6,393 research outputs found
Reducing Electricity Demand Charge for Data Centers with Partial Execution
Data centers consume a large amount of energy and incur substantial
electricity cost. In this paper, we study the familiar problem of reducing data
center energy cost with two new perspectives. First, we find, through an
empirical study of contracts from electric utilities powering Google data
centers, that demand charge per kW for the maximum power used is a major
component of the total cost. Second, many services such as Web search tolerate
partial execution of the requests because the response quality is a concave
function of processing time. Data from Microsoft Bing search engine confirms
this observation.
We propose a simple idea of using partial execution to reduce the peak power
demand and energy cost of data centers. We systematically study the problem of
scheduling partial execution with stringent SLAs on response quality. For a
single data center, we derive an optimal algorithm to solve the workload
scheduling problem. In the case of multiple geo-distributed data centers, the
demand of each data center is controlled by the request routing algorithm,
which makes the problem much more involved. We decouple the two aspects, and
develop a distributed optimization algorithm to solve the large-scale request
routing problem. Trace-driven simulations show that partial execution reduces
cost by for one data center, and by for geo-distributed
data centers together with request routing.Comment: 12 page
Minimum-cost multicast over coded packet networks
We consider the problem of establishing minimum-cost multicast connections over coded packet networks, i.e., packet networks where the contents of outgoing packets are arbitrary, causal functions of the contents of received packets. We consider both wireline and wireless packet networks as well as both static multicast (where membership of the multicast group remains constant for the duration of the connection) and dynamic multicast (where membership of the multicast group changes in time, with nodes joining and leaving the group). For static multicast, we reduce the problem to a polynomial-time solvable optimization problem, and we present decentralized algorithms for solving it. These algorithms, when coupled with existing decentralized schemes for constructing network codes, yield a fully decentralized approach for achieving minimum-cost multicast. By contrast, establishing minimum-cost static multicast connections over routed packet networks is a very difficult problem even using centralized computation, except in the special cases of unicast and broadcast connections. For dynamic multicast, we reduce the problem to a dynamic programming problem and apply the theory of dynamic programming to suggest how it may be solved
A Primal Decomposition Method with Suboptimality Bounds for Distributed Mixed-Integer Linear Programming
In this paper we deal with a network of agents seeking to solve in a
distributed way Mixed-Integer Linear Programs (MILPs) with a coupling
constraint (modeling a limited shared resource) and local constraints. MILPs
are NP-hard problems and several challenges arise in a distributed framework,
so that looking for suboptimal solutions is of interest. To achieve this goal,
the presence of a linear coupling calls for tailored decomposition approaches.
We propose a fully distributed algorithm based on a primal decomposition
approach and a suitable tightening of the coupling constraints. Agents
repeatedly update local allocation vectors, which converge to an optimal
resource allocation of an approximate version of the original problem. Based on
such allocation vectors, agents are able to (locally) compute a mixed-integer
solution, which is guaranteed to be feasible after a sufficiently large time.
Asymptotic and finite-time suboptimality bounds are established for the
computed solution. Numerical simulations highlight the efficacy of the proposed
methodology.Comment: 57th IEEE Conference on Decision and Contro
Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks
We study the system-level effects of the introduction of large populations of
Electric Vehicles on the power and transportation networks. We assume that each
EV owner solves a decision problem to pick a cost-minimizing charge and travel
plan. This individual decision takes into account traffic congestion in the
transportation network, affecting travel times, as well as as congestion in the
power grid, resulting in spatial variations in electricity prices for battery
charging. We show that this decision problem is equivalent to finding the
shortest path on an "extended" transportation graph, with virtual arcs that
represent charging options. Using this extended graph, we study the collective
effects of a large number of EV owners individually solving this path planning
problem. We propose a scheme in which independent power and transportation
system operators can collaborate to manage each network towards a socially
optimum operating point while keeping the operational data of each system
private. We further study the optimal reserve capacity requirements for pricing
in the absence of such collaboration. We showcase numerically that a lack of
attention to interdependencies between the two infrastructures can have adverse
operational effects.Comment: Submitted to IEEE Transactions on Control of Network Systems on June
1st 201
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