اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدOnline Admission Control and Resource Allocation in Network Slicing under Demand Uncertainties
80
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
One of the most important aspects of moving forward to the next generation networks like 5G/6G, is to enable network slicing in an efficient manner. The most challenging issues are the uncertainties in consumption and communication demand. Because the slices arrive to the network in different times and their lifespans vary, the solution should dynamically react to online slice requests. The joint problem of online admission control and resource allocation considering the energy consumption is formulated mathematically. It is based on Integer Linear Programming (ILP), where, the $Gamma$- Robustness concept is exploited to overcome Virtual Links (VL) bandwidths and Virtual Network Functions (VNF) workloads uncertainties. Then, an optimal algorithm that adopts this mathematical model is proposed. To overcome the high computational complexity of ILP which is NP-hard, a new heuristic algorithm is developed. The assessments results indicate that the efficiency of heuristic is vital in increasing the accepted requests count, decreasing power consumption and providing adjustable tolerance vs. the VNFs workloads and VLs traffics uncertainties, separately. Considering the acceptance ratio and power consumption that constitute the two important components of the objective function, heuristic has about 7% and 12% optimality gaps, respectively, while being about 30X faster than that of optimal algorithm.
قيم البحث
اقرأ أيضاً
Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network
and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator (RO), which can be deployed between base stations (BSs) and fog nodes to coordinate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that the proposed algorithm can minimize the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that the proposed algorithm converges much faster than some other existing algorithms. The joint network slicing with both bandwidth and computational resources can offer around 15% overall latency reduction compared to network slicing with only a single resource.
The virtualization and softwarization of modern computer networks enables the definition and fast deployment of novel network services called service chains: sequences of virtualized network functions (e.g., firewalls, caches, traffic optimizers) thr
ough which traffic is routed between source and destination. This paper attends to the problem of admitting and embedding a maximum number of service chains, i.e., a maximum number of source-destination pairs which are routed via a sequence of to-be-allocated, capacitated network functions. We consider an Online variant of this maximum Service Chain Embedding Problem, short OSCEP, where requests arrive over time, in a worst-case manner. Our main contribution is a deterministic O(log L)-competitive online algorithm, under the assumption that capacities are at least logarithmic in L. We show that this is asymptotically optimal within the class of deterministic and randomized online algorithms. We also explore lower bounds for offline approximation algorithms, and prove that the offline problem is APX-hard for unit capacities and small L > 2, and even Poly-APX-hard in general, when there is no bound on L. These approximation lower bounds may be of independent interest, as they also extend to other problems such as Virtual Circuit Routing. Finally, we present an exact algorithm based on 0-1 programming, implying that the general offline SCEP is in NP and by the above hardness results it is NP-complete for constant L.
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 challengi
ng 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.
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 t
akes 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.
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 f
or 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.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
