Microservice is an architectural style widely adopted in various latency-sensitive cloud applications. Similar to the monolith, autoscaling has attracted the attention of operators for managing the resource utilization of microservices. However, it is still challenging to optimize resources in terms of latency service-level-objective (SLO) without human intervention. In this paper, we present GRAF, a graph neural network-based SLO-aware proactive resource autoscaling framework for minimizing total CPU resources while satisfying latency SLO. GRAF leverages front-end workload, distributed tracing data, and machine learning approaches to (a) observe/estimate the impact of traffic change (b) find optimal resource combinations (c) make proactive resource allocation. Experiments using various open-source benchmarks demonstrate that GRAF successfully targets latency SLO while saving up to 19% of total CPU resources compared to the fine-tuned autoscaler. GRAF also handles a traffic surge with 36% fewer resources while achieving up to 2.6x faster tail latency convergence compared to the Kubernetes autoscaler. Moreover, we verify the scalability of GRAF on large-scale deployments, where GRAF saves 21.6% and 25.4% for CPU resources and memory resources, respectively.

Microservice is an architectural style that breaks down monolithic applications into smaller microservices and has been widely adopted by a variety of enterprises. Like the monolith, autoscaling has attracted the attention of operators in scaling microservices. However, most existing approaches of autoscaling do not consider microservice chain and severely degrade the performance of microservices when traffic surges. In this paper, we present pHPA, an autoscaling framework for the microservice chain. pHPA proactively allocates resources to the microservice chains and effectively handles traffic surges. Our evaluation using various open-source benchmarks shows that pHPA reduces 99%-tile latency and resource usage by up to 70% and 58% respectively compared to the most widely used autoscaler when traffic surges.

Microservice is an architectural style that has been widely adopted in various latency-sensitive applications. Similar to the monolith, autoscaling has attracted the attention of operators for managing resource utilization of microservices. However, it is still challenging to optimize resources in terms of latency service-level-objective (SLO) without human intervention. In this paper, we present GRAF, a graph neural network-based proactive resource allocation framework for minimizing total CPU resources while satisfying latency SLO. GRAF leverages front-end workload, distributed tracing data, and machine learning approaches to (a) observe/estimate impact of traffic change (b) find optimal resource combinations (c) make proactive resource allocation. Experiments using various open-source benchmarks demonstrate that GRAF successfully targets latency SLO while saving up to 19% of total CPU resources compared to the fine-tuned autoscaler. Moreover, GRAF handles traffic surge with 36% fewer resources while achieving up to 2.6x faster tail latency convergence compared to the Kubernetes autoscaler.

Network middleboxes provide the first line of defense for enterprise networks. Many of them typically inspect packet payload to filter malicious attack patterns. However, the widespread use of end-to-end cryptographic protocols designed to promote security and privacy, either inhibits deep packet inspection in the network or forces enterprises to use solutions that are not secure. This article introduces a complete framework for building secure and practical network middleboxes, called EVE, which enables visibility over encrypted traffic. EVE securely processes encrypted traffic using a combination of hardware-based trusted execution and software security technology. For enhanced programmability and security, EVE provides a high-level programming interface based on the Rust language. The high-level APIs of EVE provide security and significantly ease the development effort by hiding the details of cryptographic operations, enclave processing, TCP reassembly, and out-of-band key sharing. Our evaluation shows EVE supports diverse use cases with multiple encryption protocols in a secure fashion while delivering high performance.

Minimizing response time of mobile applications is critical for user experience. Existing work predominantly focuses on reducing mobile Web latency, whereas users spend more time on native mobile apps than mobile Web. Similar to Web, mobile apps contain a chain of dependencies between successive requests. However, unlike Web acceleration where object dependencies can easily be identified by parsing Web documents, App acceleration is much more difficult because the dependency is encoded in the app binary.

Android application is an important class on today's Internet. While understanding app-specific behavior is important for network operation and management, it is often difficult because it requires an in-depth application-layer protocol analysis due to the common use of HTTP(S) and standard data representations (e.g., JSON). This paper presents Extractocol, the first system to offer an automatic and comprehensive analysis of application protocol behaviors. Extractocol only uses Android application binary as input and accurately reconstructs HTTP transactions (request-response pairs) and identifies their message format and relationships using binary analysis. Our evaluation and in-depth case studies on commercial and open-source apps demonstrate that Extractocol provides high coverage and accurately characterizes network-related application behaviors.

Minimizing response times for mobile applications is critical for quality user experience that often impacts the revenue of mobile services. Generalized approaches to accelerated mobile applications (e.g., TCP acceleration, SPDY, compression) are less effective because they do not take account for application specific behaviors. In contrast, application specific approaches build application-specific proxies by leveraging the app-specific protocol behaviors to enable dynamic caching and/or prefetching. However, this is non-trivial because it requires manual analysis of application level protocols and their interactions. Therefore, only a small number of apps enjoyed the benefit.

In this work, we present a highly scalable network intrusion detection system on many-core processors. To maximize the NIDS performance, we take advantage of the underlying hardware and adhere to four design principles: shared-nothing architecture, computation offloading, lightweight data structure, and flow offloading. Through the experimental results, we find that our design choices can significantly improve the NIDS performance (79 Gbps with 1514B synthetic packets). We believe that our design decisions can be easily extended to other many-core processors and programmable NICs.