This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

Introduction to stochastic programming

Cloud service providers offer virtual machines (VMs) as services to users over Internet. As VMs are running on physical machines (PMs), PM power consumption needs to be considered. Meanwhile, VMs running on the same PM share physical resources, and there exists great resource contention, which results in VM performance degradation. Therefore, how to place VMs to reduce PM power consumption and guarantee VM performance is still one major challenge. However, existing VMPs did not study VM performance degradation, so they could not guarantee VM performance. To solve the high power consumption and VMs performance degradation problems, this paper explores the balance between saving PM power and guaranteeing VM performance, and proposes a power-aware and performance-guaranteed VMP (PPVMP). First, we investigate the relationship between power consumption and CPU utilization to build a non-linear power model, which is helpful for the following VMP. Second, we construct VM performance models to present the VM performance degradation trend. Third, based on these models, we formulate VMP as a bi-objective optimization problem, which tries to minimize PM power consumption and guarantee VM performance. We then propose an algorithm based on ant colony optimization to solve it. Finally, the results show the efficiency of our algorithm.

Power-Aware and Performance-Guaranteed Virtual Machine Placement in the Cloud

Summary
One of the key problems for Infrastructure-as-a-Service providers is finding the optimal allocation of virtual machines on the physical machines available in the provider's data center. Since the allocation has significant impact on operational costs as well as on the performance of the accommodated applications, several algorithms have been proposed for the virtual machine placement problem. So far, no objective comparison of the proposed algorithms has been provided; therefore, it is not known which one works best or what factors influence the performance of the algorithms. In this paper, we present an environment and methodology for such comparisons and compare 7 different algorithms using the proposed environment and methodology. Our results showcase differences of up to 66% between the effectiveness of different algorithms on the same real-world workload traces, thus underlining the importance of objectively comparing the performance of competing algorithms.

Which is the best algorithm for virtual machine placement optimization

We investigate the tradeoff between energy efficiency (EE) and spectrum efficiency (SE) in the heterogeneous network composed of a macro base station (BS), several pico BSs, and device-to-device (D2D) communication pairs during the uplink transmission. A utility function of the tradeoff between EE and SE is defined first. Then, we formulate the tradeoff utility maximization problem as a joint channel allocation and power control problem for cellular and D2D users. The original problem is transformed into a more tractable subtractive form, and we further decompose the problem into several subproblems that can be solved separately. Numerical results confirm the effectiveness of the proposed scheme and offer valuable insights. Compared with EE, SE is much less sensitive to the variation of the tradeoff factor, which indicates that our proposed scheme can achieve high EE while guaranteeing a fairly large value of SE with a properly chosen EE–SE tradeoff factor.

Energy Efficiency and Spectrum Efficiency Tradeoff in the D2D-Enabled HetNet

With the continuing growth of cloud computing services, power consumption has become one of the most challenging issues in data center environments. With the support of today's virtualization technology, the efficiency and flexibility of data center management can be greatly enhanced, creating great energy saving opportunities. However, effective energy aware design is a non-trivial task, considering the size of the data center, the dynamic fluctuation of workloads and the variation of computing resource requests. In this paper, we propose Cool Cloud: a practical solution for managing the mappings of VMs to physical servers. This framework solves the problem of finding the most energy efficient way (least resource wastage and least power consumption) of placing the VMs considering their resource requirements. Experiment result demonstrates our design can effectively improve data center energy efficiency and scales well to large size data centers. Comparing with industry leading product VMware's Distributed Resource Scheduler (DRS), our design offers better performance in both load balancing and power consumption.

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Cool Cloud: A Practical Dynamic Virtual Machine Placement Framework for Energy Aware Data Centers

Heterogeneous networks have been pointed out to be one of the key network architectures that help increase system capacity and reduce power consumption for efficient communications. Although conceivably, high operational efficiency brings a high profit for mobile service providers, it is noteworthy that the potential for maximizing the profit has not been explored for the heterogeneous environment. This paper investigates profitability for network operators with the spectrum-energy efficiency metric on the downlink of LTE Advanced communication systems. We pursue optimal policies by employing the techniques of cell size zooming, user migration, and sleep mode in the deployment of different base station types. The problem is formulated as a quasiconvex optimization problem and it is transformed into an equivalent form of the MILP problem; the former is solved with a bisection algorithm and the latter is approached by an off-the-shelf software package. Since the formulated optimization problem is NP hard, a sub-optimal approach with a lower computational complexity is also proposed. Numerical analysis through case studies are presented to evaluate the efficiency improvements, and demonstrate the performance of the near-optimal solution.

