 years of supercomputing experience. Informatik-Spektrum 31, 203–222 (2008), doi:10.1007/s00287-008-0240-6Nussle, M., Scherer, M., Bruning, U.: A Resource Optimized Remote-Memory-Access Architecture for Low-latency Communication. In: International Conference on Parallel Processing, pp. 220–227 (September 2009)Blocksome, M., Archer, C., Inglett, T., McCarthy, P., Mundy, M., Ratterman, J., Sidelnik, A., Smith, B., Almási, G., Castaños, J., Lieber, D., Moreira, J., Krishnamoorthy, S., Tipparaju, V., Nieplocha, J.: Design and implementation of a one-sided communication interface for the IBM eServer Blue Gene®supercomputer. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, p. 120. ACM, New York (2006)Kumar, S., Dózsa, G., Almasi, G., Heidelberger, P., Chen, D., Giampapa, M., Blocksome, M., Faraj, A., Parker, J., Ratterman, J., Smith, B.E., Archer, C.: The deep computing messaging framework: generalized scalable message passing on the blue gene/P supercomputer. In: ICS, pp. 94–103 (2008)Tipparaju, V., Kot, A., Nieplocha, J., Bruggencate, M.T., Chrisochoides, N.: Evaluation of Remote Memory Access Communication on the Cray XT3. In: IEEE International Parallel and Distributed Processing Symposium, pp. 1–7 (March 2007)HyperTransport Technology Consortium. HyperTransport I/O Link Specification Revision (October 3, 2008)Serrano, M., Sahuquillo, J., Hassan, H., Petit, S., Duato, J.: A scheduling heuristic to handle local and remote memory in cluster computers. In: High Performance Computing and Communications (2010) (accepted for publication)Serrano, M., Sahuquillo, J., Petit, S., Hassan, H., Duato, J.: A cost-effective heuristic to schedule local and remote memory in cluster computers. The Journal of Supercomputing, 1–19 (2011), doi:10.1007/s11227-011-0566-8Ubal, R., Sahuquillo, J., Petit, S., López, P.: Multi2Sim: A Simulation Framework to Evaluate Multicore-Multithreaded Processors. In: Proceedings of the 19th International Symposium on Computer Architecture and High Performance Computing (2007)Keltcher, C.N., McGrath, K.J., Ahmed, A., Conway, P.: The AMD Opteron Processor for Multiprocessor Servers. IEEE Micro 23(2), 66–76 (2003)Duato, J., Silla, F., Yalamanchili, S.: Extending HyperTransport Protocol for Improved Scalability. 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Air quality data clustering using EPLS method

[EN] Nowadays air quality data can be easily accumulated by sensors around the world. Analysis on air quality data is very useful for society decision. Among five major air pollutants which are calculated for AQI (Air Quality Index), PM2.5 data is the most concerned by the people. PM2.5 data is also cross-impacted with the other factors in the air and which has properties of non-linear non-stationary including high noise level and outlier. Traditional methods cannot solve the problem of PM2.5 data clustering very well because of their inherent characteristics. In this paper, a novel model-based feature extraction method is proposed to address this issue. The EPLS model includes: (1) Mode Decomposition, in which EEMD algorithm is applied to the aggregation dataset; (2) Dimension Reduction, which is carried out for a more significant set of vectors; (3) Least Squares Projection, in which all testing data are projected to the obtained vectors. Synthetic dataset and air quality dataset are applied to different clustering methods and similarity measures. Experimental results demonstrate that EPLS is efficient in dealing with high noise level and outlier air quality clustering problems, and which can also be adapted to various clustering techniques and distance measures. (C) 2016 Elsevier B.V. All rights reserved.This work was supported in part by the National Natural Science Foundation of China (Nos. 61440018, 61501411), the Hubei Natural Science Foundation (No. 2014CFB904), China Scholarship Council Funding.Chen, Y.; Wang, L.; Li, F.; Du, B.; Choo, KR.; Hassan Mohamed, H.; Qin, W. (2017). Air quality data clustering using EPLS method. Information Fusion. 36:225-232. https://doi.org/10.1016/j.inffus.2016.11.015S2252323

