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1. Recorp: Receiver-oriented Policies for Industrial Wireless Networks

   https://doi.org/10.1145/3460618  

   Brummet, Ryan; Hossain, Md Kowsar; Chipara, Octav; Herman, Ted; Stewart, David E. (July 2021, ACM Transactions on Sensor Networks)

   Future Industrial Internet-of-Things (IIoT) systems will require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge in such networks is to provide predictable performance and adaptability in response to link quality variations. We address this challenge by developing RECeiver ORiented Policies (Recorp), which leverages the stability of IIoT workloads by combining offline policy synthesis and run-time adaptation. Compared to schedules that service a single flow in a slot, Recorp policies share slots among multiple flows by assigning a coordinator and a list of flows that may be serviced in the same slot. At run-time, the coordinator will execute one of the flows depending on which flows the coordinator has already received. A salient feature of Recorp is that it provides predictable performance: a policy meets the end-to-end reliability and deadline of flows when the link quality exceeds a user-specified threshold. Experiments show that across IIoT workloads, policies provided a median increase of 50% to 142% in real-time capacity and a median decrease of 27% to 70% in worst-case latency when schedules and policies are configured to meet an end-to-end reliability of 99%. 

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2. A Flexible Retransmission Policy for Industrial Wireless Sensor Actuator Networks

   https://doi.org/10.1109/ICII.2018.00017  

   Brummet, Ryan; Gunatilaka, Dolvara; Vyas, Dhruv; Chipara, Octav; Lu, Chenyang (October 2018, 2018 IEEE International Conference on Industrial Internet (ICII))

   Real-time and reliable communication is essential for industrial wireless sensor-actuator networks. To this end, researchers have proposed a wide range of transmission scheduling techniques. However, these methods usually employ a link-centric policy which allocates a fixed number of retransmissions for each link of a flow. The lack of flexibility of this approach is problematic because failures do not occur uniformly across links and link quality changes over time. In this paper, we propose a flow-centric policy to flexibly and dynamically reallocate retransmissions among the links of a multi-hop flow at runtime. This contribution is complemented by a method for determining the number of retransmissions necessary to achieve a user-specified reliability level under two failures models that capture the common wireless properties of industrial environments. We demonstrate the effectiveness of flow centric policies using empirical evaluations and trace-driven simulations. Testbed experiments indicate a flow-centric policy can provide higher reliability than a link-centric policy because of its flexibility. Trace-driven experiments compare link-centric and flow-centric policies under the two reliability models. Results indicate that when the two approaches are configured to achieve the same reliability level, a flow-centric approach increases the median real-time capacity by as much as 1.42 times and reduces the end-to-end response times by as much as 2.63 times. 

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3. WARP: On-the-fly Program Synthesis for Agile, Real-time, and Reliable Wireless Networks

   https://doi.org/10.1145/3412382.3458270  

   Brummet, Ryan; Hossain, Md Kowsar; Chipara, Octav; Herman, Ted; Goddard, Steve (May 2021, Proceedings of the 20th International Conference on Information Processing in Sensor Networks)

   null (Ed.)

   Emerging Industrial Internet-of-Things systems require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge is to provide predictable performance and agility in response to fluctuations in link quality, variable workloads, and topology changes. We propose WARP to address this challenge. WARP uses programs to specify a network’s behavior and includes a synthesis procedure to automatically generate such programs from a high-level specification of the system’s workload and topology. WARP has three unique features: (1) WARP uses a domain-specific language to specify stateful programs that include conditional statements to control when a flow’s packets are transmitted. The execution paths of programs depend on the pattern of packet losses observed at runtime, thereby enabling WARP to readily adapt to packet losses due to short-term variations in link quality. (2) Our synthesis technique uses heuristics to improve network performance by considering multiple packet loss patterns and associated execution paths when determining the transmissions performed by nodes. Furthermore, the generated programs ensure that the likelihood of a flow delivering its packets by its deadline exceeds a user-specified threshold. (3) WARP can adapt to workload and topology changes without explicitly reconstructing a network’s program based on the observation that nodes can independently synthesize the same program when they share the same workload and topology information. Simulations show that WARP improves network throughput for data collection, dissemination, and mixed workloads on two realistic topologies. Testbed experiments show that WARP reduces the time to add new flows by 5 times over a state-of-the-art centralized control plane and guarantees the real-time and reliability of all flows. 

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4. Simple Policies for Multiresource Job Scheduling

   Chen, Zhongrui; Grosof, Isaac; Berg, Benjamin (September 2024, Association for Computing Machinery)

   Data center workloads are composed of multiresource jobs requiring a variety of computational resources including CPU cores, memory, disk space, and hardware accelerators. Mod- ern servers can run multiple jobs in parallel, but a set of jobs can only run in parallel if the server has sufficient resources to satisfy the demands of each job. It is generally hard to find sets of jobs that perfectly utilize all server resources, and choosing the wrong set of jobs can lead to low resource uti- lization. This raises the question of how to allocate resources across a stream of arriving multiresource jobs to minimize the mean response time across jobs — the mean time from when a job arrives to the system until it is complete. Current policies for scheduling multiresource jobs are com- plex to analyze and hard to implement. We propose a class of simple policies, called Markovian Service Rate (MSR) policies. We show that the class of MSR policies is throughput- optimal, in that if a policy exists that can stabilize the sys- tem, then an MSR policy exists that stabilizes the system. We derive bounds on the mean response time under an MSR policy, and show how our bounds can be used to choose an MSR policy that minimizes mean response time. 

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5. Cache on Track (CoT): Decentralized Elastic Caches for Cloud Environments

   Zakhary, V.; Lim, L.; Agrawal, D.; El Abbadi, A. (March 2021, International Conference on Extending Database Technology)

   Velegrakis, Y.; Zeinalipour-Yazti, D.; Chrysanthis, P.K.; Guerra, F. (Ed.)

   Distributed caches are widely deployed to serve social networks and web applications at billion-user scales. This paper presents Cache-on-Track (CoT), a decentralized, elastic, and predictive caching framework for cloud environments. CoT proposes a new cache replacement policy specifically tailored for small front-end caches that serve skewed workloads with small update percentage. Small front-end caches are mainly used to mitigate the load-imbalance across servers in the distributed caching layer. Front-end servers use a heavy hitter tracking algorithm to continuously track the top-k hot keys. CoT dynamically caches the top-C hot keys out of the tracked keys. CoT’s main advantage over other replacement policies is its ability to dynamically adapt its tracker and cache sizes in response to workload distribution changes. Our experiments show that CoT’s replacement policy consistently outperforms the hit-rates of LRU, LFU, and ARC for the same cache size on different skewed workloads. Also, CoT slightly outperforms the hit-rate of LRU-2 when both policies are configured with the same tracking (history) size. CoT achieves server size load-balance with 50% to 93.75% less front-end cache in comparison to other replacement policies. Finally, experiments show that CoT’s resizing algorithm successfully auto-configures the tracker and cache sizes to achieve back-end load-balance in the presence of workload distribution changes. 

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