Modern data centers serve workloads which are capable of exploiting parallelism. When a job parallelizes across multiple servers it will complete more quickly, but jobs receive diminishing returns from being allocated additional servers. Because allocating multiple servers to a single job is inefficient, it is unclear how best to allocate a fixed number of servers between many parallelizable jobs. This paper provides the first optimal allocation policy for minimizing the mean slowdown of parallelizable jobs of known size when all jobs are present at time 0. Our policy provides a simple closed form formula for the optimal allocations at every moment in time. Minimizing mean slowdown usually requires favoring short jobs over long ones (as in the SRPT policy). However, because parallelizable jobs have sublinear speedup functions, system efficiency is also an issue. System efficiency is maximized by giving equal allocations to all jobs and thus competes with the goal of prioritizing small jobs. Our optimal policy, high-efficiency SRPT (heSRPT), balances these competing goals. heSRPT completes jobs according to their size order, but maintains overall system efficiency by allocating some servers to each job at every moment in time. Our results generalize to also provide the optimal allocation policy with respect to mean flow time. Finally, we consider the online case where jobs arrive to the system over time. While optimizing mean slowdown in the online setting is even more difficult, we find that heSRPT provides an excellent heuristic policy for the online setting. In fact, our simulations show that heSRPT significantly outperforms state-of-the-art allocation policies for parallelizable jobs.

Spatial models for the locations of base stations (BSs) in cellular networks have long been desirable since they play a pivotal role in evaluating the mutual interference and, hence, overall performance of the network. The Poisson point process (PPP) is the most analytically tractable and widely used model for the location of BSs, but it fails to capture the underlying separation between the BSs of cellular networks. In this paper we use the most recent proposed model, the β-Ginibre point process (β-GPP), for the locations of BSs to capture the underlying separation between them. Simple yet accurate approximations for the Laplace transform of interference are still missing from the research literature for a β-GPP model. We address this shortcoming, and provide an approximate expression for the Laplace transform of interference using the diagonal approximation for computing the determinant of the kernel matrix K of a β-GPP. We apply that expression to derive a simple computable integral representation for the coverage probability of a typical user under the assumption of a Rayleigh fading channel. To show the accuracy of our derived expression, we compare it with that obtained by simulations and existing results in literature for different values of β. Further, we show that for the value of β=0, our derived expression become equal to the PPP case. Here, we also provide the extensive simulation results to validate the accuracy of our derived analytical expressions.

In this paper, we address the need for motivating applications, such as mission critical industrial control and medical applications, to operate under the Ultra-Reliable Communication (URC) mode in the future 5th Generation (5G) cellular wireless networks, while also under strict Quality of Service (QoS) constraints such as ultra-low latency. Reliability has been shown to improve by using Hybrid Automatic Repeat reQuest (HARQ) for the retransmission (RTX) of erroneous packets during poor channel conditions. However, this can increase the delay to unacceptable levels if more than 1 RTX is required. Thus, an Adaptive HARQ (A-HARQ) scheme is proposed, where RTX are done on better quality sub-bands, with resources dynamically allocated based on Channel Quality Indicator (CQI) reports. A-HARQ also increases the number of RTX within a 4 ms time period, by utilising Transmission Time Interval (TTI) bundling to decrease the delay incurred from many RTX. A performance analysis is conducted, where A-HARQ was shown to have about 35% lower delay than the legacy HARQ, with a slight decrease in throughput, for low Signal-to-Noise (SNR) values.

Wireless Body Area networks (WBANs) are a subset of wireless sensor networks that interconnect miniaturized nodes with sensor or actuator capabilities in, on, or around a human body. WBANs offer number of routing protocols i.e., CICADA etc., but inefficient usage of network resources may lead to premature network failure. To increase energy efficiency, reliability and network load balancing, this paper presents a dual channel routing protocol called DCHREL (Dual Channel Routing Protocol based on Remaining Energy and Link Quality Indicator) that uses lexical routing techniques to select routes on the basis of hop count, end-to-end link quality and path remaining energy where the priority of these metrics is applied in decreasing order. The simulation results showed that DCHREL reduces not only energy consumption but achieves more uniform energy usage between nodes resulting in longer network lifetime.

The authors have become aware that several equations in the original above-named work were presented with errors. As confirmed in this brief note, neither do the errors invalidate the game theoretic approach proposed, nor violate the equilibrium of the approach. However, the parameter selection, which is required to preserve the validity and unique equilibrium of the approach, is affected by the errors. We correct the equations with detailed mathematical derivations in the note.

