Budget Constrained Execution of Multiple Bag-of-Tasks Applications on the Cloud

Optimising the execution of Bag-of-Tasks (BoT) applications on the cloud is a hard problem due to the trade- offs between performance and monetary cost. The problem can be further complicated when multiple BoT applications need to be executed. In this paper, we propose and implement a heuristic algorithm that schedules tasks of multiple applications onto different cloud virtual machines in order to maximise performance while satisfying a given budget constraint. Current approaches are limited in task scheduling since they place a limit on the number of cloud resources that can be employed by the applications. However, in the proposed algorithm there are no such limits, and in comparison with other approaches, the algorithm on average achieves an improved performance of 10%. The experimental results also highlight that the algorithm yields consistent performance even with low budget constraints which cannot be achieved by competing approaches.Comment: 8th IEEE International Conference on Cloud Computing (CLOUD 2015

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

Spark on Entropy: A Reliable & Efficient Scheduler for Low-latency Parallel Jobs in Heterogeneous Cloud

In heterogeneous cloud, the provision of quality of service (QoS) guarantees for on-line parallel analysis jobs is much more challenging than off-line ones, mainly due to the many involved parameters, unstable resource performance, various job pattern and dynamic query workload. In this paper we propose an entropy-based scheduling strategy for running the on-line parallel analysis as a service more reliable and efficient, and implement the proposed idea in Spark. Entropy, as a measure of the degree of disorder in a system, is an indicator of a system’s tendency to progress out of order and into a chaotic condition, and it can thus serve to measure a cloud resource’s reliability for jobs scheduling. The key idea of our Entropy Scheduler is to construct the new resource entropy metric and schedule tasks according to the resources ranking with the help of the new metric so as to provide QoS guarantees for on-line Spark analysis jobs. Experiments demonstrate that our approach significantly reduces the average query response time by 15% - 20% and standard deviation by 30% - 45% compare with the native Fair Scheduler in Spark

A Hibernation Aware Dynamic Scheduler for Cloud Environments

International audienceNowadays, cloud platforms usually offer several types of Virtual Machines (VMs) which have different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in the Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs are unused instances available for a lower price. Despite the monetary advantages, a spot VM can be terminated or hibernated by EC2 at any moment. In this work, we propose the Hibernation-Aware Dynamic Scheduler (HADS), to schedule applications composed of independent tasks (bag-of-tasks) with deadline constraints in both hibernation-prone spot VMs (for cost sake) and on-demand VMs. We also consider the problem of temporal failures, that occurs when a spot VM hibernates, and does not resume within a time that guarantees the application's deadline. Our dynamic scheduling approach aims at minimizing the monetary costs of bag-of-tasks applications execution, respecting its deadline even in the presence of hibernation. It is also able to avoid temporal failures, by using task migration and work-stealing techniques. Experimental results with real executions using Amazon EC2 VMs confirm the effectiveness of our scheduling when compared with on-demand VM only based approaches, in terms of monetary costs and execution times. It is also shown that our strategy can tolerate temporal failures

Efficient resource Utilization in Cloud Computing Using Revised ROSP Algorithm (ERROSP)

Computing world these days is occupied by the Seventh Heaven. The most important question, it is necessary in this visualization is it to play an important role in the enterprise. Through this rapid development in the enterprise the most varieties of personal desire to save a lot of cash, time, hours and properties, which may increase in the area of electronic commerce. The cloud computing world is spreading rapidly on the Internet. Therefore, the basic definition of analysing cloud computing from around the world, often too, because it tells the calculation under application of services to assist the network and access to hardware and software running on the system may give the service. "A standardized IT capability (services, software or infrastructure) technology pay-per-use, self-service manner provided by the Internet." In the cloud of the most important research is Buckley RAD defines cloud computing as. The service itself has long been known as software as a service (SaaS). Data center hardware and software is what we call clouds. When the cloud is made available to the public in the way of a pay-as-you-go, which we call the public cloud; business being sold is utility computing. We use the term private cloud is an enterprise or other organization, not available to the general public within the data center. Therefore, SaaS, and cloud computing is the sum of utility computing, but not including private clouds. People can users or SaaS provider, or user or utility computing vendor. "Analysis goal of this paper is to find the user's needs, the best cloud service provider and cloud metaphysics programming algorithm is mainly based programming techniques. Actually speaking, cloud computing, programming side measure died in a cloud computing environment to take advantage of cloud computing provides a convenient broker execution management system to bring good prescription measurement programming techniques

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

Client-side resource management on the cloud: survey and future directions

Cloud computing and how to bridge the gap between various providers is getting increasing attention. In this context, efficiently scheduling tasks on heterogeneous resources is of extreme importance. The state-of-the-art for this field has been continuously growing during the last years and has reached a point in which a comprehensive overview indicating current solutions and ongoing challenges is of extreme importance for researchers. This paper aims to offer this analysis from a client-side scheduling perspective in which emphasis is not put on physical resource selection but on task to virtual machine mappings and virtual machine allocation. It provides a taxonomy for the current state-of-the-art and a unified model concerning the various metrics and goals used throughout literature. This model is designed to be sufficiently generic, extensible, and comprehensive to support most of the future work in the field. Several promising research directions and existing challenges are described

Energy-Efficient Real-Time Tasks Scheduling in Cloud Data Centers

Reducing energy consumption in cloud computing systems has been a major concern among the researchers because it not only reduce the operational cost but also increase the system reliability, and efficient scheduling approach is a promising way to achieve this goal. But unfortunately, existing energy-aware scheduling approaches are inadequate  for real-time tasks running in cloud environment because they assumes that cloud computing environment are deterministic and pre-computed schedule decisions are followed  during the execution. The above issues are addressed in this paper by considering the number of energy-efficiency factors such as energy cost, CPU power efficiency, carbon emission rate, and workload, and near-optimal energy efficient scheduling policies are proposed for cloud data center for scheduling real-time, aperiodic, independent tasks that can reduce operational cost and provide Quality of Service (QoS)