235 research outputs found
Cluster-Wide Context Switch of Virtualized Jobs
International audienceClusters are mostly used through Resources Management Systems (RMS) with a static allocation of resources for a bounded amount of time. Those approaches are known to be insufficient for an efficient use of clusters. To provide a finer RMS, job preemption, migration and dynamic allocation of resources are required. However due to the complexity of developing and using such mechanisms, advanced scheduling strategies have rarely been deployed. This trend is currently evolving thanks to the use of migration and preemption capabilities of Virtual Machines (VMs). However, although the manipulation of jobs composed of VM enables to change the state of the jobs according to the scheduling objective, changing the state and the location of numerous VMs at each decision is tedious and degrades the overall performance. In addition to the scheduling policy implementation, developers have to focus on the feasibility of the actions while executing them in the most efficient way. In this paper, we argue such an operation is independent from the policy itself and can be addressed through a generic mechanism, the cluster-wide context switch. Thanks to it, developers can implement sophisticated algorithms to schedule jobs without handling the issues related to their manipulations. They only focus on the implementation of their algorithm to select the jobs to run while the cluster-wide context switch system performs the necessary actions to switch from the current to the new situation. As a proof of concept, we evaluate the interest of the cluster-wide context switch through a sample scheduler that executes jobs as early as possible, even partially, regarding to their current resources requirements and their priority
Crux: Locality-Preserving Distributed Services
Distributed systems achieve scalability by distributing load across many
machines, but wide-area deployments can introduce worst-case response latencies
proportional to the network's diameter. Crux is a general framework to build
locality-preserving distributed systems, by transforming an existing scalable
distributed algorithm A into a new locality-preserving algorithm ALP, which
guarantees for any two clients u and v interacting via ALP that their
interactions exhibit worst-case response latencies proportional to the network
latency between u and v. Crux builds on compact-routing theory, but generalizes
these techniques beyond routing applications. Crux provides weak and strong
consistency flavors, and shows latency improvements for localized interactions
in both cases, specifically up to several orders of magnitude for
weakly-consistent Crux (from roughly 900ms to 1ms). We deployed on PlanetLab
locality-preserving versions of a Memcached distributed cache, a Bamboo
distributed hash table, and a Redis publish/subscribe. Our results indicate
that Crux is effective and applicable to a variety of existing distributed
algorithms.Comment: 11 figure
Distributed Computing in a Pandemic: A Review of Technologies Available for Tackling COVID-19
The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus
has resulted in over a million deaths and is having a grave socio-economic
impact, hence there is an urgency to find solutions to key research challenges.
Much of this COVID-19 research depends on distributed computing. In this
article, I review distributed architectures -- various types of clusters, grids
and clouds -- that can be leveraged to perform these tasks at scale, at
high-throughput, with a high degree of parallelism, and which can also be used
to work collaboratively. High-performance computing (HPC) clusters will be used
to carry out much of this work. Several bigdata processing tasks used in
reducing the spread of SARS-CoV-2 require high-throughput approaches, and a
variety of tools, which Hadoop and Spark offer, even using commodity hardware.
Extremely large-scale COVID-19 research has also utilised some of the world's
fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking
high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and
high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to
explore natural products. Grid computing has facilitated the formation of the
world's first Exascale grid computer. This has accelerated COVID-19 research in
molecular dynamics simulations of SARS-CoV-2 spike protein interactions through
massively-parallel computation and was performed with over 1 million volunteer
computing devices using the Folding@home platform. Grids and clouds both can
also be used for international collaboration by enabling access to important
datasets and providing services that allow researchers to focus on research
rather than on time-consuming data-management tasks.Comment: 21 pages (15 excl. refs), 2 figures, 3 table
VSCM: a Virtual Server Consolidation Manager for Cluster
Abstract. Virtual server consolidation is to use virtual machines to encapsulate applications which are running on multiple physical servers in the cluster and then integrate them into a small number of servers. Nowadays, with the expanding of enterprise-class data centers, virtual server consolidation can reduce large number of servers to help the enterprises reduce hardware and operating costs significantly and improve server utilization greatly. In this paper, we propose the VSCM manager for virtual cluster, which solves the problems in the consolidation from a globally optimal view and also takes migration overhead into account. Experiment results in virtual cluster demonstrate that, VSCM can greatly reduce the number of servers and the migration overhead
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