Muhammad Abdullah

Containerized workloads are gaining traction due to microservices architecture adaptation in many fields, including healthcare, finance, Internet of Things, and smart cities. Modern data centers are containerized to facilitate this growing demand. Most of the existing resource allocation methods for data centers used efficient scheduling algorithms to place the containers using static computing resources. These static resource allocation techniques are not energy efficient and do not help maximize data center utilization. Dynamic resource allocation and a migration-enabled placement method can reduce energy utilization while improving the utilization of the available computing infrastructure. This article presents and evaluates a novel dynamic resource management system that uses active migrations to minimize energy utilization to serve containerized workloads and improve data center utilization. Our approach uses a deep learning method to estimate the job execution time and then employs an unsupervised learning method to identify similar jobs. Similar jobs are placed and migrated to achieve energy efficiency and better utilization of the available data center infrastructure. Our proposed system is evaluated and compared with the existing state-of-the-art baseline methods. The proposed solution reduces the energy consumption from $\times 1.18$ to $\times 2.35$ compared to the baseline methods while maintaining similar performance.

Containers provide a lightweight runtime environment for microservices applications while enabling better server utilization. Automatic optimal allocation of CPU pins to the containers serving specific workloads can help to minimize the completion time of jobs. Most of the existing state-of-the-art focused on building new efficient scheduling algorithms for placing the containers on the infrastructure, and the resources to the containers are allocated manually and statically. An automatic method to identify and allocate optimal CPU resources to the containers can help to improve the efficiency of the scheduling algorithms. In this paper, we introduce a new deep learning-based approach to allocate optimal CPU resources to the containers automatically. Our approach uses the law of diminishing marginal returns to determine the optimal number of CPU pins for containers to gain maximum performance while maximizing the number of concurrent jobs. The proposed method is evaluated using real workloads on a Docker-based containerized infrastructure. The results demonstrate the effectiveness of the proposed solution in reducing the completion time of the jobs by 23% to 74% compared to commonly used static CPU allocation methods.

Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.

Fog computing provides microdata center (MDC) facilities closer to the users and applications, which help to overcome the application latency and response time concerns. However, guaranteeing specific service-level objectives (SLOs) for the applications running on the MDC requires automatic scaling of allocated resources by efficiently utilizing the available infrastructure capacity. In this article, we propose a novel predictive autoscaling method for microservices running on the fog MDC to satisfy the application response time SLO. Initially, our proposed approach uses a reactive rule-based autoscaling method to gather the training dataset for building the predictive autoscaling model. The proposed approach is efficient, as it can learn the predictive autoscaling model using an increasing synthetic workload. The learned predictive autoscaling model is used to manage the application resources serving different realistic workloads effectively. Our experimental evaluation using two synthetic and three realistic workloads for two benchmark microservice applications on a real MDC shows excellent performance compared to the existing state-of-the-art baseline rule-based autoscaling method. The proposed autoscaling method yields 75.51% reduction in the number of rejected requests and 77.53% fewer number of SLO violations compared to the baseline autoscaling methods by using only 9.20% additional data center resources at the fog layer.

Autoscaling methods are used for cloud-hosted applications to dynamically scale the allocated resources for guaranteeing Quality-of-Service (QoS). The public-facing application serves dynamic workloads, which contain bursts and pose challenges for autoscaling methods to ensure application performance. Existing State-of-the-art autoscaling methods are burst-oblivious to determine and provision the appropriate resources. For dynamic workloads, it is hard to detect and handle bursts online for maintaining application performance. In this paper, we propose a novel burst-aware autoscaling method which detects burst in dynamic workloads using workload forecasting, resource prediction, and scaling decision making while minimizing response time service-level objectives (SLO) violations. Our approach is evaluated through a trace-driven simulation, using multiple synthetic and realistic bursty workloads for containerized microservices, improving performance when comparing against existing state-of-the-art autoscaling methods. Such experiments show an increase of x1.09 in total processed requests, a reduction of x5.17 for SLO violations, and an increase of x0.767 cost as compared to the baseline method.