Pokemon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks

[EN] The recent popular game, Pokemon GO, created two symbiotic social networks by location-based mobile augmented reality (LMAR) technique. One is in the physical world among players, and another one is in the cyber world among players' avatars. To date, there is no study that has explored the formation of each social network and their symbiosis. In this paper, we carried out a data-driven research on the Pokemon GO game to solve this problem. We accordingly organised the collection of two real datasets. For the first dataset, we designed a questionnaire to collect players' individual behaviours in Pokemon GO, and used maps of Melbourne (Australia) to track and record their usual playing areas. Based on the data that we collected, we modelled the formation of the symbiotic social networks in both physical world (i.e. for players) and cyber world (i.e. for avatars) as well as interactions between players and Pokemon GO elements (i.e. 'bridges' of the two worlds). By investigating the mechanism of network formation, we revealed the relatively weak correlation between the formation processes of the two networks. We further incorporated the real-world pedestrian dataset collected by sensors across Melbourne CBD into the study of their symbiosis. Based on the second dataset, we examined the changes of people's social behaviours in terms of most visited places. The results suggested that the existence of the cyber social network has reciprocally changed the structure of the symbiotic physical social network. (C) 2017 Elsevier B.V. All rights reserved.This research is partially supported by the Australian Research Council projects DP150103732, DP140103649, and LP140100816. The authors extend their appreciation to the International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia for funding this work through the project No. ISPP#0069.Wang, D.; Wu, T.; Wen, S.; Liu, D.; Xiang, Y.; Zhou, W.; Hassan Mohamed, H.... (2018). Pokemon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks. Journal of Computational Science. 26:456-467. https://doi.org/10.1016/j.jocs.2017.06.009S4564672

Power-aware scheduling with effective task migration for real-time multicore embedded systems

A major design issue in embedded systems is reducing the power consumption because batteries have a limited energy budget. For this purpose, several techniques such as dynamic voltage and frequency scaling (DVFS) or task migration are being used. DVFS allows reducing power by selecting the optimal voltage supply, whereas task migration achieves this effect by balancing the workload among cores. This paper focuses on power-aware scheduling allowing task migration to reduce energy consumption in multicore embedded systems implementing DVFS capabilities. To address energy savings, the devised schedulers follow two main rules: migrations are allowed at specific points of time and only one task is allowed to migrate each time. Two algorithms have been proposed working under real-time constraints. The simpler algorithm, namely, single option migration (SOM) only checks just one target core before performing a migration. In contrast, the multiple option migration (MOM) searches the optimal target core. In general, the MOM algorithm achieves better energy savings than the SOM algorithm, although differences are wider for a reduced number of cores and frequency/voltage levels. Moreover, the MOM algorithm reduces energy consumption as much as 40% over the worst fit algorithm.This work was supported by the Spanish MICINN, Consolider Programme and Plan E funds, as well as European Commission FEDER funds, under Grants CSD2006-00046 and TIN2009-14475-C04-01.March Cabrelles, JL.; Sahuquillo Borrás, J.; Petit Martí, SV.; Hassan Mohamed, H.; Duato Marín, JF. (2013). Power-aware scheduling with effective task migration for real-time multicore embedded systems. Concurrency and Computation: Practice and Experience. 25(14):1987-2001. doi:10.1002/cpe.2899S198720012514Euiseong Seo, Jinkyu Jeong, Seonyeong Park, & Joonwon Lee. (2008). Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors. IEEE Transactions on Parallel and Distributed Systems, 19(11), 1540-1552. doi:10.1109/tpds.2008.104March, J. L., Sahuquillo, J., Hassan, H., Petit, S., & Duato, J. (2011). A New Energy-Aware Dynamic Task Set Partitioning Algorithm for Soft and Hard Embedded Real-Time Systems. The Computer Journal, 54(8), 1282-1294. doi:10.1093/comjnl/bxr008AlEnawy, T. A., & Aydin, H. (s. f.). Energy-Aware Task Allocation for Rate Monotonic Scheduling. 11th IEEE Real Time and Embedded Technology and Applications Symposium. doi:10.1109/rtas.2005.20Intel atom processor microarchitecture www.intel.com/Marvell ARMADA TM 628 Marvell Semiconductor, Inc. Santa Clara, CA, USA http://www.marvell.com/company/press_kit/assets/Marvell_ARMADA_628_Release_FINAL3.pdfMcNairy, C., & Bhatia, R. (2005). Montecito: A Dual-Core, Dual-Thread Itanium Processor. IEEE Micro, 25(2), 10-20. doi:10.1109/mm.2005.34Kalla, R., Sinharoy, B., & Tendler, J. M. (2004). IBM power5 chip: a dual-core multithreaded processor. IEEE Micro, 24(2), 40-47. doi:10.1109/mm.2004.1289290Shah A Arm plans to add multithreading to chip design 2010 http://www.itworld.com/hardware/122383/arm-plans-add-multithreading-chip-designSchranzhofer, A., Chen, J.-J., & Thiele, L. (2010). Dynamic Power-Aware Mapping of Applications onto Heterogeneous MPSoC Platforms. IEEE Transactions on Industrial Informatics, 6(4), 692-707. doi:10.1109/tii.2010.2062192Cazorla, F. J., Knijnenburg, P. M. W., Sakellariou, R., Fernandez, E., Ramirez, A., & Valero, M. (2006). Predictable performance in SMT processors: synergy between the OS and SMTs. IEEE Transactions on Computers, 55(7), 785-799. doi:10.1109/tc.2006.108Fisher, N., & Baruah, S. (2008). The feasibility of general task systems with precedence constraints on multiprocessor platforms. Real-Time Systems, 41(1), 1-26. doi:10.1007/s11241-008-9054-5Buttazzo, G., Bini, E., & Yifan Wu. (2011). Partitioning Real-Time Applications Over Multicore Reservations. IEEE Transactions on Industrial Informatics, 7(2), 302-315. doi:10.1109/tii.2011.2123902Intel Pentium M processor datasheet INTEL Corp. Santa Clara, CA, USA 2004 http://download.intel.com/support/processors/mobile/pm/sb/25261203.pdfChaparro, P., Gonzáles, J., Magklis, G., Cai, Q., & González, A. (2007). Understanding the Thermal Implications of Multi-Core Architectures. IEEE Transactions on Parallel and Distributed Systems, 18(8), 1055-1065. doi:10.1109/tpds.2007.1092WCET analysis project. WCET benchmark programs 2006 http://www.mrtc.mdh.se/projects/wcet

