Showing papers by "Nirwan Ansari published in 2016"
TL;DR: A novel approach to mobile edge computing for the IoT architecture, edgeIoT, to handle the data streams at the mobile edge by proposing a hierarchical fog computing architecture in each fog node to provide flexible IoT services while maintaining user privacy.
Abstract: In order to overcome the scalability problem of the traditional Internet of Things architecture (i.e., data streams generated from distributed IoT devices are transmitted to the remote cloud via the Internet for further analysis), this article proposes a novel approach to mobile edge computing for the IoT architecture, edgeIoT, to handle the data streams at the mobile edge. Specifically, each BS is connected to a fog node, which provides computing resources locally. On the top of the fog nodes, the SDN-based cellular core is designed to facilitate packet forwarding among fog nodes. Meanwhile, we propose a hierarchical fog computing architecture in each fog node to provide flexible IoT services while maintaining user privacy: each user's IoT devices are associated with a proxy VM (located in a fog node), which collects, classifies, and analyzes the devices' raw data streams, converts them into metadata, and transmits the metadata to the corresponding application VMs (which are owned by IoT service providers). Each application VM receives the corresponding metadata from different proxy VMs and provides its service to users. In addition, a novel proxy VM migration scheme is proposed to minimize the traffic in the SDNbased core.
594 citations
TL;DR: This article presents an architectural framework called SDNV that offers a clear holistic vision of integrating key principles of both SDN and NFV into unified network architecture, and provides guidelines for synthesizing research efforts toward combining SDN-NFV in future networks.
Abstract: SDN and NFV are two significant innovations in networking. The evolution of both SDN and NFV has shown strong synergy between these two paradigms. Recent research efforts have been made toward combining SDN and NFV to fully exploit the advantages of both technologies. However, integrating SDN and NFV is challenging due to the variety of intertwined network elements involved and the complex interaction among them. In this article, we attempt to tackle this challenging problem by presenting an architectural framework called SDNV. This framework offers a clear holistic vision of integrating key principles of both SDN and NFV into unified network architecture, and provides guidelines for synthesizing research efforts toward combining SDN and NFV in future networks. Based on this framework, we also discuss key technical challenges to realizing SDN-NFV integration and identify some important topics for future research, with a hope to arouse the research community's interest in this emerging area.
128 citations
TL;DR: In this article, the authors proposed a traffic load balancing framework that strives a balance between network utilities, e.g., the average traffic delivery latency, and the green energy utilization.
Abstract: Dramatic mobile data traffic growth has spurred a dense deployment of small cell base stations (SCBSs). Small cells enhance the spectrum efficiency and thus enlarge the capacity of mobile networks. Although SCBSs consume much less power than macro BSs (MBSs) do, the overall power consumption of a large number of SCBSs is phenomenal. As the energy harvesting technology advances, base stations (BSs) can be powered by green energy to alleviate the on-grid power consumption. For mobile networks with high BS density, traffic load balancing is critical in order to exploit the capacity of SCBSs. To fully utilize harvested energy, it is desirable to incorporate the green energy utilization as a performance metric in traffic load balancing strategies. In this paper, we have proposed a traffic load balancing framework that strives a balance between network utilities, e.g., the average traffic delivery latency, and the green energy utilization. Various properties of the proposed framework have been derived. Leveraging the software-defined radio access network architecture, the proposed scheme is implemented as a virtually distributed algorithm, which significantly reduces the communication overheads between users and BSs. The simulation results show that the proposed traffic load balancing framework enables an adjustable trade-off between the on-grid power consumption and the average traffic delivery latency, and saves a considerable amount of on-grid power, e.g., 30%, at a cost of only a small increase, e.g., 8%, of the average traffic delivery latency.
78 citations
TL;DR: This paper presents a timely survey of most relevant research activities on resource management of data centers that aim to optimize the resource utilization, and provides a comprehensive reference for further research in this field.
