Abstract
We discuss what green computing algorithmics is, and what a theory of energy as a computational resource isn’t. We then present some open problems in this area, with enough background from the literature to put the open problems in context. This background should also be a reasonably representative sample of the green computing algorithmics literature.
References
[1]
Vision and roadmap: routing telecom and data centers toward efficient energy use. In: Proceedings of Vision and Roadmap Workshop on Routing Telecom and Data Centers Toward Efficient Energy Use, May 2009
[2]
Albers, S.: Energy-efficient algorithms. Commun. ACM 53(5), 86–96 (2010)
[3]
Anand, S., Garg, N., Kumar, A.: Resource augmentation for weighted flow-time explained by dual fitting. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 1228–1241 (2012)
[4]
Andrew, L.L.H., Barman, S., Ligett, K., Lin, M., Meyerson, A., Roytman, A., Wierman, A.: A tale of two metrics: simultaneous bounds on competitiveness and regret. In: Conference on Learning Theory, pp. 741–763 (2013)
[5]
Andrews, M., Antonakopoulos, S., Zhang, L.: Minimum-cost network design with (dis)economies of scale. In: IEEE Symposium on Foundations of Computer Science, pp. 585–592 (2010)
[6]
Andrews, M., Fernández, A., Zhang, L., Zhao, W.: Routing for energy minimization in the speed scaling model. In: INFOCOM, pp. 2435–2443 (2010)
[7]
Antoniadis, A., Barcelo, N., Consuegra, M., Kling, P., Nugent, M., Pruhs, K., Scquizzato, M.: Efficient computation of optimal energy and fractional weighted flow trade-off schedules. In: Symposium on Theoretical Aspects of Computer Science (2014)
[8]
Antoniadis, A., Barcelo, N., Nugent, M., Pruhs, K., Schewior, K., Scquizzato, M.: Chasing convex bodies and functions. In: Kranakis, E., Navarro, G., Chávez, E. (eds.) LATIN 2016. LNCS, vol. 9644, pp. 68–81. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49529-2_6
[9]
Antoniadis, A., Barcelo, N., Nugent, M., Pruhs, K., Scquizzato, M.: Complexity-theoretic obstacles to achieving energy savings with near-threshold computing. In: International Green Computing Conference, pp. 1–8 (2014)
[10]
Antoniadis, A., Barcelo, N., Nugent, M., Pruhs, K., Scquizzato, M.: Energy-efficient circuit design. In: Innovations in Theoretical Computer Science, pp. 303–312 (2014)
[11]
Antoniadis, A., Im, S., Krishnaswamy, R., Moseley, B., Nagarajan, V., Pruhs, K., Stein, C.: Energy efficient virtual circuit routing. In: ACM-SIAM Symposium on Discrete Algorithms (2014)
[12]
Bansal, N., Gupta, A., Krishnaswamy, R., Nagarajan, V., Pruhs, K., Stein, C.: Multicast routing for energy minimization using speed scaling. In: Even, G., Rawitz, D. (eds.) MedAlg 2012. LNCS, vol. 7659, pp. 37–51. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34862-4_3
[13]
Bansal, N., Gupta, A., Krishnaswamy, R., Pruhs, K., Schewior, K., Stein, C.: A 2-competitive algorithm for online convex optimization with switching costs. In: Workshop on Approximation Algorithms for Combinatorial Optimization Problems, pp. 96–109 (2015)
[14]
Bansal, N., Kimbrel, T., Pruhs, K.: Speed scaling to manage energy and temperature. J. ACM 54(1), 3 (2007)
[15]
Barcelo, N., Cole, D., Letsios, D., Nugent, M., Pruhs, K.: Optimal energy trade-off schedules. Sustain. Comput.: Inf. Syst. 3, 207–217 (2013)
[16]
Barcelo, N., Kling, P., Nugent, M., Pruhs, K.: Optimal speed scaling with a solar cell. In: Chan, T.-H.H., Li, M., Wang, L. (eds.) COCOA 2016. LNCS, vol. 10043, pp. 521–535. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48749-6_38
[17]
Barcelo, N., Kling, P., Nugent, M., Pruhs, K., Scquizzato, M.: On the complexity of speed scaling. In: Italiano, G.F., Pighizzini, G., Sannella, D.T. (eds.) MFCS 2015. LNCS, vol. 9235, pp. 75–89. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48054-0_7
[18]