The ocean sampling day consortium.

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world's oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

The ocean sampling day consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

The Ocean Sampling Day Consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

An efficient cloud storage system for tele-health services

[EN] Healthcare service is a critical aspect of our daily lives. Enabled by technologies such as wearable devices and wireless sensor networks, tele-health has becoming a promising new field in IT industry. Wearable devices, which detect real-time human body conditions, form body sensor networks (BSNs) for patients. In a cloud-enabled tele-health ecosystem, health data are collected by the BSN and sent to mobile devices such as smart phones and tablets. These embedded devices process the data and forward them to remote data centers. Due to the energy and time constraints of embedded systems, the effectiveness of storage systems become a critical issue. For years, memory technologies such as SRAMs and DRAMs have been widely used in computer systems. SRAMs are fast while DRAMs have high density. However, SRAMs have the disadvantage of power leakage and low density. DRAMs are slower in read and write operations. New memory technology for embedded tele-health is needed. In the paper, we propose a hybrid memory system for embedded tele-health. We combine phase-change memory PCM with flash memory to meet energy and latency requirement while reducing capital expenditure. Moreover, the data allocation and storage on server side is also a challenging problem in tele-health. Effective storage system designs are desired to efficiently store and manage health care data from users. Therefore, in the paper, we design a ecosystem for tele-health including the memory storage for embedded devices and data storage for tele-health data centers. To fully utilize the proposed ecosystem, we design several resource allocation algorithms with dynamic programming and heuristics. The experiments show that our approaches can achieve up to 30% performance enhancement compared to greedy approaches.This work has been partially supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China ICT1600236 (Prof. Meikang Qiu)Chen, L.; Qiu, M.; Dai, W.; Hassan Mohamed, H. (2017). An efficient cloud storage system for tele-health services. The Journal of Supercomputing. 73(7):2949-2965. https://doi.org/10.1007/s11227-017-1977-yS29492965737Guthaus MR (2001) MiBench: a free, commercially representative embedded benchmark suite. In: IEEE WWC, pp 3–14Hu J (2012) Optimizing data allocation and memory configuration for non-volatile memory based hybrid SPM on embedded CMPs. In: IPDPSW. Shanghai, China, pp 982–989IHS (2012) Medical Devices & Healthcare IT. https://technology.ihs.com/researchareas/450450Lai S (2003) Current status of the phase change memory and its future. In: IEEE International on Electron Devices Meeting, 2003. IEDM’03 Technical DigestLi J, Qiu M (2011) Resource allocation robustness in multi-core embedded systems with inaccurate information. J Syst Archit 57(9):840–849Meza J (2012) Enabling efficient and scalable hybrid memories using fine-granularity DRAM cache management. IEEE Comput Archit Lett 11(2):61–64Okhonin S (2008) Ultra-scaled Z-RAM cell. In: Proceedings of the IEEE International SOI Conference, pp 157–158Qiu M, Chen Z (2014) Energy-aware data allocation with hybrid memory for mobile cloud systems. Syst J IEEE PP(99):1–10Qiu M, Ming Z (2015) Phase-change memory optimization for green cloud with genetic algorithm. IEEE Trans Comput 64(12):3528–3540Ramos LE (2011) Page placement in hybrid memory systems. In: Proceedings of the International Conference on Supercomputing, pp 85–95Shanavas A (2012) Zero capacitor RAM. http://www.edutalks.org/downloads/zram.pdfTian W (2013) Task allocation on nonvolatile-memory-based hybrid main memory. IEEE Trans Very Large Scale Integr (VLSI) Syst 21(7):1271–1284Wilton SJE, Jouppi NP (1996) CACTI: an enhanced cache access and cycle time model. IEEE J Solid-State Circuits 31(5):677–688Wong H (2010) Phase change memory. Proc IEEE 98(12):2201–2227Zhang L, Qiu M (2010) Variable partitioning and scheduling for MPSoC with virtually shared scratch pad memory. J Signal Process Syst 58(2):247–26