Abstract: To provision IT solutions with reduced operating expenses, many businesses are moving their IT infrastructures into public data centers or are starting to build their own private data centers. Data centers can provide flexible resource provisioning in order to accommodate the workload demand. In this paper, we present a comprehensive survey of most relevant research activities on resource management of data centers that aim to optimize the resource utilization. We first describe the resource overprovisioning problem in current data centers. Then, we summarize two important components in the resource management platform and present the benefit of accurately predicting the workload in resource management. Afterwards, we classify existing resource management in a data center into three categories: 1) virtual machine-based, 2) physical machine-based, and 3) application-based resource management mechanisms. We discuss the performance degradation for implementing these three kinds of resource management in a heterogeneous data center. Finally, we present three important issues arose in the data center resource management and some potential approaches to address the issues. This paper presents a timely survey on resource management in a data center, and provides a comprehensive reference for further research in this field.
73 citations
22 May 2016
TL;DR: In this article, the authors proposed a PRofIt Maximization Avatar pLacement (PRIMAL) strategy for the cloudlet network in order to optimize the tradeoff between the migration gain and the migration cost by selectively migrating the Avatars to their optimal locations.
Abstract: We propose a cloudlet network architecture to bring the computing resources from the centralized cloud to the edge. Thus, each User Equipment (UE) can communicate with its Avatar, a software clone located in a cloudlet, and can thus lower the end-to-end (E2E) delay. However, UEs are moving over time, and so the low E2E delay may not be maintained if UEs' Avatars stay in their original cloudlets. Thus, live Avatar migration (i.e., migrating a UE's Avatar to a suitable cloudlet based on the UE's location) is enabled to maintain the low E2E delay between each UE and its Avatar. On the other hand, the migration itself incurs extra overheads in terms of resources of the Avatar, which compromise the performance of applications running in the Avatar. By considering the gain (i.e., the E2E delay reduction) and the cost (i.e., the migration overheads) of the live Avatar migration, we propose a PRofIt Maximization Avatar pLacement (PRIMAL) strategy for the cloudlet network in order to optimize the tradeoff between the migration gain and the migration cost by selectively migrating the Avatars to their optimal locations. Simulation results demonstrate that as compared to the other two strategies (i.e., Follow Me Avatar and Static), PRIMAL maximizes the profit in terms of maintaining the low average E2E delay between UEs and their Avatars and minimizing the migration cost simultaneously.
67 citations
TL;DR: In this article, the authors presented a new power allocation scheme for a decode-and-forward (DF) relaying-enhanced cooperative wireless system while both the source node (SN) and relay node (RN) have limited energy storage, and the SN can also draw power from the surrounding radiofrequency (RF) signals.
Abstract: In this paper, we present a new power-allocation scheme for a decode-and-forward (DF) relaying-enhanced cooperative wireless system While both the source node (SN) and relay node (RN) have limited energy storage, the SN can also draw power from the surrounding radio-frequency (RF) signals In particular, we assume a deterministic RF energy-harvesting (EH) model under which the signals transmitted by the relay serve as the renewable energy source for the SN The amount of harvested energy is known for a given transmission power value of the forwarding signal and channel condition between the SN and RN To maximize the overall throughput while meeting the constraints imposed by the initially stored energy and the renewable RF energy source, an optimization problem is formulated and solved Based on different harvesting efficiency values and channel conditions, closed-form solutions are derived to obtain the optimal joint source and relay power allocation It is shown that, instead of demanding high on-grid power supply or high green energy availability, the system can achieve compatible or higher throughput by utilizing the harvested energy
65 citations
TL;DR: Simulation results show that the bias of the proposed method is reduced and that the Cramér-Rao lower bound accuracy is also achieved.
Abstract: We address the source localization problem by using both time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. We solve this problem in two steps, and in each step, we formulate a nonlinear weighted least squares (WLS) problem followed by a bias reduction scheme. In the first step, we formulate a nonlinear WLS problem using TDOA measurements only and derive the bias of the WLS solution, which is then used to develop an unbiased WLS solution by subtracting the bias from the WLS solution. In the second step, we formulate another nonlinear WLS problem by combining the results in the first step and the FDOA measurements. To avoid the potential risk of local convergence, this WLS problem is reduced to an approximate WLS problem, for which the globally optimal solution can be obtained. The bias of the WLS solution is also derived and then subtracted from the WLS solution to reduce the bias. Simulation results show that the bias of the proposed method is reduced and that the Cramer–Rao lower bound accuracy is also achieved.
