Overhead Minimization Research Articles

A multi‐objective optimization approach for beam pattern synthesis of UAV virtual rectangular antenna array

AbstractVirtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi‐objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi‐objective multi‐verse optimization algorithm based on the inverse decline curve type (ISDT‐MOMVO) which introduces a strategy optimization initialization solution with quasi‐opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT‐MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

Service Migration Optimization for System Overhead Minimization in VECNs via Deep Reinforcement Learning

Service Migration Optimization for System Overhead Minimization in VECNs via Deep Reinforcement Learning

An efficient Clustered IoT (CIoT) routing protocol and control overhead minimization in IoT network

Internet of Things (IoT) is a novel networking communication field in which the nodes act as physical objects. These nodes might be stationary or mobile. The majority of networking communication problems occur due to nodes’ mobility. Due to the mobility of IoT networks, excessive control overhead messages are generated. These messages are generated due to the frequent exchange of some control information, such as device identity, location information, device mobility, device direction, and others. In general, network clustering helps to alleviate the problem of generating high control overhead messages. Therefore, many proposed or enhanced Cluster-Based Routing (CBR) protocols address the shortcoming of traditional CBR protocols. In CBR protocols, each cluster selects only one node based on some parameters, and this node is called a LEaDeR (LEDR). The LEDR acts as a coordinator for its associated cluster. Furthermore, the rest of the nodes located in the cluster are called Smart DEVices (SDEV). A problem occurs when the SDEVs and the LEDR exchange their control overhead messages with each other. The exchanging of these messages utilizes the available network resources. This article proposes a Clustered IoT (CIoT) routing protocol, the CIoT routing protocol applies in a grid clustered topology. The proposed CIoT protocol aims to efficiently propagate messages between any pair of devices. The CIoT protocol selects only one route among a set of candidate routes, and the selected route is verified and evaluated based on some constraints. Also, this article proposes a Predictive Generated Hello Messages Algorithm (PGHMA). The PGHMA aims to minimize the generated control overhead messages produced by SDEVs and LEDRs. In comparison to other CBR protocols, simulation results demonstrate the effectiveness of the CIoT algorithms by increasing network throughput by 78% and decreasing end-to-end latency by 12.5%. Also, the PGHMA algorithm significantly outperforms other algorithms in terms of reducing the number of generated HELLO messages by 5.9%. Finally, we implement and evaluate our proposed algorithms by comparing them to other related algorithms published in the literature.

QaaD (Query-as-a-Data): Scalable Execution of Massive Number of Small Queries in Spark

Spark big data processing platform is heavily used in today's IT services for various critical applications such as machine learning tasks for service recommendations or massive volumes of raw sales data analysis. Spark is designed to deliver high performance by enabling a high degree of parallelism while processing various heavy-weight queries that require homogeneous operations on large data. However, it has been observed that workloads made of small and short-running queries coming from various sources are becoming dominant in practice. Unfortunately, the current Spark architecture is unfit to process workloads made of a large number of small queries optimally due to excessive I/Os with small computations. We present a technique, called QaaD, that addresses this problem fundamentally by applying i) transparent conversion of workloads made of small queries into one with large queries and ii) dynamic partition size adjustment for runtime overhead minimization. For this, we introduce a new abstraction, microRDD, to support our design of query merging, the embedding of queries as part of data, and an opportunistic sharing of common input data among queries. Comprehensive evaluation using real-world data shows that QaaD is able to deliver 10.6x to 36.6x speed-up against standard Spark executions for small query workloads.

