Straggler Root-Cause and Impact Analysis for Massive-scale Virtualized Cloud Datacenters

Increased complexity and scale of virtualized distributed systems has resulted in the manifestation of emergent phenomena substantially affecting overall system performance. This phenomena is known as “Long Tail”, whereby a small proportion of task stragglers significantly impede job completion time. While work focuses on straggler detection and mitigation, there is limited work that empirically studies straggler root-cause and quantifies its impact upon system operation. Such analysis is critical to ascertain in-depth knowledge of straggler occurrence for focusing developmental and research efforts towards solving the Long Tail challenge. This paper provides an empirical analysis of straggler root-cause within virtualized Cloud datacenters; we analyze two large-scale production systems to quantify the frequency and impact stragglers impose, and propose a method for conducting root-cause analysis. Results demonstrate approximately 5% of task stragglers impact 50% of total jobs for batch processes, and 53% of stragglers occur due to high server resource utilization. We leverage these findings to propose a method for extreme straggler detection through a combination of offline execution patterns modeling and online analytic agents to monitor tasks at runtime. Experiments show the approach is capable of detecting stragglers less than 11% into their execution lifecycle with 95% accuracy for short duration jobs

Computing at massive scale: Scalability and dependability challenges

Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications

AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities

Real-time performance diagnosis and evaluation of big data systems in cloud datacenters

PhD ThesisModern big data processing systems are becoming very complex in terms of largescale, high-concurrency and multiple talents. Thus, many failures and performance reductions only happen at run-time and are very difficult to capture. Moreover, some issues may only be triggered when some components are executed. To analyze the root cause of these types of issues, we have to capture the dependencies of each component in real-time. Big data processing systems, such as Hadoop and Spark, usually work in large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. Several systems and methods exist to detect big data processing systems’ performance degradation, perform root-cause analysis, and even overcome the issues causing such degradation. However, these solutions focus on specific problems such as stragglers and inefficient resource utilization. There is a lack of a generic and extensible framework to support the real-time diagnosis of big data systems. Performance diagnosis and prediction of big data systems are highly complex as these frameworks are typically deployed in cloud data centers that are large-scale, highly concurrent, and follows a multi-tenant model. Several factors, including hardware heterogeneity, stochastic networks and application workloads may impact the performance of big data systems. The current state-of-the-art does not sufficiently address the challenge of determining complex, usually stochastic and hidden relationships between these factors. To handle performance diagnosis and evaluation of big data systems in cloud environments, this thesis proposes multilateral research towards monitoring and performance diagnosis and prediction in cloud-based large-scale distributed systems by involving a novel combination of an effective and efficient deployment pipeline.The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors. The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors. The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors.State of the Republic of Turkey and the Turkish Ministry of National Educatio

Towards a Secure Zero-rating Framework with Three Parties

Zero-rating services allow mobile users to access contents from contracted CP free of data charge. In this thesis, we introduce attacks against the zero rating service which allows extra non-contracted traffic to be transported free of charge. We call this types of attack the \u27free-riding\u27 attack. Specifically, we create two types of free-riding attacks: 1) masquerade zero-rating CP attack; 2) response packets modification attack. We conducted multiple experiments on several major commercial cellular and WiFi ISPs in the United States and China. The experimental results show that all these ISPs are vulnerable to free-riding attacks.In this thesis, we also propose a secure and backward compatible zero-rating framework, called ZFree. ZFree authorizes network traffic from valid CP to be zero-rated. Next, we perform a formal security verification using ProVerif on ZFree. The formal verification results show that ZFree is secure in preserving packet integrity and CP server authenticity. Our evaluation shows that ZFree is lightweight, scalable and secure

