Big IoT data needs to be frequently moved between edge and cloud for efficient analysis and storage. Data movement is costly in low-bandwidth wide area network environments. Data compression can dramatically reduce data size to mitigate the bandwidth bottleneck. However, compression is compute-intensive and compression throughput can be limited by available CPU resources. The impact of available computation capability of the resource-constrained edge on the edge-to-cloud data transfer rate is apparent. Our study reveals compressors, including gzip, bzip2, lzma, and zstd, perform very differently under various resource-constrained conditions. This motivates us to propose models for the best compressor selection under CPU, network, and storage resource limitation conditions on the edge. We implement ZipMate, a middleware that enables resource-aware and adaptive compression policy based on the model. Our evaluation shows that adaptive policies consistently outperform unitary or random compressor selection policies.
Edge data centers are an appealing place for telecommunication providers to offer in-network processing such as VPN services, security monitoring, and 5G. Placing these network services closer to users can reduce latency and core network bandwidth, but the deployment of network functions at the edge poses several important challenges. Edge data centers have limited resource capacity, yet network functions are resource intensive with strict performance requirements. Replicating services at the edge is needed to meet demand, but balancing the load across multiple servers can be challenging due to diverse service costs, server and flow heterogeneity, and dynamic workload conditions. In this paper, we design and implement a model-based load balancer EdgeBalance for edge network data planes. EdgeBalance predicts the CPU demand of incoming traffic and adaptively distributes flows to servers to keep them evenly balanced. We overcome several challenges specific to network processing at the edge to improve throughput and latency over static load balancing and monitoring-based approaches.
Driven by the Internet-of-Things (IoT) and 5G, the growing size and complexity of smart home networks leads to an increased attack surface. Smart home IoT devices are typically online 24/7, have out-of-date firmware, are not regularly patched against the latest security vulnerabilities, and often collect sensitive data and send it to the cloud. In this work we propose microsegmentation as a mean to reduce the attack surface of smart home networks with the assistance of the edge cloud. We implement two network functions that cooperate to enforce fine-grained network security policies in smart homes. One function builds an inventory of all devices and their vulnerabilities. The second utilizes that information to dynamically allocate IoT devices to microsegments, and isolates them from one another using interand intrasegment network-level security policies. We evaluated our approach using three different IoT network security metrics and IoT topologies. In the best case, microsegmentation reduces the attack surface exposed to a Mirai-infected IoT webcam by as much as 65.85% at the cost of preventing 2.16% of the otherwise-valid network flows between devices.
In the resource-rich environment of data centers most failures can quickly failover to redundant resources. In contrast, failure in edge infrastructures with limited resources might require maintenance personnel to drive to the location in order to fix the problem. The operational cost of these“truck rolls” to locations at the edge infrastructure competes with the operational cost incurred by extra space and power needed for redundant resources at the edge. Computational storage devices with network interfaces can act as network-attached storage servers and offer a new design point for storage systems at the edge. In this paper we hypothesize that a system consisting of a larger number of such small “embedded” storage nodes provides higher availability due to a larger number of failure domains while also saving operational cost in terms of space and power. As evidence for our hypothesis, we compared the possibility of data loss between two different types of storage systems: one is constructed with general-purpose servers, and the other one is constructed with embedded storage nodes. Our results show that the storage system constructed with general-purpose servers has 7 to 20 times higher risk of losing data over the storage system constructed with embedded storage devices. We also compare the two alternatives in terms of power and space using the Media-Based Work Unit (MBWU) that we developed in an earlier paper as a reference point.
Edge computing is an emerging computing paradigm where data is generated and processed in the field using distributed computing devices. Many applications such as real-time video processing, augmented/virtual reality gaming, environment sensing, benefit from such decentralized, close-to-user deployments where low-latency, real-time results are expected. As with any distributed application, one of the key challenges in the development of collaborative applications is how to efficiently share data and state among multiple edge clients. The dynamic and heterogeneous environment together with diverse application’s requirements make data sharing at the edge a challenging problem. Although there have been prior efforts, a systematic understanding of the area is missing. In this paper, we conduct a methodological study of different edge applications, their data sharing needs, and designs of state-of-the-art systems. In the process, we identify design options, under-explored opportunities, and associated challenges. We then present Griffin, our edge data sharing service, and seek feedback on its design.