Collaborative computation offloading and resource allocation based on dynamic pricing in mobile edge computing

The development of collaborative computing architecture in mobile edge computing provides an effective solution for the task processing of mobile devices with low computing capability, which can make up for computation resource and physical size restrictions of mobile devices and improve the performance of mobile communication networks in the future. In this paper, we focus on network congestion caused by increased load in hotspots. In particular, we consider the integrated network architecture of mobile edge computing (MEC) and device-to-device (D2D) communication to reduce the sum overhead of all task request users (TRUs). Accordingly, the computing offloading decision, wireless resource allocation strategy, computing resource allocation strategy, and assistant selection decision are optimized to minimize the sum overhead of the energy consumption and the cost of purchasing computing resource of all TRUs. The formulated overhead minimization problem is a mixed integer nonlinear programming problem. In order to tackle this problem, an algorithm based on the distance and the transaction price is first proposed to determine the target assistant user (TAU) of each TRU. Second, for a given TAU selection strategy, the original problem is further transformed into a convex problem and decomposed to make it more tractable. Finally, a distributed algorithm based on the alternating direction method of multipliers (ADMM) is adopted to solve the optimization problem. The simulation results prove the effectiveness of the proposed scheme in reducing user overhead.

Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-Assisted Vehicular Networks

Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.

The Hidden Cost of Trustworthiness

The theory of the nonprofit institutional form developed by Henry Hansmann emphasizes the importance of organizational trustworthiness in a sector defined by hard-to-measure outputs. This body of theory effectually rationalizes a normative approach to nonprofit financial management focused on maintaining organizational trustworthiness through fiscal probity signaling. Such signals include measurable indicators of overhead minimization, fiscal leanness, revenue diversification, and debt avoidance, among others. Appropriate signaling behavior may increase organizational trustworthiness as intended, but the effects on mission impact are not well understood. Thus, this article assesses how adherence to common fiscal probity norms affects mission impact, using total spending as a proxy. Based on a panel of donative public charities spanning 1982 to 2019, analysis suggests that norm-adhering nonprofits sacrifice about half of their mission impact over a 10-year period compared with norm-busting nonprofits. This forgone mission impact is the hidden cost of trustworthiness.

Multimedia Applications Processing and Computation Resource Allocation in MEC-Assisted SIoT Systems with DVS

Due to the advancements of information technologies and the Internet of Things (IoT), the number of distributed sensors and IoT devices in the social IoT (SIoT) systems is proliferating. This has led to various multimedia applications, face recognition and augmented reality (AR). These applications are computation-intensive and delay-sensitive and have become popular in our daily life. However, IoT devices are well-known for their constrained computational resources, which hinders the execution of these applications. Mobile edge computing (MEC) has appeared and been deemed a prospective paradigm to solve this issue. Migrating the applications of IoT devices to be executed in the edge cloud can not only provide computational resources to process these applications but also lower the transmission latency between the IoT devices and the edge cloud. In this paper, computation resource allocation and multimedia applications offloading in MEC-assisted SIoT systems are investigated. We aim to optimize the resource allocation and application offloading by jointly minimizing the execution latency of multimedia applications and the consumed energy of IoT devices. The studied problem is a formulation of the total computation overhead minimization problem by optimizing the computational resources in the edge servers. Besides, as the technology of dynamic voltage scaling (DVS) can offer more flexibility for the MEC system design, we incorporate it into the application offloading. Since the studied problem is a mixed-integer nonlinear programming (MINP) problem, an efficient method is proposed to address it. By comparing with the baseline schemes, the theoretic analysis and simulation results demonstrate that the proposed multimedia applications offloading method can improve the performances of MEC-assisted SIoT systems for the most part.

Weighted utility aware computational overhead minimization of wireless power mobile edge cloud

Fifth-generation (5G) wireless networks are projected to support a large number of low-power devices, which are an essential component of Internet of Things (IoT) technologies. The existing network design is expected to face a number of challenges in serving such massive additional devices. Furthermore, these devices are bound by limited power processing and storage capacity, despite the fact that they are supposed to analyze massive volumes of data. In the last decade, the concepts of cloud and edge computing have received significant attention in order to increase network performance. Furthermore, wireless power transfer (WPT) is integrated with the cloud for energy transmission to low-power devices. This work presents a mathematical model for scheduling tasks that are being executed at low-power devices and concurrently on the cloud edge. The proposed mathematical model takes into account practical constraints and is intended to distribute resources among devices optimally while minimizing network overhead. To deal with the conflicting constraints, the model is further transformed into an unconstrained one. To solve the proposed model, evolutionary methods are being explored. Finally, a Monte Carlo simulation is used to validate the model. The simulation results show that partial offloading outperforms the edge-only computation scheme, with the partial offloading scheme demonstrating a 20% improvement in network overhead.

