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 Management in Clouds: Survey and Research Challenges

Cognitive radio (CR) has emerged as a promising technology to exploit the unused portions of spectrum in an opportunistic manner. The fixed spectrum allocation of governmental agencies results in unused portions of spectrum, which are called "spectrum holes" or "white spaces". CR technology overcomes this issue, allowing devices to sense the spectrum for unused portions and use the most suitable ones, according to some pre-defined criteria. Spectrum assignment is a key mechanism that limits the interference between CR devices and licensed users, enabling a more efficient usage of the wireless spectrum. Interference is a key factor that limits the performance in wireless networks. The scope of this work is to give an overview of the problem of spectrum assignment in cognitive radio networks, presenting the state-of-the-art proposals that have appeared in the literature, analyzing the criteria for selecting the most suitable portion of the spectrum and showing the most common approaches and techniques used to solve the spectrum assignment problem. Finally, an analysis of the techniques and approaches is presented, discussing also the open issues for future research in this area.

Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey

Spectrum decision is the ability of a cognitive radio (CR) to select the best available spectrum band to satisfy secondary users' (SUs') quality of service (QoS) requirements, without causing harmful interference to licensed or primary users (PUs). Each CR performs spectrum sensing to identify the available spectrum bands and the spectrum decision process selects from these available bands for opportunistic use. Spectrum decision constitutes an important topic which has not been adequately explored in CR research. Spectrum decision involves spectrum characterization, spectrum selection and CR reconfiguration functions. After the available spectrum has been identified, the first step is to characterize it based not only on the current radio environment conditions, but also on the PU activities. The second step involves spectrum selection, whereby the most appropriate spectrum band is selected to satisfy SUs' QoS requirements. Finally, the CR should be able to reconfigure its transmission parameters to allow communication on the selected band. Key to spectrum characterization is PU activity modelling, which is commonly based on historical data to provide the means for predicting future traffic patterns in a given spectrum band. This paper provides an up-to-date survey of spectrum decision in CR networks (CRNs) and addresses issues of spectrum characterization (including PU activity modelling), spectrum selection and CR reconfiguration. For each of these issues, we highlight key open research challenges. We also review practical implementations of spectrum decision in several CR platforms.

/pdf/spectrum-decision-in-cognitive-radio-networks-a-survey-4zyh1oas7j.pdf

Spectrum Decision in Cognitive Radio Networks: A Survey

This paper provides an overview of cognitive radio (CR) networks, with focus on the recent advances in resource allocation techniques and the CR networks architectural design. The contribution of this work is threefold. First, a systematic way to study the resource allocation problem is presented; various design approaches are introduced, such as signal-to-interference-and-noise ratio (SINR) or transmission power-based, and centralized or distributed methods. Second, CR optimization methods are presented, accompanied by a comprehensive study of the resource allocation problem formulations. Furthermore, quality of service criteria of the physical or/and the medium access control layers are investigated. Third, challenges in spectrum assignment are discussed, focusing on dynamic spectrum allocation, spectrum aggregation and frequency mobility. Such approaches constitute an emerging trend in efficient spectrum sharing and affect the performance of resource allocation techniques. The open issues for future research in this area are finally discussed, including adaptability-reconfigurability, dual accessibility, and energy efficiency.

Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio Networks

In the traditional power system demand response, customers respond to electricity price or incentive and change their original power consumption pattern accordingly to gain additional benefits. With the development of multi-energy systems (MES) in which electricity, heat, natural gas and other forms of energy are coupled with each other, all types of energy customers are able to participate in demand response, leading to the concept of integrated demand response (IDR). In IDR, energy consumers can response not only by reducing energy consumption or opting for off-peak energy consumption but also by changing the type of the consumed energy. Taking the traditional demand response in power system as a starting point, the studies of the fundamental theory, framework design and potential estimation of demand response in power system are reviewed, and the practical cases and software development of demand response are introduced. Finally, the current theoretical research and application of IDR are assessed.

/pdf/from-demand-response-to-integrated-demand-response-review-422132pr4r.pdf

From demand response to integrated demand response: review and prospect of research and application

Cognitive radio improves spectrum efficiency by allowing secondary users (SU) to dynamically exploit the idle spectrum owned by primary users (PU). This paper studies optimal bandwidth allocation for SUs. Consider the following tradeoff: a SU increases its instantaneous throughput by accessing larger bands, but channel switching overhead (due to the dynamics of PU activities) and contention among multiple SUs create higher liability for larger bandwidths. So how much is too much? In this paper, we find the optimal bandwidth allocation in both the single SU and multiple SU cases, accounting for the effects of channel switching overhead. Our result is validated through both numerical simulation and real channel activity traces. We also study the impact of various factors on SU performance, namely, PU channel idle time and probability, PU channel correlation, and SU sensing and access schemes. The work sheds light on the design of spectrum sharing protocols in cognitive radio networks.

http://ants.iis.sinica.edu.tw/3BkMJ9lTeWXTSrrvNoKNFDxRm3zFwRR/25/DySPAN%202008_Optimal%20Bandwidth%20Selection%20in%20Multi-Channel.pdf

Optimal Bandwidth Selection in Multi-Channel Cognitive Radio Networks: How Much is Too Much?

