3E: Energy-Efficient Elastic Scheduling for Independent Tasks in Heterogeneous Computing Systems

Reducing energy consumption is a major design constraint for modern heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-efficient scheduling strategies developed on these systems do not sufficiently exploit the system elasticity and adaptability for maximum energy savings, and do not simultaneously take account of user expected finish time. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, independent and non-real-time tasks with user expected finish times on DVFS-enabled heterogeneous computing systems. The 3E strategy adjusts processors’ supply voltages and frequencies according to the system workload, and makes trade-offs between energy consumption and user expected finish times. Compared with other energy-efficient strategies, 3E significantly improves the scheduling quality and effectively enhances the system elasticity

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications

Fault aware task scheduling in cloud using min-min and DBSCAN

Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of resource allocation failure that can maximize the delay in task execution. Such adverse impact in the cloud environment also raises questions on quality of service (QoS). Resource management for cloud application and service have bigger challenges and many researchers have proposed several solutions but there is room for improvement. Clustering the resources clustering and mapping them according to task can also be an option to deal with such task failure or mismanaged resource allocation. Density-based spatial clustering of applications with noise (DBSCAN) is a stochastic approach-based algorithm which has the capability to cluster the resources in a cloud environment. The proposed algorithm considers high execution enabled powerful data centers with least fault probability during resource allocation which reduces the probability of fault and increases the tolerance. The simulation is cone using CloudsSim 5.0 tool kit. The results show 25% average improve in execution time, 6.5% improvement in number of task completed and 3.48% improvement in count of task failed as compared to ACO, PSO, BB-BC (Bib = g bang Big Crunch) and WHO(Whale optimization algorithm)

Energy-Efficient Fault-Tolerant Scheduling Algorithm for Real-Time Tasks in Cloud-Based 5G Networks

© 2013 IEEE. Green computing has become a hot issue for both academia and industry. The fifth-generation (5G) mobile networks put forward a high request for energy efficiency and low latency. The cloud radio access network provides efficient resource use, high performance, and high availability for 5G systems. However, hardware and software faults of cloud systems may lead to failure in providing real-time services. Developing fault tolerance technique can efficiently enhance the reliability and availability of real-time cloud services. The core idea of fault-tolerant scheduling algorithm is introducing redundancy to ensure that the tasks can be finished in the case of permanent or transient system failure. Nevertheless, the redundancy incurs extra overhead for cloud systems, which results in considerable energy consumption. In this paper, we focus on the problem of how to reduce the energy consumption when providing fault tolerance. We first propose a novel primary-backup-based fault-tolerant scheduling architecture for real-time tasks in the cloud environment. Based on the architecture, we present an energy-efficient fault-tolerant scheduling algorithm for real-time tasks (EFTR). EFTR adopts a proactive strategy to increase the system processing capacity and employs a rearrangement mechanism to improve the resource utilization. Simulation experiments are conducted on the CloudSim platform to evaluate the feasibility and effectiveness of EFTR. Compared with the existing fault-tolerant scheduling algorithms, EFTR shows excellent performance in energy conservation and task schedulability

Computing at massive scale: Scalability and dependability challenges

Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications