503 research outputs found
Dynamic Server Allocation over Time Varying Channels with Switchover Delay
We consider a dynamic server allocation problem over parallel queues with
randomly varying connectivity and server switchover delay between the queues.
At each time slot the server decides either to stay with the current queue or
switch to another queue based on the current connectivity and the queue length
information. Switchover delay occurs in many telecommunications applications
and is a new modeling component of this problem that has not been previously
addressed. We show that the simultaneous presence of randomly varying
connectivity and switchover delay changes the system stability region and the
structure of optimal policies. In the first part of the paper, we consider a
system of two parallel queues, and develop a novel approach to explicitly
characterize the stability region of the system using state-action frequencies
which are stationary solutions to a Markov Decision Process (MDP) formulation.
We then develop a frame-based dynamic control (FBDC) policy, based on the
state-action frequencies, and show that it is throughput-optimal asymptotically
in the frame length. The FBDC policy is applicable to a broad class of network
control systems and provides a new framework for developing throughput-optimal
network control policies using state-action frequencies. Furthermore, we
develop simple Myopic policies that provably achieve more than 90% of the
stability region. In the second part of the paper, we extend our results to
systems with an arbitrary but finite number of queues.Comment: 38 Pages, 18 figures. arXiv admin note: substantial text overlap with
arXiv:1008.234
Queuing with future information
We study an admissions control problem, where a queue with service rate
receives incoming jobs at rate , and the decision maker is
allowed to redirect away jobs up to a rate of , with the objective of
minimizing the time-average queue length. We show that the amount of
information about the future has a significant impact on system performance, in
the heavy-traffic regime. When the future is unknown, the optimal average queue
length diverges at rate , as . In sharp contrast, when all future arrival and service times are revealed
beforehand, the optimal average queue length converges to a finite constant,
, as . We further show that the finite limit of
can be achieved using only a finite lookahead window starting from the current
time frame, whose length scales as , as
. This leads to the conjecture of an interesting duality between
queuing delay and the amount of information about the future.Comment: Published in at http://dx.doi.org/10.1214/13-AAP973 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Exact distributional analysis of online algorithms with lookahead
In online optimization, input data is revealed sequentially. Optimization problems in practice often exhibit this type of information disclosure as opposed to standard offline optimization where all information is known in advance. We analyze the performance of algorithms for online optimization with lookahead using a holistic distributional approach. To this end, we first introduce the performance measurement method of counting distribution functions. Then, we derive analytical expressions for the counting distribution functions of the objective value and the performance ratio in elementary cases of the online bin packing and the online traveling salesman problem. For bin packing, we also establish a relation between algorithm processing and the Catalan numbers. The paper shows that an exact analysis is strongly interconnected to the combinatorial structure of the problem and algorithm under consideration. Results further indicate that the value of lookahead heavily relies on the problem itself. The analysis also shows that exact distributional analysis could be used in order to discover key effects and identify related root causes in relatively simple problem settings. These insights can then be transferred to the analysis of more complex settings where the introduced performance measurement approach has to be used on an approximative basis (e.g., in a simulation-based optimization)
Online Algorithms for Geographical Load Balancing
It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
Merging mobile edge computing (MEC) functionality with the dense deployment
of base stations (BSs) provides enormous benefits such as a real proximity, low
latency access to computing resources. However, the envisioned integration
creates many new challenges, among which mobility management (MM) is a critical
one. Simply applying existing radio access oriented MM schemes leads to poor
performance mainly due to the co-provisioning of radio access and computing
services of the MEC-enabled BSs. In this paper, we develop a novel user-centric
energy-aware mobility management (EMM) scheme, in order to optimize the delay
due to both radio access and computation, under the long-term energy
consumption constraint of the user. Based on Lyapunov optimization and
multi-armed bandit theories, EMM works in an online fashion without future
system state information, and effectively handles the imperfect system state
information. Theoretical analysis explicitly takes radio handover and
computation migration cost into consideration and proves a bounded deviation on
both the delay performance and energy consumption compared to the oracle
solution with exact and complete future system information. The proposed
algorithm also effectively handles the scenario in which candidate BSs randomly
switch on/off during the offloading process of a task. Simulations show that
the proposed algorithms can achieve close-to-optimal delay performance while
satisfying the user energy consumption constraint.Comment: 14 pages, 6 figures, an extended version of the paper submitted to
IEEE JSA
Move Big Data to the Cloud: an Online Cost-Minimizing Approach
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A system for microarchitecture and logic optimization
This thesis spans two levels of the design process by examining optimization at both the register-transfer level and at the logic level. More specifically, this thesis addresses the following two problems: 1) performing logic synthesis for custom layout rather than the traditional approach that focuses on synthesis for standard cells, and 2) performing optimization for custom layout from register-transfer level netlists. Thus optimization is performed on the microarchitecture design and at a lower level for individual microarchitecture components.First, techniques are introduced for generating gate-level netlists that take advantage of custom layout capabilities. Such techniques include limiting serial/parallel transistor chains, transistor sizes, and capacitive loads in forming complex gates. These considerations have not been incorporated in previous logic synthesis systems.Second, techniques are introduced for improving the microarchitecture structure and using estimates from lower-level optimization tools to guide microarchitecture design optimizations that attempt to meet user specified area and time constraints. These techniques include the capability for mixing layout styles such as custom layout for random-logic components and bit-slicing for regularly structured components. In this manner the entire design, control logic and datapath, can be optimized at the same time. Further, this paper presents a new methodology for microarchitecture-level optimization that greatly reduces the amount of technology-specific knowledge necessary to perform the optimizations
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