63 citations
TL;DR: This letter formulates the anycast planning problem in the SDM EONs with MCFs, and proposes a heuristic algorithm to efficiently solve the problem.
Abstract: Elastic optical networks (EONs) play an important role for the next generation core networks, especially for supporting cloud computing. However, network traffic has been growing exponentially and almost reached the physical capacity limit of single-mode fibers. Space division multiplexing (SDM) can be potentially employed to increase the fiber capacity. Multi-core fibers (MCFs) make use of SDM to aggregate multiple cores together into the cladding of one fiber, which can greatly increase the capacity of EONs but incurs new crosstalk constraints. Anycast is more flexible as compared with unicast, and anycast communications are widely used in cloud computing, distributed computing, distributed storage system, and content delivery networks. This letter formulates the anycast planning problem in the SDM EONs with MCFs. Evaluation results show that CVX and Gurobi tools can solve small size problems, but do not scale well. Therefore, we propose a heuristic algorithm to efficiently solve the problem. To the best of our knowledge, this is the first work that considers ancyast routing in the SDM EONs with MCFs.
49 citations
Posted Content•
TL;DR: The proposed GCN architecture is aimed at providing seamless and low end-to-end delay between a UE and its Avatar in the cloudlets to facilitate the application workloads offloading process and an SDN-based core network is introduced by replacing the traditional Evolved Packet Core in the LTE network.
Abstract: This article introduces a Green Cloudlet Network (GCN) architecture in the context of mobile cloud computing. The proposed architecture is aimed at providing seamless and low End-to-End (E2E) delay between a User Equipment (UE) and its Avatar (its software clone) in the cloudlets to facilitate the application workloads offloading process. Furthermore, Software Define Networking (SDN) based core network is introduced in the GCN architecture by replacing the traditional Evolved Packet Core (EPC) in the LTE network in order to provide efficient communications connections between different end points. Cloudlet Network File System (CNFS) is designed based on the proposed architecture in order to protect Avatars' dataset against hardware failure and improve the Avatars' performance in terms of data access latency. Moreover, green energy supplement is proposed in the architecture in order to reduce the extra Operational Expenditure (OPEX) and CO2 footprint incurred by running the distributed cloudlets. Owing to the temporal and spatial dynamics of both the green energy generation and energy demands of Green Cloudlet Systems (GCSs), designing an optimal green energy management strategy based on the characteristics of the green energy generation and the energy demands of eNBs and cloudlets to minimize the on-grid energy consumption is critical to the cloudlet provider.
45 citations
TL;DR: A LU decomposition based block-recursive algorithm for large-scale matrix inversion and its well-designed implementation with optimized data structure, reduction of space complexity and effective matrix multiplication on the Spark parallel computing platform are presented.
Abstract: Matrix inversion is a fundamental operation for solving linear equations for many computational applications, especially for various emerging big data applications. However, it is a challenging task to invert large-scale matrices of extremely high order (several thousands or millions), which are common in most Web-scale systems, such as social networks and recommendation systems. In this paper, we present an lower upper decomposition-based block-recursive algorithm for large-scale matrix inversion. We present its well-designed implementation with optimized data structure, reduction of space complexity, and effective matrix multiplication on the Spark parallel computing platform. The experimental evaluation results show that the proposed algorithm is efficient to invert large-scale matrices on a cluster composed of commodity servers and is scalable for inverting even larger matrices. The proposed algorithm and implementation will become a solid foundation for building a high-performance linear algebra library on Spark for big data processing and applications.
38 citations
TL;DR: This paper introduces and investigates the green energy provisioning (GEP) problem, which aims to minimize the CAPEX of deploying green energy systems in BSs while satisfying the quality-of-service (QoS) requirements of cellular networks, and proposes a GEP solution consisting of the provision-cost-aware traffic load balancing algorithm and the binary energy system sizing algorithm.
