Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

Scheduling Periodical Multi-Stage Jobs With Fuzziness to Elastic Cloud Resources

© 2020 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] We investigate a workflow scheduling problem with stochastic task arrival times and fuzzy task processing times and due dates. The problem is common in many real-time and workflow-based applications, where tasks with fixed stage number and linearly dependency are executed on scalable cloud resources with multiple price options. The challenges lie in proposing effective, stable, and robust algorithms under stochastic and fuzzy tasks. A triangle fuzzy number-based model is formulated. Two metrics are explored: the cost and the degree of satisfaction. An iterated heuristic framework is proposed to periodically schedule tasks, which consists of a task collection and a fuzzy task scheduling phases. Two task collection strategies are presented and two task prioritization strategies are employed. In order to achieve a high satisfaction degree, deadline constraints are defined at both job and task levels. By designing delicate experiments and applying sophisticated statistical techniques, experimental results show that the proposed algorithm is more effective and robust than the two existing methods.This work was supported by the National Key Research and Development Program of China (No. 2017YFB1400800), the National Natural Science Foundation of China (Nos. 61672297, 61872077, and 61832004), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 18KJB520039) and the National Science Foundation for Post-doctoral Scientists of China (Grant No. 2018M640510). Ruben Ruiz was partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization" (No. RTI2018-094940-B-I00) financed with FEDER funds. The authors would like to thank the anonymous reviewers for their valuable feedback on this work.Zhu, J.; Li, X.; Ruiz García, R.; Li, W.; Huang, H.; Zomaya, AY. (2020). Scheduling Periodical Multi-Stage Jobs With Fuzziness to Elastic Cloud Resources. IEEE Transactions on Parallel and Distributed Systems. 31(12):2819-2833. https://doi.org/10.1109/TPDS.2020.3004134S28192833311

Scheduling Stochastic Multi-Stage Jobs to Elastic Hybrid Cloud Resources

[EN] We consider a special workflow scheduling problem in a hybrid-cloud-based workflow management system in which tasks are linearly dependent, compute-intensive, stochastic, deadline-constrained and executed on elastic and distributed cloud resources. This kind of problems closely resemble many real-time and workflow-based applications. Three optimization objectives are explored: number, usage time and utilization of rented VMs. An iterated heuristic framework is presented to schedule jobs event by event which mainly consists of job collecting and event scheduling. Two job collecting strategies are proposed and two timetabling methods are developed. The proposed methods are calibrated through detailed designs of experiments and sound statistical techniques. With the calibrated components and parameters, the proposed algorithm is compared to existing methods for related problems. Experimental results show that the proposal is robust and effective for the problems under study.This work is sponsored by the National Natural Science Foundations of China (Nos. 71401079, 61572127, 61472192), the National Key Research and Development Program of China (No. 2017YFB1400801) and the Collaborative Innovation Center of Wireless Communications Technology. Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds.Zhu, J.; Li, X.; Ruiz García, R.; Xu, X. (2018). Scheduling Stochastic Multi-Stage Jobs to Elastic Hybrid Cloud Resources. IEEE Transactions on Parallel and Distributed Systems. 29(6):1401-1415. https://doi.org/10.1109/TPDS.2018.2793254S1401141529

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users’ requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions

Twos Company, Threes A Cloud: Challenges To Implementing Service Models

Although three models are currently being used in cloud computing (Software as a Service, Platform as a Service, and infrastructure as a service, there remain many challenges before most business accept cloud computing as a reality. Virtualization in cloud computing has many advantages but carries a penalty because of state configurations, kernel drivers, and user interface environments. In addition, many non-standard architectures exist to power cloud models that are often incompatible. Another issue is adequately provisioning the resources required for a multi-tier cloud-based application in such a way that on-demand elasticity is present at vastly different scales yet is carried out efficiently. For networks that have large geographical footprints another problem arises from bottlenecks between elements supporting virtual machines and their control. While many solutions have been proposed to alleviate these problems, some of which are already commercial, much remains to be done to see whether these solutions will be practicable at scale up and address business concerns

SCHEDULING ALGORITHMS IN MULTI CLOUD ENVIRONMENTS – A SHORT REVIEW

It is essential to schedule the workloads to cloud in an efficient manner. Whether user is using single cloud or multi cloud environments, according to the available resources and needs, the incoming jobs or tasks has to be scheduled. Hence this short review paper focuses on different available scheduling algorithms and its categories

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022

The Inter-cloud meta-scheduling

Inter-cloud is a recently emerging approach that expands cloud elasticity. By facilitating an adaptable setting, it purposes at the realization of a scalable resource provisioning that enables a diversity of cloud user requirements to be handled efficiently. This study’s contribution is in the inter-cloud performance optimization of job executions using metascheduling concepts. This includes the development of the inter-cloud meta-scheduling (ICMS) framework, the ICMS optimal schemes and the SimIC toolkit. The ICMS model is an architectural strategy for managing and scheduling user services in virtualized dynamically inter-linked clouds. This is achieved by the development of a model that includes a set of algorithms, namely the Service-Request, Service-Distribution, Service-Availability and Service-Allocation algorithms. These along with resource management optimal schemes offer the novel functionalities of the ICMS where the message exchanging implements the job distributions method, the VM deployment offers the VM management features and the local resource management system details the management of the local cloud schedulers. The generated system offers great flexibility by facilitating a lightweight resource management methodology while at the same time handling the heterogeneity of different clouds through advanced service level agreement coordination. Experimental results are productive as the proposed ICMS model achieves enhancement of the performance of service distribution for a variety of criteria such as service execution times, makespan, turnaround times, utilization levels and energy consumption rates for various inter-cloud entities, e.g. users, hosts and VMs. For example, ICMS optimizes the performance of a non-meta-brokering inter-cloud by 3%, while ICMS with full optimal schemes achieves 9% optimization for the same configurations. The whole experimental platform is implemented into the inter-cloud Simulation toolkit (SimIC) developed by the author, which is a discrete event simulation framework