Efficient Resource Management Mechanism for 802.16 Wireless Networks Based on Weighted Fair Queuing

Wireless Networking continues on its path of being one of the most commonly used means of communication. The evolution of this technology has taken place through the design of various protocols. Some common wireless protocols are the WLAN, 802.16 or WiMAX, and the emerging 802.20, which specializes in high speed vehicular networks, taking the concept from 802.16 to higher levels of performance. As with any large network, congestion becomes an important issue. Congestion gains importance as more hosts join a wireless network. In most cases, congestion is caused by the lack of an efficient mechanism to deal with exponential increases in host devices. This can effectively lead to very huge bottlenecks in the network causing slow sluggish performance, which may eventually reduce the speed of the network. With continuous advancement being the trend in this technology, the proposal of an efficient scheme for wireless resource allocation is an important solution to the problem of congestion. The primary area of focus will be the emerging standard for wireless networks, the 802.16 or “WiMAX”. This project, attempts to propose a mechanism for an effective resource management mechanism between subscriber stations and the corresponding base station

Performance Analysis of QoS in PMP Mode WiMax Networks

IEEE 802.16 standard supports two different topologies: point to multipoint (PMP) and Mesh. In this paper, a QoS mechanism for point to multipoint of IEEE 802.16 and BS scheduler for PMP Mode is proposed. This paper also describes quality of service over WiMAX networks. Average WiMAX delay, Average WiMAX load and Average WiMAX throughput at base station is analyzed and compared by applying different scheduler at Base station and at fixed nodes

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation

Myriad of graph-based algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a large-scale distributed environment in order to scale to massive data sets. To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the "changes" between iterations. By DAIC, we can process only the "changes" to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the high-cost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60x speedup over Hadoop and outperforms other state-of-the-art frameworks.Comment: ScienceCloud 2012, TKDE 201

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

Weighted Round Robin (WRR) Based Replenishment Model in Vendor Managed Inventory (VMI) System

Vendor managed inventory (VMI) is a popular supply chain system where vendor or supplier take responsibility and decision in managing its customers’ inventory. Two important goals of the VMI are improving service level and maintaining inventory still low and available. Many studies in VMI compare their performance with the traditional system. Unfortunately, studies in improving VMI performance are rare. This work aims to improve VMI by implementing Weighted Round Robin (WRR), a popular scheduling model in computer system, in the replenishment model in VMI. WRR is popular because of its load balancing nature. Environment in this work is two-echelon supply chain. The vendor is a multi-product manufacturer. The customers are retailers. This WRR based replenishment model is then compared with two common replenishment models: (s, S) model and (r, Q) model. In this work, we observe two performance parameters: sales and inventory condition. Based on the simulation result, it is shown that the WRR model performs better than the existing (s, S) model and (r, Q) model and it occurs in most of the observed variables. In the certain condition, performance of the WRR model compared with the (s, S) model and the (r, Q) model is as follows. The WRR model performs 31 percent better than the (s, S) model and 12 percent better than the (r, Q) model in success ratio. Manufacturer’s stock in the WRR model is only 36 percent than in the (s, S) model and 40 percent than in the (r, Q) model. Total stock in the supply chain in the WRR model is only 63 percent than in the (s, S) model and 89 percent than in the (r, Q) model