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 Smart home IoT devices are typically online 24/7, have out-of-date firmware, are not regularly patched against the latest security vulnerabilities, and often collect sensitive data and send it to the cloud.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.