Barcelo, N., Nugent, M., Pruhs, K., Scquizzato, M.: Almost all functions require exponential energy. In: Italiano, G.F., Pighizzini, G., Sannella, D.T. (eds.) MFCS 2015. LNCS, vol. 9235, pp. 90–101. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48054-0_8
[19]
Barcelo, N., Nugent, M., Pruhs, K., Scquizzato, M.: The power of heterogeneity in near-threshold computing. In: International Green and Sustainable Computing Conference, pp. 1–4 (2015)
[20]
Bartal, Y., Bollobás, B., Mendel, M.: Ramsey-type theorems for metric spaces with applications to online problems. J. Comput. Syst. Sci. 72(5), 890–921 (2006)
[21]
Bartal, Y., Linial, N., Mendel, M., Naor, A.: On metric Ramsey-type phenomena. In: ACM Symposium on Theory of Computing, pp. 463–472 (2003)
[22]
Borodin, A., El-Yaniv, R.: Online Computation and Competitive Analysis. Cambridge University Press, Cambridge (1998)
[23]
Borodin, A., Linial, N., Saks, M.E.: An optimal on-line algorithm for metrical task system. J. ACM 39(4), 745–763 (1992)
[24]
Bower, F.A., Sorin, D.J., Cox, L.P.: The impact of dynamically heterogeneous multicore processors on thread scheduling. IEEE Micro 28(3), 17–25 (2008)
[25]
Brooks, D.M., Bose, P., Schuster, S.E., Jacobson, H., Kudva, P.N., Buyuktosunoglu, A., Wellman, J.-D., Zyuban, V., Gupta, M., Cook, P.W.: Power-aware microarchitecture: design and modeling challenges for next-generation microprocessors. IEEE Micro 20(6), 26–44 (2000)
[26]
Butts, J.A., Sohi, G.S.: A static power model for architects. In: ACM/IEEE International Symposium on Microarchitecture, pp. 191–201 (2000)
[27]
Chekuri, C., Khanna, S., Shepherd, F.B.: Multicommodity flow, well-linked terminals, and routing problems. In: ACM Symposium on Theory of Computing, pp. 183–192 (2005)
[28]
Dreslinski, R.G., Wieckowski, M., Blaauw, D., Sylvester, D., Mudge, T.N.: Near-threshold computing: reclaiming Moore’s law through energy efficient integrated circuits. Proc. IEEE 98(2), 253–266 (2010)
[29]
Esmaeilzadeh, H., Blem, E.R., Amant, R.S., Sankaralingam, K., Burger, D.: Dark silicon and the end of multicore scaling. IEEE Micro 32(3), 122–134 (2012)
[30]
Fiat, A., Mendel, M.: Better algorithms for unfair metrical task systems and applications. SIAM J. Comput. 32(6), 1403–1422 (2003)
[31]
Friedman, J., Linial, N.: On convex body chasing. Discret. Comput. Geom. 9, 293–321 (1993)
[32]
Fujiwara, H., Iwama, K., Yonezawa, K.: Online chasing problems for regular polygons. Inf. Process. Lett. 108(3), 155–159 (2008)
[33]
Gács, P.: Reliable computation. In: Algorithms in Informatics, vol. 2. ELTE Eötvös Kiadó, Budapest (2005)
[34]
Gupta, A., Im, S., Krishnaswamy, R., Moseley, B., Pruhs, K.: Scheduling heterogeneous processors isn’t as easy as you think. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 1242–1253 (2012)
[35]
Gupta, A., Krishnaswamy, R., Pruhs, K.: Online primal-dual for non-linear optimization with applications to speed scaling. In: Erlebach, T., Persiano, G. (eds.) WAOA 2012. LNCS, vol. 7846, pp. 173–186. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38016-7_15
[36]
Im, S., Kulkarni, J., Munagala, K.: Competitive algorithms from competitive equilibria: non-clairvoyant scheduling under polyhedral constraints. In: Symposium on Theory of Computing, pp. 313–322 (2014)
[37]
Im, S., Kulkarni, J., Munagala, K., Pruhs, K.: Selfishmigrate: a scalable algorithm for non-clairvoyantly scheduling heterogeneous processors. In: Symposium on Foundations of Computer Science, pp. 531–540 (2014)
[38]
Im, S., Moseley, B., Pruhs, K.: A tutorial on amortized local competitiveness in online scheduling. SIGACT News 42(2), 83–97 (2011)
[39]
Irani, S., Pruhs, K.: Algorithmic problems in power management. SIGACT News 36(2), 63–76 (2005)
[40]
Khandekar, R., Rao, S., Vazirani, U.V.: Graph partitioning using single commodity flows. J. ACM 56(4), 19 (2009)
[41]