Abstract: Cellular networks are among the biggest energy hogs of communication networks, and their contributions to the global energy consumption rapidly increase due to the surge of data traffic. With the development of green energy technologies, base stations (BSs) can be powered by green energy to reduce on-grid energy consumption and subsequently reduce carbon footprints. However, equipping a BS with a green energy system incurs additional capital expenditure (CAPEX) that is determined by the size of the green energy generator, the battery capacity, and other installation expenses. In this paper, we introduce and investigate the green energy provisioning (GEP) problem, which aims to minimize the CAPEX of deploying green energy systems in BSs while satisfying the quality-of-service (QoS) requirements of cellular networks. The GEP problem is challenging because it involves optimization over multiple time slots and across multiple BSs. We decompose the GEP problem into the weighted energy minimization problem and the green energy system sizing problem and propose a GEP solution consisting of the provision-cost-aware traffic load balancing algorithm and the binary energy system sizing algorithm to solve the subproblems and subsequently solve the GEP problem. We validate the performance and the viability of the proposed GEP solution through extensive simulations, which also conform to our analytical results.
TL;DR: In this paper, the authors proposed a new hierarchical model by introducing the concept of field, shallow, and deep cloudlets where the cloudlet tier itself is designed in three hierarchical levels based on the principle of LTE-Advanced backhaul network.
Abstract: The multi-tiered concept of Internet of Things (IoT) devices, cloudlets and clouds is facilitating a user-centric IoT. However, in such three tier network, it is still desirable to investigate efficient strategies to offer the computing, storage and communications resources to the users. To this end, this paper proposes a new hierarchical model by introducing the concept of field, shallow, and deep cloudlets where the cloudlet tier itself is designed in three hierarchical levels based on the principle of LTE-Advanced backhaul network. Accordingly, we explore a two time scale approach in which the computing resources are offered in an auction-based profit maximization manner and then the communications resources are allocated to satisfy the users' QoS.
03 Apr 2016
TL;DR: This paper proposes a heuristic NEAT algorithm to approximate the optimal solution with low computational complexity in the two-tier green heterogeneous network, that enables a BS depleting of green energy to offload its traffic load to other BSs with excessive green energy.
Abstract: Greening information and communications technology is becoming an environmental and economic sine qua non, and has attracted much research attention. For a cellular network, the base stations (BSs) cost more than 50% of the energy consumption of the whole network. Therefore, BSs can be powered by green energy to reduce its on-grid power consumption. In this paper, we propose a greeN Energy Aware user associaTion (NEAT) scheme in the two-tier green heterogeneous network, that enables a BS depleting of green energy to offload its traffic load to other BSs with excessive green energy. Since the Macro BS (MBS) and Pico BS (PBS) employ different partitions of the licensed spectrum, we also consider the bandwidth allocation and adjust the two spectrum partitions dynamically. However, in the NEAT scheme, achieving the optimal user association in terms of minimizing the on-grid power consumption of BSs, is NP-hard. Therefore, we propose a heuristic NEAT algorithm to approximate the optimal solution with low computational complexity. Finally, the performance and viability of the algorithm are substantiated by simulation results.
10 Apr 2016
TL;DR: A LU decomposition based block-recursive algorithm for large-scale matrix inversion and its well-designed implementation with optimized data structure, reduction of space complexity and effective matrix multiplication on the Spark parallel computing platform are presented.
Abstract: Matrix inversion is a fundamental operation to solve linear equations for many computational applications. However, it is a challenging task to invert large-scale matrices of extremely high order (several thousands), which are common in most of web-scale systems like social networks and recommendation systems. In this paper, we present a LU decomposition based block-recursive algorithm for large-scale matrix inversion, and its well-designed implementation with optimized data structure, reduction of space complexity and effective matrix multiplication on the Spark parallel computing platform. The experimental evaluation results show that the proposed algorithm is efficient to invert large-scale matrices on a cluster composed of commodity servers and scalable to invert even larger matrices. The proposed algorithm and implementation will be a solid base to build a high-performance linear algebra library on Spark for big data processing.