Efficient discovery and effective sharing of spectrum opportunities are the most challenging issues in a multi-channel cognitive radio network (CRN) with multiple secondary users (SUs). In this paper, we propose a novel spectrum sensing and access protocol in CRNs where SUs are allowed to sequentially sense two channels in a single time slot and are coordinated to access the potentially unused channels. The proposed protocol is formulated as a channel selection problem coupled with a channel assignment problem. We subsequently investigate the myopic sensing policy and propose a Markov chain-based greedy channel assignment scheme (MCGA) to maximize the expected total SU throughput. Moreover, we extend our proposal to the scenario with imperfect spectrum sensing and obtain the optimal sensing time to enhance the performance improvement by considering spectrum sensing and spectrum access in a joint manner. Finally, we evaluate the performance of our proposal in a saturated network. Numerical and simulation results demonstrate that compared to the existing work, our approach can achieve significant performance improvements in SU throughput.

In this paper we address the problem of rate scheduling in the Impulse Radio (IR) ultra-wideband (UWB) wireless body area networks (WBANs) and the minimum energy required to stabilize the queuing system. Targeting low complexity WBAN applications, we assume noncoherent receivers based on energy detection and autocorrelation for all nodes. The coordinating node can minimize the average energy consumption of the system and achieve the queue backlog stability of the sensor nodes by controlling the number of pulses per symbol. We first illustrate the necessary and sufficient conditions of network stability for a multi-mode UWB system and then propose a feasible rate scheduling algorithm based on the Lyapunov optimization theory. The scheduling algorithm uses the instantaneous channel state information and the length of the local queue of all sensor nodes and can approach the optimal energy-delay tradeoff of the network. We apply our theoretical framework to the IR-UWB physical layer of the IEEE 802.15.6 standard and extract the optimal physical layer modes that can achieve the desired energy-delay tradeoff.

Cooperative spectrum sensing has been proposed to significantly improve spectrum sensing accuracy by taking advantage of the cooperation among multiple secondary users (SUs). Most of existing work assumes that all SUs have the same SNR values of primary users' signal while the difference of SNR values among SUs, although it is very common in practice, is largely ignored. In this paper, we investigate two cooperative spectrum sensing scenarios where multiple geographically diverse SUs may have different SNR values. In the first scenario a cognitive radio network (CRN) with a single primary channel is considered. We aim to optimize the individual thresholds of SUs to maximize SU throughput subject to the constraint of missed detection probability. In the second scenario where there are multiple channels in a CRN, we jointly optimize the allocation of SUs to sense different channels and the individual detection thresholds of SUs. Our objective is to maximize total SU throughput over multiple channels while guaranteeing the missed detection probability below a given threshold. Simulation results demonstrate that our proposed schemes achieve significant improvements in SU throughput over the existing schemes.

Efficient data transmission in cognitive radio networks (CRNs) is critical for cognitive radio (CR) users to communicate with each other in an opportunistic manner. Even with successful access to required channels, the transmission could still suffer from failures due to channel fading. In this paper, we propose a random linear coded scheme for efficient data transmission in multi-channel CRNs under practical fading channel conditions. We develop theoretical analysis and derive general form solutions for the batch delay associated with the proposed scheme. We also use our theoretical model to analyze the performances of two multi-channel automatic repeat request (ARQ) based schemes. Simulation results validate the analysis and show that the coded scheme outperforms the ARQ based schemes in terms of batch transmission delay. Additionally, the coded scheme is less dependent on feedback channels than the other schemes.

Cooperative spectrum sensing has been proposed to significantly improve spectrum sensing accuracy by taking advantage of the cooperation among secondary users (SUs), but also this incurs some sensing cost. In this paper, we present a cooperative spectrum sensing model with consideration to spectrum sensing cost in distributed cognitive radio networks where each SU aims to maximize its utility. Under the scenario with selfish SUs, we formulate cooperative spectrum sensing as a non-cooperative game and obtain the mixed strategy Nash equilibrium of the formulated spectrum sensing game by deriving the sensing probabilities of SUs. Under the scenario with limited collaboration of SUs, we formulate cooperative spectrum sensing as a nonlinear optimization problem and derive the optimal sensing strategy of SUs by using our proposed Newton-Raphson based algorithm. Numerical results demonstrate that SUs with limited collaboration are able to achieve much better performance than the outcome of the Nash equilibrium and by choosing the optimal sensing strategy SUs are able to maximize their utility, which is an effective tradeoff between SU throughput and sensing cost.

In this paper we present a novel dynamic spectrum sensing and access model in cognitive radio networks. This model allows secondary users (SUs) to sequentially sense two channels in a single time slot and provides coordinated access of multiple SUs to the available channels. The presented access model is formulated as a channel assignment optimization problem which is shown to be NP-hard. We subsequently propose and analyze a Markov chain based greedy channel assignment scheme (MCGA) which allows for sequential sensing of two channels with a priority order per time slot. Finally, we analyze and evaluate the performance of our approach in a saturated network. Our analytical results, validated by simulation, indicate that compared to the existing work, our approach can achieve significant improvements in terms of SU throughput and MAC delay.

