306 research outputs found
Task Scheduling on the Cloud with Hard Constraints
Scheduling Bag-of-Tasks (BoT) applications on the cloud can be more
challenging than grid and cluster environ- ments. This is because a user may
have a budgetary constraint or a deadline for executing the BoT application in
order to keep the overall execution costs low. The research in this paper is
motivated to investigate task scheduling on the cloud, given two hard
constraints based on a user-defined budget and a deadline. A heuristic
algorithm is proposed and implemented to satisfy the hard constraints for
executing the BoT application in a cost effective manner. The proposed
algorithm is evaluated using four scenarios that are based on the trade-off
between performance and the cost of using different cloud resource types. The
experimental evaluation confirms the feasibility of the algorithm in satisfying
the constraints. The key observation is that multiple resource types can be a
better alternative to using a single type of resource.Comment: Visionary Track of the IEEE 11th World Congress on Services (IEEE
SERVICES 2015
Economic-based Distributed Resource Management and Scheduling for Grid Computing
Computational Grids, emerging as an infrastructure for next generation
computing, enable the sharing, selection, and aggregation of geographically
distributed resources for solving large-scale problems in science, engineering,
and commerce. As the resources in the Grid are heterogeneous and geographically
distributed with varying availability and a variety of usage and cost policies
for diverse users at different times and, priorities as well as goals that vary
with time. The management of resources and application scheduling in such a
large and distributed environment is a complex task. This thesis proposes a
distributed computational economy as an effective metaphor for the management
of resources and application scheduling. It proposes an architectural framework
that supports resource trading and quality of services based scheduling. It
enables the regulation of supply and demand for resources and provides an
incentive for resource owners for participating in the Grid and motives the
users to trade-off between the deadline, budget, and the required level of
quality of service. The thesis demonstrates the capability of economic-based
systems for peer-to-peer distributed computing by developing users'
quality-of-service requirements driven scheduling strategies and algorithms. It
demonstrates their effectiveness by performing scheduling experiments on the
World-Wide Grid for solving parameter sweep applications
An economic market for the brokering of time and budget guarantees
Grids offer best effort services to users. Service level agreements offer the opportunity to provide guarantees upon services offered, in such a way that it captures the users’ requirements, while also considering concerns of the service providers. This is achieved via a process of converging requirements and service cost values from both sides towards an agreement. This paper presents the intelligent scheduling for quality of service market-oriented mechanism for brokering guarantees upon completion time and cost for jobs submitted to a batch-oriented compute service. Web Services agreement (negotiation) is used along with the planning of schedules in determining pricing, ensuring that jobs become prioritised depending on their budget constraints. An evaluation is performed to demonstrate how market mechanisms can be used to achieve this, whilst also showing the effects that scheduling algorithms can have upon the market in terms of rescheduling. The evaluation is completed with a comparison of the broker’s capabilities in relation to the literature
Scientific Workflow Scheduling for Cloud Computing Environments
The scheduling of workflow applications consists of assigning their tasks to computer resources to fulfill a final goal such as minimizing total workflow execution time. For this reason, workflow scheduling plays a crucial role in efficiently running experiments. Workflows often have many discrete tasks and the number of different task distributions possible and consequent time required to evaluate each configuration quickly becomes prohibitively large. A proper solution to the scheduling problem requires the analysis of tasks and resources, production of an accurate environment model and, most importantly, the adaptation of optimization techniques. This study is a major step toward solving the scheduling problem by not only addressing these issues but also optimizing the runtime and reducing monetary cost, two of the most important variables. This study proposes three scheduling algorithms capable of answering key issues to solve the scheduling problem. Firstly, it unveils BaRRS, a scheduling solution that exploits parallelism and optimizes runtime and monetary cost. Secondly, it proposes GA-ETI, a scheduler capable of returning the number of resources that a given workflow requires for execution. Finally, it describes PSO-DS, a scheduler based on particle swarm optimization to efficiently schedule large workflows. To test the algorithms, five well-known benchmarks are selected that represent different scientific applications. The experiments found the novel algorithms solutions substantially improve efficiency, reducing makespan by 11% to 78%. The proposed frameworks open a path for building a complete system that encompasses the capabilities of a workflow manager, scheduler, and a cloud resource broker in order to offer scientists a single tool to run computationally intensive applications
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Non-clairvoyant Scheduling of Multiple Bag-of-Tasks Applications
International audienceThe bag-of-tasks application model, albeit simple, arises in many application domains and has received a lot of attention in the scheduling literature. Previous works propose either theoretically sound solutions that rely on unrealistic assumptions, or ad-hoc heuristics with no guarantees on performance. This work attempts to bridge this gap through the design of non-clairvoyant heuristics based on solid theoretical foundations. The performance achieved by these heuristics is studied via simulations in a view to comparing them both to previously proposed solutions and to theoretical upper bounds on achievable performance. Also, an interesting theoretical result in this work is that a straightforward on-demand heuristic delivers asymptotically optimal performance when the communications or the computations can be neglected
Broker-mediated Multiple-Cloud Orchestration Mechanisms for Cloud Computing
Ph.DDOCTOR OF PHILOSOPH
Negotiated resource brokering for quality of service provision of grid applications
Grid Computing is a distributed computing paradigm where many computers often formed from different organisations work together so that their computing power may be
aggregated. Grids are often heterogeneous and resources vary significantly in CPU power, available RAM, disk space, OS, architecture and installed software etc. Added to this lack of uniformity is that best effort services are usually offered, as opposed to services that offer guarantees upon completion time via the use of Service Level Agreements (SLAs). The lack of guarantees means the uptake of Grids is stifled. The challenge tackled here is to add such guarantees, thus ensuring users are more willing to use the Grid given an obvious reluctance to pay or contribute, if the quality of the services returned lacks any guarantees.
Grids resources are also finite in nature, hence priorities need establishing in order to best meet any guarantees placed upon the limited resources available. An economic
approach is hence adopted to ensure end users reveal their true priorities for jobs, whilst also adding incentive for provisioning services, via a service charge.
An economically oriented model is therefore proposed that provides SLAs with bicriteria constraints upon time and cost. This model is tested via discrete event simulation
and a simulator is presented that is capable of testing the model. An architecture is then established that was developed to utilise the economic model for negotiating SLAs. Finally experimentation is reported upon from the use of the software developed when it was deployed upon a testbed, including admission control and steering of jobs within the Grid. Results are presented that show the interactions and relationship between the time
and cost constraints within the model, including transitions between the dominance of one constraint over the other and other things such as the effects of rescheduling upon the market
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