Shih-Chang Chen

Optimizing Energy Consumption with Task Consolidation in Clouds

Task consolidation is a way to maximize utilization of cloud computing resources. Maximizing resource utilization provides various benefits such as the rationalization of maintenance, IT service customization, QoS and reliable services, etc. However, maximizing resource utilization does not mean efficient energy use. Much of the literature shows that energy consumption and resource utilization in clouds are highly coupled. Consequently, some of the literature aims to decrease resource utilization in order to save energy, while others try to reach a balance between resource utilization and energy consumption. In this paper, we present an energy-aware task consolidation (ETC) technique that minimizes energy consumption. ETC achieves this by restricting CPU use below a specified peak threshold. ETC does this by consolidating tasks amongst virtual clusters. In addition, the energy cost model considers network latency when a task migrates to another virtual cluster. To evaluate the performance of ETC we compare it against MaxUtil. MaxUtil is a recently developed greedy algorithm that aims to maximize cloud computing resources. The simulation results show that ETC can significantly reduce power consumption in a cloud system, with 17% improvement over MaxUtil.

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.

JackHare: a framework for SQL to NoSQL translation using MapReduce

As data exploration has increased rapidly in recent years, the datastore and data processing are getting more and more attention in extracting important information. To find a scalable solution to process the large-scale data is a critical issue in either the relational database system or the emerging NoSQL database. With the inherent scalability and fault tolerance of Hadoop, MapReduce is attractive to process the massive data in parallel. Most of previous researches focus on developing the SQL or SQL-like queries translator with the Hadoop distributed file system. However, it could be difficult to update data frequently in such file system. Therefore, we need a flexible datastore as HBase not only to place the data over a scale-out storage system, but also to manipulate the changeable data in a transparent way. However, the HBase interface is not friendly enough for most users. A GUI composed of SQL client application and database connection to HBase will ease the learning curve. In this paper, we propose the JackHare framework with SQL query compiler, JDBC driver and a systematical method using MapReduce framework for processing the unstructured data in HBase. After importing the JDBC driver to a SQL client GUI, we can exploit the HBase as the underlying datastore to execute the ANSI-SQL queries. Experimental results show that our approaches can perform well with efficiency and scalability.

Alleviating reader collision problem in mobile RFID networks

With the emergence of wireless RFID technologies, the problem of scheduling transmissions in dynamic RFID systems has been arousing attention. In recent year, it has also instigated researches to propose different heuristic algorithms for scheduling transactions between RFID readers and tags. In this paper, we present a two phase dynamic modulation (TPDM) technique, which consists of regional scheduling and hidden terminal scheduling phases, aims to efficiently perform communications between readers and tags in high density and mobile RFID networks. TPDM is a simple mechanism for coordinating simultaneous transmissions among multiple readers and hidden terminals. A significant improvement of this approach is that TPDM can prevent reader collisions by using a distributed self-scheduling scheme. An advantage of the proposed technique is that TPDM is adaptive in both static and dynamic RFID environments. To evaluate the performance of the proposed technique, we have implemented the TPDM scheme along with the Colorwave and Pulse protocols. The experimental results show that the TPDM scheduling techniques provide superior and stable performance in both static and dynamic circumstance, especially in mobile and high density RFID environments. The TPDM is shown to be effective in terms of throughput, system efficiency, and easy to implement.

Data adapter for querying and transformation between SQL and NoSQL database

As the growing of applications with big data in cloud computing become popular, many existing systems expect to expand their service to support the explosive increase of data. We propose a data adapter system to support hybrid database architecture including a relational database (RDB) and NoSQL database. It can support query from application and deal with database transformation at the same time. We provide three modes of query approach in data adapter system: blocking transformation mode (BT mode), blocking dump mode (BD mode), and direct access mode (DA mode). We provide a data synchronization mechanism and describe the design and implementation in detail. This paper focuses on velocity with proposed three modes and partly variety with data stored in RDB, NoSQL database and temporary files. With the proposed data adapter system, we can provide a seamless mechanism to use RDB and NoSQL database at the same time. This paper presents data adapter to make possible the automated transformation of multi-structured data in Relational Database (RDB) and NoSQL systems.With the proposed data adapter, a seamless mechanism is provided for constructing hybrid database systems.With the proposed data adapter, hybrid database systems can be performed in an elastic manner, i.e., access can be either RDB or NoSQL, depending on the size of data.