A Wireless Body Area Network (WBAN) comprises a number of tiny devices implanted in/on the body that sample physiological signals of the human body and send them to a coordinator node for medical or other purposes. As these miniature devices run on built-in batteries, energy is the most valuable resource in WBANs. This makes signal interference between neighboring WBANs a serious threat because it causes energy waste in these systems. To mitigate this internetwork interference, we propose a dynamic power control mechanism in WBANs which employs reinforcement learning (RL) to learn from experience and improve its performance. This paper presents guidelines in designing efficient RL power controllers in WBANs and provides an analysis of the effect of the reward function, discount factor, learning rate and eligibility trace parameter where the main performance criteria used are convergence and solution optimality in terms of throughput and energy consumption per bit.

A Wireless Body Area Network (WBAN) is made up of multiple tiny physiological sensors implanted in/on the human body with each sensor equipped with a wireless transceiver that communicates to a coordinator in a star topology. Energy is the scarcest resource in WBANs. Power control mechanisms to achieve a certain level of utility while using as little power for transmission as possible can play an important role in reducing energy consumption in such very energy-constrained networks. In this paper, we propose a novel power controller to mitigate internetwork interference in WBANs and increase the maximum achievable throughput with the minimum energy consumption. The proposed power controller employs reinforcement learning with approximation to learn from the environment and improve its performance. We compare the performance of the proposed controller to two other power controllers, one based on game theory and the other one based on fuzzy logic. Simulation results show that compared to the other two approaches, RLPC provides a substantial saving in energy consumption per bit, with a substantial increase in network lifetime.

Wireless Body Area Networks (WBANs) are an emerging technology for short-range wireless communication inside, on or around the human body, mainly for medical applications. A WBAN's scarcest resource is power. Due to the mobility of WBANs as well as the limited number of available channels, signals of neighboring WBANs can cause interference that may severely degrade the reliability and performance of the system and lead to more power consumption. In this paper, we propose a fast converging fuzzy power controller (FPC) with feedback whose inputs are the current interference power level, Signal-to-Interference-and-Noise (SINR) and the current transmission power level to provide interference mitigation in WBANs. We utilize a genetic algorithm to design and optimize the FPC to simultaneously maximize capacity, minimize power consumption and minimize convergence time. We compare the performance of the proposed approach with two game-theory power control approaches. Our simulation results show that compared to these other approaches, the proposed FPC provides a substantial saving in power consumption as well as quick convergence that is independent of the number of nodes in the system, while sacrificing only a small amount of capacity.

This paper studies optimal channel reservation in cooperative cognitive radio networks (CRNs) where secondary users (SUs) have access to the combined spectrum pool of cooperating CRNs. Motivated by SU high forced termination in cooperative CRNs, we propose two channel reservation schemes, Fixed Channel Placement Reservation (FCPR) and Dynamic Channel Placement Reservation (DCPR), and theoretically analyze their performances using the Markov chain approach. Our numerical results, validated by simulation, indicate that for a given number of reserved channels, the DCPR algorithm achieves better user experience by reducing the forced termination probability. Based on this analysis, we propose two enhanced reservation algorithms: Algorithm A maximizes the overall capacity of CRNs to enable network operators to increase their revenue; Algorithm B minimizes the user experience cost function to provide better services.

In this paper we consider the inter-network interference problem in Wireless Body Area Networks (WBANs). We propose a distributed inter-network interference aware power control algorithm motivated by game theory. A power control game is formulated considering both interference between nearby networks and energy efficiency of WBANs. We derive a distributed power control algorithm called ProActive Power Update (PAPU), which can efficiently find the Nash Equilibrium representing the best tradeoff between energy and network utility. A realistic power control procedure is proposed assuming limited cooperation between WBANs. We compare our algorithm with the ADP algorithm where users are punished for interfering with others and we show that our solution can utilize energy much more efficiently by only sacrificing a small amount of network utility. In addition, we show that by adjusting the energy price, PAPU provides a methodology for application scenarios where WBANs have different energy constraints and quality of service requirements.

Server farms, consisting of a collection of hosts and a front-end router that dispatches incoming jobs to hosts, are now commonplace. When job service requirements (job sizes) are highly variable, the Size-Interval task assignment policy is an excellent rule for assigning jobs to hosts. For a long time it was believed that the size cutoff separating "short" jobs from "long " ones should be chosen to balance the load at the hosts in the server farm. However, recent literature has provided evidence that load balancing is not always optimal for minimizing mean response time. Previous collaborative work by the author presented simple sufficient criteria for determining which host should be underloaded in a two host system using Size-Interval task assignment. In this paper, we derive asymptotic closed-form expressions for the optimal size cutoff, separating short jobs from large jobs, and the optimal load point at the separation in a two-host system. The asymptotic expressions are for when the maximum job size in a Bounded Pareto distribution approaches infinity and are derived using perturbation analysis. We show that the asymptotics can be one of three forms depending on whether the system load (mean number of busy hosts) is less than 1, equal to 1 or greater than 1 with different mechanisms dominating in each case. Our results allow the effects of both load and tail index to be easily determined. Numerical analysis shows that the asymptotic expressions in most cases are very accurate.