Krishnaswamy, R., Nagarajan, V., Pruhs, K., Stein, C.: Cluster before you hallucinate: approximating node-capacitated network design and energy efficient routing (2014)
[42]
Kumar, R., Tullsen, D.M., Jouppi, N.P.: Core architecture optimization for heterogeneous chip multiprocessors. In: International Conference on Parallel Architectures and Compilation Techniques, pp. 23–32. ACM (2006)
[43]
Kumar, R., Tullsen, D.M., Ranganathan, P., Jouppi, N.P., Farkas, K.I.: Single-ISA heterogeneous multi-core architectures for multithreaded workload performance. SIGARCH Comput. Archit. News 32(2), 64 (2004)
[44]
Kurose, J.F., Ross, K.W.: Computer Networking: A Top-Down Approach. Addison-Wesley Publishing Company, Boston (2009)
[45]
Leighton, F.T., Rao, S.: Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. J. ACM 46(6), 787–832 (1999)
[46]
Lin, M., Liu, Z., Wierman, A., Andrew, L.L.H.: Online algorithms for geographical load balancing. In: International Green Computing Conference, pp. 1–10 (2012)
[47]
Lin, M., Wierman, A., Andrew, L.L.H., Thereska, E.: Online dynamic capacity provisioning in data centers. In: Allerton Conference on Communication, Control, and Computing, pp. 1159–1163 (2011)
[48]
Lin, M., Wierman, A., Andrew, L.L.H., Thereska, E.: Dynamic right-sizing for power-proportional data centers. IEEE/ACM Trans. Netw. 21(5), 1378–1391 (2013)
[49]
Lin, M., Wierman, A., Roytman, A., Meyerson, A., Andrew, L.L.H.: Online optimization with switching cost. SIGMETRICS Perform. Eval. Rev. 40(3), 98–100 (2012)
[50]
Linial, N., London, E., Rabinovich, Y.: The geometry of graphs and some of its algorithmic applications. Combinatorica 15(2), 215–245 (1995)
[51]
Liu, Z., Lin, M., Wierman, A., Low, S.H., Andrew, L.L.H.: Greening geographical load balancing. In: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, pp. 233–244 (2011)
[52]
Merritt, R.: CPU designers debate multi-core future. EE Times, February 2008
[53]
Morad, T.Y., Weiser, U.C., Kolodny, A., Valero, M., Ayguade, E.: Performance, power efficiency and scalability of asymmetric cluster chip multiprocessors. IEEE Comput. Archit. Lett. 5(1), 4 (2006)
[54]
Mudge, T.: Power: a first-class architectural design constraint. Computer 34(4), 52–58 (2001)
[55]
Pippenger, N.: On networks of noisy gates. In: Symposium on Foundations of Computer Science, pp. 30–38 (1985)
[56]
Pruhs, K.: Competitive online scheduling for server systems. In: Special Issue of SIGMETRICS Performance Evaluation Review on New Perspectives in Scheduling, no. 4 (2007)
[57]
Pruhs, K., Sgall, J., Torng, E.: Online scheduling. In: Handbook of Scheduling: Algorithms, Models, and Performance Analysis (2004)
[58]
Pruhs, K., Uthaisombut, P., Woeginger, G.J.: Getting the best response for your erg. ACM Trans. Algorithms 4(3), 38:1–38:17 (2008)
[59]
Rao, S., Zhou, S.: Edge disjoint paths in moderately connected graphs. SIAM J. Comput. 39(5), 1856–1887 (2010)
[60]
Sergent, J.E., Krum, A.: Thermal Management Handbook. McGraw-Hill, New York (1998)
[61]
Shannon, C.E.: The synthesis of two-terminal switching circuits. Bell Syst. Tech. J. 28, 59–98 (1949)
[62]
Sitters, R.: The generalized work function algorithm is competitive for the generalized 2-server problem. SIAM J. Comput. 43(1), 96–125 (2014)
[63]
Valiant, L.G.: Short monotone formulae for the majority function. J. Algorithms 5(3), 363–366 (1984)
[64]
von Neumann, J.: Probabilistic logics and the synthesis of reliable organisms from unreliable components. In: Shannon, C.E., McCarthy, J. (eds.) Automata Studies, pp. 329–378. Princeton University Press, Princeton (1956)
[65]
Wang, K., Lin, M., Ciucu, F., Wierman, A., Lin, C.: Characterizing the impact of the workload on the value of dynamic resizing in data centers. In: IEEE INFOCOM, pp. 515–519 (2013)
[66]
Williamson, D.P., Shmoys, D.B.: The Design of Approximation Algorithms. Cambridge University Press, Cambridge (2011)
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