TL;DR: A new service efficiency parameter for data centers in satisfying the QoS requirements based on the queuing analysis is defined and the idea of modeling geo-dispersed data centers with an information flow graph to capture a total-brown power consumption tradeoff region is proposed.
Abstract: This letter aims at deriving a fundamental tradeoff between the total and brown power consumption associated with geographical dispersed data centers, where utilizing more green energy mostly happens at the cost of increasing the total power consumption. To this end, we define a new service efficiency parameter for data centers in satisfying the QoS requirements based on the queuing analysis. More importantly, we propose the idea of modeling geo-dispersed data centers with an information flow graph to capture a total-brown power consumption tradeoff region. Accordingly, we characterize the achievable tradeoff between total and brown power consumption.
TL;DR: This paper introduces the request dependency graph (RDG), a graph representation of the relationships among HTTP requests, and proposes a methodology to establish such a graph by mining the temporal and causal information among aggregated HTTP requests.
Abstract: In the Web of Things (WoT) environment, Web traffic logs contain valuable information of how people interact with smart devices and Web servers. Mining the wealth of information available in the Web access logs has theoretical and practical significance for many important applications like network optimization and security management. The first critical step of the mining task is modeling the relationships among HyperText Transfer Protocol (HTTP) requests for accessing Web objects to investigate the behavior of Web clients. In this paper, we introduce the request dependency graph (RDG), a graph representation of the relationships among HTTP requests. Conceptually, a directed link from A to B in the graph means that the accessing of Web object B is caused by the accessing of A, i.e., B depends on A. We propose a methodology to establish such a graph by mining the temporal and causal information among aggregated HTTP requests. To demonstrate the value and effectiveness of the proposed model, we design and implement an algorithm for primary requests identification, which is a critical task of Web usage mining, based on the RDG. Evaluation results from a large-scale real-world Web access log shows that the RDG is a useful tool for Web usage mining.
01 Jan 2016
TL;DR: A stochastic geometry based framework to analyze the coverage probability and ergodic rate with different channel allocations for device-to-device (D2D) communications and results indicate that the framework can help to find the optimal channel allocation to achieve the optimal system performance.
Abstract: In this paper, we present a stochastic geometry based framework to analyze the coverage probability and ergodic rate with different channel allocations for device-to-device (D2D) communications. Different from existing works, we assume there are two different kinds of users, cellular users and D2D users, in the muti-channel uplink cellular network. Specifically, cellular users can upload data to the nearest base station (BS) directly through cellular channels. However, D2D users must upload data to their own D2D relays through D2D channels and then the D2D relays communicate with the nearest BS through cellular channels. There is no overlapping between cellular channels and D2D channels. Each cellular user and D2D relay adopt the channel inversion power control with maximum transmit power limit. Our results indicate that the framework can help to find the optimal channel allocation to achieve the optimal system performance in terms of coverage probability and average rate.
01 Dec 2016
TL;DR: This work proposes a novel architecture of green relay assisted D2D communications with dual battery for IoT that adopts low power small base stations (BSs) as the relay BSs in the network and achieves the goal of furthest saving the on-grid energy.
Abstract: As the era of Internet of Things (IoT) approaches, we are facing a new level of awareness about our world. It has been predicted that almost 50 billion devices will be connected by 2020 to realize the Internet of Things. A large number of devices will communicate with each other to gather, share and forward information to connect people in a more intelligent, convenient and efficient way. Therefore, Device-to-Device (D2D) communications is expected to be the intrinsic part of IoT. However, most existing researches in D2D communications are based on the D2D and cellular communications coexisted architecture. Provisioning D2D and cellular communications in the same cellular network quickly exhaust the limited resources, thus leading to performance degradation. We envision a novel architecture of green relay assisted D2D communications with dual battery for IoT. We adopt low power small base stations (BSs) as the relay BSs in the network. By optimally allocating the network resource, our proposed architecture enables the source- destination device pairs to reach their required transmission data rates to satisfy their different application services. The relay BSs are powered by both green energy and on-grid energy, and equipped with dual battery. By balancing the residual green energy among the relay BSs, we maximize the utilization of green energy in the network and achieve the goal of furthest saving the on-grid energy. Finally, we validate the performance of the proposed architecture through extensive simulations.