Motivated from Software Defined Networking (SDN), LTE standard body (3GPP) has recently proposed splitting monolithic LTE Network Functions (NFs) into their control-plane and data-plane modules for better performance, flexibility and agility. The data-plane logic is pushed at the edge of the network, while retaining control-plane functionality at the core. However, both network edge and the core modules involve in executing LTE control-plane procedures (e.g. device registration/deregistration, and mobility etc.) as well as LTE dataplane services (e.g. voice over LTE, and video streaming, etc.). We discover that these decoupled modules, being part of the same LTE network function, interact frequently and cause deadlocks and races. In this paper, we argue that SDN style approach may not work for LTE NFs due to their monolithic design. We reason to retain LTE legacy design by not splitting its NFs. Our idea is to keep all types of LTE control-plane procedures handling at the core; while moving the execution of LTE dataplane services to the edge as microservices. We propose FERRET that is inspired from the success of the Circuit Switch Fall Back (CSFB) procedure, and falls-back to specific LTE microservice for the user requesting a particular LTE service. It first records signaling messages exchange as part of LTE service establishment phase at the core and then replays these messages at dedicated microservice to enable that service handling. FERRET lets microservice to facilitate LTE service execution that provides data forwarding at the edge.
Containers are becoming the canonical way of deploying compute tasks at the edge. Unfortunately, container startup latency and overhead remain high, limiting responsiveness and resource efficiency of edge deployments. This latency comes mostly from fetching container dependencies including system libraries, tools, configuration files, and data files. To address this, we propose that schedulers in container or-chestrators take into account a task’s dependencies. Hence, in dependency scheduling , the scheduler tries to place a task at a node that has the maximum number of the task’s dependencies stored locally. We implement dependency scheduling within Kubernetes and evaluate it through extensive experiments and measurement-driven simulations. We show that, for typical scenarios, dependency scheduling improves task startup latency by 1.4-2.3x relative to current dependency-agnostic schedulers. Our implementation of dependency scheduling has been adopted into the mainline Kubernetes codebase.
The decreasing cost and power consumption of intelligent, interconnected, and interactive devices at the edge of the internet are creating massive opportunities to instrument our cities, factories, farms, and environment to improve efficiency, safety and productivity. Developing, debugging, deploying and securing software for the estimated trillion connected devices present substantial challenges. As part of the SMARTER (Secure Municipal, Agricultural, Rural, and Telco Edge Research) project, Arm has been exploring the use of cloudnative technology and methodologies in edge environments to evaluate their effectiveness at addressing these problems at scale.
Traditional LTE networks route Internet traffic through a packet gateway. Enterprise LTE networks with a cloudbased core use a similarly faraway gateway. To provide lowlatency services, such as accessing nearby mobile devices, fog services, or localized information, a “local-exit” to the Internet is needed to avoid traveling through the LTE core. To create a local-exit, we build P4EC, a terabit capable mobile edge cloud using a programmable switch to distinguish and reroute traffic. P4EC is placed physically near the cellular deployment and reroutes specifically identified traffic to and from the mobile device. The P4EC implements packet redirection using the P4 programmable switching hardware that supports terabit throughput in inexpensive equipment. P4EC operates without any modification to the LTE core. This work describes a working proof-of-concept operating in an actual LTE network.
Connected and Autonomous Vehicles (CAVs) is becoming more and more popular for automobile academic and industry community. Communication mechanisms play an important role in CAVs applications and services. However, lack of detailed comparison of different communication mechanisms is the main obstacle for the deployment of CAVs applications and services. In this paper, we set up an end-to-end prototype which supports WiFi, LTE, and DSRC based communications and evaluate the performance in latency, power dissipation, and system utilization. Three observations are summarized for the real deployment of VEC applications.
Hardware accelerator devices have emerged as an alternative to traditional CPUs since they not only help perform computations faster but also consume much less energy than a traditional CPU thereby helping to lower both capex (i.e., lower procurement costs) and opex (i.e., lesser energy usage). However, since different accelerator technologies can illustrate different traits for different application types that run at the edge, there is a critical need for effective mechanisms that help developers select the right technology (or a mix of) to use in their context, which is currently lacking. To address this critical need, we propose a recommender system to help users rapidly and cost-effectively select the right hardware accelerator technology for a given compute intensive task. Our framework comprises the following workflow. First, we collect realistic execution traces of computations on real, single hardware accelertor devices. Second, we utilize these traces to deduce the achievable latencies and amortized costs of device deployments at scale, which serves as the guidance in selecting the right hardware.
