Showing papers on "Resource management published in 2015"

Practical Non-Linear Energy Harvesting Model and Resource Allocation for SWIPT Systems

Abstract: In this letter, we propose a practical non-linear energy harvesting model and design a resource allocation algorithm for simultaneous wireless information and power transfer (SWIPT) systems. The algorithm design is formulated as a non-convex optimization problem for the maximization of the total harvested power at energy harvesting receivers subject to minimum required signal-to-interference-plus-noise ratios (SINRs) at multiple information receivers. We transform the considered non-convex objective function from sum-of-ratios form into an equivalent objective function in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. In each iteration, a rank-constrained semidefinite program (SDP) is solved optimally by SDP relaxation. Numerical results unveil a substantial performance gain that can be achieved if the resource allocation design is based on the proposed non-linear energy harvesting model instead of the traditional linear model.

Matching theory for future wireless networks: fundamentals and applications

Abstract: The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this article, the first comprehensive tutorial on the use of matching theory, a Nobel Prize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then the key solution concepts and algorithmic implementations of this framework are exposed. The developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.

Resource Management in Clouds: Survey and Research Challenges

Abstract: Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .

Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach

Abstract: Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.

Resource orchestration in practice: CEO emphasis on SHRM, commitment‐based HR systems, and firm performance

Abstract: In order to be effective, managers at all levels of the firm must engage in resource management activities, and these efforts are synchronized and orchestrated by top management. Using a specific type of strategic resource, commitment-based human resource systems, we examine the effect of CEO resource orchestration in a multi-industry sample of 190 Korean firms. Our results demonstrate that CEO emphasis on strategic HRM is a significant antecedent to commitment-based HR systems. Furthermore, our results also suggest that CEO emphasis on strategic HRM has its primary effects on firm performance through commitment-based HR systems. This finding underscores the importance of middle managers in operationalizing top management's strategic emphasis, lending empirical support to a fundamental tenet of resource orchestration arguments.

Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT

Abstract: Pervasive and ubiquitous computing services have recently been under focus of not only the research community, but developers as well. Prevailing wireless sensor networks (WSNs), Internet of Things (IoT), and healthcare related services have made it difficult to handle all the data in an efficient and effective way and create more useful services. Different devices generate different types of data with different frequencies. Therefore, amalgamation of cloud computing with IoTs, termed as Cloud of Things (CoT) has recently been under discussion in research arena. CoT provides ease of management for the growing media content and other data. Besides this, features like: ubiquitous access, service creation, service discovery, and resource provisioning play a significant role, which comes with CoT. Emergency, healthcare, and latency sensitive services require real-time response. Also, it is necessary to decide what type of data is to be uploaded in the cloud, without burdening the core network and the cloud. For this purpose, Fog computing plays an important role. Fog resides between underlying IoTs and the cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. For this purpose, Fog requires an effective and efficient resource management framework for IoTs, which we provide in this paper. Our model covers the issues of resource prediction, customer type based resource estimation and reservation, advance reservation, and pricing for new and existing IoT customers, on the basis of their characteristics. The implementation was done using Java, while the model was evaluated using CloudSim toolkit. The results and discussion show the validity and performance of our system.

Global Waste Management Outlook

Abstract: The Global Waste Management Outlook, a collective effort of the United Nations Environment Programme and the International Waste Management Association, is a pioneering scientific global assessment on the state of waste management and a call for action to the international community. Prepared as a follow up to the Rio+20 Summit and as a response to UNEP Governing Council decision GC 27/12, the document establishes the rationale and the tools for taking a holistic approach towards waste management and recognizing waste and resource management as a significant contributor to sustainable development and climate change mitigation. To complement the Sustainable Development Goals of the Post-2015 Development Agenda, the Outlook sets forth Global Waste Management Goals and a Global Call to Action to achieve those goals.

