# Energy-efficient adaptive networked datacenters for the QoS support of real-time applications

## Abstract

In this paper, we develop the optimal minimum-energy scheduler for the adaptive joint allocation of the task sizes, computing rates, communication rates and communication powers in virtualized networked data centers (VNetDCs) that operate under hard per-job delay-constraints. The considered VNetDC platform works at the Middleware layer of the underlying protocol stack. It aims at supporting real-time stream service (such as, for example, the emerging big data stream computing (BDSC) services) by adopting the software-as-a-service (SaaS) computing model. Our objective is the minimization of the overall computing-plus-communication energy consumption. The main new contributions of the paper are the following ones: (i) the computing-plus-communication resources are *jointly* allotted in an *adaptive* fashion by accounting in *real-time* for both the (possibly, unpredictable) time fluctuations of the offered workload and the reconfiguration costs of the considered VNetDC platform; (ii) *hard* per-job delay-constraints on the overall allowed computing-plus-communication latencies are enforced; and, (iii) to deal with the inherently *nonconvex* nature of the resulting resource optimization problem, a novel solving approach is developed, that leads to the *lossless* decomposition of the afforded problem into the cascade of two simpler sub-problems. The sensitivity of the energy consumption of the proposed scheduler on the allowed processing latency, as well as the peak-to-mean ratio (PMR) and the correlation coefficient (i.e., the smoothness) of the offered workload is numerically tested under both synthetically generated and real-world workload traces. Finally, as an index of the attained energy efficiency, we compare the energy consumption of the proposed scheduler with the corresponding ones of some benchmark static, hybrid and sequential schedulers and numerically evaluate the resulting percent energy gaps.

## Keywords

Big data stream computing (BDSC) Virtualized networked data centers Real-time cloud computing Adaptive resource management Energy saving## References

