No Arabic abstract
In the last few years there has been significant growth in the area of wireless communication. IEEE 802.16/WiMAX is the network which is designed for providing high speed wide area broadband wireless access; WiMAX is an emerging wireless technology for creating multi-hop Mesh network. Future generation networks will be characterized by variable and high data rates, Quality of Services (QoS), seamless mobility both within a network and between networks of different technologies and service providers. A technology is developed to accomplish these necessities is regular by IEEE, is 802.16, also called as WiMAX (Worldwide Interoperability for Microwave Access). This architecture aims to apply Long range connectivity, High data rates, High security, Low power utilization and Excellent Quality of Services and squat deployment costs to a wireless access technology on a metropolitan level. In this paper we have observed the performance analysis of location based resource allocation for WiMAX and WLAN-WiMAX client and in second phase we observed the rate-adaptive algorithms. We know that base station (BS) is observed the ranging first for all subscribers then established the link between them and in final phase they will allocate the resource with Subcarriers allocation according to the demand (UL) i.e. video, voice and data application. We propose linear approach, Active-Set optimization and Genetic Algorithm for Resource Allocation in downlink Mobile WiMAX networks. Purpose of proposed algorithms is to optimize total throughput. Simulation results show that Genetic Algorithm and Active-Set algorithm performs better than previous methods in terms of higher capacities but GA have high complexity then active set.
In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improv
We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool.
To address the rising demand for strong packet delivery guarantees in networking, we study a novel way to perform graph resource allocation. We first introduce allocation graphs, in which nodes can independently set local resource limits based on physical constraints or policy decisions. In this scenario we formalize the distributed path-allocation (PAdist) problem, which consists in allocating resources to paths considering only local on-path information -- importantly, not knowing which other paths could have an allocation -- while at the same time achieving the global property of never exceeding available resources. Our core contribution, the global myopic allocation (GMA) algorithm, is a solution to this problem. We prove that GMA can compute unconditional allocations for all paths on a graph, while never over-allocating resources. Further, we prove that GMA is Pareto optimal with respect to the allocation size, and it has linear complexity in the input size. Finally, we show with simulations that this theoretical result could be indeed applied to practical scenarios, as the resulting path allocations are large enough to fit the requirements of practically relevant applications.
Joint channel and rate allocation with power minimization in orthogonal frequency-division multiple access (OFDMA) has attracted extensive attention. Most of the research has dealt with the development of sub-optimal but low-complexity algorithms. In this paper, the contributions comprise new insights from revisiting tractability aspects of computing optimum. Previous complexity analyses have been limited by assumptions of fixed power on each subcarrier, or power-rate functions that locally grow arbitrarily fast. The analysis under the former assumption does not generalize to problem tractability with variable power, whereas the latter assumption prohibits the result from being applicable to well-behaved power-rate functions. As the first contribution, we overcome the previous limitations by rigorously proving the problems NP-hardness for the representative logarithmic rate function. Next, we extend the proof to reach a much stronger result, namely that the problem remains NP-hard, even if the channels allocated to each user is restricted to a consecutive block with given size. We also prove that, under these restrictions, there is a special case with polynomial-time tractability. Then, we treat the problem class where the channels can be partitioned into an arbitrarily large but constant number of groups, each having uniform gain for every individual user. For this problem class, we present a polynomial-time algorithm and prove optimality guarantee. In addition, we prove that the recognition of this class is polynomial-time solvable.
IEEE 802.16m amends the IEEE 802.16 Wireless MAN-OFDMA specification to provide an advanced air interface for operation in licenced bands. It will meet the cellular layer requirements of IMT-Advanced next generation mobile networks. It will be designed to provide significantly improved performance compared to other high rate broadband cellular network systems. For the next generation mobile networks, it is important to consider increasing peak, sustained data reates, corresponding spectral efficiencies, system capacity and cell coverage as well as decreasing latency and providing QoS while carefully considering overall system complexity. In this paper we provide an overview of the state-of-the-art mobile WiMAX technology and its development. We focus our discussion on Physical Layer, MAC Layer, Schedular,QoS provisioning and mobile WiMAX specification.