Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers

funcX: A Federated Function Serving Fabric for Science

Exploding data volumes and velocities, new computational methods and platforms, and ubiquitous connectivity demand new approaches to computation in the sciences. These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e.g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available. They also require new design approaches in which monolithic applications can be decomposed into smaller components, that may in turn be executed separately and on the most suitable resources. To address these needs we present funcX---a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. funcX's endpoint software can transform existing clouds, clusters, and supercomputers into function serving systems, while funcX's cloud-hosted service provides transparent, secure, and reliable function execution across a federated ecosystem of endpoints. We motivate the need for funcX with several scientific case studies, present our prototype design and implementation, show optimizations that deliver throughput in excess of 1 million functions per second, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap with arXiv:1908.0490

Distributed Computing Framework Based on Software Containers for Heterogeneous Embedded Devices

The Internet of Things (IoT) is represented by millions of everyday objects enhanced with sensing and actuation capabilities that are connected to the Internet. Traditional approaches for IoT applications involve sending data to cloud servers for processing and storage, and then relaying commands back to devices. However, this approach is no longer feasible due to the rapid growth of IoT in the network: the vast amount of devices causes congestion; latency and security requirements demand that data is processed close to the devices that produce and consume it; and the processing and storage resources of devices remain underutilized. Fog Computing has emerged as a new paradigm where multiple end-devices form a shared pool of resources where distributed applications are deployed, taking advantage of local capabilities. These devices are highly heterogeneous, with varying hardware and software platforms. They are also resource-constrained, with limited availability of processing and storage resources. Realizing the Fog requires a software framework that simplifies the deployment of distributed applications, while at the same time overcoming these constraints. In Cloud-based deployments, software containers provide a lightweight solution to simplify the deployment of distributed applications. However, Cloud hardware is mostly homogeneous and abundant in resources. This work establishes the feasibility of using Docker Swarm -- an existing container-based software framework -- for the deployment of distributed applications on IoT devices. This is realized with the use of custom tools to enable minimal-size applications compatible with heterogeneous devices; automatic configuration and formation of device Fog; remote management and provisioning of devices. The proposed framework has significant advantages over the state of the art, namely, it supports Fog-based distributed applications, it overcomes device heterogeneity and it simplifies device initialization