Energy management is a key issue for mobile devices. On current Android devices, power management relies heavily on OS modules known as governors. These modules are created for various hardware components, including the CPU, to support DVFS. They implement algorithms that attempt to balance performance and power consumption. In this paper we make the observation that the existing governors are (1) general-purpose by nature (2) focused on power reduction and (3) are not energy-optimal for many applications. We thus establish the need for an application-specific approach that could overcome these drawbacks and provide higher energy efficiency for suitable applications. We also show that existing methods manage power and performance in an independent and isolated fashion and that co-ordinated control of multiple components can save more energy. In addition, we note that on mobile devices, energy savings cannot be achieved at the expense of performance. Consequently, we propose a solution that minimizes energy consumption of specific applications while maintaining a user-specified performance target. Our solution consists of two stages: (1) offline profiling and (2) online controlling. Utilizing the offline profiling data of the target application, our control theory based online controller dynamically selects the optimal system configuration (in this paper, combination of CPU frequency and memory bandwidth) for the application, while it is running. Our energy management solution is tested on a Nexus 6 smartphone with 6 real-world applications. We achieve 4 - 31% better energy than default governors with a worst case performance loss of <; 1%.

Performance scaling is now synonymous with scaling the number of cores. One of the consequences of this shift is the increasing difficulty of designing processors with predictable and controllable performance. To address this challenge this paper proposes a chip-scale throughput regulation technique that is based on dynamic tracking of instruction execution dynamics in each core. A new variable gain controller design is developed for regulating the throughput of modern out-of-order cores. The gain is adjusted based on an on-line sensitivity analysis of the core's throughput to the control parameter. We explore throughput regulation using two control paramaters - core frequency and instruction issue width and demonstrate via cycle-level, full system simulation the utility of the proposed regulator on both compute and memory intensive workloads. Performance results are presented for the application to a 16 core, cache coherent 3D multicore processor.

This paper demonstrates the application of Infinitesimal Perturbation Analysis (IPA) to performance optimization of fluid marked graphs. Recent developments in the theory of stochastic hybrid systems concern IPA techniques for gradient estimation of performance functions defined on Petri nets and their use in sample-path optimization. This paper takes a first step towards eventual applications in manufacturing by considering an example of workload balancing in a marked graph. In particular, it addresses the problem of balancing parts' inventories and product backorders in a production-system's model by controlling the parts' inflow rates. The paper defines the problem, describes the algorithm, and presents simulation results. Although the considered system is simple it captures the salient features of our approach, and the simulation results suggest its potential viability in future applications.

We consider the case of a single resource. A given schedule (possibly optimal) is evaluated by means of the sum of the delays of the tasks. A task appears in the system at a random time. The duration of this task is random, as well as its due date. The goal is to complete the task at the latest by its due date while increasing as little as possible the sum of the delays of the initial tasks. We have to find an algorithm that reduces as most as possible the amount of computation to be performed in real time at the expenses of the amount of computation to be performed off-line.

We consider an optimal control problem in single-stage manufacturing systems that are modeled as a deterministic queue. The objective is to determine the release times of parts while minimizing a measure of the discrepancy between the jobs' completion times and the given due dates. Although this optimal control problem is neither convex nor differentiable, we cast it in the form of a convex differentiable nonlinear programming problem. By exploiting a special structural property, we develop an efficient algorithm for computing optimal controls, and it turns out that the algorithm has linear complexity with respect to the dimension of the original optimal control problem.

This paper presents a low-complexity algorithm for online job scheduling at workcenters along a given route in a manufacturing system. At each workcenter, the job has to be processed by any one of a given set of identical machines. Each machine has a preset schedule of operations, leaving out time-windows during which the job's processing must be scheduled. The manufacturing system has no internal buffers and the job cannot wait between two consecutive operations. There is some flexibility in the job's processing times, which must be confined to given time intervals. The scheduling algorithm minimizes the job's completion time, and it is executable in real time whenever the job requirement is generated.