Kuan-Chou Lai

Energy-Aware Task Consolidation Technique for Cloud Computing

Task consolidation is a way of maximizing cloud computing resource, which brings many benefits such as better use of resources, rationalization of maintenance, IT service customization, QoS and reliable services, etc. However, maximizing resource utilization does not mean efficient energy usage. Many literature show that energy consumption and resource utilization in clouds are highly coupled. Some research works aim to decrease resource utilization for saving energy while some try to find the balance between resource utilization and energy consumption. In this paper, an energy-aware task consolidation (ETC) technique is presented aims to optimize energy consumption of virtual clusters in cloud data center. Conforming most cloud systems, a 70% principle of CPU utilization is proposed to manage task consolidation among virtual clusters. The simulation results show that ETC can significantly reduce power consumption in managing task consolidation for cloud systems. Up to 17% improvement as compare to a recent work in [10] that aims to maximize resource utilization can be obtained.

A dominant predecessor duplication scheduling algorithm for heterogeneous systems

Abstract This paper proposes a new duplication-based task scheduling algorithm for distributed heterogeneous computing (DHC) systems. For such systems, many researchers have focused on solving the NP-complete problem of scheduling directed acyclic task graphs to minimize the makespan. However, the heterogeneity of computational resources and communication mechanisms poses some major obstacles to achieving high parallel efficiency. This paper proposes a heuristic strategy called the Dominant Predecessor Duplication (DPD) scheduling algorithm, which allows for system heterogeneities and communication bandwidth to exploit the potential of parallel processing. This algorithm can improve system utilization and avoid redundant resource consumption, resulting in better schedules. Experimental results show that the system heterogeneities and program structures of applications affect scheduling performance, and that our presented algorithm is better able to avoid these problems than those presented in previous literature. Here, we show that our algorithm can be applied to design efficient distributed systems to overcome performance bottlenecks caused by system heterogeneities.

A Novel Approach for Cooperative Overlay-Maintenance in Multi-overlay Environments

Overlay networks are widely adopted in many distributed systems for efficient resource sharing. Recently, issues in overlay network have also been introduced into cloud systems, in order to organize thousands of virtualized resources. In parallel, the explosion of P2P applications introduces the multi-overlay environment in which a number of nodes simultaneously participate in multiple overlays. When multiple applications running over a large set of nodes, some of nodes may take repeated efforts to preserve multi-overlay networks. Therefore, maintaining these co-existing overlays brings the redundant maintenance overhead. This paper presents a cooperative strategy to analyze the overlay maintenance of multi-overlay environments and to elaborate multiple overlays for simplifying the overlay maintenance. The proposed strategy exploits the synergy of co-existing overlays to handle their common overlay-maintenance, so that the redundant maintenance overhead could be eliminated while keeping performance. To evaluate the system performance, this paper not only analyzes several overlays but also considers realistic multi-overlay environments by varying the intersection ratio of diverse overlays and the combination of multiple overlays. Experimental results show that the proposed cooperative strategy significantly decreases the redundant overlay-maintenance overhead, where the reduction ratio of maintaining multiple overlays is higher than 60 percent in some of cases.

Energy-Efficient Resource Provisioning with SLA Consideration on Cloud Computing

Cloud computing aims at reducing energy consumption and maximizing resource efficiency without violating service level agreement (SLA). To address these important issues, this study proposes an energy-efficient resource provisioning technology with SLA consideration for virtual machine scheduling. According to SLAs, the resource manager could consolidate virtual machines onto the physical machine for meeting customers SLA requests. Experimental results show that the proposed approach outperforms other proposed ones in power consumption.

Pervasive health service system: insights on the development of a grid-based personal health service system

Although medical technologies developed in the twenty-first century have successfully increased mans life span, the pressure of modern life has consequently brought many modern civilization diseases and chronic illness. When all these problems are tackled by hospitals, they will consume considerable amount of medical resources. Alternatively, providing health care services at home is an important issue for improving personal health and save hospital resources. In this paper, we present an ongoing project that designs and implements a pervasive health service infrastructure based on the grid system which is integrated with the P2Ps resource sharing mechanism, to provide the personal health service. The personal health status is recorded, monitored, and even mined in/from the proposed pervasive health service system for preventive medicine. Additionally, wireless sensor equipments for mobile personal health services are also integrated into the pervasive health service system, in order to construct a situation-aware, context-aware and environment-aware mobile-health-service platform.

