Abstract
Recently, as the amount of data from both the cyber and physical worlds are exploding, database functionalities are increasingly embedded into mobile/embedded systems to provide systematic in situ data services. Such databases, often called embedded databases, are supposed to provide timely data services under various constraints, such as limited time and energy. However, current embedded databases do not support timely and energy-efficient processing of transactions. Further, most existing academic results from real-time and embedded databases depend on the adaptation of quality-of-data (QoD) and admission control techniques to achieve timeliness. However, the range of QoD adaptation is often limited by the requirements of applications. We might consider different tuning knobs exposed by modern embedded systems, such as dynamic voltage/frequency scaling (DVFS), since they can be applied to control the latency of transactions without degrading QoD as far as the system is not overloaded. However, since DVFS has only a few discrete voltage/frequency steps, fine-grained control to support desired response time is hard to achieve. In this paper, we present a method of combining the advantages of these two very different control knobs to achieve the energy-efficiency while still supporting fine-grained control of response time of transactions. We formally design the feedback control loop using these two complementing control knobs, and demonstrate the feasibility of our approach by implementing it in a modern embedded device. The experimental results show that our approach closely supports the desired response time of transactions while achieving lower energy consumption compared to baseline approaches.










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Abadi DJ, Ahmad Y, Balazinska M, Cetintemel U, Cherniack M, Hwang JH, Lindner W, Maskey A, Rasin A, Ryvkina E, Tatbul N (2005) The Design of the Borealis Stream Processing Engine. In: CIDR, vol 5. pp 277–289
Amirijoo M, Hansson J, Son SH (2006) Specification and management of QoS in real-time databases supporting imprecise computations. IEEE Trans Comput 55(3):304–319
Ayoub RZ, Ogras U, Gorbatov E, Jin Y, Kam T, Diefenbaugh P, Rosing T (2011) OS-level power minimization under tight performance constraints in general purpose systems. In: Proceedings of the 17th IEEE/ACM international symposium on low-power electronics and design. IEEE Press, Piscataway, p 321–326 (2011)
Bertini L, Leite J, Mosse D (2009) Generalized tardiness quantile metric: distributed DVS for soft real-time web clusters. In: 21st Euromicro conference on real-time systems, 2009. ECRTS ’09, p 227–236
Bonnet P, Gehrke J, Seshadri P (2001) Towards sensor database systems. In: Mobile data management. Springer, Heidelberg, p 3–14
Chen JJ, Kuo CF (2007) Energy-efficient scheduling for real-time systems on dynamic voltage scaling (DVS) platforms. In: 13th IEEE international conference on embedded and real-time computing systems and applications, 2007, p 28–38
Diao Y, Gandhi N, Hellerstein J (2001) Using MIMO feedback control to enforce policies for interrelated metrics with application to the Apache web server. In: Network operations and management, April 2002
Fire information and rescue equipment (FIRE) project (2008). http://fire.me.berkeley.edu. Accessed 20 Feb 2008
Fu X, Wang X (2011) Utilization-controlled task consolidation for power optimization in multi-core real time systems. In: 2011 IEEE 17th international conference on embedded and real-time computing systems and applications (RTCSA), 2011, vol 1, p 73–82
Fu Y, Kottenstette N, Lu C, Koutsoukos XD (2012) Feedback thermal control of real-time systems on multicore processors. In: Proceedings of the tenth ACM international conference on embedded software, EMSOFT ’12. ACM, New York, p 113–122
HardKernel products (2016). http://www.hardkernel.com/main/products/prdt_info.php?g_code=G137361754360. Accessed 26 Dec 2016
Harizopoulos S, Shah MA, Meza J, Ranganathan P (2009) Energy efficiency: the new holy grail of data management systems research. In: CIDR, 2009
Hellerstein JL, Diao Y, Parekh S, Tilbury DM (2004) Feedback control of computing systems. Wiley IEEE Press, Hoboken
Ishihara T, Yasuura H (1998) Voltage scheduling problem for dynamically variable voltage processors. In: 1998 International symposium on low power electronics and design, 1998. Proceedings. IEEE, New York, p 197–202
Jain A, Chang EY, Wang YF (2004) Adaptive stream resource management using Kalman filters. In: SIGMOD ’04: proceedings of the 2004 ACM SIGMOD international conference on management of data. ACM Press, New York, p 11–22
Jiang X, Chen NY, Hong JI, Wang K, Takayama L, Landay JA (2004) Siren: context-aware computing for firefighting. Springer, Heidelberg
Kang KD, Oh J, Son SH (2007) Chronos: feedback control of a real database system performance. In: RTSS, 2007