Smart collaborative optimizations strategy for mobile edge computing based on deep reinforcement learning

With the arrival of the 5th generation mobile networks (5 G) era, the data needed by mobile devices (MDs) is explosively growing. High-consumption, low-latency applications are huge challenges for resource-constrained Internet of things (IoT) devices. Mobile edge computing overcomes the limitations of computing resources on MDs by offloading tasks generated by MDs and assigning them to nearby MEC servers. Therefore, mobile edge computing (MEC) becomes important. This paper presents a task offloading strategy for the multi-device multi-server system. To meet the task requirements of different MDs, we formulate an overhead minimization problem to optimize the delay and energy consumption of the system. We propose the Double Deep Q Network (Double-DQN) algorithm to perform location selection strategies for tasks generated on the mobile devices and allocate respective computing resources. Simulation results show that the algorithm can allocate resources reasonably and reduce the overhead of the entire system.

Multi-threaded checksum computation for ATLAS high-performance storage software

ATLAS is one of the general purpose experiments observing hadron collisions at the LHC at CERN. Its trigger and data acquisition system (TDAQ) is responsible for selecting and transporting interesting physics events from the detector to permanent storage where the data are used for further processing. The transient storage of ATLAS TDAQ is the last component of the online system in the data flow. It records selected events at several GB/s to non-volatile storage before transfer to offline permanent storage. The transient storage is a distributed system consisting of high-performance direct-attached storage servers accounting for 480 hard drives. A distributed multi-threaded C++ application operates the hardware. The transient storage is also responsible for computing a checksum for the data, which is used to ensure data integrity of the transferred data. Reliability and efficiency of this system are critical for the operations of TDAQ as well. This paper presents the existing multi-threading strategy of the software and how the available hardware resources are used. We then introduce how multi-threaded checksum computation was introduced to increase significantly the maximum throughput of the system. We discuss the key concepts of the implementation with a focus on the importance of overhead minimization. Finally the paper reports on the tests done on the production system to demonstrate the validity of the implementation and measurements of the performance improvement in the view of future LHC and ATLAS upgrades.

An energy efficient distributed queuing random access (EE-DQRA) MAC protocol for wireless body sensor networks

In wireless sensor networks, a significant amount of energy is consumed by the sensor nodes during data packet transmission and reception. An IEEE 802.15.4 MAC protocol is not able to completely satisfy all the requirements of wireless body sensor networks (BSNs) in a healthcare environment. Hence there is a demand for the design of a new scalable and energy saving MAC protocols. In this paper the challenging healthcare requirements are considered and based upon these requirements, an energy efficient distributed queuing random access (EE-DQRA) MAC protocol is proposed for BSN scenarios which utilizes the concept of distributed queuing for enhanced radio channel utilization. The theoretical analysis of energy efficient EE-DQRA MAC protocol is being carried out systematically considering the limitations of IEEE 802.15.4 and DQ-MAC protocol. Further, EE-DQRA performance is validated with IEEE 802.15.4 system parameters using computer simulations. The effect of relative traffic load and payload length on the energy consumption, delay and throughput are also analyzed. The simulation results shows that the proposed EE-DQRA MAC with M/M/K queuing has better energy performance than the existing DQ-MAC and IEEE 802.15.4 MAC in BSN scenarios due to collisionless transmission in the data transmission queuing (DTQ) module while keeping the control packet overhead smaller in collision resolution queuing (CRQ) module. It is also evident that EE-DQRA requires a minimum delay in comparison to IEEE802.15.4 and DQ-MAC, due to overhead minimization and M/M/K queuing utilization in DTQ system.

