The Inter-cloud meta-scheduling

Inter-cloud is a recently emerging approach that expands cloud elasticity. By facilitating an adaptable setting, it purposes at the realization of a scalable resource provisioning that enables a diversity of cloud user requirements to be handled efficiently. This study’s contribution is in the inter-cloud performance optimization of job executions using metascheduling concepts. This includes the development of the inter-cloud meta-scheduling (ICMS) framework, the ICMS optimal schemes and the SimIC toolkit. The ICMS model is an architectural strategy for managing and scheduling user services in virtualized dynamically inter-linked clouds. This is achieved by the development of a model that includes a set of algorithms, namely the Service-Request, Service-Distribution, Service-Availability and Service-Allocation algorithms. These along with resource management optimal schemes offer the novel functionalities of the ICMS where the message exchanging implements the job distributions method, the VM deployment offers the VM management features and the local resource management system details the management of the local cloud schedulers. The generated system offers great flexibility by facilitating a lightweight resource management methodology while at the same time handling the heterogeneity of different clouds through advanced service level agreement coordination. Experimental results are productive as the proposed ICMS model achieves enhancement of the performance of service distribution for a variety of criteria such as service execution times, makespan, turnaround times, utilization levels and energy consumption rates for various inter-cloud entities, e.g. users, hosts and VMs. For example, ICMS optimizes the performance of a non-meta-brokering inter-cloud by 3%, while ICMS with full optimal schemes achieves 9% optimization for the same configurations. The whole experimental platform is implemented into the inter-cloud Simulation toolkit (SimIC) developed by the author, which is a discrete event simulation framework

Meta-scheduling Issues in Interoperable HPCs, Grids and Clouds

Over the last years, interoperability among resources has been emerged as one of the most challenging research topics. However, the commonality of the complexity of the architectures (e.g., heterogeneity) and the targets that each computational paradigm including HPC, grids and clouds aims to achieve (e.g., flexibility) remain the same. This is to efficiently orchestrate resources in a distributed computing fashion by bridging the gap among local and remote participants. Initially, this is closely related with the scheduling concept which is one of the most important issues for designing a cooperative resource management system, especially in large scale settings such as in grids and clouds. Within this context, meta-scheduling offers additional functionalities in the area of interoperable resource management, this is because of its great agility to handle sudden variations and dynamic situations in user demands. Accordingly, the case of inter-infrastructures, including InterCloud, entitle that the decentralised meta-scheduling scheme overcome issues like consolidated administration management, bottleneck and local information exposition. In this work, we detail the fundamental issues for developing an effective interoperable meta-scheduler for e-infrastructures in general and InterCloud in particular. Finally, we describe a simulation and experimental configuration based on real grid workload traces to demonstrate the interoperable setting as well as provide experimental results as part of a strategic plan for integrating future meta-schedulers

Topology-aware GPU scheduling for learning workloads in cloud environments

Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments. This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.This project is supported by the IBM/BSC Technology Center for Supercomputing collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We thank our IBM Research colleagues Alaa Youssef and Asser Tantawi for the valuable discussions. We also thank SC17 committee member Blair Bethwaite of Monash University for his constructive feedback on the earlier drafts of this paper.Peer ReviewedPostprint (published version

Hybrid approach for energy aware management of multi-cloud architecture integrating user machines

International audienceThe arrival and development of remotely accessible services via the cloud has transfigured computer technology. However, its impact on personal computing remains limited to cloud-based applications. Meanwhile, acceptance and usage of telephony and smartphones have exploded. Their sparse administration needs and general user friendliness allows all people, regardless of technology literacy, to access, install and use a large variety of applications.We propose in this paper a model and a platform to offer personal computing a simple and transparent usage similar to modern telephony. In this model, user machines are integrated within the classical cloud model, consequently expanding available resources and management targets. In particular, we defined and implemented a modular architecture including resource managers at different levels that take into account energy and QoS concerns. We also propose simulation tools to design and size the underlying infrastructure to cope with the explosion of usage. Functionalities of the resulting platform are validated and demonstrated through various utilization scenarios. The internal scheduler managing resource usage is experimentally evaluated and compared with classical method-ologies, showing a significant reduction of energy consumption with almost no QoS degradation