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Measurement-based efficient resource allocation with demand-side adjustments. (English) Zbl 1429.93238

Summary: The problem of efficient resource allocation has drawn significant attention in many scientific disciplines due to its direct societal benefits, such as energy savings. Traditional approaches in addressing online resource allocation problems neglect the potential benefit of feedback information available from the running tasks/loads as well as the potential flexibility of a task to adjust its operation/service-level in order to increase efficiency. The present paper builds upon recent developments in the area of bandwidth allocation in computing systems and proposes a generalized design approach for resource allocation when only performance measurements of the running tasks are available, possibly corrupted by noise. We demonstrate through analysis and simulations the potential of the proposed scheme in providing fair and efficient allocation of resources in a large class of resource allocation problems.

MSC:

93C83 Control/observation systems involving computers (process control, etc.)
93C40 Adaptive control/observation systems
91B32 Resource and cost allocation (including fair division, apportionment, etc.)
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[1] Angelis, F. D.; Boaro, M.; Fuselli, D.; Squartini, S.; Piazza, F.; Wei, Q., Optimal home energy management under dynamic electrical and thermal constraints, IEEE Transactions on Industrial Informatics, 9, 3, 1518-1527 (2013)
[2] Bini, E.; Buttazzo, G. C.; Eker, J.; Schorr, S.; Guerra, R.; Fohler, G., Resource management on multicore systems: The ACTORS approach, IEEE Micro, 31, 3, 72-81 (2011)
[3] Brecht, T., On the importance of parallel application placement in NUMA multiprocessors, (Symposium on experiences with distributed and multiprocessor systems (SEDMS IV) (1993)), 1-18
[4] Broquedis, F.; Furmento, N.; Goglin, B.; Wecrenier, P.-A.; Namyst, R., ForestGOMP: An efficient openMP environment for NUMA architectures, International Journal of Parallel Programming, 38, 418-439 (2010) · Zbl 1213.68141
[5] Chasparis, G. C., Reinforcement-learning-based efficient resource allocation with demand-side adjustments, (European control conference (ECC) (2015)), 3066-3072
[6] Chasparis, G. C. (2017). Measurement-based Efficient Resource Allocation with Demand-Side Adjustments, [cs.SY].; Chasparis, G. C. (2017). Measurement-based Efficient Resource Allocation with Demand-Side Adjustments, [cs.SY].
[7] Chasparis, G. C., Stochastic stability of perturbed learning automata in positive-utility games, IEEE Transactions on Automatic Control (2019), 1-1 · Zbl 1482.93664
[8] Chasparis, G. C.; Arapostathis, A.; Shamma, J. S., Aspiration learning in coordination games, SIAM Journal on Control and Optimization, 51, 1 (2013) · Zbl 1262.68149
[9] Chasparis, G. C.; Maggio, M.; Bini, E.; Årzén, K.-E., Design and implementation of distributed resource management for time-sensitive applications, Automatica, 64, 44-53 (2016) · Zbl 1329.93098
[10] Chasparis, G. C.; Natschlaeger, T., Regression models for output prediction of thermal dynamics in buildings, Journal of Dynamic Systems, Measurement, and Control, 139, 2 (2016), 021006-021006
[11] Epstein, L.; Kleiman, E., Selfish bin packing, (European symposium on algorithms (2008), Springer), 368-380 · Zbl 1158.91333
[12] Inaltekin, H.; Wicker, S., A one-shot random access game for wireless networks, (International conference on wireless networks, communications and mobile computing (2005))
[13] Johansson, B.; Rabi, M.; Johansson, M., A randomized incremental subgradient method for distributed optimization in networked systems, SIAM Journal on Optimization, 20, 3, 1157-1170 (2009) · Zbl 1201.65100
[14] Kushner, H. J.; Yin, G. G., Stochastic approximation and recursive algorithms and applications (2003), Springer-Verlag New York, Inc. · Zbl 1026.62084
[15] Marden, J. R.; Wierman, A., Overcoming limitations of game-theoretic distributed control, (48th IEEE Conference on Decision and Control (2009)), 6466-6471
[16] Marden, J. R.; Young, H. P.; Pao, L., Achieving Pareto optimality through distributed learning, SIAM Journal on Control and Optimization, 52, 5, 2753-2770 (2014) · Zbl 1305.91045
[17] Meinhardt, H., Common pool games are convex games, Journal of Public Economic Theory, 1, 2, 247-270 (1999)
[18] Nedic, A.; Ozdaglar, A., Distributed subgradient methods for multi-agent optimization, IEEE Transactions on Automatic Control, 54, 1, 48-61 (2009) · Zbl 1367.90086
[19] Shamma, J. S.; Arslan, G., Dynamic fictitious play, dynamic gradient play, and distributed convergence to nash equilibria, IEEE Transactions on Automatic Control, 50, 3, 312-327 (2005) · Zbl 1366.91028
[20] Steere, D. C.; Goel, A.; Gruenberg, J.; McNamee, D.; Pu, C.; Walpole, J., A feedback-driven proportion allocator for real-rate scheduling, (Proc. of the 3rd symposium on operating systems design and implementation (1999))
[21] Subrata, R.; Zomaya, A. Y.; Landfeldt, B., A cooperative game framework for QoS guided job allocation schemes in grids, IEEE Transactions on Computers, 57, 10, 1413-1422 (2008) · Zbl 1390.91231
[22] Tatarenko, T.; Kamgarpour, M., Learning generalized Nash equilibria in a class of convex games, IEEE Transactions on Automatic Control, 64, 4, 1426-1439 (2019) · Zbl 1482.91036
[23] Tatarenko, T.; Touri, B., Non-convex distributed optimization, IEEE Transactions on Automatic Control, 62, 8, 3744-3757 (2017) · Zbl 1373.90123
[24] Tembine, H.; Altman, E.; ElAzouri, R.; Hayel, Y., Correlated evolutionary stable strategies in random medium access control, (International conference on game theory for networks (2009)), 212-221
[25] Vöcking, B., Selfish load balancing, Algorithmic Game Theory, 20, 517-542 (2007) · Zbl 1151.91322
[26] Wei, G.; Vasilakos, A. V.; Zheng, Y.; Xiong, N., A game-theoretic method of fair resource allocation for cloud computing services, The Journal of Supercomputing, 54, 2, 252-269 (2010)
[27] Yi, P.; Hong, Y.; Liu, F., Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and application to economic dispatch of power systems, Automatica, 74, 259-269 (2016) · Zbl 1348.93024
[28] Zhu, M.; Martinez, S., An approximate dual subgradient algorithm for multi-agent non-convex optimization, IEEE Transactions on Automatic Control, 58, 6, 1534-1539 (2013) · Zbl 1369.90140
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