01 Sep 2016
TL;DR: Numerical results validate the effectiveness and efficiency of the sub-channel allocation method as well as performance gain of the optimal structure of SUs that maximize the total throughput while guaranteeing the rate requirements of both real-time (RT) and non-real- time (NRT) SUs.
Abstract: This paper aims at maximizing the sum rate of secondary users (SUs) in Orthogonal Frequency Division Multiplexing (OFDM)-based Heterogeneous Cognitive Radio (CR) Networks by considering some practical limitations such as various traffic demands of SUs, interference constraint and imperfect spectrum sensing. The CR system operates in a slotted mode, in which the SUs have no power supplies and harvest energy from ambient radio signal. Assuming SUs operate in a time switching fashion, the time slot is partitioned into three non-overlapping parts devoted for energy harvesting, spectrum sensing and data transmission. Then, the general problem of sum rate maximization is formulated as a mixed integer programming task. Since this problem is intractable, we propose a sub-channel allocation scheme based on a factor called Energy Figure of Merit to approximately satisfy SUs' rate requirements. Then, the integer constraints of the optimization problem are removed and the problem can be reduced to a convex optimization task. Considering the trade-off between fractions of the time slot, we focus on finding the optimal structures of SUs that maximize the total throughput while guaranteeing the rate requirements of both real-time (RT) and non-real-time (NRT) SUs. The numerical results validate the effectiveness and efficiency of our sub-channel allocation method as well as performance gain of the optimal structure.
22 May 2016
TL;DR: To advance green communications, an orthogonal frequency division multiplexing (OFDM) based cooperative relay system, where the relay node not only can forward the data to the destination node, but is also capable of transferring energy to the source node, is proposed.
Abstract: To advance green communications, we propose an orthogonal frequency division multiplexing (OFDM) based cooperative relay system, where the relay node not only can forward the data to the destination node, but is also capable of transferring energy to the source node. In particular, to maximize the overall system capacity in multiple subchannels and multiple time slots while meeting the power constraints, a power allocation optimization problem is formulated and solved in three steps. First, at each data transmission and data forwarding cycle, we split the total transmission power of relay into two parts, one for data forwarding and the other as power supplement for the source node. Then, our analysis indicates that at each cycle, once all of the subchannels are sorted in a certain order, the relay node will only provide forwarding power to the subchannels with index greater than a certain value. Meanwhile, the incentive for the relay node to provide power supplement should be strong enough such that relay chooses not to simultaneously transmit data and energy. Then, an equivalent convex constrained optimization problem is formulated and the solution is derived by solving the Lagrange function. The solution takes the form of water-filling in combination with a cooperative feature. Numerical results demonstrate that energy cooperation notably improves the system capacity.
22 May 2016
TL;DR: A green energy driven user-BS association with dual battery system to maximize the utilization of green energy at BSs and approximate the optimal user association with low computational complexity is proposed.
Abstract: Green communications has received much attention in recent years. In cellular networks, base stations (BSs) account for more than 50 percent of the energy consumption. Reducing energy consumption of BSs is essential to realizing green cellular networks. Utilizing green energy to power BSs is a promising way to reduce the on-grid energy consumption. Maximizing the utilization of green energy has thus been proposed to furthest save the on-grid energy. In this paper, we propose a green energy driven user-BS association with dual battery system to maximize the utilization of green energy at BSs. The BSs of cellular networks are powered by both on-grid energy and green energy. The optimal usage of green energy is achieved by balancing the mobile users among BSs according to the amount of residual green energy in their batteries. This green energy driven user association optimization problem is NP-hard. Hence, we propose some heuristics to maximize the green energy utilization and approximate the optimal user association with low computational complexity. Finally, we validate the performance of the proposed algorithm through extensive simulations.
01 Dec 2016
TL;DR: The joint scheduling and caching (JSC) algorithm has been proposed to tap on the potential of the coded multicasting scheme between MBS and SNs and can be extended to networks with arbitrary number of SNs.