- 1.Cugola G, Magara A (2012) Processing flows of information: from data stream to complex event processing. ACM Comput Surveys (CSUR) 44(3)Google Scholar
- 2.Baliga J, Ayre RWA, Hinton K, Tucker RS (2011) Green cloud computing: balancing energy in processing. Storage Transp Proc IEEE 99(1):149–167Google Scholar
- 3.Mishra A, Jain R, Durresi A (2012) Cloud computing: networking and communication challenges. IEEE Commun Mag 50(9):24–25Google Scholar
- 4.Azodolmolky S, Wieder P, Yahyapour R (2013) Cloud computing networking: challanges and opportunities for innovations. IEEE Commun Mag 51(7):54–62Google Scholar
- 5.Scheneider S, Hirzel M, Gedik B (2013) Tutorial: stream processing optimizations. ACM DEBS 249–258Google Scholar
- 6.Lu T, Chen M (2012) Simple and effective dynamic provisioning for power-proportional data centers. Proc CISSGoogle Scholar
- 7.Rajaraman A, Ullman JD (2011) Mining of massive datasets. Cambridge University Press, Cambridge, p 326Google Scholar
- 8.Chakravarthy Sh, Jiang Q (2009) Stream data processing: a quality of service perspective, vol 36. Springer, Berlin, p 348Google Scholar
- 9.Krempl G, Brzezinski D, Hllermeier E, Last M (2014) Open challenges for data stream mining research. ACM SIGKDD Explor NewslettGoogle Scholar
- 10.Mittal S (2014 ) Power management techniques for data centers: a survey. arXiv:1404.6681
- 11.Baccarelli E, Biagi M, Pelizzoni C, Cordeschi N (2007) Optimized power allocation for multiantenna systems impaired by multiple access interference and imperfect channel estimation. IEEE Trans Veh Technol 56(5):3089–3105CrossRefMathSciNetGoogle Scholar
- 12.Neumeyer L, Robbins B, Nair A, Kesari A (2010) S4: distributed stream computing platform. In: International workshop on knowledge discovery using cloud and distributed computing platforms, ICDMW ’10, pp 170–177Google Scholar
- 13.Zaharia M, Das T, Li H, Shenker S, Stoica I (2012) Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters. Hot CloudGoogle Scholar
- 14.Loesing S, Hentschel M, Kraska T (2012) Storm: an elastic and highly available streaming service in the cloud. EDBT-ICDT ’12, pp 55–60Google Scholar
- 15.Qian Z, He Y, Su C, Wu Z, Zhu H, Zhang T (2013) TimeStream: reliable stream computation in the cloud. In: EuroSys, pp 1–14Google Scholar
- 16.Kumbhare A et al (2014) PLAstiCC: predictive look-ahead scheduling for continuous data- flows on clouds. CCGRIDGoogle Scholar
- 17.Mathew V, Sitaraman R, Rowstrom A (2012) Energy-aware load balancing in content delivery networks. IEEE INFOCOMGoogle Scholar
- 18.Padala P, You KY, Shin KG, Zhu X, Uysal M, Wang Z, Singhal S, Merchant M (2009) Automatic control of multiple virtualized resources. . In: Proceedings of the 4th ACM European conference on computer systems, pp 13–26Google Scholar
- 19.Kusic D, Kandasamy N (2008) Power and performance management of virtualized computing environments via look-ahead control. . In: Proceedings of the international conference on automatic computing, vol 1, pp 3–12Google Scholar
- 20.Govindan S, Choi J, Urgaonkar B, Sasubramanian A, Baldini A (2009) Statistical profiling-based techniques for effective power provisioning in data centers. Proc Euro SystGoogle Scholar
- 21.Lin M, Wierman A, Andrew L, Thereska E (2011) Dynamic right-sizing for power-proportional data centers. IEEE INFOCOMGoogle Scholar
- 22.Zhou Z et al (2013) Carbon-aware load balancing for geo-distributed cloud services. IEEE MASCOTS, pp 232–241Google Scholar
- 23.Tamm O, Hersmeyer C, Rush AM (2010) Eco-sustainable system and network architectures for future transport networks. Bell Labs Tech J 14:311–327CrossRefGoogle Scholar
- 24.Liu J, Zhao F, Liu X, He W (2009) Challenges towards elastic power management in internet data centers. In: Proceedings on IEEE international conference on distributed computing systems workshops, Los AlamitosGoogle Scholar
- 25.Khan AN, Mat Kiah ML, Madani SA, Ali M, Khan AR, Shamshirband S (2014) Incremental proxy re-encryption scheme for mobile cloud computing environment. J Supercomput 68(2):624–651Google Scholar
- 26.Nathuji R, Schwan K (2007) VirtualPower: coordinated power management in virtualized enterprise systems. In: ACM 21th SOSP’07, pp 265–278Google Scholar
- 27.Kim KH, Beloglazov A, Buyya R (2009) Power-aware provisioning of cloud resources for real-time services. Proc ACM MGC’09Google Scholar
- 28.Koller R, Verma A, Neogi A (2010) WattApp: an application aware power meter for shared data centers. ICAC’10Google Scholar
