Cost-Efficient NFV-Enabled Mobile Edge-Cloud for Low Latency Mobile Applications

Mobile edge-cloud (MEC) aims to support low la- tency mobile services by bringing remote cloud services nearer to mobile users. However, in order to deal with dynamic workloads, MEC is deployed in a large number of fixed-location micro- clouds, leading to resource wastage during stable/low work- load periods. Limiting the number of micro-clouds improves resource utilization and saves operational costs, but faces service performance degradations due to insufficient physical capacity during peak time from nearby micro-clouds. To efficiently support services with low latency requirement under varying workload conditions, we adopt the emerging Network Function Virtualization (NFV)-enabled MEC, which offers new flexibility in hosting MEC services in any virtualized network node, e.g., access points, routers, etc. This flexibility overcomes the limitations imposed by fixed-location solutions, providing new freedom in terms of MEC service-hosting locations. In this paper, we address the questions on where and when to allocate resources as well as how many resources to be allocated among NFV- enabled MECs, such that both the low latency requirements of mobile services and MEC cost efficiency are achieved. We propose a dynamic resource allocation framework that consists of a fast heuristic-based incremental allocation mechanism that dynamically performs resource allocation and a reoptimization algorithm that periodically adjusts allocation to maintain a near- optimal MEC operational cost over time. We show through ex- tensive simulations that our flexible framework always manages to allocate sufficient resources in time to guarantee continuous satisfaction of applications’ low latency requirements. At the same time, our proposal saves up to 33% of cost in comparison to existing fixed-location MEC solutions

Seamless Support of Low Latency Mobile Applications with NFV-Enabled Mobile Edge-Cloud

Emerging mobile multimedia applications, such as augmented reality, have stringent latency requirements and high computational cost. To address this, mobile edge-cloud (MEC) has been proposed as an approach to bring resources closer to users. Recently, in contrast to conventional fixed cloud locations, the advent of network function virtualization (NFV) has, with some added cost due to the necessary decentralization, enhanced MEC with new flexibility in placing MEC services to any nodes capable of virtualizing their resources. In this work, we address the question on how to optimally place resources among NFV-enabled nodes to support mobile multimedia applications with low latency requirement and when to adapt the current resource placements to address workload changes. We first show that the placement optimization problem is NP-hard and propose an online dynamic resource allocation scheme that consists of an adaptive greedy heuristic algorithm and a detection mechanism to identify the time when the system will no longer be able to satisfy the applications' delay requirement. Our scheme takes into account the effect of current existing techniques (i.e., auto-scaling and load balancing). We design and implement a realistic NFV-enabled MEC simulated framework and show through extensive simulations that our proposal always manages to allocate sufficient resources on time to guarantee continuous satisfaction of the application latency requirements under changing workload while incurring up to 40% less cost in comparison to existing overprovisioning approaches

Efficient Three-stage Auction Schemes for Cloudlets Deployment in Wireless Access Network

Cloudlet deployment and resource allocation for mobile users (MUs) have been extensively studied in existing works for computation resource scarcity. However, most of them failed to jointly consider the two techniques together, and the selfishness of cloudlet and access point (AP) are ignored. Inspired by the group-buying mechanism, this paper proposes three-stage auction schemes by combining cloudlet placement and resource assignment, to improve the social welfare subject to the economic properties. We first divide all MUs into some small groups according to the associated APs. Then the MUs in same group can trade with cloudlets in a group-buying way through the APs. Finally, the MUs pay for the cloudlets if they are the winners in the auction scheme. We prove that our auction schemes can work in polynomial time. We also provide the proofs for economic properties in theory. For the purpose of performance comparison, we compare the proposed schemes with HAF, which is a centralized cloudlet placement scheme without auction. Numerical results confirm the correctness and efficiency of the proposed schemes.Comment: 22 pages,12 figures, Accepted by Wireless Network

On the design of a cost-efficient resource management framework for low latency applications

The ability to offer low latency communications is one of the critical design requirements for the upcoming 5G era. The current practice for achieving low latency is to overprovision network resources (e.g., bandwidth and computing resources). However, this approach is not cost-efficient, and cannot be applied in large-scale. To solve this, more cost-efficient resource management is required to dynamically and efficiently exploit network resources to guarantee low latencies. The advent of network virtualization provides novel opportunities in achieving cost-efficient low latency communications. It decouples network resources from physical machines through virtualization, and groups resources in the form of virtual machines (VMs). By doing so, network resources can be flexibly increased at any network locations through VM auto-scaling to alleviate network delays due to lack of resources. At the same time, the operational cost can be largely reduced by shutting down low-utilized VMs (e.g., energy saving). Also, network virtualization enables the emerging concept of mobile edge-computing, whereby VMs can be utilized to host low latency applications at the network edge to shorten communication latency. Despite these advantages provided by virtualization, a key challenge is the optimal resource management of different physical and virtual resources for low latency communications. This thesis addresses the challenge by deploying a novel cost-efficient resource management framework that aims to solve the cost-efficient design of 1) low latency communication infrastructures; 2) dynamic resource management for low latency applications; and 3) fault-tolerant resource management. Compared to the current practices, the proposed framework achieves 80% of deployment cost reduction for the design of low latency communication infrastructures; continuously saves up to 33% of operational cost through dynamic resource management while always achieving low latencies; and succeeds in providing fault tolerance to low latency communications with a guaranteed operational cost