Abstract: To facilitate content delivery to mobile users, we propose a content caching and distribution framework for the heterogeneous OFDM networks, where a library of files available at the macro base station (MBS) can be distributively cached in multiple serving nodes (SNs). SNs are capable of both receiving unstored files from MBS and transmitting files to the associated users. For a given group of file downloading requests, the user scheduling scheme is jointly designed with the content caching scheme so that the number of served users is maximized for a given amount of spectrum and time. First, the corresponding downlink throughput maximization problem is shown to be NP-hard. Then, for the system with one MBS and one SN, the design of the joint user scheduling and caching scheme is transformed into a binary linear programming problem. For the system with one MBS and two SNs, the joint scheduling and caching (JSC) algorithm has been proposed to tap on the potential of the coded multicasting scheme between MBS and SNs. The proposed algorithm can be extended to networks with arbitrary number of SNs. Simulation results demonstrate that the JSC algorithm provides a significant sum throughput gain.
TL;DR: FreeNet is introduced, figuratively synonymous with “Free Network,” which engineers spectrum and energy harvesting techniques to alleviate Spectrum and energy constraints by sensing and harvesting spare spectrum for data communications and utilizing renewable energy as power supplies, respectively.
Abstract: The dramatic growth in mobile data traffic is resulting in spectrum crunch, and is also leading to exorbitant energy consumption. Thus, it is desirable to liberate mobile and wireless networks from the constraint of spectrum scarcity and to rein in growing energy consumption. This article introduces FreeNet, figuratively synonymous with “Free Network,” which engineers spectrum and energy harvesting techniques to alleviate spectrum and energy constraints by sensing and harvesting spare spectrum for data communications and utilizing renewable energy as power supplies, respectively. Hence, FreeNet increases the spectrum and energy efficiency of wireless networks and enhances network availability. As a result, FreeNet can be deployed to alleviate network congestion in urban areas, provision broadband services in rural areas, and upgrade emergency communication capacity. This article provides a brief analysis of the design of FreeNet that accommodates the dynamics of the spare spectrum and employs renewable energy.
TL;DR: In this article, the authors proposed a scheme that can efficiently reduce the energy consumption of optical line terminals (OLTs) in time division multiplexing passive optical networks (PONs).
Abstract: This paper proposes a novel scheme that can efficiently reduce the energy consumption of optical line terminals (OLTs) in time division multiplexing passive optical networks (PONs) such as ethernet PONs and gigabit PONs. Currently, OLTs consume a significant amount of energy in PONs, which is one of the major fiber-to-the-X technologies. To be environmentally friendly, it is desirable to reduce energy consumption of OLTs as much as possible, a requirement that becomes even more urgent as OLTs keep increasing their provisioning data rate; higher data rate provisioning usually implies higher energy consumption. In this paper, we propose a novel energy-efficient OLT structure that guarantees services to end users with the smallest number of power-on OLT line cards. More specifically, we adapt the number of power-on OLT line cards to the real-time incoming traffic. Also, in order to avoid service disruption resulting from powering off OLT line cards, proper optical switches are equipped in OLT to dynamically configure the communications between OLT line cards and optical network units.
01 Jan 2016
TL;DR: This is the first study of the revenue driven VM management problem in green DC networks towards big data with VM migration by integer linear programming and results show that optimal results can be reached for small size problems.
Abstract: The big data era is presenting unprecedented opportunities for generating new revenues in various sectors ranging from health care, economics, life science, to manufacturing. Datacenters (DCs) are widely deployed to provision various application services as well as to process big data. Since more and more servers are installed in DCs, the cost of electricity incurs a financial burden for the DC operators. Many DCs are equipped with renewable energy to reduce the electricity bill. However, the locations of the energy demands do not match the locations of the renewable energy generation. This mismatch may be addressed by virtual machine (VM) migration. The DCs, which lack renewable energy, can migrate their workloads to other DCs, which have abundant renewable energy. In addition, DC operators always want to maximize revenue and minimize operation cost. In this paper, the problem of maximizing revenue and minimizing operation cost of a green DC network enabled with and without VM migration is formulated by integer linear programming. Simulation results show that optimal results can be reached for small size problems. Two heuristic algorithms are proposed to efficiently solve large size problems. To our best knowledge, this is the first study of the revenue driven VM management problem in green DC networks towards big data with VM migration.