Cognitive radio (CR) improves spectrum efficiency by allowing secondary users (SUs) to dynamically exploit the idle spectrum owned by primary users (PUs). This paper studies optimal bandwidth allocation of SUs for throughput efficiency. Consider the following tradeoff: an SU increases its instantaneous throughput by accessing more spectrum, but channel access/switching overhead, contention among multiple SUs, and dynamic PU activity create higher liability for larger bandwidths. So how much is too much? In this paper, we study the optimal bandwidth allocation for multiple SUs. Our approach is twofold. We first study the optimal bandwidth an SU should use to maximize the per-SU throughput in the long term. The optimal bandwidth is derived in the context of dynamic PU activity, where we consider both independent and correlated PU channel scenarios while accounting for the effects of channel switching overhead. We further consider the case of suboptimal spectrum use by SUs in the short term due to PU activity dynamics. We propose an efficient channel reconfiguration (CREC) scheme to improve SUs' performance. We use real PU channel activity traces in the simulations to validate our results. The work sheds light on the design of spectrum sharing protocols in cognitive radio networks.

Efficient and Fair Bandwidth Allocation in Multichannel Cognitive Radio Networks

To reduce datacenter energy consumption and cost, current practice has considered demand-proportional resource provisioning schemes, where servers are turned on/off according to the load of requests. 
Most existing work considers instantaneous (Internet) requests only, which are explicitly or implicitly assumed to be delay-sensitive. On the other hand, in datacenters, there exist a vast amount of delay-tolerant jobs, such as background/maintainance jobs. In this paper, we explicitly differentiate delay-sensitive jobs and delay tolerant jobs. We focus on the problem of using delay-tolerant jobs to fill the extra capacity of datacenters, referred to as trough/valley filling. Giving a higher priority to delay-sensitive jobs, our schemes complement to most existing demand-proportional resource provisioning schemes. Our goal is to design intelligent trough filling mechanisms that are energy efficient and also achieve good delay performance. Specifically, we propose two joint dynamic speed scaling and traffic shifting schemes, one subgradient-based and the other queue-based. Our schemes assume little statistical information of the system, which is usually difficult to obtain in practice. In both schemes, energy cost saving comes from dynamic speed scaling, statistical multiplexing, electricity price diversity, and service efficiency diversity. In addition, good delay performance is achieved in the queue-based scheme via load shifting and capacity allocation based on queue conditions. Practical issues that may arise in datacenter networks are considered, including capacity and bandwidth constraint, service agility constraint, and load shifting cost. We use both artificial and real datacenter traces to evaluate the proposed schemes.

Geographic Trough Filling for Internet Datacenters

To reduce datacenter energy consumption and cost, current practice has considered demand-proportional resource provisioning schemes, where servers are turned on/off according to the load of requests. Most existing work considers instantaneous (Internet) requests only, which are explicitly or implicitly assumed to be delay-sensitive. On the other hand, in datacenters, there exist a vast amount of delay-tolerant jobs, such as background/maintainance jobs. In this paper, we explicitly differentiate delay-sensitive jobs and delay tolerant jobs. We focus on the problem of using delay-tolerant jobs to fill the extra capacity of datacenters, referred to as trough/valley filling. Giving a higher priority to delay-sensitive jobs, our scheme complements most existing demand-proportional resource provisioning schemes. Our goal is to design an intelligent trough filling mechanism that is energy efficient and also achieves good delay performance. Specifically, we propose a joint dynamic speed scaling and traffic shifting scheme. The scheme does not need statistical information of the system, which is usually difficult to obtain. In the proposed scheme, energy cost saving comes from dynamic speed scaling, statistical multiplexing, electricity price diversity, and service efficiency diversity. In addition, good delay performance is achieved via load shifting and capacity allocation based on queue conditions. We show the efficiency of the proposed scheme by both analysis and simulation.

Geographic trough filling for internet datacenters

Market mechanisms have been exploited as important means for spectrum acquisition and access in cognitive radio networks. In this paper, we propose a two-tier market for decentralized dynamic spectrum access. In the proposed Tier-1 market, spectrum is traded from a primary user (PU) to secondary users (SUs) in a relatively large time scale to reduce signaling overhead. Then, driven by dynamic traffic demands, SUs set up the Tier-2 market to redistribute channels among themselves in a small time scale. More specifically, we use a Nash bargain game to model the spectrum acquisition of SUs in the Tier-1 market and derive the equilibrium prices. We then employ a strategic bargain game to study the spectrum redistribution in the Tier-2 market, where SUs can exchange channels with low overhead through random matching, bilateral bargain, and the predetermined market equilibrium price. We investigate how various factors, such as the availability of channels and bargain partners, matching strategies, and traffic dynamics, affect the market relationships. This work provides new understanding on the spectrum market and valuable guidelines to primary and secondary network operators.

Dan Xu

Papers

Optimal Bandwidth Selection in Multi-Channel Cognitive Radio Networks: How Much is Too Much?

Efficient and Fair Bandwidth Allocation in Multichannel Cognitive Radio Networks

Geographic Trough Filling for Internet Datacenters

Geographic trough filling for internet datacenters

Decentralized Bargain: A Two-Tier Market for Efficient and Flexible Dynamic Spectrum Access