Joint Offloading and Transmission Power Control for Mobile Edge Computing

With the exploding growth of smart devices and application of 5G communications, there are more and more computation-intensive tasks which are delay-sensitive. Mobile edge computing (MEC) is a new paradigm that provides rich computing resources in close proximity to mobile users. However, how to make the decision of whether offloading or not as well as control the transmission power jointly remains a challenge. In this paper, we first study the joint multiuser offloading and transmission power control optimization problem in a multi-channel wireless interference scenario. We formulate the joint optimization problem into a system-wide computation overhead minimization problem as a mixed integer non-linear program. We then propose an efficient semi-distributed algorithm consisting of two subalgorithms of offloading scheme and transmission power control. The numerical simulation results demonstrate that the proposed algorithm can effectively reduce system-wide computation overhead and increase the number of beneficial edge computing users, compared with all local or edge computing and fixed transmission power schemes.

Computation Offloading for Mobile Edge Computing Enabled Vehicular Networks

The emergence of computation-intensive and delay-sensitive vehicular applications poses a great challenge for individual vehicles with limited computation resources. Mobile edge computing (MEC) is a new paradigm shift that can enhance vehicular services through computation offloading. However, the high mobility of vehicles will affect offloading performance. In this paper, we investigate the vehicular user (VU) computation overhead minimization problem in MEC-enabled vehicular networks by jointly optimizing the computation and communication resources' allocation (transmit power and uploading time for communication, and the offloading ratio and local CPU frequency for computation). This optimization problem is nonconvex and difficult to solve directly. To deal with this issue, we first transform the original problem into an equivalent one. Then, we decompose the equivalent problem into a two-level problem. In addition, we develop a low-complexity algorithm to obtain the optimal solution. The numerical results demonstrate that the proposed algorithm can significantly outperform benchmark algorithms in terms of computation overhead.

A Distributed Computation Offloading Strategy in Small-Cell Networks Integrated With Mobile Edge Computing

Mobile edge computing is conceived as an appealing technology to enhance cloud computing capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks’ high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.

Overhead minimization of online codes

We introduce a novel method and two algorithms for designing efficient degree distributions with minimum overhead for online codes. The first algorithm is the general form for solving the problem of overhead minimization and the second one is an adaptive method based on the first method. In these two approaches, the codes are designed based on a discretized non-linear optimization problem. One direct result of the presented algorithms is decreasing the number of samples for solving the non-linear programming problem of the overhead minimization problem, which is not easy to solve without discretization. Also, we find a simple criteria in order to choose the critical sample points and show that the convergence rate improves. By considering these algorithms, one can construct an online code with minimum overhead for any given erasure channel parameter. Furthermore, the complexity of our algorithms has a linear relation with the number of samples because linear programming model is used. Simulation results are presented that show the efficiency of the proposed algorithms.

A preload cooperative sensing scheme with low overhead in cognitive radio networks

AbstractIn cognitive radio networks (CRNs), users can collaborate to improve the accuracy of spectrum sensing, but a large number of secondary users reporting their local sensing results may create significant overhead. In this paper, we propose a new pre‐sensing scheme, called preload cooperative sensing (PCS), which not only attains the given sensing accuracy for CRNs but also reduces the whole sensing time T. In order to reduce the sensing overhead in CRNs, the proposed scheme adopts two key technologies: selective reporting technology and pre‐sensing sequential detection technology. Selective reporting technology implies that only those users, which detect the presence of primary users, need to report the results, while pre‐sensing sequential detection technology is an asynchronous parallel scheme, which sets a threshold to determine the presence of primary users. Considering the preload sensing slots, we derive a formula to express the overall miss detection probability, and at a given Quality of Service (QoS) value, the sensing overheads of PCS are analyzed over Rayleigh fading channel. Also, we consider the overhead minimization problems in PCS. Simulation results show the superiority and efficiency of the PCS scheme. Copyright © 2015 John Wiley & Sons, Ltd.

