An elastic software architecture for extreme-scale big data analytics

This chapter describes a software architecture for processing big-data analytics considering the complete compute continuum, from the edge to the cloud. The new generation of smart systems requires processing a vast amount of diverse information from distributed data sources. The software architecture presented in this chapter addresses two main challenges. On the one hand, a new elasticity concept enables smart systems to satisfy the performance requirements of extreme-scale analytics workloads. By extending the elasticity concept (known at cloud side) across the compute continuum in a fog computing environment, combined with the usage of advanced heterogeneous hardware architectures at the edge side, the capabilities of the extreme-scale analytics can significantly increase, integrating both responsive data-in-motion and latent data-at-rest analytics into a single solution. On the other hand, the software architecture also focuses on the fulfilment of the non-functional properties inherited from smart systems, such as real-time, energy-efficiency, communication quality and security, that are of paramount importance for many application domains such as smart cities, smart mobility and smart manufacturing.The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the ELASTIC Project (www.elastic-project.eu), grant agreement No 825473.Peer ReviewedPostprint (published version

Data-Driven Intelligent Scheduling For Long Running Workloads In Large-Scale Datacenters

Cloud computing is becoming a fundamental facility of society today. Large-scale public or private cloud datacenters spreading millions of servers, as a warehouse-scale computer, are supporting most business of Fortune-500 companies and serving billions of users around the world. Unfortunately, modern industry-wide average datacenter utilization is as low as 6% to 12%. Low utilization not only negatively impacts operational and capital components of cost efficiency, but also becomes the scaling bottleneck due to the limits of electricity delivered by nearby utility. It is critical and challenge to improve multi-resource efficiency for global datacenters. Additionally, with the great commercial success of diverse big data analytics services, enterprise datacenters are evolving to host heterogeneous computation workloads including online web services, batch processing, machine learning, streaming computing, interactive query and graph computation on shared clusters. Most of them are long-running workloads that leverage long-lived containers to execute tasks. We concluded datacenter resource scheduling works over last 15 years. Most previous works are designed to maximize the cluster efficiency for short-lived tasks in batch processing system like Hadoop. They are not suitable for modern long-running workloads of Microservices, Spark, Flink, Pregel, Storm or Tensorflow like systems. It is urgent to develop new effective scheduling and resource allocation approaches to improve efficiency in large-scale enterprise datacenters. In the dissertation, we are the first of works to define and identify the problems, challenges and scenarios of scheduling and resource management for diverse long-running workloads in modern datacenter. They rely on predictive scheduling techniques to perform reservation, auto-scaling, migration or rescheduling. It forces us to pursue and explore more intelligent scheduling techniques by adequate predictive knowledges. We innovatively specify what is intelligent scheduling, what abilities are necessary towards intelligent scheduling, how to leverage intelligent scheduling to transfer NP-hard online scheduling problems to resolvable offline scheduling issues. We designed and implemented an intelligent cloud datacenter scheduler, which automatically performs resource-to-performance modeling, predictive optimal reservation estimation, QoS (interference)-aware predictive scheduling to maximize resource efficiency of multi-dimensions (CPU, Memory, Network, Disk I/O), and strictly guarantee service level agreements (SLA) for long-running workloads. Finally, we introduced a large-scale co-location techniques of executing long-running and other workloads on the shared global datacenter infrastructure of Alibaba Group. It effectively improves cluster utilization from 10% to averagely 50%. It is far more complicated beyond scheduling that involves technique evolutions of IDC, network, physical datacenter topology, storage, server hardwares, operating systems and containerization. We demonstrate its effectiveness by analysis of newest Alibaba public cluster trace in 2017. We are the first of works to reveal the global view of scenarios, challenges and status in Alibaba large-scale global datacenters by data demonstration, including big promotion events like Double 11 . Data-driven intelligent scheduling methodologies and effective infrastructure co-location techniques are critical and necessary to pursue maximized multi-resource efficiency in modern large-scale datacenter, especially for long-running workloads

Contributions to Edge Computing

Efforts related to Internet of Things (IoT), Cyber-Physical Systems (CPS), Machine to Machine (M2M) technologies, Industrial Internet, and Smart Cities aim to improve society through the coordination of distributed devices and analysis of resulting data. By the year 2020 there will be an estimated 50 billion network connected devices globally and 43 trillion gigabytes of electronic data. Current practices of moving data directly from end-devices to remote and potentially distant cloud computing services will not be sufficient to manage future device and data growth. Edge Computing is the migration of computational functionality to sources of data generation. The importance of edge computing increases with the size and complexity of devices and resulting data. In addition, the coordination of global edge-to-edge communications, shared resources, high-level application scheduling, monitoring, measurement, and Quality of Service (QoS) enforcement will be critical to address the rapid growth of connected devices and associated data. We present a new distributed agent-based framework designed to address the challenges of edge computing. This actor-model framework implementation is designed to manage large numbers of geographically distributed services, comprised from heterogeneous resources and communication protocols, in support of low-latency real-time streaming applications. As part of this framework, an application description language was developed and implemented. Using the application description language a number of high-order management modules were implemented including solutions for resource and workload comparison, performance observation, scheduling, and provisioning. A number of hypothetical and real-world use cases are described to support the framework implementation

Tram-tastic Cloud Computing

This master’s thesis evaluates the scalability and cost-effectiveness of the AWS cloud platform used to collect and utilize data generated by the 87 digitally equipped trams. The SL-18 Cloud Platform was developed before the trams arrived, and resource configuration estimates were made to handle the data generated by the trams. However, with a few trams currently operational, it is crucial to evaluate the allocation of resources to the services based on actual data. Thus, the thesis's objective is to estimate the data generated by all 87 trams and evaluate the current resource provisioning on the AWS Cloud Platform in terms of scalability and cost. By doing so, this study will provide insights into the optimal resource allocation required for the AWS Cloud Platform to accommodate the data generated by the trams. In this study, we use an existing Digital Twin tool for the trams to evaluate the scalability of the platform, ensuring that it can handle the load while keeping the cost low. To achieve this, the existing Digital Twin is modified to run 87 or more instances concurrently. Using this modified tool, the SL-18 IT platform, which processes real-time data from all 87 trams simultaneously, is evaluated. We monitored the metrics of AWS services to identify any issues. Then based on measurements, we make recommendations for each service's upgrading, downgrading, or keeping the current configuration. Most services are recommended to scale down to reduce costs, while three services require scaling up to be operational. Although our process is well-defined and could be replicated by other studies, it is crucial to have in-depth discussions with the relevant teams for each service and perform repeated validations and evaluations. This is also a necessary protocol in Sporveien to present the results to the various stakeholders and implement the recommended changes. With these changes, Sporveien can save costs and most importantly have a platform capable of handling the data load of 87 SL-18 trams