Modern cloud applications increasingly rely on adaptive control modules, such as dynamic resource tuning or system reconfiguration, to meet strict quality-of-service (QoS) objectives. However, when multiple independently developed adaptation modules are colocated on a shared infrastructure, their uncoordinated behavior causes interference leading to QoS violations. Existing approaches require centralized control or inter-module communication, violating modularity and limiting adoption in multi-tenant environments.

Analyzing time-series correlations is key to anomaly detection and incident diagnosis, but existing methods that rely on fixed time windows often fail or are inaccurate in environments with (a) missing adequate ground truth, (b) asynchronous signals, or (c) mixed sampling rates containing irregularities in signals. To provide anomaly-relevant signal correlations given these challenges, we propose AdaptWin, a novel method to adaptively select windows for a signal-pair of the same size with distinct positions, i.e., the specific start and end timestamps can differ, for correlation-aware ranking. Our window selection is based on deviations in (1) inter-arrival times and (2) observed values. This flexible window selection approach improves ranking by better capturing signal variations aligned with anomalies. Across three real-world datasets, AdaptWin improves anomaly-relevant ranking by over 3 x compared to adaptive baselines.

Production HPC clusters endure failures reducing computational capability and resource availability. Despite the presence of various failure prediction schemes for large-scale computing systems, a comprehensive understanding of how nodes fail considering various components and layers of the system is required for sustained resilience. This work performs a holistic diagnosis of node failures using a measurement-driven approach on contemporary system logs that can help vendors and system administrators support exascale resilience.Our work shows that external environmental influence is not strongly correlated with node failures in terms of the root cause. Though hardware and software faults trigger failures, the underlying root cause often lies in the application malfunctioning causing the system to fail. Furthermore, lead time enhancements are feasible for nodes showing fail slow characteristics. This study excavates such helpful empirical observations, which could facilitate better failure handling in production systems.

Large-scale production systems are well known to encounter node failures, which affect compute capacity and energy. Both in HPC systems and enterprise data centers, combating failures is becoming challenging with increasing hardware and software complexity. Several data mining solutions of logs have been investigated in the context of anomaly detection in such systems. However, with subsequent proactive failure mitigation, the existing log mining solutions are not sufficiently fast for real-time anomaly detection. Machine learning (ML)-based training can produce high accuracy but the inference scheme needs to be enhanced with rapid parsers to assess anomalies in real-time. This work tackles online anomaly prediction in computing systems by exploiting context free grammar-based rapid event analysis. We present our framework Aarohi1, which describes an effective way to predict failures online. Aarohi is designed to be generic and scalable making it suitable as a real-time predictor. Aarohi obtains more than 3 minutes lead times to node failures with an average of 0.31 msecs prediction time for a chain length of 18. The overall improvement obtained w.r.t. the existing state-of-the-art is over a factor of 27.4×. Our compiler-based approach provides new research directions for lead time optimization with a significant prediction speedup required for the deployment of proactive fault tolerant solutions in practice.

Today's large-scale supercomputers encounter faults on a daily basis. Exascale systems are likely to experience even higher fault rates due to increased component count and density. Triggering resilience-mitigating techniques remains a challenge due to the absence of well defined failure indicators. System logs consist of unstructured text that obscures essential system health information contained within. In this context, efficient failure prediction via log mining can enable proactive recovery mechanisms to increase reliability.

Summary Distributed key‐value stores have become indispensable for large‐scale cluster applications. Many cloud services have deployed in‐memory data grids for their enterprise infrastructures and support multi‐tenancy services. However, most services do not offer fine‐grained multi‐tenant resource sharing. To this front, we present KeyValueServe, a low overhead cloud service with features aiding resource management. Results based on Hazelcast, a popular open source data grid, indicate that KeyValueServe can efficiently provide services to tenants without degrading performance. Providing consistent performance to all tenants for fluctuating workloads is still difficult. Performance problems occur at scale with diverse tenant requirements. To address this, the paper provides insights to contention and performance bottlenecks. Through experimental analysis, we uncover scenarios of performance degradation and demonstrate optimized performance via coalescing multiple clients' requests. Our work indicates that a Hazelcast cluster can get congested with multiple concurrent connections when processing client requests, resulting in poor performance. KeyValueServe can reduce the number of parallel connections maintained for client requests, resulting in improved performance.

Distributed key-value stores have become indispensable for large scale low latency applications. Many cloud services have deployed in-memory data grids for their enterprise infrastructures and support multi-tenancy services. But it is still difficult to provide consistent performance to all tenants for fluctuating workloads that need to scale out. Many popular key-value stores suffer from performance problems at scale and different tenant requirements. To this front, we present our study with Hazelcast, a popular open source data grid, and provide insights to contention and performance bottlenecks. Through experimental analysis, this paper uncovers scenarios of performance degradation followed by optimized performance via end-point multiplexing. Our study suggests that processing increasing number of client requests spawning fewer number of threads help improve performance.