Related papers: Flora: Efficient Cloud Resource Selection for Big …
Recent trends of technology have explored a numerous applications of cloud services, which require a significant amount of energy. In the present scenario, most of the energy sources are limited and have a greenhouse effect on the…
Orchestrating centralised service-oriented workflows presents significant scalability challenges that include: the consumption of network bandwidth, degradation of performance, and single points of failure. This paper presents a high-level…
In the past few years, we have envisioned an increasing number of businesses start driving by big data analytics, such as Amazon recommendations and Google Advertisements. At the back-end side, the businesses are powered by big data…
Spark is an in-memory analytics platform that targets commodity server environments today. It relies on the Hadoop Distributed File System (HDFS) to persist intermediate checkpoint states and final processing results. In Spark, immutable…
Modern cloud-based data analytics systems must efficiently process petabytes of data residing on cloud storage. A key optimization technique in state-of-the-art systems like Snowflake is partition pruning - skipping chunks of data that do…
We propose Flock; a simple and scalable protocol that enables live migration of Virtual Machines (VMs) across heterogeneous edge and conventional cloud platforms to improve the responsiveness of cloud services. Flock is designed with…
Many companies now use crowdsourcing to leverage external (as well as internal) crowds to perform specialized work, and so methods of improving efficiency are critical. Tasks in crowdsourcing systems with specialized work have multiple…
Federated Learning (FL) is a machine learning paradigm that safeguards privacy by retaining client data on edge devices. However, optimizing FL in practice can be challenging due to the diverse and heterogeneous nature of the learning…
This paper explores resource allocation in serverless cloud computing platforms and proposes an optimization approach for autoscaling systems. Serverless computing relieves users from resource management tasks, enabling focus on application…
Increasing scale and heterogeneity in data centers have led to the development of federated clusters such as KubeFed, Hydra, and Pigeon, that federate individual data center clusters. In our work, we introduce Megha, a novel decentralized…
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…
Large-scale cloud data centers have gained popularity due to their high availability, rapid elasticity, scalability, and low cost. However, current data centers continue to have high failure rates due to the lack of proper resource…
Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job…
Cloud Computing is a new era of remote computing / Internet based computing where one can access their personal resources easily from any computer through Internet. Cloud delivers computing as a utility as it is available to the cloud…
Scientific workflow management systems like Nextflow support large-scale data analysis by abstracting away the details of scientific workflows. In these systems, workflows consist of several abstract tasks, of which instances are run in…
Finding the optimal set of cloud resources to deploy a given workload at minimal cost while meeting a defined service level agreement is an active area of research. Combining tens of parameters applicable across a large selection of…
With rapid growth in the amount of unstructured data produced by memory-intensive applications, large scale data analytics has recently attracted increasing interest. Processing, managing and analyzing this huge amount of data poses several…
With the increasing prevalence of computationally intensive workflows in cloud environments, it has become crucial for cloud platforms to optimize energy consumption while ensuring the feasibility of user workflow schedules with respect to…
The problem of complex data analysis is a central topic of modern statistical science and learning systems and is becoming of broader interest with the increasing prevalence of high-dimensional data. The challenge is to develop statistical…
Various performance characteristics of distributed file systems have been well studied. However, the performance efficiency of distributed file systems on small-file problems with complex machine learning algorithms scenarios is not well…