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We investigate the use of SmartNIC-accelerated servers to execute microservice-based applications in the data center. By offloading suitable microservices to the SmartNIC’s low-power processor, we can improve server energy-efficiency without latency loss. However, as a heterogeneous computing substrate in the data path of the host, SmartNICs bring several challenges to a microservice platform: network traffic routing and load balancing, microservice placement on heterogeneous hardware, and contention on shared SmartNIC resources. We present E3, a microservice execution platform for SmartNIC-accelerated servers. E3 follows the design philosophies of the Azure Service Fabric microservice platform and extends key system components to a SmartNIC to address the above-mentioned challenges. E3 employs three key techniques: ECMP-based load balancing via SmartNICs to the host, network topology-aware microservice placement, and a data-plane orchestrator that can detect SmartNIC overload. Our E3 prototype using Cavium LiquidIO SmartNICs shows that SmartNIC offload can improve cluster energy-efficiency up to 3× and cost efficiency up to 1.9× at up to 4% latency cost for common microservices, including real-time analytics, an IoT hub, and virtual network functions.
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EdgeBalance: Model-Based Load Balancing for Network Edge Data Planes
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 re-source 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.
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- Award ID(s):
- 1763548
- PAR ID:
- 10211600
- Date Published:
- Journal Name:
- Workshop on Hot Topics in Edge Computing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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