Intelligent Straggler Mitigation in Massive-Scale Computing Systems

In order to satisfy increasing demands for Cloud services, modern computing systems are often massive in scale, typically consisting of hundreds to thousands of heterogeneous machine nodes. Parallel computing frameworks such as MapReduce are widely deployed over such cluster infrastructure to provide reliable yet prompt services to customers. However, complex characteristics of Cloud workloads, including multi-dimensional resource requirements and highly changeable system environments, e.g. dynamic node performance, are introducing new challenges to service providers in terms of both customer experience and system efficiency. One primary challenge is the straggler problem, whereby a small subset of the parallelized tasks take abnormally longer execution time in comparison with the siblings, leading to extended job response and potential late-timing failure. The state-of-the-art approach to straggler mitigation is speculative execution. Although it has been deployed in several real-world systems with a variety of implementation optimizations, the analysis from this thesis has shown that speculative execution is often inefficient. According to various production tracelogs of data centers, the failure rate of speculative execution could be as high as 71%. Straggler mitigation is a complicated problem in its own nature: 1) stragglers may lead to different consequences to parallel job execution, possibly with different degrees of severity, 2) whether a task should be regarded as a straggler is highly subjective, depending upon different application and system conditions, 3) the efficiency of speculative execution would be improved if dynamic node performance could be modelled and predicted appropriately, and 4) there are other types of stragglers, e.g. those caused by data skews, that are beyond the capability of speculative execution. This thesis starts with a quantitative and rigorous analysis of issues with stragglers, including their root-causes and impacts, the execution environment running them, and the limitations to their mitigation. Scientific principles of straggler mitigation are investigated and new algorithms are developed. An intelligent system for straggler mitigation is then designed and developed, being compatible with the majority of current parallel computing frameworks. Combined with historical data analysis and online adaptation, the system is capable of mitigating stragglers intelligently, dynamically judging a task as a straggler and handling it, avoiding current weak nodes, and dealing with data skew, a special type of straggler, with a dedicated method. Comprehensive analysis and evaluation of the system show that it is able to reduce job response time by up to 55%, as compared with the speculator used in the default YARN system, while the optimal improvement a speculative-based method may achieve is around 66% in theory. The system also achieves a much higher success rate of speculation than other production systems, up to 89%

Distributed Tracing for Troubleshooting of Native Cloud Applications via Rule-Induction Systems

Diagnosing IT issues is a challenging problem for large-scale distributed cloud environments due to complex and non-deterministic interrelations between the system components. Modern monitoring tools rely on AI-empowered data analytics for detection, root cause analysis, and rapid resolution of performance degradation. However, the successful adoption of AI solutions is anchored on trust. System administrators will not unthinkingly follow the recommendations without sufficient interpretability of solutions. Explainable AI is gaining popularity by enabling improved confidence and trust in intelligent solutions. For many industrial applications, explainable models with moderate accuracy are preferable to highly precise black-box ones. This paper shows the benefits of rule-induction classification methods, particularly RIPPER, for the root cause analysis of performance degradations. RIPPER reveals the causes of problems in a set of rules system administrators can use in remediation processes. Native cloud applications are based on the microservices architecture to consume the benefits of distributed computing. Monitoring such applications can be accomplished via distributed tracing, which inspects the passage of requests through different microservices. We discuss the application of rule-learning approaches to trace traffic passing through a malfunctioning microservice for the explanations of the problem. Experiments performed on datasets from cloud environments proved the applicability of such approaches and unveiled the benefits

Proactive Interference-aware Resource Management in Deep Learning Training Cluster

Deep Learning (DL) applications are growing at an unprecedented rate across many domains, ranging from weather prediction, map navigation to medical imaging. However, training these deep learning models in large-scale compute clusters face substantial challenges in terms of low cluster resource utilisation and high job waiting time. State-of-the-art DL cluster resource managers are needed to increase GPU utilisation and maximise throughput. While co-locating DL jobs within the same GPU has been shown to be an effective means towards achieving this, co-location subsequently incurs performance interference resulting in job slowdown. We argue that effective workload placement can minimise DL cluster interference at scheduling runtime by understanding the DL workload characteristics and their respective hardware resource consumption. However, existing DL cluster resource managers reserve isolated GPUs to perform online profiling to directly measure GPU utilisation and kernel patterns for each unique submitted job. Such a feedback-based reactive approach results in additional waiting times as well as reduced cluster resource efficiency and availability. In this thesis, we propose Horus: an interference-aware and prediction-based DL cluster resource manager. Through empirically studying a series of microbenchmarks and DL workload co-location combinations across heterogeneous GPU hardware, we demonstrate the negative effects of performance interference when colocating DL workload, and identify GPU utilisation as a general proxy metric to determine good placement decisions. From these findings, we design Horus, which in contrast to existing approaches, proactively predicts GPU utilisation of heterogeneous DL workload extrapolated from the DL model computation graph features when performing placement decisions, removing the need for online profiling and isolated reserved GPUs. By conducting empirical experimentation within a medium-scale DL cluster as well as a large-scale trace-driven simulation of a production system, we demonstrate Horus improves cluster GPU utilisation, reduces cluster makespan and waiting time, and can scale to operate within hundreds of machines