The Fishery Performance Indicators: A Management Tool for Triple Bottom Line Outcomes

Abstract: Pursuit of the triple bottom line of economic, community and ecological sustainability has increased the complexity of fishery management; fisheries assessments require new types of data and analysis to guide science-based policy in addition to traditional biological information and modeling. The authors introduce the Fishery Performance Indicators (FPIs), a broadly applicable and flexible tool for assessing performance in individual fisheries, and for establishing cross-sectional links between enabling conditions, management strategies and triple bottom line outcomes. Conceptually separating measures of performance, the FPIs use 68 individual outcome metrics, coded on a 1 to 5 scale based on expert assessment to facilitate application to data poor fisheries and sectors that can be partitioned into sector based or triple-bottom-line sustainability-based interpretative indicators. Variation among outcomes is explained with 54 similarly structured metrics of inputs, management approaches and enabling conditions. Using 61 initial fishery case studies drawn from industrial and developing countries around the world, the authors demonstrate the inferential importance of tracking economic and community outcomes, in addition to resource status.

A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing

Abstract: For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the user’s resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the user’s budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this method’s performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.

Distributed Optimal Resource Management Based on the Consensus Algorithm in a Microgrid

Abstract: A microgrid is a promising approach to provide clean, renewable, and reliable electricity by integrating various distributed generations and energy storage systems into power systems. However, highly intermittent renewable generations and various load demands pose new challenges to the optimal resource management in a microgrid. This paper proposes a fully distributed control strategy based on the consensus algorithm for the optimal resource management in an islanded microgrid. The proposed strategy is implemented through a multiagent system framework, which only requires information exchange among neighboring agents through a local network. The objective is achieved through a two-level control strategy. The upper control level is a consensus-based optimization algorithm that discovers the reference of optimal power generation or demand while maintaining the supply–demand balance. The lower control level is responsible for reference tracking of the associated component. Simulation results in the IEEE 14- and 162-bus systems are presented to demonstrate the effectiveness of the proposed control strategy.

An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems

Abstract: Vehicular ad hoc networks are expected to significantly improve traffic safety and transportation efficiency while providing a comfortable driving experience. However, available communication, storage, and computation resources of the connected vehicles are not well utilized to meet the service requirements of intelligent transportation systems. Vehicular cloud computing (VCC) is a promising approach that makes use of the advantages of cloud computing and applies them to vehicular networks. In this paper, we propose an optimal computation resource allocation scheme to maximize the total long-term expected reward of the VCC system. The system reward is derived by taking into account both the income and cost of the VCC system as well as the variability feature of available resources. Then, the optimization problem is formulated as an infinite horizon semi-Markov decision process (SMDP) with the defined state space, action space, reward model, and transition probability distribution of the VCC system. We utilize the iteration algorithm to develop the optimal scheme that describes which action has to be taken under a certain state. Numerical results demonstrate that the significant performance gain can be obtained by the SMDP-based scheme within the acceptable complexity.

Energy-Efficient Resource Assignment and Power Allocation in Heterogeneous Cloud Radio Access Networks

Abstract: Taking full advantage of both heterogeneous networks and cloud access radio access networks, heterogeneous cloud radio access networks (H-CRANs) are presented to enhance both spectral and energy efficiencies, where remote radio heads (RRHs) are mainly used to provide high data rates for users with high quality of service (QoS) requirements, whereas the high-power node (HPN) is deployed to guarantee seamless coverage and serve users with low-QoS requirements. To mitigate the intertier interference and improve energy efficiency (EE) performances in H-CRANs, characterizing user association with RRH/HPN is considered in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based on the RRH/HPN association constraint and the enhanced S-FFR, an energy-efficient optimization problem with the resource assignment and power allocation for the orthogonal-frequency-division-multiple-access-based H-CRANs is formulated as a nonconvex objective function. To deal with the nonconvexity, an equivalent convex feasibility problem is reformulated, and closed-form expressions for the energy-efficient resource allocation solution to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition method. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation solution can enhance the EE significantly.

