An official website of the United States government
Edge cloud solutions that bring the cloud closer to the sensors can be very useful to meet the low latency requirements of many Internet-of-Things (IoT) applications. However, IoT traffic can also be intermittent, so running applications constantly can be wasteful. Therefore, having a serverless edge cloud that is responsive and provides low-latency features is a very attractive option for a resource and cost-efficient IoT application environment.In this paper, we discuss the key components needed to support IoT traffic in the serverless edge cloud and identify the critical challenges that make it difficult to directly use existing serverless solutions such as Knative, for IoT applications. These include overhead from heavyweight components for managing the overall system and software adaptors for communication protocol translation used in off-the-shelf serverless platforms that are designed for large-scale centralized clouds. The latency imposed by ‘cold start’ is a further deterrent.To address these challenges we redesign several components of the Knative serverless framework. We use a streamlined protocol adaptor to leverage the MQTT IoT protocol in our serverless framework for IoT event processing. We also create a novel, event-driven proxy based on the extended Berkeley Packet Filter (eBPF), to replace the regular heavyweight Knative queue proxy. Our preliminary experimental results show that the event-driven proxy is a suitable replacement for the queue proxy in an IoT serverless environment and results in lower CPU usage and a higher request throughput.
more »
« less
The hidden cost of the edge: a performance comparison of edge and cloud latencies
Edge computing has emerged as a popular paradigm for running latency-sensitive applications due to its ability to offer lower network latencies to end-users. In this paper, we argue that despite its lower network latency, the resource-constrained nature of the edge can result in higher end-to-end latency, especially at higher utilizations, when compared to cloud data centers. We study this edge performance inversion problem through an analytic comparison of edge and cloud latencies and analyze conditions under which the edge can yield worse performance than the cloud. To verify our analytic results, we conduct a detailed experimental comparison of the edge and the cloud latencies using a realistic application and real cloud workloads. Both our analytical and experimental results show that even at moderate utilizations, the edge queuing delays can offset the benefits of lower network latencies, and even result in performance inversion where running in the cloud would provide superior latencies. We finally discuss practical implications of our results and provide insights into how application designers and service providers should design edge applications and systems to avoid these pitfalls.
more »
« less
- PAR ID:
- 10356561
- Date Published:
- Journal Name:
- International Conference for High Performance Computing, Networking, Storage and Analysis
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Edge computing is an emerging paradigm whose goal is to boost with cloud resources available at the edge the computational capability of otherwise weak devices. This paradigm is mostly attractive to reduce user perceived latency. A central mechanism in edge computing is cyber-foraging, i.e., the search and delegation to capable edge cloud processes of tasks too complex, time consuming or resource intensive to be running on user devices or low-latency demanding to be running remotely, as a form of edge function. An edge function is any network or device-specific process that may be run on an edge process instead. Despite the recent interest for this technology from industry and academia, cyber-foraging techniques and protocols have yet to be standardized. In this paper, we leverage decomposition theory to propose an architecture providing insights in the design and implementation of protocols for cyber-foraging of multiple edge functions. In contrast with several existing solutions, we argue that the (distributed) cyber-foraging orchestration should be policy-based and not an ad-hoc solution, i.e., either a pure edge cloud burden or a device decision. To this end, via simulations, we show how our approach can be used by edge computing providers and application programmers to compare and evaluate different alternative cyber-foraging solutions. Our decomposition-based approach has general applicability to other network utility maximization problems, even outside the edge computing domain.more » « less
-
With the explosion of intelligent and latency-sensitive applications such as AR/VR, remote health and autonomous driving, mobile edge computing (MEC) has emerged as a promising solution to mitigate the high end-to-end latency of mobile cloud computing (MCC). However, the edge servers have significantly less computing capability compared to the resourceful central cloud. Therefore, a collaborative cloud-edge-local offloading scheme is necessary to accommodate both computationally intensive and latency-sensitive mobile applications. The coexistence of central cloud, edge servers and the mobile device (MD), forming a multi-tiered heterogeneous architecture, makes the optimal application deployment very challenging especially for multi-component applications with component dependencies. This paper addresses the problem of energy and latency efficient application offloading in a collaborative cloud-edgelocal environment. We formulate a multi-objective mixed integer linear program (MILP) with the goal of minimizing the systemwide energy consumption and application end-to-end latency. An approximation algorithm based on LP relaxation and rounding is proposed to address the time complexity. We demonstrate that our approach outperforms existing strategies in terms of application request acceptance ratio, latency and system energy consumption.more » « less
-
The rapid growth in technology and wide use of internet has increased smart applications such as intelligent transportation control system, and Internet of Things, which heavily rely on an efficient and reliable connectivity network. To overcome high bandwidth work load on the network, as well as minimize latency for real-time applications, the computation can be moved from the central cloud to a distributed edge cloud. The edge computing benefits various smart applications that uses distributed network for data analytics and services. Different from the existing cloud management solutions, edge computing needs to move cloud management services towards distributed heterogeneous edge nodes for multi-tenant user applications. However, existing cloud management services do not offer remote deployment of multi-tenant user applications on the cloud of edge nodes. In this paper, we propose a practical edge cloud software framework for deploying multi-tenant distributed smart applications. Having multiple distributed end nodes, auto discovery of all active end nodes is required for deploying multi-tenant user applications. However, existing cloud solutions require either private network or fixed IP address, which is not achievable for the distributed edge nodes. Most of the edge nodes connected through the public internet without fixed IP, and some of them even connect through IEEE 802.15 based sensor networks. We propose to build a software platform to manage the distributed edge nodes as well as support services to deploy and launch isolated, multi-tenant user applications through a lightweight container. We propose an architectural solution to remotely access edge cloud management services through intermittent internet connections. We open sourced our whole set of software solutions, and analyzed the major performance metrics of the edge cloud platform.more » « less
-
5G edge clouds promise a pervasive computational infrastructure a short network hop away, enabling a new breed of smart devices that respond in real-time to their physical surroundings. Unfortunately, today’s operating system designs fail to meet the goals of scalable isolation, dense multi-tenancy, and high performance needed for such applications. In this paper we introduce EdgeOS that emphasizes system-wide isolation as fine-grained as per-client. We propose a novel memory movement accelerator architecture that employs data copying to enforce strong isolation without performance penalties. To support scalable isolation, we introduce a new protection domain implementation that offers lightweight isolation, fast startup and low latency even under high churn. We implement EdgeOS in a microkernel based OS and demonstrate running high scale network middleboxes using the Click software router and endpoint applications such as memcached, a TLS proxy, and neural network inference. We reduce startup latency by 170X compared to Linux processes, and improve latency by three orders of magnitude when running 300 to 1000 edge-cloud memcached instances on one server.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3458817.3476142
