Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks

The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm which incorporates the cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets. Characterizing the cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improving energy efficiency (EE) in H-CRANs. An energy-efficient optimization objective function with individual fronthaul capacity and inter-tier interference constraints is presented in this paper for queue-aware multimedia H-CRANs. To solve this non-convex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework. Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power and inter-tier interference constraints, which can be regarded as the weighted sum EE maximization problem and solved by a generalized weighted minimum mean square error approach. The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint

Resource Allocation Optimization in Critical Chain Method

The paper presents resource allocation optimization in Critical Chain Project Management (CCPM). The cheapest project schedule is searched with respect to time constraints. The algorithm originally developed for the hardware-software co-design of heterogeneous distributed systems is adapted to work with human resources and CCPM method. The results of the optimization showed significant efficiency of the algorithm in comparison with a greedy algorithm. On average, the optimization gives 14.10% of cost reduction using the same number of resources. The gain varies depending on the number of resources and the time constraints. Advantages and disadvantages of such an approach are also discussed

Benchmarking and comparison of software project human resource allocation optimization approaches

For the Staffing and Scheduling a Software Project (SSSP), one has to find an allocation of resources to tasks while considering parameters such skills and availability to identify the optimal delivery of the project. Many approaches have been proposed that solve SSSP tasks by representing them as optimization problems and applying optimization techniques and heuristics. However, these approaches tend to vary in the parameters they consider, such as skill and availability, as well as the optimization techniques, which means their accuracy, performance, and applicability can vastly differ, making it difficult to select the most suitable approach for the problem at hand. The fundamental reason for this lack of comparative material lies in the absence of a systematic evaluation method that uses a validation dataset to benchmark SSSP approaches. We introduce an evaluation process for SSSP approaches together with benchmark data to address this problem. In addition, we present the initial evaluation of five SSSP approaches. The results shows that SSSP approaches solving identical challenges can differ in their computational time, preciseness of results and that our approach is capable of quantifying these differences. In addition, the results highlight that focused approaches generally outperform more sophisticated approaches for identical SSSP problems

Encryption Mechanism And Resource Allocation Optimization Based On Edge Computing Environment

A method for optimizing encryption mechanism and resource allocation based on edge computing environment is proposed. A local differential privacy algorithm based on a histogram algorithm is used to protect user information during task offloading, which allows accurate preservation of user contextual information while reducing interference with the playback decision. To efficiently offload tasks and improve offloading performance, a joint optimization algorithm for task offloading and resource allocation is proposed that optimizes overall latency. A balance will be found between privacy protection and task offloading accuracy. The impact of contextual data interference on task offloading decisions is minimized while ensuring a predefined level of privacy protection. In the concrete connected vehicle example, the method distributes tasks among roadside devices and neighboring vehicles with sufficient computational resources

Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges

As a promising paradigm for fifth generation (5G) wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between a baseband unit (BBU) and a remote radio head (RRH), requires high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained C-RANs, including system architectures and key techniques. In particular, key techniques for alleviating the impact of constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin note: text overlap with arXiv:1407.3855 by other author

Context-Aware Resource Allocation in Cellular Networks

We define and propose a resource allocation architecture for cellular networks. The architecture combines content-aware, time-aware and location-aware resource allocation for next generation broadband wireless systems. The architecture ensures content-aware resource allocation by prioritizing real-time applications users over delay-tolerant applications users when allocating resources. It enables time-aware resource allocation via traffic-dependent pricing that varies during different hours of day (e.g. peak and off-peak traffic hours). Additionally, location-aware resource allocation is integrable in this architecture by including carrier aggregation of various frequency bands. The context-aware resource allocation is an optimal and flexible architecture that can be easily implemented in practical cellular networks. We highlight the advantages of the proposed network architecture with a discussion on the future research directions for context-aware resource allocation architecture. We also provide experimental results to illustrate a general proof of concept for this new architecture.Comment: (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work