ReCAP: A distributed CAPTCHA service at the edge of the network to handle server overload

Mobile edge computing (MEC) literally pushes cloud computing from remote datacenters to the life radius of end users. By leveraging the widely adopted European Telecommunications Standards Institute network function virtualization architecture, MEC provisions elastic and resilient mobile edge applications with proximity. Typical MEC virtualization infrastructure allows a 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, whereas 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 the complexity challenge, we also propose a heuristic algorithm called EdgePlace. 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 colocation. When enforcing anti‐affinity, experimental results show increases of both availability and interhost 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 environment.

Section: Related Workmentioning

confidence: 99%

EdgePlace: Availability‐aware placement for chained mobile edge applications

Zhu

Huang

2018

“…Recent years have witnessed a large amount of efforts by different nations, companies, standardization bodies, etc, poured into the research and development of the fifth generation of communication networks, ie, fifth‐generation (5G) networks. It is well acknowledged that 5G covers a wide spectrum of research topics in wired core networks and wireless networks, where network heterogeneity, security, core‐edge collaboration, self‐adaptiveness, spectrum efficiency, quality of service/experience (QoS/QoE), etc, should be collectively considered to push forward its development.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning–based QoS/QoE‐aware service function chaining in software‐driven 5G slices

Chen

Zhang

et al. 2018

With the ever‐growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged quality of experience (QoE) that satisfies the end user's functional and quality‐of‐service (QoS) requirements is necessary. Software‐defined networking (SDN) and network function virtualization (NFV) are considered key enabling technologies for 5G core networks. In this regard, this paper proposes a reinforcement learning–based QoS/QoE‐aware service function chaining (SFC) scheme in SDN/NFV‐enabled 5G slices. First, it implements a lightweight QoS information collector based on the Link Layer Discovery Protocol, which works in a piggyback fashion on the southbound interface of the SDN controller, to enable QoS‐awareness. Then, a deep Q‐network–based orchestration agent is designed to support SFC in the context of NFV. The agent takes into account the QoE and QoS as key aspects to formulate the reward so that it is expected to maximize QoE while respecting QoS constraints. The experiment results show that the proposed framework exhibits good performance in QoE provisioning and QoS requirements maintenance for SFC in dynamic network environments.

“…The property of live migration offered by cloud infrastructure is the core heart of VM consolidations that helps to improve the resource utilization of a physical host, by migrating VMs from least loaded hosts to normal loaded hosts. In this way, smaller resource capabilities available with physical hosts are able to handle higher incoming workloads and thus reduce computing and cooling energy requirements …”

Section: Introductionmentioning

confidence: 99%

Enabling green computing in cloud environments: Network virtualization approach toward 5G support

Verma

Kumar

Kaiwartya

et al. 2018

Virtualization technology has revolutionized the mobile network and widely used in fifth‐generation innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The proliferation of these services among the users led to the establishment of large‐scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models, namely, median migration time, smallest void detection, and maximum fill technique, that can reduce energy consumption with minimal variation in service‐level agreements negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem, and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on linear relationship between power consumption and resource utilization. A virtual machine (VM) migration technique is considered, focusing on synchronization‐oriented shorter stop‐and‐copy phase. The complete operational steps as algorithms are developed for energy‐aware heuristic models including median migration time, smallest void detection, and maximum fill technique. To evaluate the proposed heuristic models, we conduct experimentations using PlanetLab server data often in ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab servers. Through evaluation process, we deduce that the proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining high system performance.