Artificial intelligence empowered virtual network function deployment and service function chaining for next-generation networks

The entire Internet of Things (IoT) ecosystem is directing towards a high volume of diverse applications. From smart healthcare to smart cities, every ubiquitous digital sector provisions automation for an immersive experience. Augmented/Virtual reality, remote surgery, and autonomous driving expect high data rates and ultra-low latency. The Network Function Virtualization (NFV) based IoT infrastructure of decoupling software services from proprietary devices has been extremely popular due to cutting back significant deployment and maintenance expenditure in the telecommunication industry. Another substantially highlighted technological trend for delaysensitive IoT applications has emerged as multi-access edge computing (MEC). MEC brings NFV to the network edge (in closer proximity to users) for faster computation. Among the massive pool of IoT services in NFV context, the urgency for efficient edge service orchestration is constantly growing. The emerging challenges are addressed as collaborative optimization of resource utilities and ensuring Quality-ofService (QoS) with prompt orchestration in dynamic, congested, and resource-hungry IoT networks. Traditional mathematical programming models are NP-hard, hence inappropriate for time-sensitive IoT environments. In this thesis, we promote the need to go beyond the realms and leverage artificial intelligence (AI) based decision-makers for “smart” service management. We offer different methods of integrating supervised and reinforcement learning techniques to support future-generation wireless network optimization problems. Due to the combinatorial explosion of some service orchestration problems, supervised learning is more superior to reinforcement learning performance-wise. Unfortunately, open access and standardized datasets for this research area are still in their infancy. Thus, we utilize the optimal results retrieved by Integer Linear Programming (ILP) for building labeled datasets to train supervised models (e.g., artificial neural networks, convolutional neural networks). Furthermore, we find that ensemble models are better than complex single networks for control layer intelligent service orchestration. Contrarily, we employ Deep Q-learning (DQL) for heavily constrained service function chaining optimization. We carefully address key performance indicators (e.g., optimality gap, service time, relocation and communication costs, resource utilization, scalability intelligence) to evaluate the viability of prospective orchestration schemes. We envision that AI-enabled network management can be regarded as a pioneering tread to scale down massive IoT resource fabrication costs, upgrade profit margin for providers, and sustain QoS mutuall

Cost-Effective Resource Allocation and Throughput Maximization in Mobile Cloudlets and Distributed Clouds

With the advance in communication networks and the use explosion of mobile devices, distributed clouds consisting of many small and medium datacenters in geographical locations and cloudlets defined as "mini" datacenters are envisioned as the next-generation cloud computing platform. In particular, distributed clouds enable disaster-resilient and scalable services by scaling the services into multiple datacenters, while cloudlets allow pervasive and continuous services with low access delay by further enabling mobile users to access the services within their proximity. To realize the promises provided by distributed clouds and mobile cloudlets, it is urgently to optimize various system performance of distributed clouds and cloudlets, such as system throughput and operational cost by developing efficient solutions. In this thesis, we aim to devise novel solutions to maximize the system throughput of mobile cloudlets, and minimize the operational costs of distributed clouds, while meeting the resource capacity constraints and users' resource demands. This however poses great challenges, that is, (1) how to maximize the system throughput of a mobile cloudlet, considering that a mobile cloudlet has limited resources to serve energy-constrained mobile devices, (2) how to efficiently and effectively manage and evaluate big data in distributed clouds, and (3) how to efficiently allocate the resources of a distributed cloud to meet the resource demands of various users. Existing studies mainly focused on implementing systems and lacked systematic optimization methods to optimize the performance of distributed clouds and mobile cloudlets. Novel techniques and approaches for performance optimization of distributed clouds and mobile cloudlets are desperately needed. To address these challenges, this thesis makes the following contributions. We firstly study online request admissions in a cloudlet with the aim of maximizing the system throughput, assuming that future user requests are not known in advance. We propose a novel admission cost model to accurately model dynamic resource consumption, and devise efficient algorithms for online request admissions. We secondly study a novel collaboration- and fairness-aware big data management problem in a distributed cloud to maximize the system throughput, while minimizing the operational cost of service providers, subject to resource capacities and users' fairness constraints, for which, we propose a novel optimization framework and devise a fast yet scalable approximation algorithm with an approximation ratio. We thirdly investigate online query evaluation for big data analysis in a distributed cloud to maximize the query acceptance ratio, while minimizing the query evaluation cost. For this problem, we propose a novel metric to model the costs of different resource consumptions in datacenters, and devise efficient online algorithms under both unsplittable and splittable source data assumptions. We fourthly address the problem of community-aware data placement of online social networks into a distributed cloud, with the aim of minimizing the operational cost of the cloud service provider, and devise a fast yet scalable algorithm for the problem, by leveraging the close community concept that considers both user read rates and update rates. We also deal with social network evolutions, by developing a dynamic evaluation algorithm for the problem. We finally evaluate the performance of all proposed algorithms in this thesis through experimental simulations, using real and/or synthetic datasets. Simulation results show that the proposed algorithms significantly outperform existing algorithms