Seamless Support of Low Latency Mobile Applications with NFV-Enabled Mobile Edge-Cloud (original) (raw)
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Availability-Aware Mobile Edge Application Placement in 5G Networks
GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017
Mobile edge computing (MEC) literally pushes cloud computing from remote datacenters to the life radius of end users. By leveraging the widely adopted ETSI network function virtualization (NFV) architecture, MEC provisions elastic and resilient mobile edge applications with proximity. Typical MEC virtualization infrastructure allows configurable placement policy to deploy mobile edge applications as virtual machines (VMs): affinity can be used to put VMs on the same host for inter-VM networking performance, while anti-affinity is to separate VMs for high availability. In this paper, we propose a novel model to track the availability and cost impact from placement policy changes of the mobile edge applications. We formulate our model as a stochastic programming problem. To minimize complexity challenge, we also propose a heuristic algorithm. With our model, the unit resource cost increases when there are less resources left on a host. Applying affinity would take up more resources of the host but saves network bandwidth cost because of co-location. When enforcing anti-affinity, experimental results show increases of both availability and inter-host network bandwidth cost. For applications with different resource requirements, our model is able to find their sweet points with the consideration of both resource cost and application availability, which is vital in a less robust MEC cloud environment.
Dynamic application placement in the Mobile Cloud Network
• Proposes a holistic cost-optimal management approach for the forthcoming Mobile Cloud Network. • The proposed method shows significant improvements in aggregate system cost and resource utilisation skewness, and proves to be very capable at accommodating user mobility. • The evaluations reveal that high cost of user mobility and the amount of coordinated effort is required to accommodate it. • The proposed method provides an upper bound to which we can contrast more tractable distributed solutions. a b s t r a c t To meet the challenges of consistent performance, low communication latency, and a high degree of user mobility, cloud and Telecom infrastructure vendors and operators foresee a Mobile Cloud Network that incorporates public cloud infrastructures with cloud augmented Telecom nodes in forthcoming mobile access networks. A Mobile Cloud Network is composed of distributed cost-and capacity-heterogeneous resources that host applications that in turn are subject to a spatially and quantitatively rapidly changing demand. Such an infrastructure requires a holistic management approach that ensures that the resident applications' performance requirements are met while sustainably supported by the underlying infrastructure. The contribution of this paper is threefold. Firstly, this paper contributes with a model that captures the cost-and capacity-heterogeneity of a Mobile Cloud Network infrastructure. The model bridges the Mobile Edge Computing and Distributed Cloud paradigms by modelling multiple tiers of resources across the network and serves not just mobile devices but any client beyond and within the network. A set of resource management challenges is presented based on this model. Secondly, an algorithm that holistically and optimally solves these challenges is proposed. The algorithm is formulated as an application placement method that incorporates aspects of network link capacity, desired user latency and user mobility, as well as data centre resource utilisation and server provisioning costs. Thirdly, to address scalability, a tractable locally optimal algorithm is presented. The evaluation demonstrates that the placement algorithm significantly improves latency, resource utilisation skewness while minimising the operational cost of the system. Additionally, the proposed model and evaluation method demonstrate the viability of dynamic resource management of the Mobile Cloud Network and the need for accommodating rapidly mobile demand in a holistic manner.
Cost-Efficient NFV-Enabled Mobile Edge-Cloud for Low Latency Mobile Applications
IEEE Transactions on Network and Service Management
Mobile edge-cloud (MEC) aims to support low latency 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 microclouds, leading to resource wastage during stable/low workload 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 NFVenabled 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 nearoptimal MEC operational cost over time. We show through extensive 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.
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Mobile edge computing (MEC) is an emerging architecture for accommodating latency sensitive virtualized services (VSs). Many of these VSs are expected to be safety critical, and will have some form of reliability requirements. In order to support provisioning reliability to such VSs in MEC in an efficient and confidentiality preserving manner, in this paper we consider the joint resource dimensioning and placement problem for VSs with diverse reliability requirements, with the objective of minimizing the energy consumption. We formulate the problem as an integer programming problem, and prove that it is NPhard. We propose a two-step approximation algorithm with bounded approximation ratio based on Lagrangian relaxation. We benchmark our algorithm against two greedy algorithms in realistic scenarios. The results show that the proposed solution is computationally efficient, scalable and can provide up to 30% reduction in energy consumption compared to greedy algorithms.
Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, 2019
The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.
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Multi-access edge computing (MEC) aims to execute mobile cloud services in edge servers located near end-users to provide higher Quality of Experience (QoE). Centralised methods for dynamic service placement in MEC require central access to control every server, which is challenging where servers belong to different administrative domains. Other approaches use distributed decision-making, but most of them do not consider cooperation between servers. Dynamic, distributed service placement can potentially provide workload orchestration and higher QoE. However, this is challenging where service demand patterns are non-stationary. This paper proposes a multi-armed banditbased method where servers cooperatively decide to host service replicas by applying reinforcement learning that uses service characteristics and context information to minimise response time and backhaul traffic. By sharing cached services, the servers achieve better workload distribution while autonomously making placement decisions. The simulations demonstrate improvements in response time and reduction in backhaul traffic compared to close baselines.
QoS-Aware VNF Placement and Service Chaining for IoT Applications in Multi-Tier Mobile Edge Networks
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Mobile edge computing and network function virtualization (NFV) paradigms enable new flexibility and possibilities of the deployment of extreme low-latency services for Internet-of-Things (IoT) applications within the proximity of their users. However, this poses great challenges to find optimal placements of virtualized network functions (VNFs) for data processing requests of IoT applications in a multi-tier cloud network, which consists of many small- or medium-scale servers, clusters, or cloudlets deployed within the proximity of IoT nodes and a few large-scale remote data centers with abundant computing and storage resources. In particular, it is challenging to jointly consider VNF instance placement and routing traffic path planning for user requests, as they are not only delay sensitive but also resource hungry. In this article, we consider admissions of NFV-enabled requests of IoT applications in a multi-tier cloud network, where users request network services by issuing serv...
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Edge computing enables a wide variety of application services for the Internet of Things, including those with performance-critical requirements. To achieve this, it brings cloud computing capabilities to network edges. A key challenge therein is to decide where and when to place or migrate application services considering their load variation and seeking the optimization of multiple performance objectives. In this paper, we address this optimal service placement issue by further considering how to distribute the load of an application placed in different locations. By estimating the performance-cost trade-off of services migration, we propose a dynamic service placement and load distribution strategy that uses limited lookahead prediction to handle load fluctuations. Evaluation analysis demonstrates that our proposal outperforms other benchmarks solutions in terms of multiple conflicting objectives.
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