A Review of Workflow Scheduling in Cloud Computing Environment

Abstract Over the years, distributed environments have evolved from shared community platforms to utility-based models; the latest of these being Cloud computing. This technology enables the delivery of IT resources over the Internet and follows a pay-as-you-go model where users are charged based on their usage. There are various types of Cloud providers each of which has different product offerings. They are classified into a hierarchy of as-a-service terms: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). There are a mass of researches on the issue of scheduling in cloud computing, most of them, however, are about workflow and job scheduling. A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. Many scheduling policies have been proposed till now which aim to maximize the amount of work completed while meeting QoS constraints such as deadline and budget. However many of them are not optimal to incorporate some basic principles of Cloud Computing such as the elasticity and heterogeneity of the computing resources. Therefore our work focuses on studying various problems and issues related to workflow scheduling

ASSOCIATING USER’S PSYCHOLOGY INTO QUALITY OF SERVICE: AN EXAMPLE OF WEB ADAPTATION SERVICES

Content adaptation is a potential solution for tailoring multimedia web content according to the users’ preferences and heterogeneous devices’ constraints. Content adaptation can be done as third party service over the Internet. Users may pay for the service thus demand quality. The quality should include the human psychological factors. One of these factors is the maximum time a user can wait for the output to be displayed. Thus, response time is one of the qualities of service (QoS) to be considered in assessing the deliverability of content adaptation services. However, the advertised response time may not be deliverable accordingly during the actual service execution due to heavy load. Practically, the service provider should able to determine a current deliverable response time before the service level agreement (SLA) is settled with the users. In this paper, we propose a strategy for service providers to evaluate incoming requests and capable of offering the new response time. The proposed strategy takes into account the current server load and enables a mechanism for the user to evaluate whether the new response time can be accepted or not. We analyzed the performance of the proposed strategy in terms of SLA settlement under various conditions. The results indicate that the proposed strategy performs well

Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

In this paper, new multi-objective optimization algorithm is proposed. It optimizes the execution time, the energy consumption and the cost of booked nodes in the grid architecture at the same time. The proposed algorithm selects the best frequencies depends on a new optimization function that optimized these three objectives, while giving equivalent trade-off for each one. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption of the message passing parallel iterative method executed over grid. DVFS is also reduced the computing power of each processor executing the parallel applications. Therefore, the performance of these applications is decreased and so on the payed cost for the booking nodes is increased.  However, the proposed multi-objective algorithm gives the minimum energy consumption and minimum cost with maximum performance at the same time. The proposed algorithm is evaluated on the SimGrid/SMPI simulator while running the parallel iterative Jacobi method. The experiments show that it reduces on average the energy consumption by up to 19.7 %, while limiting the performance and cost degradations to 3.2 % and 5.2 % respectively

Scheduling independent stochastic tasks under deadline and budget constraints

International audienceThis paper discusses scheduling strategies for the problem of maximizing the expected number of tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The execution times of tasks follow IID probability laws. The main questions are how many processors to enroll and whether and when to interrupt tasks that have been executing for some time. We provide complexity results and an asymptotically optimal strategy for the problem instance with discrete probability distributions and without deadline. We extend the latter strategy for the general case with continuous distributions and a deadline and we design an efficient heuristic which is shown to outperform standard approaches when running simulations for a variety of useful distribution laws

A compromised-time-cost scheduling algorithm in SwinDeW-C for instance-intensive cost-constrained workflows on a cloud computing platform

The concept of cloud computing continues to spread widely, as it has been accepted recently. Cloud computing has many unique advantages which can be utilized to facilitate workflow execution. Instance-intensive cost-constrained cloud workflows are workflows with a large number of workflow instances (i.e. instance intensive) bounded by a certain budget for execution (i.e. cost constrained) on a cloud computing platform (i.e. cloud workflows). However, there are, so far, no dedicated scheduling algorithms for instance-intensive cost-constrained cloud workflows. This paper presents a novel compromised-time-cost scheduling algorithm which considers the characteristics of cloud computing to accommodate instance-intensive cost-constrained workflows by compromising execution time and cost with user input enabled on the fly. The simulation performed demonstrates that the algorithm can cut down the mean execution cost by over 15% whilst meeting the user-designated deadline or shorten the mean execution time by over 20% within the user-designated execution cost