A mathematical programming approach for resource allocation of data analysis workflows on heterogeneous clusters

Scientific communities are motivated to schedule their large-scale data analysis workflows in heterogeneous cluster environments because of privacy and financial issues. In such environments containing considerably diverse resources, efficient resource allocation approaches are essential for reaching high performance. Accordingly, this research addresses the scheduling problem of workflows with bag-of-task form to minimize total runtime (makespan). To this aim, we develop a mixed-integer linear programming model (MILP). The proposed model contains binary decision variables determining which tasks should be assigned to which nodes. Also, it contains linear constraints to fulfill the tasks requirements such as memory and scheduling policy. Comparative results show that our approach outperforms related approaches in most cases. As part of the post-optimality analysis, some secondary preferences are imposed on the proposed model to obtain the most preferred optimal solution. We analyze the relaxation of the makespan in the hope of significantly reducing the number of consumed nodes

Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems

Includes bibliographical references.2015 Summer.As high performance computing systems increase in size, new and more efficient algorithms are needed to schedule work on the machines, understand the performance trade-offs inherent in the system, and determine which machines to provision. The extreme scale of these newer systems requires unique task scheduling algorithms that are capable of handling millions of tasks and thousands of machines. A highly scalable scheduling algorithm is developed that computes high quality schedules, especially for large problem sizes. Large-scale computing systems also consume vast amounts of electricity, leading to high operating costs. Through the use of novel resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. Trading-off energy and makespan is often difficult for companies because it is unclear how each affects the profit. A monetary-based model of high performance computing is presented and a highly scalable algorithm is developed to quickly find the schedule that maximizes the profit per unit time. As more high performance computing needs are being met with cloud computing, algorithms are needed to determine the types of machines that are best suited to a particular workload. An algorithm is designed to find the best set of computing resources to allocate to the workload that takes into account the uncertainty in the task arrival rates, task execution times, and power consumption. Reward rate, cost, failure rate, and power consumption can be optimized, as desired, to optimally trade-off these conflicting objectives

A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids

Peer ReviewedPostprint (published version

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Budget-aware scheduling algorithm for scientific workflow applications across multiple clouds. A Mathematical Optimization-Based Approach

Scientific workflows have become a prevailing means of achieving significant scientific advances at an ever-increasing rate. Scheduling mechanisms and approaches are vital to automating these large-scale scientific workflows efficiently. On the other hand, with the advent of cloud computing and its easier availability and lower cost of use, more attention has been paid to the execution and scheduling of scientific workflows in this new paradigm environment. For scheduling large-scale workflows, a multi-cloud environment will typically have a more significant advantage in various computing resources than a single cloud provider. Also, the scheduling makespan and cost can be reduced if the computing resources are used optimally in a multi-cloud environment. Accordingly, this thesis addressed the problem of scientific workflow scheduling in the multi-cloud environment under budget constraints to minimize associated makespan. Furthermore, this study tries to minimize costs, including fees for running VMs and data transfer, minimize the data transfer time, and fulfill budget and resource constraints in the multi-clouds scenario. To this end, we proposed Mixed-Integer Linear Programming (MILP) models that can be solved in a reasonable time by available solvers. We divided the workflow tasks into small segments, distributed them among VMs with multi-vCPU, and formulated them in mathematical programming. In the proposed mathematical model, the objective of a problem and real and physical constraints or restrictions are formulated using exact mathematical functions. We analyzed the treatment of optimal makespan under variations in budget, workflow size, and different segment sizes. The evaluation's results signify that our proposed approach has achieved logical and expected results in meeting the set objectives

Multi-objective and Scalable Heuristic Algorithm for Workflow Task Scheduling in Utility Grids

To use services transparently in a distributed environment, the Utility Grids develop a cyber-infrastructure. The parameters of the Quality of Service such as the allocation-cost and makespan have to be dealt with in order to schedule workflow application tasks in the Utility Grids. Optimization of both target parameters above is a challenge in a distributed environment and may conflict one another. We, therefore, present a novel heuristic algorithm for scheduling a workflow application on Utility Grids. Our proposed algorithm optimizes the allocation-cost and makespan in a scalable and very low runtime. The results of the wide-spread simulation indicate that the proposed algorithm is scalable against an increase in the application size and task parallelism of the application. The proposed algorithm effectively outperforms the current algorithms in terms of the allocation-cost, makespan and runtime scalability