A scalable multi-attribute hybrid overlay for range queries on the cloud

Cloud computing has become a promising paradigm as next generation computing model, by providing computation, software, data access, and storage services that do not need to know the location of physical resources interconnected across the globe providing such services. In such an environment, important issues as information sharing and resource/service discovery arise. In order to overcome critical limitations in centralized approaches for information sharing and resource/service discovery, this paper proposes a framework of a scalable multi-attribute hybrid overlay featured with decentralized information sharing, flexible resource/service discovery, fault tolerance and load balancing. Additionally, the proposed hybrid overlay integrates a structured P2P system with an unstructured one to support complex queries. Mechanisms such as load balancing and fault tolerance implemented in our proposed system to improve the overall system performance are also discussed. Experimental results show that the performance of the proposed approach is feasible and stable, as the proposed hybrid overlay improves system performance by reducing the number of routing hops and balancing the load by migrating requests.

GA-Based Job Scheduling Strategies for Fault Tolerant Grid Systems

This work mainly aims at the designs of the genetic algorithm based scheduling strategies by considering four different fault tolerance techniques in the grid environment, including retry, migration, checkpoint, replication. We also take into account the risk relationship between jobs and nodes to improve the system reliability in the scheduling algorithm. According to the simulation results, we can find out that the performance of fault tolerant algorithms is better than risky algorithm whether in makespan, average turnaround time, or the job failure rate. Checkpoint algorithm has the best performance in all algorithms. On the other hand, retry algorithm is recommended for the system where the job sizes are usually smaller because of its simplicity. Finally, replicated algorithm is not suitable for the grid since it imposes too much overhead.

Design and Implementation of Job Migration Policies in P2P Grid Systems

Grid computing systems integrate geographical computing resources across virtual organizations. In grid systems, one of the most important challenges is how to efficiently exploit shared computing resources. This study addresses the job migration policies for exploiting computing resources in grid computing systems. In this study, we propose two job migration policies, the load barrier policy and the minimal job turnaround time policy. By adopting the P2P technique, we construct a job migration mechanism to improve the utilization of idle computing resources across virtual organizations in P2P Grid systems. We quantify the performance of our job migration mechanism using several influencing parameters such as the job migration policy, load barrier, job size, and data transfer rate. Experimental results show that our job migration mechanism could efficiently distribute load in P2P Grid systems.

Taiwan UniCloud: A Cloud Testbed with Collaborative Cloud Services

This paper introduces a prototype of Taiwan UniCloud, a community-driven hybrid cloud platform for academics in Taiwan. The goal is to leverage resources in multiple clouds among different organizations. Each self-managing cloud can join the UniCloud platform to share its resources and simultaneously benefit from other clouds with scale-out capabilities. Accordingly, resources are elastic and sharable with each other such as to afford unexpected resource demands to each cloud. The proposed platform provides a web portal to operate each cloud via a uniform user interface. The construction of virtual clusters with multi-core VMs is supplied for parallel and distributed processing models. An object-based storage system is also delivered to federate different storage providers. This paper not only presents the architectural design of Taiwan UniCloud, but also evaluates the performance to demonstrate the possibility of current implementation. Experimental results show the feasibility of the proposed platform as well as the benefit from the cloud federation.

Direction-aware resource discovery in large-scale distributed computing environments

As a system scales up, the peer-to-peer (P2P) approach is attractive to distributed computing environments, such as Grids and Clouds, due to the amount of resources increased. The major issue in large-scale distributed systems is to prevent the phenomenon of a communication bottleneck or a single point of failure. Conventional approaches may not be able to apply directly to such environments due to restricted queries and varied resource characteristics. Alternatively, a fully decentralized resource discovery service based on an unstructured overlay, which relies only on the information of resource attributes and characteristics, may be a feasible solution. One major challenge of such service is to locate desired and suitable resources without the global knowledge of distributed sharing resources. As a consequence, the more nodes the resource discovery service involves, the higher the network overhead incurs. In this paper, we proposed a direction-aware strategy which can alleviate the network traffic among unstructured information systems for distributed resource discovery service. Experimental results have demonstrated that the proposed approach achieves higher success rate at low cost and higher scalability.