Spectrum-Energy Efficiency Optimization for Downlink LTE-A for Heterogeneous Networks

The popularity of smart mobile devices has brought significant growth of data services for mobile service providers. Mobile users of data services are charged based on the amount of data used. Raising served data amount seemingly increases the profit; energy consumption rises correspondingly. Besides, spectral resources are licensed and limited for mobile operators to allocate. Increasing data services over the spectrum for the profit does not count the cost of energy. To assess the profitability, considered is the revenue-to-cost ratio. Optimizing the ratio is an economic incentive for mobile operators. Revenue is regarded as efficiency in spectrum use, the cost as energy consumption; therefore we interpret the revenue-to-cost ratio as spectrum-energy efficiency. In this paper, we study the spectrum-energy efficiency optimization problem where BSs are with the ability to perform cell zooming, sleep mode, and user migration. We formulate the problem into an integer linear program which is solvable by CPLEX to maximize spectrum-energy efficiency; meanwhile traffic demands by associated users in multicell/multiuser networks are met. To avoid high computation time, a heuristic algorithm is proposed to efficiently solve the formulated problem. Numerical analysis through case studies demonstrates energy consumption and efficiency improvements, and comparisons between near-optimal solutions against optimality.

Optimizing Spectrum-Energy Efficiency in Downlink Cellular Networks

To steadily gaining benefit from the exponential growth in mobile traffic, operators are eager to find solutions to maximize profits. Two very attractive strategies have been proposed to complement the existing macro cellular architecture: deploying low power bases stations and offloading data traffic to other networks. Each strategy has different costs and yields different benefits for operators. The offloading option could be cheaper in the short run; nevertheless, it might be more expensive in the long run than cell densification due to the varying cost. On the other hand, small cells, since having to be deployed in advance, may be underutilized or not fully meet future demands. In the latter case, offloading techniques can be used to increase capacity with additional costs. Further, uncertainty of future data demands and electricity prices also impact operators profitability, making the best network strategy difficult to achieve. To address such problem, an optimal cell configuration algorithm is proposed by formulating a stochastic programming model that considers both network design and data offloading. This algorithm can maximize the profit, under future demand and price uncertainty. Numerical studies are extensively performed in which the results show that operators' profits can be improved with our proposed algorithm.

Joint optimization for cell configuration and offloading in heterogeneous networks

The popularity of smart mobile devices has brought significant growth of data service for mobile service providers. Very often, mobile users of data service are charged based on the amount of data used. With the increasing revenue, mobile operators are also facing the increasing operation cost due to the high energy consumption. Meanwhile, as the access to spectral resource is limited, increasing spectrum efficiency is an economic incentive for operators alike. However, we are faced with the dilemma that focusing only on reducing energy consumption might incur the cost of decreasing spectrum efficiency. In this paper, we study the spectrum-energy efficiency enhancement problem by way of jointly considering the mechanisms of cell zooming, sleep mode and user migration. To gain the benefits economically, we formulate an optimization framework into an integer linear program which is solvable by CPLEX to maximize spectrum and energy efficiencies jointly as well as to meet the minimal traffic demand by associated users in multicell/multi-user networks. To avoid high computation time, a heuristic algorithm is proposed to efficiently solve the formulated problem. Numerical analysis through case studies demonstrates the energy saved and efficiency improvements and the comparison between the proposed heuristic algorithm against the optimal solutions.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=3873&context=etd

Chan-Ching Hsu

Papers

Cool Cloud: A Practical Dynamic Virtual Machine Placement Framework for Energy Aware Data Centers

Spectrum-Energy Efficiency Optimization for Downlink LTE-A for Heterogeneous Networks

Optimizing Spectrum-Energy Efficiency in Downlink Cellular Networks

Joint optimization for cell configuration and offloading in heterogeneous networks

optimizing spectrum-energy efficiency in wireless networks