Scheduling contention-free irregular redistributions in parallelizing compilers

Abstract Irregular array redistribution has been paid attention recently since it can distribute different size of data segment to heterogeneous processors according to their computational ability. It’s also the reason why it has been kept an eye on load balance. High Performance Fortran Version 2 (HPF2) provides GEN_BLOCK distribution format which facilitates generalized block distributions. In this paper, we present a two-phase degree-reduction (TPDR) method for scheduling HPF2 irregular array redistribution. Using a bipartite communication graph, the first phase of TPDR schedules communication links adjacent to processors that with degree greater than two. A communication step will be scheduled follow each degree-reduction iteration. The second phase of TPDR schedules remaining messages of all processors that with degree-2 and degree-1 using an adjustable coloring mechanism. An extended algorithm based on TPDR is also presented in this paper. Effectiveness of the proposed methods not only avoids node contention but also shortens the overall communication cost. The proposed methods are also practicable due to low algorithmic complexity. To evaluate the performance of our methods, we have implemented both algorithms along with the divide-and-conquer algorithm and two scheduling mechanism. The simulation results show improvement of total communication costs.

CAD: an efficient data management and migration scheme across clouds for data-intensive scientific applications

Data management and migration are important research challenges of novel Cloud environments. While moving data among different geographical domains, it is important to lower the transmission cost for performance purposes. Efficient scheduling methods allow us to manage data transmissions with lower number of steps and shorter transmission time. In previous research efforts, several methods have been proposed in literature in order to manage data and minimize transmission cost for the case of Single Cluster environments. Unfortunately, these methods are not suitable to large-scale and complicated environments such as Clouds, with particular regard to the case of scheduling policies. Starting from these motivations, in this paper we propose an efficient data transmission method for data-intensive scientific applications over Clouds, called Cloud Adaptive Dispatching (CAD). This method adapts to specialized characteristics of Cloud systems and successfully shortens the transmission cost, while also avoiding node contention during moving data from sites to sites. We conduct an extensive campaign of experiments focused to test the effective performance of CAD. Results clearly demonstrate the improvements offered by CAD in supporting data transmissions across Clouds for data-intensive scientific applications.

Efficient selection strategies towards processor reordering techniques for improving data locality in heterogeneous clusters

Grid architecture integrates geographically distributed nodes to manage and provide resources to execute scientific applications. For data locality, applications with different computational phases require data redistribution for realignment. The tradeoff between high efficiency computation and communication cost of data redistribution accompanies. This paper introduces a research model and two methods to derive new lists of processor logical id according to the characteristics of heterogeneous network. Both methods provide choices of more low-cost communication schedules in grid. The simulations show both proposed methods yield outstanding performance in grid.

A Xen-based paravirtualization system toward efficient high performance computing environments

A virtual machine provides platforms to install an OS within another OS which provides resources. It can be accomplished to construct a computational cluster system on a single machine. The real cluster with machines provides full utilization of its resource for users while a virtual machine assigns the resources of the host to residing OSs. Xen is such kind of virtual machine to construct the virtualization system. It is chosen to be our systems virtual machine monitor because it provides better efficiency, supports different operating system work simultaneously, and gives each operating system an independent system environment. The performance of the virtualization system is examined by comparing with a non-virtualization system which is a real cluster system. The experiments show less power consumption and better computing efficiency by executing programs such as matrix multiplication, LINPACK, lower-upper triangular and Primes test sets. The results show better choices of constructing a large-scaled computing system using a virtual machine.

On Improving Message Passing in Unstructured Peer-to-Peer Overlay Networks

With the advance of the network technologies, peer- to-peer (P2P) has become a new network application model for state of the art distributed computing and has instigated many researches on it. Message passing is one of the most important operation to accomplish high scalable and reliable services in P2P networks. Among number of techniques, flooding is the most frequent used scheme to perform resource discovery and message forwarding. However, flooding usually causes communication redundancy and network congestion. In this paper, we present different messages passing strategies aim to alleviate drawbacks of flooding in distributed and unstructured P2P overlay networks. Objective of the proposed techniques are twofold, reduce amount of querying and increase resource utilization. Advantages of our techniques are simple, low complexity and easy to implement. Due to the property of distributed techniques, they are easy to be applied in distributed and unstructured P2P overlay network. The simulation results show that the proposed methods can efficiently reduce the number of querying and provide reasonable message coverage for different unstructured P2P overlay networks. The detailed analysis, which weighs the pros and cons of these methods, points out their properties and suggests the better message passing mechanism for different P2P environment is also contribution of this paper.