3E: Energy-Efficient Elastic Scheduling for Independent Tasks in Heterogeneous Computing Systems

Reducing energy consumption is a major design constraint for modern heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-efficient scheduling strategies developed on these systems do not sufficiently exploit the system elasticity and adaptability for maximum energy savings, and do not simultaneously take account of user expected finish time. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, independent and non-real-time tasks with user expected finish times on DVFS-enabled heterogeneous computing systems. The 3E strategy adjusts processors’ supply voltages and frequencies according to the system workload, and makes trade-offs between energy consumption and user expected finish times. Compared with other energy-efficient strategies, 3E significantly improves the scheduling quality and effectively enhances the system elasticity

Hybridising local search with Branch-and-Bound for constrained portfolio selection problems

In this paper, we investigate a constrained portfolio selection problem with cardinality constraint, minimum size and position constraints, and non-convex transaction cost. A hybrid method named Local Search Branch-and-Bound (LS-B&B) which integrates local search with B&B is proposed based on the property of the problem, i.e. cardinality constraint. To eliminate the computational burden which is mainly due to the cardinality constraint, the corresponding set of binary variables is identified as core variables. Variable fixing (Bixby, Fenelon et al. 2000) is applied on the core variables, together with a local search, to generate a sequence of simplified sub-problems. The default B&B search then solves these restricted and simplified subproblems optimally due to their reduced size comparing to the original one. Due to the inherent similar structures in the sub-problems, the solution information is reused to evoke the repairing heuristics and thus accelerate the solving procedure of the subproblems in B&B. The tight upper bound identified at early stage of the search can discard more subproblems to speed up the LS-B&B search to the optimal solution to the original problem. Our study is performed on a set of portfolio selection problems with non-convex transaction costs and a number of trading constraints based on the extended mean-variance model. Computational experiments demonstrate the effectiveness of the algorithm by using less computational time

Particle diode: Rectification of interacting Brownian ratchets

Transport of Brownian particles interacting with each other via the Morse potential is investigated in the presence of an ac driving force applied locally at one end of the chain. By using numerical simulations, we find that the system can behave as a particle diode for both overdamped and underdamped cases. For low frequencies, the transport from the free end to the ac acting end is prohibited, while the transport from the ac acting end to the free end is permitted. However, the polarity of the particle diode will reverse for medium frequencies. There exists an optimal value of the well depth of the interaction potential at which the average velocity takes its maximum. The average velocity $\upsilon$ decreases monotonically with the system size $N$ by a power law $\upsilon \propto N^{-1}$.Comment: 7 pages, 9 figure