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1. Mitigating Large Response Time Fluctuations through Fast Concurrency Adapting in Clouds

   https://doi.org/10.1109/IPDPS47924.2020.00046  

   Liu, Jianshu ; Zhang, Shungeng ; Wang, Qingyang ; Wei, Jinpeng ( May 2020 , 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS))

   null (Ed.)

   Dynamically reallocating computing resources to handle bursty workloads is a common practice for web applications (e.g., e-commerce) in clouds. However, our empirical analysis on a standard n-tier benchmark application (RUBBoS) shows that simply scaling an n-tier application by reallocating hardware resources without fast adapting soft resources (e.g., server threads, connections) may lead to large response time fluctuations. This is because soft resources control the workload concurrency of component servers in the system: adding or removing hardware resources such as Virtual Machines (VMs) can implicitly change the workload concurrency of dependent servers, causing either under- or over-utilization of the critical hardware resource in the system. To quickly identify the optimal soft resource allocation of each server in the system and stabilize response time fluctuation, we propose a novel Scatter-Concurrency-Throughput (SCT) model based on the monitoring of each server's real-time concurrency and throughput. We then implement a Concurrency-aware system Scaling (ConScale) framework which integrates the SCT model to fast adapt the soft resource allocations of key servers during the system scaling process. Our experiments using six realistic bursty workload traces show that ConScale can effectively mitigate the response time fluctuations of the target web application compared to the state-of-the-art cloud scaling strategies such as EC2-AutoScaling. 

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2. Constrained Network Slicing Games: Achieving Service Guarantees and Network Efficiency

   https://doi.org/10.1109/TNET.2023.3262810  

   Zheng, Jiaxiao ; Banchs, Albert ; de Veciana, Gustavo ( January 2023 , IEEE/ACM Transactions on Networking)

   Abstract—Network slicing is a key capability for next gen- eration mobile networks. It enables infrastructure providers to cost effectively customize logical networks over a shared infrastructure. A critical component of network slicing is resource allocation, which needs to ensure that slices receive the resources needed to support their services while optimizing network effi- ciency. In this paper, we propose a novel approach to slice-based resource allocation named Guaranteed seRvice Efficient nETwork slicing (GREET). The underlying concept is to set up a con- strained resource allocation game, where (i) slices unilaterally optimize their allocations to best meet their (dynamic) customer loads, while (ii) constraints are imposed to guarantee that, if they wish so, slices receive a pre-agreed share of the network resources. The resulting game is a variation of the well-known Fisher mar- ket, where slices are provided a budget to contend for network resources (as in a traditional Fisher market), but (unlike a Fisher market) prices are constrained for some resources to ensure that the pre-agreed guarantees are met for each slice. In this way, GREET combines the advantages of a share-based approach (high efficiency by flexible sharing) and reservation-based ones (which provide guarantees by assigning a fixed amount of resources). We characterize the Nash equilibrium, best response dynamics, and propose a practical slice strategy with provable convergence properties. Extensive simulations exhibit substantial improvements over network slicing state-of-the-art benchmarks. 

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3. Data-driven resource flexing for network functions visualization

   https://doi.org/10.1145/3230718.3230725  

   Cao, Lianjie ; Fahmy, Sonia ; Sharma, Puneet ; Zhe, Shandian ( July 2018 , ANCS '18: Symposium on Architectures for Networking and Communications Systems, July 23--24, 2018, Ithaca, NY, USA)

   Resource flexing is the notion of allocating resources on-demand as workload changes. This is a key advantage of Virtualized Network Functions (VNFs) over their non-virtualized counterparts. However, it is difficult to balance the timeliness and resource efficiency when making resource flexing decisions due to unpredictable workloads and complex VNF processing logic. In this work, we propose an Elastic resource flexing system for Network functions VIrtualization (ENVI) that leverages a combination of VNF-level features and infrastructure-level features to construct a neural-network-based scaling decision engine for generating timely scaling decisions. To adapt to dynamic workloads, we design a window-based rewinding mechanism to update the neural network with emerging workload patterns and make accurate decisions in real time. Our experimental results for real VNFs (IDS Suricata and caching proxy Squid) using workloads generated based on real-world traces, show that ENVI provisions significantly fewer (up to 26%) resources without violating service level objectives, compared to commonly used rule-based scaling policies. 

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4. AWARE: Automate Workload Autoscaling with Reinforcement Learning in Production Cloud Systems

   Qiu, Haoran ; Mao, Weichao ; Wang, Chen ; Franke, Hubertus ; Yousseff, Alaa ; Kalbarczyk, Zbigniew T. ; Iyer, Ravishankar K ( July 2023 , 2023 USENIX Annual Technical Conference (USENIX ATC 23))

   Workload autoscaling is widely used in public and private cloud systems to maintain stable service performance and save resources. However, it remains challenging to set the optimal resource limits and dynamically scale each workload at runtime. Reinforcement learning (RL) has recently been proposed and applied in various systems tasks, including resource management. In this paper, we first characterize the state-of-the-art RL approaches for workload autoscaling in a public cloud and point out that there is still a large gap in taking the RL advances to production systems. We then propose AWARE, an extensible framework for deploying and managing RL-based agents in production systems. AWARE leverages meta-learning and bootstrapping to (a) automatically and quickly adapt to different workloads, and (b) provide safe and robust RL exploration. AWARE provides a common OpenAI Gym-like RL interface to agent developers for easy integration with different systems tasks. We illustrate the use of AWARE in the case of workload autoscaling. Our experiments show that AWARE adapts a learned autoscaling policy to new workloads 5.5x faster than the existing transfer-learning-based approach and provides stable online policy-serving performance with less than 3.6% reward degradation. With bootstrapping, AWARE helps achieve 47.5% and 39.2% higher CPU and memory utilization while reducing SLO violations by a factor of 16.9x during policy training. 

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5. O-RAN Performance Analyzer: Platform Design, Development, and Deployment

   https://doi.org/10.1109/MCOM.002.2300822  

   Kouchaki, Mohammadreza ; Natanzi, Seyed_Bagher Hashemi ; Zhang, Minglong ; Tang, Bo ; Marojevic, Vuk ( September 2024 , IEEE Communications Magazine)

   The open radio access network (O-RAN) represents a paradigm shift in RAN architecture, integrating intelligence into communication networks via xApps -- control applications for managing RAN resources. This integration facilitates the adoption of AI for network optimization and resource management. However, there is a notable gap in practical network performance analyzers capable of assessing the functionality and efficiency of xApps in near real-time within operational networks. Addressing this gap, this article introduces a comprehensive network performance analyzer, tailored for the near-real time RAN intelligent controller. We present the design, development, and application scenarios for this testing framework, including its components, software, and tools, providing an end-to-end solution for evaluating the performance of xApps in O-RAN environments. 

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