Robust Joint Transmit Beamforming With QoS Guarantees in Time-Asynchronous DAS

This paper studies the joint transmit beamforming optimization in the downlink of a single-cell multiuser distributed antenna system. Due to the distributed nature of transmit antennas, the signals received from different distributed antenna units (DAUs) will experience various transmission delays, and it is difficult to realize accurate symbol-level time synchronization at the user terminal. Thus, we turn to finding a robust joint transmit beamforming algorithm by replacing the received signal-to-interference-plus-noise ratio (SINR) with its lower-bound value. Moreover, the joint transmission incurs huge amount of data sharing among DAUs. Under the per-DAU maximum transmit power constraint, we aim to minimize the system backhaul overhead while satisfying the quality-of-service (QoS) requirement of each user. Since the backhaul overhead minimization requires the transmit beam vectors to have a group sparse structure and is NP-hard, we first make a convex relaxation and then apply the reweighted method to improve the sparsity of the beam vectors. Furthermore, an admission control scheme is applied to guarantee the feasibility of the problem, which iteratively removes the unsatisfied user with the worst quality of experience (QoE) away from the system. Simulation results show that our proposed robust algorithm can achieve a much better performance than the traditional nonrobust algorithm when time asynchronism is less than 30% of the symbol duration and can significantly reduce the system backhaul overhead by designing group sparse transmit beam vectors.

SD-PCM

Phase Change Memory (PCM) has better scalability and smaller cell size comparing to DRAM. However, further scaling PCM cell in deep sub-micron regime results in significant thermal based write disturbance (WD). Naively allocating large inter-cell space increases cell size from 4F 2 ideal to 12F 2 . While a recent work mitigates WD along word-lines through disturbance resilient data encoding, it is ineffective for WD along bit-lines, which is more severe due to widely adopted $\mu$Trench structure in constructing PCM cell arrays. Without mitigating WD along bit-lines, a PCM cell still has 8F2, which is 100% larger than the ideal. In this paper, we propose SD-PCM for achieving reliable write operations in super dense PCM. In particular, we focus on mitigating WD along bit-lines such that we can construct super dense PCM chips with 4F 2 cell size, i.e., the minimal for diode-switch based PCM. Based on simple verification-n-correction (VnC), we propose LazyCorrection and PreRead to effectively reduce VnC overhead and minimize cascading verification during write. We further propose (n:m)-Alloc for achieving good tradeoff between VnC overhead minimization and memory capacity loss. Our experimental results show that, comparing to a WD-free low density PCM, SD-PCM achieves 80% capacity improvement in cell arrays while incurring around 0-10% performance degradation when using different (n:m) allocators.

SD-PCM

Phase Change Memory (PCM) has better scalability and smaller cell size comparing to DRAM. However, further scaling PCM cell in deep sub-micron regime results in significant thermal based write disturbance (WD). Naively allocating large inter-cell space increases cell size from 4F 2 ideal to 12F 2 . While a recent work mitigates WD along word-lines through disturbance resilient data encoding, it is ineffective for WD along bit-lines, which is more severe due to widely adopted $\mu$Trench structure in constructing PCM cell arrays. Without mitigating WD along bit-lines, a PCM cell still has 8F2, which is 100% larger than the ideal. In this paper, we propose SD-PCM for achieving reliable write operations in super dense PCM. In particular, we focus on mitigating WD along bit-lines such that we can construct super dense PCM chips with 4F 2 cell size, i.e., the minimal for diode-switch based PCM. Based on simple verification-n-correction (VnC), we propose LazyCorrection and PreRead to effectively reduce VnC overhead and minimize cascading verification during write. We further propose (n:m)-Alloc for achieving good tradeoff between VnC overhead minimization and memory capacity loss. Our experimental results show that, comparing to a WD-free low density PCM, SD-PCM achieves 80% capacity improvement in cell arrays while incurring around 0-10% performance degradation when using different (n:m) allocators.