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Poweraware scheduling for makespan and flow
 In Proc. 18th Annual ACM Symp. Parallelism in Algorithms and Architectures
, 2006
"... We consider offline scheduling algorithms that incorporate speed scaling to address the bicriteria problem of minimizing energy consumption and a scheduling metric. For makespan, we give a lineartime algorithm to compute all nondominated solutions for the general uniprocessor problem and a fast a ..."
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Cited by 42 (1 self)
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We consider offline scheduling algorithms that incorporate speed scaling to address the bicriteria problem of minimizing energy consumption and a scheduling metric. For makespan, we give a lineartime algorithm to compute all nondominated solutions for the general uniprocessor problem and a fast arbitrarilygood approximation for multiprocessor problems when every job requires the same amount of work. We also show that the multiprocessor problem becomes NPhard when jobs can require different amounts of work. For total flow, we show that the optimal flow corresponding to a particular energy budget cannot be exactly computed on a machine supporting exact real arithmetic, including the extraction of roots. This hardness result holds even when scheduling equalwork jobs on a uniprocessor. We do, however, extend previous work by Pruhs et al. to give an arbitrarilygood approximation for scheduling equalwork jobs on a multiprocessor. 1
Control and optimization meet the smart power grid: Scheduling of power demands for optimal energy management
 in Proc. 2nd Int. Conf. EnergyEfficient Computing Networking (eEnergy ’11
, 2011
"... The smart power grid uses information and communication technologies to enforce sensible use of energy through effective demand load management. We envision a scenario with realtime communication between the grid operator and the consumers. The operator controller receives consumer power demand r ..."
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Cited by 17 (1 self)
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The smart power grid uses information and communication technologies to enforce sensible use of energy through effective demand load management. We envision a scenario with realtime communication between the grid operator and the consumers. The operator controller receives consumer power demand requests with different power requirements, durations, and deadlines by which they are to be activated. The objective of the operator is to devise a power demand task scheduling policy that minimizes the grid operational cost over a time horizon. The cost is a convex function of total instantaneous power consumption, thus reflecting the fact that each additional unit of power needed to serve demands is more expensive, as the demand load increases. First, we study the offline demand scheduling
On resource allocation for machinetomachine (M2M) communications in cellular networks
 In Proceedings of the 2012 IEEE, Globecom Workshops (GC Wkshps
, 2012
"... Abstract—Cellular networks are an attractive option for handling the growing number of sensing and monitoring devices due to their ubiquitous presence. While this growing popularity of cellular network based machinetomachine (M2M) communications is opening new avenues for the mobile network oper ..."
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Cited by 5 (4 self)
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Abstract—Cellular networks are an attractive option for handling the growing number of sensing and monitoring devices due to their ubiquitous presence. While this growing popularity of cellular network based machinetomachine (M2M) communications is opening new avenues for the mobile network operators, it is also bringing forth new system design challenges mainly because of the significant difference in the nature of M2M traffic and the current commercial traffic for which the cellular networks are designed and optimized. In this paper, we consider the M2M operational regime characterized by large number of small transactions and study the problem of power optimal uplink resource allocation both for Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA). We derive tractable results for the maximum load a base station can handle and the optimal transmit power for both access strategies and show that FDMA supports an order of magnitude higher load than TDMA under the peak power constraint. We also show that the value of optimizing uplink resource allocation in the M2M parameter space of interest is typically insignificant and simpler access strategies, such as channel gain based allocation or even equal resource allocation, lead to near optimal performance. We also derive accurate closed form approximations for optimum power levels indicative of the actual performance in this regime. I.
Powerefficient system design for cellularbased machinetomachine communications
 IEEE Trans. on Wireless Commun
, 2013
"... Abstract—The growing popularity of MachinetoMachine (M2M) communications in cellular networks is driving the need to optimize networks based on the characteristics of M2M, which are significantly different from the requirements that current networks are designed to meet. First, M2M requires large ..."
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Cited by 2 (2 self)
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Abstract—The growing popularity of MachinetoMachine (M2M) communications in cellular networks is driving the need to optimize networks based on the characteristics of M2M, which are significantly different from the requirements that current networks are designed to meet. First, M2M requires large number of short sessions as opposed to small number of long lived sessions required by the human generated traffic. Second, M2M constitutes a number of battery operated devices that are static in locations such as basements and tunnels, and need to transmit at elevated powers compared to the traditional devices. Third, replacing or recharging batteries of such devices may not be feasible. All these differences highlight the importance of a systematic framework to study the power and energy optimal system design in the regime of interest for M2M, which is the main focus of this paper. For a variety of coordinated and uncoordinated transmission strategies, we derive results for the optimal transmit power, energy per bit, and the maximum load supported by the base station, leading to the following design guidelines: (i) frequency division multiple access (FDMA), including equal bandwidth allocation, is sumpower optimal in the asymptotically low spectral efficiency regime, (ii) while FDMA is the best practical strategy overall, uncoordinated code division multiple access (CDMA) is almost as good when the base station is lightly loaded, (iii) the value of optimization within FDMA is not significant in the regime of interest for M2M. Index Terms—Machinetomachine communications, cellular network, wireless access, optimal resource allocation. I.
SCHEDULING AND ADMISSION CONTROL
, 2006
"... We present algorithms and hardness results for three resource allocation problems. The first is an abstract admission control problem where the system receives a series of requests and wants to satisfy as many as possible, but has bounded resources. This occurs, for example, when allocating network ..."
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We present algorithms and hardness results for three resource allocation problems. The first is an abstract admission control problem where the system receives a series of requests and wants to satisfy as many as possible, but has bounded resources. This occurs, for example, when allocating network bandwidth to incoming calls so the calls receive guaranteed quality of service. Algorithms can have performance guarantees for this problem either with respect to acceptances or with respect to rejections. These types of guarantees are incomparable and algorithms having different types of guarantee can have nearly opposite behavior. We give two procedures for combining one algorithm of each type into a single algorithm having both types of guarantee simultaneously. Specifically, if we combine an algorithm that is cAcompetitive for acceptances with an algorithm that is cRcompetitive for rejections, the combined algorithm is O(cA)competitive for acceptance and O(cAcR)competitive for rejections. If both the input algorithms are deterministic, then so is the combined algorithm. In addition, one of the combining procedures does not need to know the value of cA and neither needs to know the value of cR. The second problem we consider is scheduling with rejections, a combination of scheduling