E2FS: an elastic storage system for cloud computing

[EN] In cloud storage, replication technologies are essential to fault tolerance and high availability of data. While achieving the goal of high availability, replication brings extra number of active servers to the storage system. Extra active servers mean extra power consumption and capital expenditure. Furthermore, the lack of classification of data makes replication scheme fixed at the very beginning. This paper proposes an elastic and efficient file storage called E2FS for big data applications. E2FS can dynamically scale in/out the storage system based on real-time demands of big data applications. We adopt a novel replication scheme based on data blocks, which provides a fine-grained maintenance of the data in the storage system. E2FS analyzes features of data and makes dynamic replication decision to balance the cost and performance of cloud storage. To evaluate the performance of proposed work, we implement a prototype of E2FS and compare it with HDFS. Our experiments show E2FS can outperform HDFS in elasticity while achieving guaranteed performance for big data applications.This work is supported by NSF CNS-1457506 and NSF CNS-1359557Chen, L.; Qiu, M.; Song, J.; Xiong, Z.; Hassan Mohamed, H. (2018). E2FS: an elastic storage system for cloud computing. The Journal of Supercomputing. 74(3):1045-1060. https://doi.org/10.1007/s11227-016-1827-3S10451060743Chen M, Hai J, Wen Y, Leung VC (2013) Enabling technologies for future data center networking: a primer. IEEE Netw 27(4):8–15Li J, Qiu M, Niu J, Gao W, Zong Z, Qin X (2010) Feedback dynamic algorithms for preemptable job scheduling in cloud systems. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, DC, USA, pp 561–564Dai W, Qiu M (2015) Energy optimization with dynamic task scheduling mobile cloud computing. Syst J IEEE PP(99):1–10Chen M, Mao S, Zhang Y, Leung VC (2014) Big data: related technologies, challenges and future prospects. Springer Briefs in Computer ScienceZhang Y, Chen M, Mao S, Hu L, Leung VC (2014) Cap: Community activity prediction based on big data analysis. IEEE Netw 28(4):52–57Chen M, Hao Y, Li Y, Lai C, Wu D (2015) On the computation offloading at ad hoc cloudlet: architecture and service modes. IEEE Commun Mag 53(6):18–24Cidon A et al (2013) Copysets: reducing the frequency of data loss in cloud storage. In: USENIX Annual Technical Conference 2013 (USENIXATC 13). San Jose, pp 37–48Qiu M, Ming Z (2013) Informer homed routing fault tolerance mechanism for wireless sensor networks. J Syst Archit 59(4):260–270CISCO (2014) Cisco Visual Networking Index: Forecast and Methodology, 2014–2019 White Paper. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/ip-ngn-ip-next-generation-network/white_paper_c11-481360.html . Accessed 18 Feb 2016CNET (2013) Cloud storage comparison. http://www.cnet.com/how-to/onedrive-dropbox-google-drive-and-box-which-cloud-storage-service-is-right-for-you/ . Accessed 18 Feb 2016Gai K, Qiu M (2015) Dynamic Energy-aware Cloudlet-based Mobile Cloud Computing Model for Green Computing. J Netw Comput Appl 59:46–54Wu G, Qiu M (2013) A decentralized approach for mining event correlations in dis- tributed system monitoring. J Parallel Distrib Comput 73(3):330–340Xu L et al (2014) SpringFS: bridging agility and performance in elastic distributed storage. In: Proceedings of the 12th USENIX Conference on File and Storage Technologies (FAST 14). Santa Clara, CA, pp 243–255Harter T et al (2014) Analysis of hdfs under hbase: A facebook messages case study. In: Proceedings of the 12th USENIX Conference on File and Storage Technologies (FAST 14), pp 199–212Wang H, Varman P (2014) Balancing fairness and effciency in tiered storage systems with bottleneck-aware allocation. In: Proceedings of the 12th USENIX Conferenceon File and Storage Technologies (FAST 14), pp 229–242Cidon A et al (2015) Tiered replication: a cost-effective alternative to full cluster geo-replication. In: 2015 USENIX Annual Technical Conference (USENIX ATC 15), pp 31–43Bowers KD, Juels A, Oprea A (2009) Hail: a high-availability and integrity layer for cloud storage. In: Proceedings of the 16th ACM Conference on Computer and Communications Security. ACM, New York, pp 187–19