Related papers: Optimizing simultaneous autoscaling for serverless…
Cloud computing is a new archetype that provides dynamic computing services to cloud users through the support of datacenters that employs the services of datacenter brokers which discover resources and assign them Virtually. The focus of…
The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing…
Resource autoscaling mechanisms in cloud environments depend on accurate performance metrics to make optimal provisioning decisions. When infrastructure faults including hardware malfunctions, network disruptions, and software anomalies…
As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance…
Autoscaling is a technology that automatically scales resources for applications without human intervention to ensure runtime Quality of Service (QoS) while reducing costs. However, user-facing cloud applications serve dynamic workloads…
Applications are moving away from monolithic designs to microservice and serverless architectures, where fleets of lightweight and independently deployable components run on public clouds. Autoscaling serves as the primary control mechanism…
Today's data centers have an abundance of computing resources, hosting server clusters consisting of as many as tens or hundreds of thousands of machines. To execute a complex computing task over a data center, it is natural to distribute…
This paper considers a cost minimization problem for data centers with N servers and randomly arriving service requests. A central router decides which server to use for each new request. Each server has three types of states (active, idle,…
We investigate an optimal scheduling problem in a discrete-time system of L parallel queues that are served by K identical, randomly connected servers. Each queue may be connected to a subset of the K servers during any given time slot.…
Edge computing allows for the decentralization of computing resources. This decentralization is achieved through implementing microservice architectures, which require low latencies to meet stringent service level agreements (SLA) such as…
Problem Definition: Allocating sufficient capacity to cloud services is a challenging task, especially when demand is time-varying, heterogeneous, contains batches, and requires multiple types of resources for processing. In this setting,…
A parallel server system is a stochastic processing network with applications in manufacturing, supply chain, ride-hailing, call centers, etc. Heterogeneous customers arrive in the system, and only a subset of servers can serve any customer…
Experiment-in-the-Loop Computing (EILC) requires support for numerous types of processing and the management of heterogeneous infrastructure over a dynamic range of scales: from the edge to the cloud and HPC, and intermediate resources.…
Distributed Machine Learning refers to the practice of training a model on multiple computers or devices that can be called nodes. Additionally, serverless computing is a new paradigm for cloud computing that uses functions as a…
The applications that are deployed in the cloud to provide services to the users encompass a large number of interconnected dependent cloud components. Multiple identical components are scheduled to run concurrently in order to handle…
Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions. However, it is still non-trivial for users to allocate appropriate resources due to…
We consider an automatic overload control for two large service systems modeled as multi-server queues, such as call centers. We assume that the two systems are designed to operate independently, but want to help each other respond to…
In cloud computing resource management plays a significant role in data centres and it is directly dependent on the application workload. Various services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and…
Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these…
Modern day continued demand for resource hungry services and applications in IT sector has led to development of Cloud computing. Cloud computing environment involves high cost infrastructure on one hand and need high scale computational…