Statistical characterization of business-critical workloads hosted in cloud datacenters

Abstract: Business-critical workloads -- web servers, mail servers, app servers, etc. -- are increasingly hosted in virtualized data enters acting as Infrastructure-as-a-Service clouds (cloud data enters). Understanding how business-critical workloads demand and use resources is key in capacity sizing, in infrastructure operation and testing, and in application performance management. However, relatively little is currently known about these workloads, because the information is complex -- larges-scale, heterogeneous, shared-clusters -- and because datacenter operators remain reluctant to share such information. Moreover, the few operators that have shared data (e.g., Google and several supercomputing centers) have enabled studies in business intelligence (MapReduce), search, and scientific computing (HPC), but not in business-critical workloads. To alleviate this situation, in this work we conduct a comprehensive study of business-critical workloads hosted in cloud data enters. We collect two large-scale and long-term workload traces corresponding to requested and actually used resources in a distributed datacenter servicing business-critical workloads. We perform an in-depth analysis about workload traces. Our study sheds light into the workload of cloud data enters hosting business-critical workloads. The results of this work can be used as a basis to develop efficient resource management mechanisms for data enters. Moreover, the traces we released in this work can be used for workload verification, modelling and for evaluating resource scheduling policies, etc.

Dynamic Ocean Management: Identifying the Critical Ingredients of Dynamic Approaches to Ocean Resource Management

Abstract: Dynamic ocean management, or management that uses near real-time data to guide the spatial distribution of commercial activities, is an emerging approach to balance ocean resource use and conservation. Employing a wide range of data types, dynamic ocean management can be used to meet multiple objectives—for example, managing target quota, bycatch reduction, and reducing interactions with species of conservation concern. Here, we present several prominent examples of dynamic ocean management that highlight the utility, achievements, challenges, and potential of this approach. Regulatory frameworks and incentive structures, stakeholder participation, and technological applications that align with user capabilities are identified as key ingredients to support successful implementation. By addressing the variability inherent in ocean systems, dynamic ocean management represents a new approach to tackle the pressing challenges of managing a fluid and complex environment.

An intelligent SDN framework for 5G heterogeneous networks

Abstract: In fifth-generation (5G) mobile networks, a major challenge is to effectively improve system capacity and meet dynamic service demands. One promising technology to solve this problem is heterogeneous networks (HetNets), which involve a large number of densified low power nodes (LPNs). This article proposes a software defined network (SDN) based intelligent model that can efficiently manage the heterogeneous infrastructure and resources. In particular, we first review the latest SDN standards and discuss the possible extensions. We then discuss the advantages of SDN in meeting the dynamic nature of services and requirements in 5G HetNets. Finally, we develop a variety of schemes to improve traffic control, subscriber management, and resource allocation. Performance analysis shows that our proposed system is reliable, scalable, and implementable.

Recent Advances in Underlay Heterogeneous Networks: Interference Control, Resource Allocation, and Self-Organization

Abstract: By deploying additional low power nodes (LPNs) within the coverage area of traditional high power nodes (HPNs) and bringing them closer to users, underlay heterogeneous networks (HetNets) can significantly boost the overall spectral efficiency (SE) and energy efficiency (EE) through a full spatial resource reuse. Considering that the severe intra-tier interference among dense LPNs and inter-tier interference between LPNs and HPNs are challenging the successful rollout and commercial operations of underlay HetNets, a great emphasis is given towards advanced techniques that take interference control, radio resource allocation, and self-organization into account to enhance both SE and EE in this paper. The interference control techniques presented in this paper are classified as the spatial interference coordination at the transmitter and the interference cancelation at the receiver. For the radio resource allocation, the multi-dimensional optimization, cross-layer optimization, and cooperative radio resource management are comprehensively summarized. The self-configuration, self-optimization, and self-healing techniques for the self-organized underlay HetNets are surveyed. Furthermore, this paper outlines the potential open issues for underlay HetNets to improve SE and EE when combining with energy harvesting and cloud computing.

Energy-Efficient Resource Allocation for Device-to-Device Underlay Communication

Abstract: Device-to-device (D2D) communication underlaying cellular networks is expected to bring significant benefits for utilizing resources, improving user throughput, and extending the battery life of user equipment. However, the allocation of radio and power resources to D2D communication needs elaborate coordination, as D2D communication can cause interference to cellular communication. In this paper, we study joint channel and power allocation to improve the energy efficiency of user equipments. To solve the problem efficiently, we introduce an iterative combinatorial auction algorithm, where the D2D users are considered bidders that compete for channel resources and the cellular network is treated as the auctioneer. We also analyze important properties of D2D underlay communication and present numerical simulations to verify the proposed algorithm.

