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Numerous studies have underscored the significant privacy risks associated with various leakage patterns in encrypted data stores. While many solutions have been proposed to mitigate these leakages, they either (1) incur substantial…
Heuristic search is often used for motion planning and pathfinding problems, for finding the shortest path in a graph while also promising completeness and optimal efficiency. The drawback is it's space complexity, specifically storing all…
The rapid development of cloud-native architecture has promoted the widespread application of container technology, but the optimization problems in container scheduling and resource management still face many challenges. This paper…
Airline operations are subject to many uncertainties, such as weather, varying demand, maintenance events, congestion, etc. Large amounts of information are currently ignored due to difficulties in processing big data sets. We explore the…
Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance. Here we propose a novel cloud-based HyperOpt (CHOPT) framework which can efficiently utilize shared…
Utilitarian algorithm configuration identifies a parameter setting for a given algorithm that maximizes a user's utility. Utility functions offer a theoretically well-grounded approach to optimizing decision-making under uncertainty and are…
The flexibility and the variety of computing resources offered by the cloud make it particularly attractive for executing user workloads. However, IaaS cloud environments pose non-trivial challenges in the case of workflow scheduling under…
The performance levels of a computing machine running a given workload configuration are crucial for both users and providers of computing resources. Knowing how well a computing machine is running with a given workload configuration is…
Spacecraft increasingly rely on heterogeneous computing resources spanning onboard flight computers, orbital data centers, ground station edge nodes, and terrestrial cloud infrastructure. Selecting where a workload should execute is a…
Distributed dataflow systems enable data-parallel processing of large datasets on clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. Yet, selecting appropriate cloud…
This paper addresses the challenge of deadline-aware online scheduling for jobs in hybrid cloud environments, where jobs may run on either cost-effective but unreliable spot instances or more expensive on-demand instances, under hard…
Speculative decoding enables collaborative Large Language Model (LLM) inference across cloud and edge by separating lightweight token drafting from heavyweight verification. While prior systems show performance and cost benefits, practical…
Resource orchestration and configuration parameter search are key concerns for container-based infrastructure in cloud data centers. Large configuration search space and cloud uncertainties are often mitigated using contextual bandit…
We formulate selecting the best optimizing system (SBOS) problems and provide solutions for those problems. In an SBOS problem, a finite number of systems are contenders. Inside each system, a continuous decision variable affects the…
We present a framework for scheduling multifunction serverless applications over a hybrid public-private cloud. A set of serverless jobs is input as a batch, and the objective is to schedule function executions over the hybrid platform to…
Distributed dataflow systems like Apache Flink and Apache Spark simplify processing large amounts of data on clusters in a data-parallel manner. However, choosing suitable cluster resources for distributed dataflow jobs in both type and…
Query processing over big data is ubiquitous in modern clouds, where the system takes care of picking both the physical query execution plans and the resources needed to run those plans, using a cost-based query optimizer. A good cost…
Transitioning cloud-based Hadoop from IaaS to PaaS, which are commercially conceptualized as pay-as-you-go or pay-per-use, often reduces the associated system costs. However, managed Hadoop systems do present a black-box behavior to the…
Given the rapid rise in energy demand by data centers and computing systems in general, it is fundamental to incorporate energy considerations when designing (scheduling) algorithms. Machine learning can be a useful approach in practice by…
Cloud Computing has emerged as a key technology to deliver and manage computing, platform, and software services over the Internet. Task scheduling algorithms play an important role in the efficiency of cloud computing services as they aim…