With the development of IT infrastructures, applications and systems generate a tsunami of data that keeps growing. Traditional IT management solutions can’t keep up with volume and complexity. Artificial intelligence for IT operations (AIOps) is an extremely effective method that could simplify IT operations management and accelerate & automate problem resolution in complex modern IT environments. Alarm root cause location is an important scenario and key function of AIOps. At present, the relevant research work mainly focuses on the association mining of historical alarm data, forming alarm rules, and processing offline alarm compression. However, the practical applications require faster and more accurate root cause location of alarms, which could put forward higher requirements for its real-time performance. The online real-time alarm root cause location algorithm proposed in this paper can fully explore the relationship between alarms in the dimension of time and space, and achieve online alarm data compression through technologies such as alarm association time, alarm event division, and alarm event topology generation. Real-time accurate division of alarm events and real-time location of key alarms greatly improve the velocity and accuracy of root cause location. The algorithm has been launched on a mobile network operator's 5G network management system. With the application of the proposed algorithm, over 10,000 alarms are processed per minute, and the accuracy of the root cause of the alarm has reached 85%, which has achieved good online effects.

In a stream data analytics system, input data arrive continuously and trigger the processing and updating of analytics results. We focus on applications with real-time constraints, in which, any data unit must be completely processed within a given time duration. To handle fast data, it is common to place the stream data analytics system on top of a cloud infrastructure. Because stream properties, such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time responses. It is essential, for existing systems or future developments, to possess the ability of scaling resources dynamically according to the instantaneous workload, in order to avoid wasting resources or failing in delivering the correct analytics results on time. Motivated by this, we propose DRS, a dynamic resource scaling framework for cloud-based stream data analytics systems. DRS overcomes three fundamental challenges: 1) how to model the relationship between the provisioned resources and the application performance, 2) where to best place resources, and 3) how to measure the system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of Jackson open queueing networks and is capable of handling arbitrary operator topologies, possibly with loops, splits, and joins. Extensive experiments with real data show that DRS is capable of detecting sub-optimal resource allocation and making quick and effective resource adjustment.

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources, and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of Jackson open queueing networks and is capable of handling arbitrary operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.

We present LiveTraj, a novel system for tracking trajectories in a live video stream in real time, backed by a cloud platform. Although trajectory tracking is a well-studied topic in computer vision, so far most attention has been devoted to improving the accuracy of trajectory tracking, rather than the efficiency. To our knowledge, LiveTraj is the first that achieves real-time efficiency in trajectory tracking, which can be a key enabler in many important applications such as video surveillance, action recognition and robotics. LiveTraj is based on a state-of-the-art approach to (offline) trajectory tracking; its main innovation is to adapt this base solution to run on an elastic cloud platform to achieve real-time tracking speed at an affordable cost. The video demo shows the offline base solution and LiveTraj side by side, both running on a video stream containing human actions. Besides demonstrating the real-time efficiency of LiveTraj, our video demo also exhibits important system parameters to the audience such as latency and cloud resource usage for different components of the system. Further, if the conference venue provides sufficiently fast Internet connection to our cloud platform, we also plan to demonstrate LiveTraj on-site, during which we will show LiveTraj identifying and tracking trajectories from a live video stream captured by a camera.

The emergence of the cloud computing paradigm has greatly enabled innovative service models, such as Platform as a Service (PaaS), and distributed computing frameworks, such as Map Reduce. However, most existing cloud systems fail to distinguish users with different preferences, or jobs of different natures. Consequently, they are unable to provide service differentiation, leading to inefficient allocations of cloud resources. Moreover, contentions on the resources exacerbate this inefficiency, when prioritizing crucial jobs is necessary, but impossible. Motivated by this, we propose Abacus, a generic resource management framework addressing this problem. Abacus interacts with users through an auction mechanism, which allows users to specify their priorities using budgets, and job characteristics via utility functions. Based on this information, Abacus computes the optimal allocation and scheduling of resources. Meanwhile, the auction mechanism in Abacus possesses important properties including incentive compatibility (i.e., the users' best strategy is to simply bid their true budgets and job utilities) and monotonicity (i.e., users are motivated to increase their budgets in order to receive better services). In addition, when the user is unclear about her utility function, Abacus automatically learns this function based on statistics of her previous jobs. An extensive set of experiments, running on Hadoop, demonstrate the high performance and other desirable properties of Abacus.