QoS-Aware Autonomic Resource Management in Cloud Computing: A Systematic Review

Abstract: As computing infrastructure expands, resource management in a large, heterogeneous, and distributed environment becomes a challenging task. In a cloud environment, with uncertainty and dispersion of resources, one encounters problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. To provide efficient performance of workloads and applications, the aforementioned characteristics should be addressed effectively. This research depicts a broad methodical literature analysis of autonomic resource management in the area of the cloud in general and QoS (Quality of Service)-aware autonomic resource management specifically. The current status of autonomic resource management in cloud computing is distributed into various categories. Methodical analysis of autonomic resource management in cloud computing and its techniques are described as developed by various industry and academic groups. Further, taxonomy of autonomic resource management in the cloud has been presented. This research work will help researchers find the important characteristics of autonomic resource management and will also help to select the most suitable technique for autonomic resource management in a specific application along with significant future research directions.

Cross-Layer Resource Allocation With Elastic Service Scaling in Cloud Radio Access Network

Abstract: Cloud radio access network (C-RAN) aims to improve spectrum and energy efficiency of wireless networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We propose and investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in the BBU pool, fiber links and the remote radio heads (RRHs). We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which jointly considers elastic service scaling, RRH selection, and joint beamforming. The MINLP is however a combinatorial optimization problem and NP-hard. We relax the original MINLP problem into an extended sum-utility maximization (ESUM) problem, and propose two different solution approaches. We also propose a low-complexity Shaping-and-Pruning (SP) algorithm to obtain a sparse solution for the active RRH set. Simulation results suggest that the average sparsity of the solution given by our SP algorithm is close to that obtained by a recently proposed greedy selection algorithm, which has higher computational complexity. Furthermore, our proposed cross-layer resource allocation is more energy efficient than the greedy selection and successive selection algorithms.

On inclusion of water resource management in Earth system models – Part 1: Problem definition and representation of water demand

Abstract: Human activities have caused various changes to the Earth system, and hence the interconnections between human activities and the Earth system should be recognized and reflected in models that simulate Earth system processes. One key anthropogenic activity is water resource management, which determines the dynamics of human–water interactions in time and space and controls human livelihoods and economy, including energy and food production. There are immediate needs to include water resource management in Earth system models. First, the extent of human water requirements is increasing rapidly at the global scale and it is crucial to analyze the possible imbalance between water demands and supply under various scenarios of climate change and across various temporal and spatial scales. Second, recent observations show that human–water interactions, manifested through water resource management, can substantially alter the terrestrial water cycle, affect land–atmospheric feedbacks and may further interact with climate and contribute to sea-level change. Due to the importance of water resource management in determining the future of the global water and climate cycles, the World Climate Research Program's Global Energy and Water Exchanges project (WRCP-GEWEX) has recently identified gaps in describing human–water interactions as one of the grand challenges in Earth system modeling (GEWEX, 2012). Here, we divide water resource management into two interdependent elements, related firstly to water demand and secondly to water supply and allocation. In this paper, we survey the current literature on how various components of water demand have been included in large-scale models, in particular land surface and global hydrological models. Issues of water supply and allocation are addressed in a companion paper. The available algorithms to represent the dominant demands are classified based on the demand type, mode of simulation and underlying modeling assumptions. We discuss the pros and cons of available algorithms, address various sources of uncertainty and highlight limitations in current applications. We conclude that current capability of large-scale models to represent human water demands is rather limited, particularly with respect to future projections and coupled land–atmospheric simulations. To fill these gaps, the available models, algorithms and data for representing various water demands should be systematically tested, intercompared and improved. In particular, human water demands should be considered in conjunction with water supply and allocation, particularly in the face of water scarcity and unknown future climate.

