Near real-time optimization of fog service placement for responsive edge computing

In recent years, computing workloads have shifted from the cloud to the fog, and IoT devices are becoming powerful enough to run containerized services. While the combination of IoT devices and fog computing has many advantages, such as increased efficiency, reduced network traffic and better end user experience, the scale and volatility of the fog and edge also present new problems for service deployment scheduling.Fog and edge networks contain orders of magnitude more devices than cloud data centers, and they are often less stable and slower. Additionally, frequent changes in network topology and the number of connected devices are the norm in edge networks, rather than the exception as in cloud data centers.This article presents a service scheduling algorithm, labeled "Swirly", for fog and edge networks containing hundreds of thousands of devices, which is capable of incorporating changes in network conditions and connected devices. The theoretical performance is explored, and a model of the behaviour and limits of fog nodes is constructed. An evaluation of Swirly is performed, showing that it is capable of managing service meshes for at least 300.000 devices in near real-time

Dynamic service placement in geographically distributed clouds

Abstract-Large-scale online service providers have been increasingly relying on geographically distributed cloud infrastructures for service hosting and delivery. In this context, a key challenge faced by service providers is to determine the locations where service applications should be placed such that the hosting cost is minimized while key performance requirements (e.g. response time) are assured. Furthermore, the dynamic nature of both demand pattern and infrastructure cost favors a dynamic solution to this problem. Currently most of the existing solutions for service placement have either ignored dynamics, or provided inadequate solutions that achieve both objectives at the same time. In this paper, we present a framework for dynamic service placement problems based on control-and game-theoretic models. In particular, we present a solution that optimizes the desired objective dynamically over time according to both demand and resource price fluctuations. We further consider the case where multiple service providers compete for resource in a dynamic manner, and show that there is a Nash equilibrium solution which is socially optimal. Using simulations based on realistic topologies, demand and resource prices, we demonstrate the effectiveness of our solution in realistic settings

Doctor of Philosophy

dissertationNetwork emulation has become an indispensable tool for the conduct of research in networking and distributed systems. It offers more realism than simulation and more control and repeatability than experimentation on a live network. However, emulation testbeds face a number of challenges, most prominently realism and scale. Because emulation allows the creation of arbitrary networks exhibiting a wide range of conditions, there is no guarantee that emulated topologies reflect real networks; the burden of selecting parameters to create a realistic environment is on the experimenter. While there are a number of techniques for measuring the end-to-end properties of real networks, directly importing such properties into an emulation has been a challenge. Similarly, while there exist numerous models for creating realistic network topologies, the lack of addresses on these generated topologies has been a barrier to using them in emulators. Once an experimenter obtains a suitable topology, that topology must be mapped onto the physical resources of the testbed so that it can be instantiated. A number of restrictions make this an interesting problem: testbeds typically have heterogeneous hardware, scarce resources which must be conserved, and bottlenecks that must not be overused. User requests for particular types of nodes or links must also be met. In light of these constraints, the network testbed mapping problem is NP-hard. Though the complexity of the problem increases rapidly with the size of the experimenter's topology and the size of the physical network, the runtime of the mapper must not; long mapping times can hinder the usability of the testbed. This dissertation makes three contributions towards improving realism and scale in emulation testbeds. First, it meets the need for realistic network conditions by creating Flexlab, a hybrid environment that couples an emulation testbed with a live-network testbed, inheriting strengths from each. Second, it attends to the need for realistic topologies by presenting a set of algorithms for automatically annotating generated topologies with realistic IP addresses. Third, it presents a mapper, assign, that is capable of assigning experimenters' requested topologies to testbeds' physical resources in a manner that scales well enough to handle large environments

Design and evaluation of virtual network migration mechanisms on shared substrate

The Internet faces well-known challenges in realizing modifications to the core architecture. To help overcome these limitations, the use of network virtualization has been proposed. Network virtualization enables the deployment of novel network architectures and services on existing Internet infrastructure. Virtual networks run over physical networks and use Internet paths and protocols as essentially a link layer in the virtual network. Virtual networks can also share the resources in the physical substrate. Effective use of the underlying substrate network requires intelligent placement of virtual networks so that underlying resources do not incur over-subscription. Because virtual networks can come and go over time, and underlying networks can experience their own dynamic changes, virtual networks need to be migrated---re-mapped to the physical network during active operation---to maintain good performance. While virtual network placement, and to a lesser extent migration, has been studied in the past, little attention has been devoted to designing, deploying, and evaluating migration mechanisms for virtual networks. In this dissertation, we design virtual network migration mechanisms for different substrate platforms and further design a system to mitigate the effects of virtual network migration. In particular this dissertation makes the following contributions: 1. With the goal of minimizing the disruption during a virtual network migration, we design three algorithms for scheduling the sequence of virtual router moves that takes a virtual network from its original placement to its new placement. 2. We design and implement a controller-based architecture for virtual network migration on PlanetLab. This work explores the challenges in implementing virtual network migration on real infrastructure. Recommendations are given for infrastructure that support virtual network migration. 3. We propose and implement a mechanism to mitigate the performance degradation resulting from virtual network migration through transport and application layer collaboration. We utilize a centralized controller to notify the end-systems or the gateways about the time of the virtual network migration such that we prevent packet loss to the application traffic of the end-systems.Ph.D

