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

Reliable Provisioning of Spot Instances for Compute-intensive Applications

Cloud computing providers are now offering their unused resources for leasing in the spot market, which has been considered the first step towards a full-fledged market economy for computational resources. Spot instances are virtual machines (VMs) available at lower prices than their standard on-demand counterparts. These VMs will run for as long as the current price is lower than the maximum bid price users are willing to pay per hour. Spot instances have been increasingly used for executing compute-intensive applications. In spite of an apparent economical advantage, due to an intermittent nature of biddable resources, application execution times may be prolonged or they may not finish at all. This paper proposes a resource allocation strategy that addresses the problem of running compute-intensive jobs on a pool of intermittent virtual machines, while also aiming to run applications in a fast and economical way. To mitigate potential unavailability periods, a multifaceted fault-aware resource provisioning policy is proposed. Our solution employs price and runtime estimation mechanisms, as well as three fault tolerance techniques, namely checkpointing, task duplication and migration. We evaluate our strategies using trace-driven simulations, which take as input real price variation traces, as well as an application trace from the Parallel Workload Archive. Our results demonstrate the effectiveness of executing applications on spot instances, respecting QoS constraints, despite occasional failures.Comment: 8 pages, 4 figure

CASCH: a tool for computer-aided scheduling

A software tool called Computer-Aided Scheduling (CASCH) for parallel processing on distributed-memory multiprocessors in a complete parallel programming environment is presented. A compiler automatically converts sequential applications into parallel codes to perform program parallelization. The parallel code that executes on a target machine is optimized by CASCH through proper scheduling and mapping.published_or_final_versio

Analytical Performance Comparison of BNP Scheduling Algorithms

Parallel computing is related to the application of many computers running in parallel to solve computationally intensive problems. One of the biggest issues in parallel computing is efficient task scheduling. In this paper, we survey the algorithms that allocate a parallel program represented by an edge-directed acyclic graph (DAG) to a set of homogenous processors with the objective of minimizing the completion time. We examine several such classes of algorithms and then compare the performance of a class of scheduling algorithms known as the bounded number of processors (BNP) scheduling algorithms. Comparison is based on various scheduling parameters such as makespan, speed up, processor utilization and scheduled length ratio. The main focus is given on measuring the impact of increasing the number of tasks and processors on the performance of these four BNP scheduling algorithms