Timely Long Tail Identification through Agent Based Monitoring and Analytics

The increasing complexity and scale of distributed systems has resulted in the manifestation of emergent behavior which substantially affects overall system performance. A significant emergent property is that of the "Long Tail", whereby a small proportion of task stragglers significantly impact job execution completion times. To mitigate such behavior, straggling tasks occurring within the system need to be accurately identified in a timely manner. However, current approaches focus on mitigation rather than identification, which typically identify stragglers too late in the execution lifecycle. This paper presents a method and tool to identify Long Tail behavior within distributed systems in a timely manner, through a combination of online and offline analytics. This is achieved through historical analysis to profile and model task execution patterns, which then inform online analytic agents that monitor task execution at runtime. Furthermore, we provide an empirical analysis of two large-scale production Cloud data enters that demonstrate the challenge of data skew within modern distributed systems, this analysis shows that approximately 5% of task stragglers caused by data skew impact 50% of the total jobs for batch processes. Our results demonstrate that our approach is capable of identifying task stragglers less than 11% into their execution lifecycle with 98% accuracy, signifying significant improvement over current state-of-the-art practice and enables far more effective mitigation strategies in large-scale distributed systems worldwide

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

Mitigate data skew caused stragglers through ImKP partition in MapReduce

Speculative execution is the mechanism adopted by current MapReduce framework when dealing with the straggler problem, and it functions through creating redundant copies for identified stragglers. The result of the quicker task will be adopted to improve the overall job execution performance. Although proved to be effective for contention caused stragglers, speculative execution can easily meet its bottleneck when mitigating data skew caused stragglers due to its replication nature: the identical unbalanced input data will lead to a slow speculative task. The Map inputs are typically even in size according to the HDFS block configuration, therefore the skew caused stragglers happen mainly in the Reduce phase because of the unknown intermediate key distribution. In this paper, we focus on mitigating data skew caused Reduce stragglers, propose ImKP, an Intermediate Key Pre-processing framework that enables the even distributed partition for Reduce inputs. A group based ranking technique has been developed that dramatically decreases the pre-processing time, and ImKP manages to eliminate this timing overhead through parallelizing the pre-processing with the file uploading procedure (from local file system to HDFS). For jobs that take input directly from HDFS, ImKP minimizes the overhead by storing the mapping result on every node within the cluster for reuse. Experiments are conducted on different datasets with various workloads. Results show that, compared to the popular hash partition, ImKP can dramatically decrease Reduce skew, achieving a 99.8% reduction in the coefficient of variation of the input sizes in average, and improve up to 29.37% job response performance

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%

Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres

Cloud computing systems are splitting compute- and data-intensive jobs into smaller tasks to execute them in a parallel manner using clusters to improve execution time. However, such systems at increasing scale are exposed to stragglers, whereby abnormally slow running tasks executing within a job substantially affect job performance completion. Such stragglers are a direct threat towards attaining fast execution of data-intensive jobs within cloud computing. Researchers have proposed an assortment of different mechanisms, frameworks, and management techniques to detect and mitigate stragglers both proactively and reactively. In this paper, we present a comprehensive review of straggler management techniques within large-scale cloud data centres. We provide a detailed taxonomy of straggler causes, as well as proposed management and mitigation techniques based on straggler characteristics and properties. From this systematic review, we outline several outstanding challenges and potential directions of possible future work for straggler research

A prescriptive analytics approach for energy efficiency in datacentres.

Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.N/

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues

Optimising data centre operation by removing the transport bottleneck

Data centres lie at the heart of almost every service on the Internet. Data centres are used to provide search results, to power social media, to store and index email, to host “cloud” applications, for online retail and to provide a myriad of other web services. Consequently the more efficient they can be made the better for all of us. The power of modern data centres is in combining commodity off-the-shelf server hardware and network equipment to provide what Google’s Barrosso and Ho ̈lzle describe as “warehouse scale” computers. Data centres rely on TCP, a transport protocol that was originally designed for use in the Internet. Like other such protocols, TCP has been optimised to maximise throughput, usually by filling up queues at the bottleneck. However, for most applications within a data centre network latency is more critical than throughput. Consequently the choice of transport protocol becomes a bottleneck for performance. My thesis is that the solution to this is to move away from the use of one-size-fits-all transport protocols towards ones that have been designed to reduce latency across the data centre and which can dynamically respond to the needs of the applications. This dissertation focuses on optimising the transport layer in data centre networks. In particular I address the question of whether any single transport mechanism can be flexible enough to cater to the needs of all data centre traffic. I show that one leading protocol (DCTCP) has been heavily optimised for certain network conditions. I then explore approaches that seek to minimise latency for applications that care about it while still allowing throughput-intensive applications to receive a good level of service. My key contributions to this are Silo and Trevi. Trevi is a novel transport system for storage traffic that utilises fountain coding to max- imise throughput and minimise latency while being agnostic to drop, thus allowing storage traffic to be pushed out of the way when latency sensitive traffic is present in the network. Silo is an admission control system that is designed to give tenants of a multi-tenant data centre guaranteed low latency network performance. Both of these were developed in collaboration with others

Suffolk Journal, vol. 69, no. 19, 3/11/2009

https://dc.suffolk.edu/journal/1513/thumbnail.jp