The transition from monolithic architecture to microservices has enhanced flexibility in application design and its scalable execution. This approach typically uses a computing cluster managed by a container orchestration platform to deploy microservices. However, this shift introduces significant challenges, particularly in the efficient scheduling of containerized services. These challenges are compounded by unpredictable scenarios such as dynamic incoming workloads with various execution traffic and variable communication delays among cluster nodes. Existing works often overlook the real-time traffic impacts of dynamic requests on running microservices, as well as the varied communication delays across cluster nodes. Consequently, even optimally deployed microservices could suffer from significant performance degradation over time. To address these issues, we propose a network and traffic-aware adaptive scheduling framework,TraDE, which can adaptively redeploy microservice instances to maintain desired performance amid changing traffic and network conditions within the hosting cluster. We have implementedTraDEas an extension to the Kubernetes platform. Additionally, we deployed realistic microservice applications in a real compute cluster and conducted extensive experiments to assess our framework's performance in various scenarios. The results demonstrate the effectiveness ofTraDEin rescheduling running microservices to enhance end-to-end performance while maintaining a high goodput ratio. Compared with the existing method NetMARKS,TraDEoutperforms it by reducing the average response time of the application by up to 48.3%, and improving the throughput by up to 1.2-1.5× across workloads while maintaining a goodput ratio of 95.36%, and showing robust adaptive capability to meet QoS targets under sustained workloads and dynamic networking conditions.

The rapid development of emerging vehicular edge computing (VEC) brings new opportunities and challenges for dynamic resource management. The increasing number of edge data centers, roadside units (RSUs), and network devices, how-ever, makes resource management a complex task in VEC. On the other hand, the exponential growth of service applications and end-users makes corresponding QoS hard to maintain. Intent-Based Networking (IBN), based on Software-Defined Networking, was introduced to provide the ability to automatically handle and manage the networking requirements of different applications. Motivated by the IBN concept, in this paper, we propose a novel approach to jointly orchestrate networking and computing resources based on user requirements. The proposed solution constantly monitors user requirements and dynamically re-configures the system to satisfy desired states of the application. We compared our proposed solution with the state-of-the-art networking embedding algorithms using real-world taxi GPS traces. Results show that our proposed method is significantly faster (up to 95%) and can improve resource utilization (up to 76%) and the acceptance ratio of computing and networking requests with various priorities (up to 71%). We also present a small-scale prototype of the proposed intent management framework to validate our solution.

Big data frameworks such as Apache Spark is becoming prominent to perform large-scale data analytics jobs in various domains. However, due to limited resource availability, the local or on-premise computing resources are often not sufficient to run these jobs. Therefore, public cloud resources can be hired on a pay-per-use basis from the cloud service providers to deploy a Spark cluster entirely on the cloud. Nevertheless, using only cloud resources can be costly. Hence, both local and cloud resources nowadays are used together to deploy a hybrid cloud computing cluster. However, scheduling jobs in a cluster deployed on hybrid clouds is challenging in the presence of various Service-Level Agreement (SLA) demands such as cost minimization and job deadline guarantee. Most of the existing works either consider a public or a locally deployed cluster and mainly focus on improving job performance in the cluster. In this article, we propose efficient scheduling algorithms that leverage from different VM instance pricing in a hybrid cloud deployed cluster to optimize the Virtual Machine (VM) usage cost for both local and cloud resources and maximize the job deadline met percentage. We have conducted extensive simulation-based experiments to compare our proposed algorithms with the baseline approaches. In addition, we have developed a prototype system on top of Apache Mesos cluster manager and performed real experiments to evaluate the applicability of our proposed approaches in a real platform with benchmark applications. The results show that our proposed algorithms are highly scalable and reduce the cost of VM usage of a hybrid cluster for up to 20 percent.

Big data frameworks such as Spark and Hadoop are widely adopted to run analytics jobs in both research and industry. Cloud offers affordable compute resources which are easier to manage. Hence, many organizations are shifting towards a cloud deployment of their big data computing clusters. However, job scheduling is a complex problem in the presence of various Service Level Agreement (SLA) objectives such as monetary cost reduction, and job performance improvement. Most of the existing research does not address multiple objectives together and fail to capture the inherent cluster and workload characteristics. In this article, we formulate the job scheduling problem of a cloud-deployed Spark cluster and propose a novel Reinforcement Learning (RL) model to accommodate the SLA objectives. We develop the RL cluster environment and implement two Deep Reinforce Learning (DRL) based schedulers in TF-Agents framework. The proposed DRL-based scheduling agents work at a fine-grained level to place the executors of jobs while leveraging the pricing model of cloud VM instances. In addition, the DRL-based agents can also learn the inherent characteristics of different types of jobs to find a proper placement to reduce both the total cluster VM usage cost and the average job duration. The results show that the proposed DRL-based algorithms can reduce the VM usage cost up to 30%.

Large-scale data processing framework like Apache Spark is becoming more popular to process large amounts of data either in a local or a cloud deployed cluster. When an application is deployed in a Spark cluster, all the resources are allocated to it unless users manually set a limit on the available resources. In addition, it is not possible to impose any user-specific constraints and minimize the cost of running applications. In this paper, we present dSpark, a lightweight, pluggable resource allocation framework for Apache Spark. In dSpark, we have modelled the application completion time with respect to the number of executors and application input/iteration. This model is further used in our proposed resource allocation model where a deadlinebased, cost-efficient resource allocation scheme can be selected for any application. As opposed to the existing frameworks that focus more on modelling the number of VMs to use for an application, we have modelled both the application cost and completion time with respect to executors, hence providing a finegrained resource allocation scheme. In addition, users do not need to specify any application types in dSpark. We have evaluated our proposed framework through extensive experimentation, which shows significant performance benefits. The application completion time prediction model has a mean relative error (RE) less than 7% for different types of applications. Furthermore, we have shown that our proposed resource allocation model minimizes the cost of running applications and selects effective resource allocation schemes under varying user-specific deadlines.