combinatorial optimisation technology has come a long way. We now have mature high-level modelling languages in which to specify a model of the particular problem of interest [18, 7, 24, 6];robust complete solvers in ...
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To tackle the massive access and different computation offloading requirements of IoT devices in 5G MEC system, Dynamic User Pairing NOMA-based offloading is considered in this paper. An overall energy consumption min...
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To tackle the massive access and different computation offloading requirements of IoT devices in 5G MEC system, Dynamic User Pairing NOMA-based offloading is considered in this paper. An overall energy consumption minimization framework is established by joint optimizing user pairing, of-floading decision and uplink power control under the time and maximum multiplexing number of user constraints. Due to the combinatorial nature of the formulated mixed integer non-linear programming problem, we adapt two-phase approach to solve the challenging problem. First, a low-complexity heuristic algorithm is designed to solve the user pairing problem. After we get the sub-optimal user pairing scheme, the original problem is proved to be a convex problem, we then get the optimal offloading decision and transmission power of devices. Numerical results show that the proposed DUP NOMA-based offloading scheme can significantly improve system performance in terms of energy consumption comparing to OMA and Fixed User Pairing NOMA based offloading.
the paper deals with low-power adaptive scheduling of synchronous and flexible real-time OS tasks. A software reconfiguration scenario is assumed to be any run-time operation allowing the addition-removal-update of OS...
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the paper deals with low-power adaptive scheduling of synchronous and flexible real-time OS tasks. A software reconfiguration scenario is assumed to be any run-time operation allowing the addition-removal-update of OS tasks to adapt the system to its environment under well-defined conditions. the problem is that any reconfiguration can push the system to an unfeasible behavior where temporal properties are violated or the energy consumption is possibly high and unacceptable. A task in the system can change its characteristics at any time when a reconfiguration scenario is applied, it can also be stopped or replaced by another one. the difficulty is how to find the new temporal parameters of the systems tasks after any reconfiguration. We use a DVS processor which is with a variable speed to support run-time solutions that re-obtain the system's feasibility. the challenge is how to compute the best combinations between available processor speeds for a good compromise between execution time and energy consumption. We propose a combinatorialoptimization method based on integerprogramming and heuristics. We propose also a solution when the available speeds do not allow the feasibility of the system. Both approaches include a mechanism to adjust the deadlines of tasks to satisfy the feasibility conditions and overcome the problem of rejected tasks. this mechanism makes the scheduling more flexible and able to react in accordance with its environment.
作者：
Saal, David S.Parker, DavidWeyman-Jone, TomAston Univ
Aston Business Sch Econ & Strategy Grp Birmingham B4 7ET W Midlands England Aston Univ
Aston Business Sch Ctr Performance Measurement & Managemment Birmingham B4 7ET W Midlands England Cranfield Univ
Sch Management Cranfield Ctr Competit & Regulat Res Cranfield MK43 0AL Beds England Univ Loughborough
Dept Econ Loughborough LE11 3TU Leics England
the water and sewerage industry of England and Wales was privatized in 1989 and subjected to a new regime of environmental, water quality and RPI+K price cap regulation. this paper estimates a quality-adjusted input d...
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the water and sewerage industry of England and Wales was privatized in 1989 and subjected to a new regime of environmental, water quality and RPI+K price cap regulation. this paper estimates a quality-adjusted input distance function, with stochastic frontier techniques in order to estimate productivity growth rates for the period 1985-2000. Productivity is decomposed so as to account for the impact of technical change, efficiency change, and scale change. Compared with earlier studies by Saal and Parker [(2000) Managerial Decision Econ 21(6):253-268, (2001) J Regul Econ 20(1): 61-90], these estimates allow a more careful consideration of how and whether privatization and the new regulatory regime affected productivity growth in the industry. Strikingly, they suggest that while technical change improved after privatization, productivity growth did not improve, and this was attributable to efficiency losses as firms appear to have struggled to keep up with technical advances after privatization. Moreover, the results also suggest that the excessive scale of the WaSCs contributed negatively to productivity growth.
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