A survey and taxonomy of resource optimisation for executing Bag-of-Task applications on public clouds

Cloud computing has been widely adopted due to the flexibility in resource provisioning and on-demand pricing models. Entire clusters of Virtual Machines (VMs) can be dynamically provisioned to meet the computational demands of users. However, from a user’s perspective, it is still challenging to utilise cloud resources efficiently. This is because an overwhelmingly wide variety of resource types with different prices and significant performance variations are available. This paper presents a survey and taxonomy of existing research in optimising the execution of Bag-of-Task applications on cloud resources. A BoT application consists of multiple independent tasks, each of which can be executed by a VM in any order; these applications are widely used by both the scientific communities and commercial organisations. The objectives of this survey are as follows: (i) to provide the reader with a concise understanding of existing research on optimising the execution of BoT applications on the cloud, (ii) to define a taxonomy that categorises current frameworks to compare and contrast them, and (iii) to present current trends and future research directions in the area.PostprintPeer reviewe

Optimising the usage of cloud resources for execution bag-of-tasks applications

Cloud computing has been widely adopted by many organisations, due to its flexibility in resource provisioning and on-demand pricing models. Entire clusters of machines can now be dynamically provisioned to meet the computational demands of users. By moving operations to the cloud, users hope to reduce the costs of building and maintaining a computational cluster without sacrificing the quality of service. However, cloud computing has presented challenges in scheduling and managing the usage of resources, which users of more traditional resource pooling models, such as grid and clusters, have never encountered before. Firstly, the costs associated with resource usage changes dynamically, and is based on the type and duration of resources used; this prevents users from greedily acquiring as many resources as possible due to the associated costs. Secondly, the cloud computing marketplace offers an assortment of on-demand resources with a wide range of performance capabilities. Given the variety of resources, this makes it difficult for users to construct a cluster which is suitable for their applications. As a result, it is challenging for users to ensure the desired quality of service while running applications on the cloud. The research in this thesis focuses on optimising the usage of cloud computing resources. We propose approaches for scheduling the execution of applications on to the cloud, such that the desired performance is met whilst the incurred monetary cost is minimised. Furthermore, this thesis presents a set of mechanisms which manages the execution at runtime, in order to detect and handle unexpected events with undesirable consequences, such as the violation of quality of service, or cost overheads. Using both simulated and real world experiments, we validate the feasibility of the proposed research by executing applications on the cloud with low costs without sacrificing performance. The key result is that it is possible to optimise the usage of cloud resources for user applications by using the research reported in this thesis

Minimising the execution of unknown Bag-of-Task jobs with deadlines on the Cloud

Scheduling jobs with deadlines, each of which de nes the latest time that a job must be completed, can be challenging on the cloud due to incurred costs and unpredictable performance. This problem is further complicated when thereis not enough information to e ectively schedule a job such that its deadline is satis ed, and the cost is minimised. In this paper, we present an approach to schedule jobs, whose performance are unknown before execution, with deadlines on the cloud. By performing a sampling phase to collectthe necessary information about those jobs, our approach delivers the scheduling decision within 10% cost and 16% violation rate when compared to the ideal setting, which has complete knowledge about each of the jobs from the beginning. It is noted that our proposed algorithm outperforms existing approaches, which use a xed amount of resources by reducing the violation cost by at least two times