An Evolutionary Game for Distributed Resource Allocation in Self-Organizing Small Cells

Abstract: We propose an evolutionary game theory (EGT)-based distributed resource allocation scheme for small cells underlaying a macro cellular network. EGT is a suitable tool to address the problem of resource allocation in self-organizing small cells since it allows the players with bounded-rationality to learn from the environment and take individual decisions for attaining the equilibrium with minimum information exchange. EGT-based resource allocation can also provide fairness among users. We show how EGT can be used for distributed subcarrier and power allocation in orthogonal frequency-division multiple access (OFDMA)-based small cell networks while limiting interference to the macrocell users below given thresholds. Two game models are considered, where the utility of each small cell depends on average achievable signal-to-interference-plus-noise ratio (SINR) and data rate, respectively. For the proposed distributed resource allocation method, the average SINR and data rate are obtained based on a stochastic geometry analysis. Replicator dynamics is used to model the strategy adaptation process of the small cell base stations and an evolutionary equilibrium is obtained as the solution. Based on the results obtained using stochastic geometry, the stability of the equilibrium is analyzed. We also extend the formulation by considering information exchange delay and investigate its impact on the convergence of the algorithm. Numerical results are presented to validate our theoretical findings and to show the effectiveness of the proposed scheme in comparison to a centralized resource allocation scheme.

Multi-Resource Fair Allocation in Heterogeneous Cloud Computing Systems

Abstract: We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no coalition behavior of misreporting resource demands can benefit all its members. DRFH also ensures some level of service isolation among the users. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.

Joint Access Control and Resource Allocation for Concurrent and Massive Access of M2M Devices

Abstract: Machine-to-machine (M2M) communications, also known as machine-type communications (MTC) in 3GPP LTE systems, provide autonomous connectivity between machines without human intervention to create new service, e.g., the Internet of Things and the smart grid. M2M communications normally involve a large number of MTC devices (MTCDs) to support a variety of sensor applications. Consequently, concurrent and massive access attempts of MTCDs to radio access networks (RANs) may cause intolerable delay, packet loss, and even service unavailability. In this paper, we propose a joint optimal physical random access channel (PRACH) resource allocation and access control mechanism to address the performance degradation caused by concurrent and massive access attempts of MTCDs in LTE systems. We define the notion of random access efficiency and formulate an optimization problem for maximization of the random access efficiency with random access delay constraint. We also propose a dynamic resource allocation and access control algorithm based on estimation of the number of MTCDs for a system with dynamically varying numbers of massive MTCDs. Then, an analytical model is provided using a discrete-time Markov chain for the proposed mechanism. The effectiveness of the proposed algorithm is demonstrated via analysis and simulations. The proposed algorithm was able to maintain the optimal random access efficiency while satisfying the average random access delay requirement of MTCDs to handle massive and dynamic MTCDs per cell.

Toward a systematized framework for resource efficiency indicators

Abstract: The transition toward resource efficient production and consumption patterns is currently one of the main challenges in engineering, environmental science and especially in governmental policies. This transition has led to a proliferation of meanings related to the resource efficiency concept, resulting in a wide variety of indicators. In this paper, we propose a systematized framework in which resource efficiency indicators can be structured and comprehensively positioned. The aim is to provide a proper understanding of the scope and limitations of particular existing resource efficiency indicators in order to assist policy makers and the scientific community in the application and further development of indicators. This framework covers all different resource use-related aspects evaluated in existing approaches, including simple accounting of resource extraction and use; environmental impact assessment due to resource extraction and use; accounting and environmental impact assessment of specific processes and of full supply chains; analyses at micro-scale and macro-scale; and analysis of both natural resources versus waste-as-resources. To illustrate the potential application of the framework, a set of currently used indicators was selected, whereupon these indicators were structured and evaluated within the framework.

Architecture Harmonization Between Cloud Radio Access Networks and Fog Networks

Abstract: To guarantee the ubiquitous and fully autonomous Internet connections in our daily life, the new technical challenges of mobile communications lie on the efficient utilization of resource and social information. To facilitate the innovation of the fifth generation (5G) networks, the cloud radio access network (RAN) and fog network have been proposed to respond newly emerging traffic demands. The cloud RAN functions more toward centralized resource management to achieve optimal transmissions. The fog network takes advantage of social information and edge computing to efficiently alleviate the end-to-end latency. In this paper, we conduct a comprehensive survey of these two network structures, and then investigate possible harmonization to integrate both for the diverse needs of 5G mobile communications. We analytically study the harmonization of cloud RAN and fog network from various points of view, including the cache of Internet contents, mobility management, and radio access control. The performance of transition between the cloud RAN and the fog network has been presented and the subsequent switching strategy has been proposed to ensure engineering flexibility and success.