Related papers: Call Scheduling to Reduce Response Time of a FaaS …
We study the scheduling polices for asymptotically optimal delay in queueing systems with switching overhead. Such systems consist of a single server that serves multiple queues, and some capacity is lost whenever the server switches to…
Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant benefits in terms of scalability, rapid development,…
Modern high performance computing (HPC) systems exhibit a rapid growth in size, both "horizontally" in the number of nodes, as well as "vertically" in the number of cores per node. As such, they offer additional levels of hardware…
In parking lots with large groups of electric vehicles (EVs), charging has to happen in a coordinated manner, among others, due to the high load per vehicle and the limited capacity of the electricity grid. To achieve such coordination,…
AWS Lambda is a serverless event-driven compute service, part of a category of cloud compute offerings sometimes called Function-as-a-service (FaaS). When we first released AWS Lambda, functions were limited to 250MB of code and…
In cloud computing systems, assigning a job to multiple servers and waiting for the earliest copy to finish is an effective method to combat the variability in response time of individual servers. Although adding redundant replicas always…
Beowulf clusters are very popular and deployed worldwide in support of scientific computing, because of the high computational power and performance. However, they also pose several challenges, and yet they need to provide high…
The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency…
In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request…
Emerging workloads, such as graph processing and machine learning are approximate because of the scale of data involved and the stochastic nature of the underlying algorithms. These algorithms are often distributed over multiple machines…
High-performance computing systems are complex machines whose behaviour is governed by the correct functioning of its many subsystems. Among these, the workload scheduler has a crucial impact on the timely execution of the jobs continuously…
Serverless query processing has become increasingly popular due to its auto-scaling, high elasticity, and pay-as-you-go pricing. It allows cloud data warehouse (or lakehouse) users to focus on data analysis without the burden of managing…
In current cellular networks, schedulers allocate wireless channel resources to users based on instantaneous channel gains and short-term moving averages of user rates and queue lengths. By using only such short-term information, schedulers…
In queueing systems, effective scheduling algorithms are essential for optimizing performance. Optimal scheduling for the M/G/k queue has been explored in the heavy traffic limit, but much remains unknown in the intermediate load regime. In…
We present a new framework for designing nonpreemptive and job-size oblivious scheduling policies in the multiserver-job queueing model. The main requirement is to identify a static and balanced sub-partition of the server set and ensure…
Modern data center workloads are composed of multiserver jobs, computational jobs that require multiple servers in order to run. A data center server can run many multiserver jobs in parallel, as long as it has sufficient resources to meet…
With the escalating complexity and volume of genomic data, the capacity of biology institutions' HPC faces limitations. While the Cloud presents a viable solution for short-term elasticity, its intricacies pose challenges for bioinformatics…
FaaS (Function-as-a-Service) revolutionized cloud computing by replacing persistent virtual machines with dynamically allocated resources. This shift trades locality and statefulness for a pay-as-you-go model more suited to variable and…
Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational resources for dataflow jobs is often challenging. For efficient execution, individual…
Metadata hotspots remain one of the key obstacles to scalable Input/Output (I/O) in both High-Performance Computing (HPC) and cloud-scale storage environments. Situations such as job start-ups, checkpoint storms, or heavily skewed namespace…