Kang KD, Son SH, Stankovic JA (2004) Managing deadline miss ratio and sensor data freshness in real-time databases. IEEE Trans Knowl Data Eng 16(10):1200–1216
Kang W, Son SH, Stankovic JA (2008) Power-aware data buffer cache management in real-time embedded databases. In: RTCSA ’08: proceedings of the 14th IEEE international conference on embedded and real-time computing systems and applications, 2008
Kang W, Son SH, Stankovic JA (2012) Design, implementation, and evaluation of a QoS-aware real-time embedded database. IEEE Trans Comput 61(1):45–59
Kim GJ, Baek SC, Lee HS, Lee HD, Joe MJ (2006) LGeDBMS: a small DBMS for embedded system with flash memory. In: Proceedings of the 32nd international conference on very large data bases (VLDB), 2006
Lang W, Patel JM (2009) Towards eco-friendly database management systems. In: CIDR, 2009
Lee SW, Moon B (2007) Design of flash-based DBMS: an in-page logging approach. In: SIGMOD ’07: proceedings of the 2007 ACM SIGMOD international conference on management of data, 2007
Ljung L (1999) Systems identification: theory for the user, 2nd edn. Prentice Hall PTR, Englewood Cliffs
Lu C, Abdelzaher TF, Stankovic JA, Son SH (2001) A feedback control approach for guaranteeing relative delays in web servers. In: RTAS ’01: proceedings of the seventh real-time technology and applications symposium, 2001
Lu C, Wang X, Gill C (2003) Feedback control real-time scheduling in ORB middleware. In: RTAS ’03: proceedings of the 9th IEEE real-time and embedded technology and applications symposium, 2003. IEEE Computer Society, Washington, DC, p 37
Lu Y, Abdelzaher TF, Saxena A (2004) Design, implementation, and evaluation of differentiated caching services. IEEE Trans Parallel Distrib Syst 15(5):440–452
Madden SR, Franklin MJ, Hellerstein JM, Hong W (2005) Tinydb: an acquisitional query processing system for sensor networks. ACM Trans Database Syst 30(1):122–173
Mathur G, Desnoyers P, Ganesan D, Shenoy PJ (2006) Capsule: an energy-optimized object storage system for memory-constrained sensor devices. In: SenSys, 2006
Nath S, Kansal A (2007) FlashDB: dynamic self-tuning database for NAND flash. In: The international conference on information processing in sensor networks (IPSN), 2007
Oh J, Kang KD (2013) A predictive-reactive method for improving the robustness of real-time data services. IEEE Trans Knowl Data Eng 25(5):974–986
Oracle Berkeley DB (2016). https://oss.oracle.com/berkeley-db.html. Accessed 26 Dec 2016
Pallipadi V, Starikovskiy A (2006) The ondemand governor. Proc Linux Symp 2:215–230
Parekh S, Gandhi N, Hellerstein J, Tilbury D, Jayram T, Bigus J (2002) Using control theory to achieve service level objectives in performance management. Real Time Syst 23(1–2):127–141
Park J, Shin D, Chang N, Pedram M (2010) Accurate modeling and calculation of delay and energy overheads of dynamic voltage scaling in modern high-performance microprocessors. In: 2010 ACM/IEEE international symposium on low-power electronics and design (ISLPED), 2010, p 419–424
Ramamritham K, Son SH, Dipippo LC (2004) Real-time databases and data services. Real Time Syst 28(2–3):179–215
SQLite (2016). http://www.sqlite.org. Accessed 26 Dec 2016
StreamBase (2016). http://www.tibco.com/streaming-analytics. Accessed 26 Dec 2016
Tatbul N, Çetintemel U, Zdonik S (2007) Staying fit: efficient load shedding techniques for distributed stream processing. In: Proceedings of the 33rd international conference on very large data bases. VLDB Endowment, Vienna, p 159–170 (2007)
Tsiftes N, Dunkels A (2011) A database in every sensor. In: Proceedings of the 9th ACM conference on embedded networked sensor systems. ACM, New York, p 316–332 (2011)
Tsirogiannis D, Harizopoulos S, Shah MA (2010) Analyzing the energy efficiency of a database server. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data. ACM, New York, p 231–242 (2010)
Wang J, Feng L, Xue W, Song Z (2011) A survey on energy-efficient data management. ACM SIGMOD Rec 40(2):17–23
Whang KY, Song IY, Kim TY, Lee KH (2010) The ubiquitous DBMS. ACM SIGMOD Rec 38(4):14–22
Wu B, Li P (2012) Load-aware stochastic feedback control for DVFS with tight performance guarantee. In: 2012 IEEE/IFIP 20th international conference on VLSI and system-on-chip (VLSI-SoC), p 231–236
Xu Z, Tu YC, Wang X (2010) Exploring power-performance tradeoffs in database systems. In: 2010 IEEE 26th international conference on data engineering (ICDE). IEEE, Los Alamitos, p 485–496
Yao F, Demers A, Shenker S (1995) A scheduling model for reduced CPU energy. In: 36th Annual symposium on foundations of computer science, 1995. Proceedings, p 374–382
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2014R1A1A1005781).
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Kang, W., Chung, J. Energy-efficient response time management for embedded databases. Real-Time Syst 53, 228–253 (2017). https://doi.org/10.1007/s11241-016-9264-1
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DOI: https://doi.org/10.1007/s11241-016-9264-1