- 29.Warneke D, Kao O (2011) Exploiting dynamic resource allocation for efficient parallel data processing in the cloud. IEEE Trans Parallel Disturb Syst 22(6):985–997CrossRefGoogle Scholar
- 30.Zhu D, Melhem R, Childers BR (2003) Scheduling with dynamic voltage/rate adjustment using slack reclamation in multiprocessor real-time systems. IEEE Trans Parllel Distrib Syst 14(7):686–700CrossRefGoogle Scholar
- 31.Vasudevan V et al (2009) Safe and effective fine-grained TCP retransmissions for datacenter communication. ACM SIGCOMM, pp 303–314Google Scholar
- 32.Alizadeh M, Greenberg A, Maltz DA (2010) J Padhye “Data center TCP (DCTCP)”, ACM SIGCOMM.Google Scholar
- 33.Das T, Sivalingam KM (2013) TCP improvements for data center networks. COMSNETS, pp 1–10Google Scholar
- 34.Kurose JF, Ross KW (2013) Computer networking: a top-down approach featuring the internet, 6th edn. Addison WesleyGoogle Scholar
- 35.Jin S, Guo L, Matta I, Bestravos A (2003) A spectrum of TCP-friendly window-based congestion control algorithms. IEEE/ACM Trans Netw 11(3):341–355CrossRefGoogle Scholar
- 36.Baccarelli E, Biagi M, Pelizzoni C, Cordeschi N (2008) Optimal MIMO UWB-IR transceiver for Nakagami-fading and Poisson-arrivals. J Commun 3(1):27–40CrossRefGoogle Scholar
- 37.Cordeschi N, Patriarca T, Baccarelli E (2012) Stochastic traffic engineering for real-time applications over wireless networks. J Netw Comput Appl 35(2):681–694CrossRefGoogle Scholar
- 38.Baccarelli E, Cordeschi N, Polli V (2013) Optimal self-adaptive QoS resource management in interference-affected multicast wireless networks. IEEE/ACM Trans Netw 21(6):1750–1759CrossRefGoogle Scholar
- 39.Al-Fares M, Loukissas A, Vahdat A (2008) A scalable commodity data center network architecture. ACM SIGCOMM, pp 63–74Google Scholar
- 40.Gulati A, Merchant A, Varman PJ (2010) mClock: handling throughput variability for hypervisor IO scheduling, OSDI’10Google Scholar
- 41.Ballami H, Costa P, Karagiannis T, Rowstron A (2011) Towards predicable datacenter networks, SIGCOMM ’11Google Scholar
- 42.Greenberg A et al (2011) VL2: a scalable and flexible data center network. Commun ACM 54(3):95–104Google Scholar
- 43.Guo C et al (2010) SecondNet: a data center network virtualization architecture with bandwidth guarantees. ACM CoNEXTGoogle Scholar
- 44.Xia L, Cui Z, Lange J (2012) VNET/P: bridging the cloud and high performance computing through fast overaly networking, HPDC’12Google Scholar
- 45.Wang L, Zhang F, Aroca JA, Vasilakos AV, Zheng K, Hou C, Li D, Liu Z (2014) Green DCN: a general framework for achieving energy efficiency in data center networks. IEEE JSAC 32(1):4–15Google Scholar
- 46.Khan AN, Mat Kiah ML, Ali M, Madani SA, Khan AR, Shamshirband S (2014) BSS: block-based sharing scheme for secure data storage services in mobile cloud environment. J Supercomput. doi: 10.1007/s11227-014-1269-8
- 47.Neely MJ, Modiano E, Rohs CE (2003) Power allocation and routing in multi beam satellites with time-varying channels. IEEE/ACM Trans Netw 19(1):138–152CrossRefGoogle Scholar
- 48.Wang L, Zhang F, Hou C, Aroca JA, Liu Z (2013) Incorporating rate adaptation into Green networking for future data centers. IEEE NCA, pp 106–109Google Scholar
- 49.Balter MH (2013) Performance modeling and design of computer systems. Cambridge Press, CambridgeGoogle Scholar
- 50.Chiang M, Low SH, Calderbank AR, Doyle JC (2007) Layering as optimization decomposition: a mathematical theory of network architectures. Proc IEEE 95(1):255–312CrossRefGoogle Scholar
- 51.Cordeschi N, Shojafar M, Baccarelli E (2013) Energy-saving self-configuring networked data centers. Comput Netw 57(17):3479–3491CrossRefGoogle Scholar
- 52.Bazaraa MS, Sherali HD, Shetty CM (2006) Nonlinear programming, 3rd edn. Wiley, New YorkGoogle Scholar
- 53.Kushner HJ, Yang J (1995) Analysis of adaptive step-size SA algorithms for parameter tracking. IEEE Trans Autom Control 40(8):1403–1410CrossRefMATHMathSciNetGoogle Scholar
- 54.Baccarelli E, Cusani R (1996) Recursive Kalman-type optimal estimation and detection of hidden Markov chains. Signal Process 51(1):55–64CrossRefMATHGoogle Scholar
- 55.Urgaonkar B, Pacifici G, Shenoy P, Spreitzer M, Tantawi A (2007) Analytic modeling of multitier internet applications. ACM Trans Web 1(1)Google Scholar
- 56.Srikant R (2004) The mathematics of internet congestion control. Birkhauser, BaselGoogle Scholar