TL;DR: An overview on the design and optimization of energy-efficient broadband access networks is provided, the energy- efficient design of passive optical networks are analyzed, the enabling technologies for next generation broadband wireless access networks are discussed, and emerging technologies for enhancing the energy efficiency of the last mile access of the network infrastructure are elicited.
Abstract: Dramatic data traffic growth, especially in wireless data, is driving a significant surge in energy consumption in the last mile access of the telecommunications infrastructure. The growing energy consumption not only escalates operators' operational expenditures, but also leads to a significant rise in carbon footprints. Therefore, enhancing the energy efficiency of broadband access networks is becoming a necessity to bolster social, environmental, and economic sustainability. This article provides an overview on the design and optimization of energy-efficient broadband access networks, analyzes the energy-efficient design of passive optical networks, discusses the enabling technologies for next generation broadband wireless access networks, and elicits emerging technologies for enhancing the energy efficiency of the last mile access of the network infrastructure.
03 Apr 2016
TL;DR: An intelligent battery management mechanism to optimize the green energy utilization in BSs based on the Markov Decision Process (MDP) is proposed, and the performance of the proposed algorithm is validated through extensive simulations.
Abstract: Green communications has received much attention in recent years. In cellular networks, base stations (BSs) account for more than 50 percent of the energy consumption. Reducing energy consumption of BSs is essential to realize green cellular networks. Utilizing green energy to power BSs is a promising way to reduce the on-grid energy consumption. Owing to the dynamics of both mobile traffic loads and green energy, the mismatch between the energy demands and green energy generation in a BS results in inefficient green energy utilization. Managing the battery in BSs can control the green energy usage in individual time slots, thus alleviating the inefficiency caused by the mismatch. In this paper, we propose an intelligent battery management mechanism to optimize the green energy utilization in BSs based on the Markov Decision Process (MDP). A large number of states in the Markov chain are required to model the dynamics of solar radiation and BS workload demands. Thus, the original MDP optimal policy iteration method incurs a high computational complexity. Therefore, we propose some heuristics to approximate the optimal energy dispatching strategy with low computational complexity, and validate the performance of the proposed algorithm through extensive simulations.
TL;DR: This Special Issue covers the most recent research results that address challenges of big data for networking and selects nine high quality papers, organized into two groups.
Abstract: Big data analytics has shown great potential in optimizing operations, making decisions, spotting business trends, preventing threats, and capitalizing on new sources of revenues in various fields such as manufacturing, healthcare, finance, insurance, and retail. The management of various networks has become inefficient and difficult because of their high complexities and interdependencies. Big data, in forms of device logs, software logs, media content, and sensed data, provide rich information and facilitate a fundamentally different and novel approach to explore, design, and develop reliable and scalable networks. This Special Issue covers the most recent research results that address challenges of big data for networking. We received 45 submissions, and ultimately nine high quality papers, organized into two groups, have been selected for inclusion in this Special Issue.
TL;DR: Seven papers that cover a broad range of this feature topic were selected and are expected to stimulate new ideas and developments in the research community, providing readers with relevant background information and proposed solutions to various technical design issues of future mobile internet.
Posted Content•
TL;DR: In this paper, the authors proposed a novel localization framework by fusing a group of fingerprints via multiple antennas for the indoor environment, which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low order moment, and fourth-order cumulant.
Abstract: Most existing fingerprints-based indoor localization approaches are based on some single fingerprints, such as received signal strength (RSS), channel impulse response (CIR), and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multi-path, and non-line-of-sight (NLOS) propagation. Furthermore, building the fingerprints is a very time consuming process. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, the prediction probability of our proposed approach is improved significantly. The process for building fingerprints can also be reduced drastically. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral (USRP) platform with four antennas.