5,265 research outputs found
Energy-aware Load Balancing Policies for the Cloud Ecosystem
The energy consumption of computer and communication systems does not scale
linearly with the workload. A system uses a significant amount of energy even
when idle or lightly loaded. A widely reported solution to resource management
in large data centers is to concentrate the load on a subset of servers and,
whenever possible, switch the rest of the servers to one of the possible sleep
states. We propose a reformulation of the traditional concept of load balancing
aiming to optimize the energy consumption of a large-scale system: {\it
distribute the workload evenly to the smallest set of servers operating at an
optimal energy level, while observing QoS constraints, such as the response
time.} Our model applies to clustered systems; the model also requires that the
demand for system resources to increase at a bounded rate in each reallocation
interval. In this paper we report the VM migration costs for application
scaling.Comment: 10 Page
Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle
This paper presents a nonlinear-model based hybrid optimal control technique
to compute a suboptimal power-split strategy for power/energy management in a
parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained
as model predictive control solution to the power management control problem
(PMCP) of the PHEV, i.e., to decide upon the power distribution among the
internal combustion engine, an electric drive, and other subsystems. A
hierarchical control structure of the hybrid vehicle, i.e., supervisory level
and local or subsystem level is assumed in this study. The PMCP consists of a
dynamical nonlinear model, and a performance index, both of which are
formulated for power flows at the supervisory level. The model is described as
a bi-modal switched system, consistent with the operating mode of the electric
ED. The performance index prescribing the desired behavior penalizes vehicle
tracking errors, fuel consumption, and frictional losses, as well as sustaining
the battery state of charge (SOC). The power-split strategy is obtained by
first creating the embedded optimal control problem (EOCP) from the original
bi-modal switched system model with the performance index. Direct collocation
is applied to transform the problem into a nonlinear programming problem. A
nonlinear predictive control technique (NMPC) in conjunction with a sequential
quadratic programming solver is used to compute suboptimal numerical solutions
to the PMCP. Methods for approximating the numerical solution to the EOCP with
trajectories of the original bi-modal PHEV are also presented in this paper.
The usefulness of the approach is illustrated via simulation results on several
case studies
Phase Space Navigator: Towards Automating Control Synthesis in Phase Spaces for Nonlinear Control Systems
We develop a novel autonomous control synthesis strategy called Phase Space Navigator for the automatic synthesis of nonlinear control systems. The Phase Space Navigator generates global control laws by synthesizing flow shapes of dynamical systems and planning and navigating system trajectories in the phase spaces. Parsing phase spaces into trajectory flow pipes provide a way to efficiently reason about the phase space structures and search for global control paths. The strategy is particularly suitable for synthesizing high-performance control systems that do not lend themselves to traditional design and analysis techniques
Extending classical planning with state constraints: Heuristics and search for optimal planning
We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate - sometimes determine - the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems - power networks, for example - in which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for cost-optimal planning known in the classical setting - in particular, relaxation-based admissible heuristics - can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning contextThis work was supported by ARC project DP140104219, “Robust AI Planning for Hybrid Systems”, and in part by ARO grant W911NF1210471 and ONR grant N000141210430
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