4 research outputs found
Metascheduling of HPC Jobs in Day-Ahead Electricity Markets
High performance grid computing is a key enabler of large scale collaborative
computational science. With the promise of exascale computing, high performance
grid systems are expected to incur electricity bills that grow super-linearly
over time. In order to achieve cost effectiveness in these systems, it is
essential for the scheduling algorithms to exploit electricity price
variations, both in space and time, that are prevalent in the dynamic
electricity price markets. In this paper, we present a metascheduling algorithm
to optimize the placement of jobs in a compute grid which consumes electricity
from the day-ahead wholesale market. We formulate the scheduling problem as a
Minimum Cost Maximum Flow problem and leverage queue waiting time and
electricity price predictions to accurately estimate the cost of job execution
at a system. Using trace based simulation with real and synthetic workload
traces, and real electricity price data sets, we demonstrate our approach on
two currently operational grids, XSEDE and NorduGrid. Our experimental setup
collectively constitute more than 433K processors spread across 58 compute
systems in 17 geographically distributed locations. Experiments show that our
approach simultaneously optimizes the total electricity cost and the average
response time of the grid, without being unfair to users of the local batch
systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System
Incentives and Two-Sided Matching - Engineering Coordination Mechanisms for Social Clouds
The Social Cloud framework leverages existing relationships between members of a social network for the exchange of resources. This thesis focuses on the design of coordination mechanisms to address two challenges in this scenario. In the first part, user participation incentives are studied. In the second part, heuristics for two-sided matching-based resource allocation are designed and evaluated
DRIVE: A Distributed Economic Meta-Scheduler for the Federation of Grid and Cloud Systems
The computational landscape is littered with islands of disjoint resource providers including
commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers.
These providers are independent and isolated due to a lack of communication and coordination,
they are also often proprietary without standardised interfaces, protocols, or execution environments.
The lack of standardisation and global transparency has the effect of binding consumers
to individual providers. With the increasing ubiquity of computation providers there is an opportunity
to create federated architectures that span both Grid and Cloud computing providers
effectively creating a global computing infrastructure. In order to realise this vision, secure and
scalable mechanisms to coordinate resource access are required. This thesis proposes a generic
meta-scheduling architecture to facilitate federated resource allocation in which users can provision
resources from a range of heterogeneous (service) providers.
Efficient resource allocation is difficult in large scale distributed environments due to the inherent
lack of centralised control. In a Grid model, local resource managers govern access to a
pool of resources within a single administrative domain but have only a local view of the Grid
and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to
submit jobs to multiple resource managers, however they are most often deployed on a per-client
basis and are therefore concerned with only their allocations, essentially competing against one
another. In a federated environment the widespread adoption of utility computing models seen in
commercial Cloud providers has re-motivated the need for economically aware meta-schedulers.
Economies provide a way to represent the different goals and strategies that exist in a competitive
distributed environment. The use of economic allocation principles effectively creates an
open service market that provides efficient allocation and incentives for participation.
The major contributions of this thesis are the architecture and prototype implementation of the
DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler
in which members of the VO collaboratively allocate services or resources. Providers
joining the VO contribute obligation services to the VO. These contributed services are in effect
membership “dues” and are used in the running of the VOs operations – for example allocation,
advertising, and general management. DRIVE is independent from a particular class of provider
(Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in
federated environments composed of heterogeneous providers in vastly different scenarios. Protocol
independence facilitates the use of arbitrary protocols based on specific requirements and
infrastructural availability. For instance, within a single organisation where internal trust exists,
users can achieve maximum allocation performance by choosing a simple economic protocol.
In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used
with a secure protocol which ensures the allocation is carried out fairly in the absence of trust.
DRIVE establishes contracts between participants as the result of allocation. A contract describes
individual requirements and obligations of each party. A unique two stage contract negotiation
protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of
the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a
distributed environment without requiring large scale dedicated resources.
This thesis presents several other contributions related to meta-scheduling and open service
markets. To overcome the perceived performance limitations of economic systems four high utilisation
strategies have been developed and evaluated. Each strategy is shown to improve occupancy,
utilisation and profit using synthetic workloads based on a production Grid trace. The
gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications.
The gRAVI toolkit has been extended for this thesis such that it creates economically
aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring
developer input. The final contribution of this thesis is the definition and architecture of
a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources
contributed by members of a Social network. The Social Cloud prototype is based on DRIVE
and highlights the ease in which dynamic DRIVE markets can be created and used in different
domains