Efficient Resource Management for Cloud Computing Environments

Cloud computing has recently gained popularity as a cost-effective model for hosting and delivering services over the Internet. In a cloud computing environment, a cloud provider packages its physical resources in data centers into virtual resources and offers them to service providers using a pay-as-you-go pricing model. Meanwhile, a service provider uses the rented virtual resources to host its services. This large-scale multi-tenant architecture of cloud computing systems raises key challenges regarding how data centers resources should be controlled and managed by both service and cloud providers. This thesis addresses several key challenges pertaining to resource management in cloud environments. From the perspective of service providers, we address the problem of selecting appropriate data centers for service hosting with consideration of resource price, service quality as well as dynamic reconfiguration costs. From the perspective of cloud providers, as it has been reported that workload in real data centers can be typically divided into server-based applications and MapReduce applications with different performance and scheduling criteria, we provide separate resource management solutions for each type of workloads. For server-based applications, we provide a dynamic capacity provisioning scheme that dynamically adjusts the number of active servers to achieve the best trade-off between energy savings and scheduling delay, while considering heterogeneous resource characteristics of both workload and physical machines. For MapReduce applications, we first analyzed task run-time resource consumption of a large variety of MapReduce jobs and discovered it can vary significantly over-time, depending on the phase the task is currently executing. We then present a novel scheduling algorithm that controls task execution at the level of phases with the aim of improving both job running time and resource utilization. Through detailed simulations and experiments using real cloud clusters, we have found our proposed solutions achieve substantial gain compared to current state-of-art resource management solutions, and therefore have strong implications in the design of real cloud resource management systems in practice

Efficient Resource Management for Cloud Computing Environments

Cloud computing has recently gained popularity as a cost-effective model for hosting and delivering services over the Internet. In a cloud computing environment, a cloud provider packages its physical resources in data centers into virtual resources and offers them to service providers using a pay-as-you-go pricing model. Meanwhile, a service provider uses the rented virtual resources to host its services. This large-scale multi-tenant architecture of cloud computing systems raises key challenges regarding how data centers resources should be controlled and managed by both service and cloud providers. This thesis addresses several key challenges pertaining to resource management in cloud environments. From the perspective of service providers, we address the problem of selecting appropriate data centers for service hosting with consideration of resource price, service quality as well as dynamic reconfiguration costs. From the perspective of cloud providers, as it has been reported that workload in real data centers can be typically divided into server-based applications and MapReduce applications with different performance and scheduling criteria, we provide separate resource management solutions for each type of workloads. For server-based applications, we provide a dynamic capacity provisioning scheme that dynamically adjusts the number of active servers to achieve the best trade-off between energy savings and scheduling delay, while considering heterogeneous resource characteristics of both workload and physical machines. For MapReduce applications, we first analyzed task run-time resource consumption of a large variety of MapReduce jobs and discovered it can vary significantly over-time, depending on the phase the task is currently executing. We then present a novel scheduling algorithm that controls task execution at the level of phases with the aim of improving both job running time and resource utilization. Through detailed simulations and experiments using real cloud clusters, we have found our proposed solutions achieve substantial gain compared to current state-of-art resource management solutions, and therefore have strong implications in the design of real cloud resource management systems in practice

Service Placement in a Shared Wide-Area Platform

Emerging federated computing environments offer attractive platforms to test and deploy global-scale distributed applications. When nodes in these platforms are time-shared among competing applications, available resources vary across nodes and over time. Thus, one open architectural question in such systems is how to map applications to available nodes—that is, how to discover and select resources. Using a six-month trace of PlanetLab resource utilization data and of resource demands from three long-running PlanetLab services, we quantitatively characterize resource availability and application usage behavior across nodes and over time, and investigate the potential to mitigate the application impact of resource variability through intelligent service placement and migration. We find that usage of CPU and network resources is heavy and highly variable. We argue that this variability calls for intelligently mapping applications to available nodes. Further, we find that node placement decisions can become illsuited after about 30 minutes, suggesting that some applications can benefit from migration at that timescale, and that placement and migration decisions can be safely based on data collected at roughly that timescale. We find that inter-node latency is stable and is a good predictor of available bandwidth; this observation argues for collecting latency data at relatively coarse timescales and bandwidth data at even coarser timescales, using the former to predict the latter between measurements. Finally, we find that although the utilization of a particular resource on a particular node is a good predictor of that node’s utilization of that resource in the near future, there do not exist correlations to support predicting one resource’s availability based on availability of other resources on the same node at the same time, on availability of the same resource on other nodes at the same site, or on time-series forecasts that assume a daily or weekly regression to the mean. 1