OS Scheduling Algorithms for Memory Intensive Workloads in Multi-socket Multi-core servers

Major chip manufacturers have all introduced multicore microprocessors. Multi-socket systems built from these processors are routinely used for running various server applications. Depending on the application that is run on the system, remote memory accesses can impact overall performance. This paper presents a new operating system (OS) scheduling optimization to reduce the impact of such remote memory accesses. By observing the pattern of local and remote DRAM accesses for every thread in each scheduling quantum and applying different algorithms, we come up with a new schedule of threads for the next quantum. This new schedule potentially cuts down remote DRAM accesses for the next scheduling quantum and improves overall performance. We present three such new algorithms of varying complexity followed by an algorithm which is an adaptation of Hungarian algorithm. We used three different synthetic workloads to evaluate the algorithm. We also performed sensitivity analysis with respect to varying DRAM latency. We show that these algorithms can cut down DRAM access latency by up to 55% depending on the algorithm used. The benefit gained from the algorithms is dependent upon their complexity. In general higher the complexity higher is the benefit. Hungarian algorithm results in an optimal solution. We find that two out of four algorithms provide a good trade-off between performance and complexity for the workloads we studied

Local heuristic for the refinement of multi-path routing in wireless mesh networks

We consider wireless mesh networks and the problem of routing end-to-end traffic over multiple paths for the same origin-destination pair with minimal interference. We introduce a heuristic for path determination with two distinguishing characteristics. First, it works by refining an extant set of paths, determined previously by a single- or multi-path routing algorithm. Second, it is totally local, in the sense that it can be run by each of the origins on information that is available no farther than the node's immediate neighborhood. We have conducted extensive computational experiments with the new heuristic, using AODV and OLSR, as well as their multi-path variants, as underlying routing methods. For two different CSMA settings (as implemented by 802.11) and one TDMA setting running a path-oriented link scheduling algorithm, we have demonstrated that the new heuristic is capable of improving the average throughput network-wide. When working from the paths generated by the multi-path routing algorithms, the heuristic is also capable to provide a more evenly distributed traffic pattern

Integrated optimization of smart home appliances with cost-effective energy management system

Smart grid enables consumers to control and schedule the consumption pattern of their appliances, minimize energy cost, peak-to-average ratio (PAR) and peak load demand. In this paper, a general architecture of home energy management system (HEMS) is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residential customers. The optimization problem is developed under the time of use pricing (TOUP) scheme. To optimize the formulated problem, a powerful meta-heuristic algorithm called grey wolf optimizer (GWO) is utilized, which is compared with particle swarm optimization (PSO) algorithm to show its effectiveness. A rooftop photovoltaic (PV) system is integrated with the system to show the cost effectiveness of the appliances. For analysis, eight different cases are considered under various time scheduling algorithm

Production Scheduling in Integrated Steel Manufacturing

Steel manufacturing is both energy and capital intensive, and it includes multiple production stages, such as iron-making, steelmaking, and rolling. This dissertation investigates the order schedule coordination problem in a multi-stage manufacturing context. A mixed-integer linear programming model is proposed to generate operational (up to the minute) schedules for the steelmaking and rolling stages simultaneously. The proposed multi-stage scheduling model in integrated steel manufacturing can provide a broader view of the cost impact on the individual stages. It also extends the current order scheduling literature in steel manufacturing from a single-stage focus to the coordinated multi-stage focus. Experiments are introduced to study the impact of problem size (number of order batches), order due time and demand pattern on solution performance. Preliminary results from small data instances are reported. A novel heuristic algorithm, Wind Driven Algorithm (WDO), is explained in detail, and numerical parameter study is presented. Another well-known and effective heuristic approach based on Particle Swarm Optimization (PSO) is used as a benchmark for performance comparison. Both algorithms are implemented to solve the scheduling model. Results show that WDO outperforms PSO for the proposed model on solving large sample data instances. Novel contributions and future research areas are highlighted in the conclusion

Online scheduling for vehicle-to-grid regulation service

Electric vehicle (EV) fleets can provide ancillary services, such as frequency regulation, to the utility grid, if their charging/discharging schedules are coordinated appropriately. In this paper, a multi-level architecture for bidirectional vehicleto-grid regulation service is proposed. In this architecture, aggregators coordinate the charging/discharging schedules of EVs in order to meet their shares of regulation demand requested by the grid operator. Based on this architecture, the scheduling problem of V2G regulation is then formulated as a convex optimization problem, which in turn degenerates to an online scheduling problem for charging/discharging of EVs. It requires only the current and past regulation profiles, and does not depend on the accurate forecast of regulation demand. A decentralized algorithm, which enables every EV to solve its local optimization problem and obtain its own schedule, is applied to solve the online scheduling problem. Based on the household driving pattern and regulation signal data from the PJM market, a simulation study of 1,000 EVs has been performed. The simulation results show that the proposed online scheduling algorithm is able to smooth out the power fluctuations of the grid by coordinating the EV schedules, demonstrating the potential of V2G in providing regulation service to the grid.published_or_final_versio