MBA: A market-based approach to data allocation and migration for cloud database

With the coming shift to cloud computing, cloud database is emerging to provide database service over the Internet. In the cloud-based environment, data are distributed at internet scale and the system needs to handle a huge number of user queries simultaneously without delay. How data are distributed among the servers has a crucial impact on the query load distribution and the system response time. In this paper, we propose a market-based control method, called MBA, to achieve query load balance via reasonable data distribution. In MBA, database nodes are treated as traders in a market, and certain market rules are used to intelligently decide data allocation and migration. We built a prototype system and conducted extensive experiments. Experimental results show that the MBA method signicantly improves system performance in terms of average query response time and fairness

MBA: A market-based approach to data allocation and dynamic migration for cloud database

With the coming shift to cloud computing, cloud database is emerging to provide database service over the Internet. In the cloud-based environment, data are distributed at Internet scale and the system needs to handle a huge number of user queries simultaneously without delay. How data are distributed among the servers has a crucial impact on the query load distribution and the system response time. In this paper, we propose a market-based control method, called MBA, to achieve query load balance via reasonable data distribution. In MBA, database nodes are treated as traders in a market, and certain market rules are used to intelligently decide data allocation and migration. We built a prototype system and conducted extensive experiments. Experimental results show that the MBA method signicantly improves system performance in terms of average query response time and fairness

A resource aware distributed LSI algorithm for scalable information retrieval

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Latent Semantic Indexing (LSI) is one of the popular techniques in the information retrieval fields. Different from the traditional information retrieval techniques, LSI is not based on the keyword matching simply. It uses statistics and algebraic computations. Based on Singular Value Decomposition (SVD), the higher dimensional matrix is converted to a lower dimensional approximate matrix, of which the noises could be filtered. And also the issues of synonymy and polysemy in the traditional techniques can be overcome based on the investigations of the terms related with the documents. However, it is notable that LSI suffers a scalability issue due to the computing complexity of SVD. This thesis presents a resource aware distributed LSI algorithm MR-LSI which can solve the scalability issue using Hadoop framework based on the distributed computing model MapReduce. It also solves the overhead issue caused by the involved clustering algorithm. The evaluations indicate that MR-LSI can gain significant enhancement compared to the other strategies on processing large scale of documents. One remarkable advantage of Hadoop is that it supports heterogeneous computing environments so that the issue of unbalanced load among nodes is highlighted. Therefore, a load balancing algorithm based on genetic algorithm for balancing load in static environment is proposed. The results show that it can improve the performance of a cluster according to heterogeneity levels. Considering dynamic Hadoop environments, a dynamic load balancing strategy with varying window size has been proposed. The algorithm works depending on data selecting decision and modeling Hadoop parameters and working mechanisms. Employing improved genetic algorithm for achieving optimized scheduler, the algorithm enhances the performance of a cluster with certain heterogeneity levels

Dynamic Management Of Heterogeneous Resources

This paper presents techniques for dynamic load balancing in heterogeneous computing environments. That is, the techniques are designed for sets of machines with varying processing capabilities and memory capacities. These methods can also be applied to homogenous systems in which the effective compute speed or memory availability is reduced by the presence of other programs running outside the target computation. To handle heterogeneous systems, a precise distinction is made between an abstract quantity of work, which might be measured as the number of iterations of a loop or the count of some data structure, and the utilization of resources, measured in seconds of processor time or bytes of memory, required by that work. Once that distinction is clearly drawn, the modifications to existing load balancing techniques are fairly straight-forward. The effectiveness of the resulting load balancing system is demonstrated for a large-scale particle simulation on a network of heterogeneous PC’s, workstations and multiprocessor servers

Dynamic load balancing in image retargeting using pipeline architecture

In today’s smart world demand of efficient multimedia based communication has increased at a rapid rate. Diversity on display sizes of gadgets used for multimedia communication confines the quality of images. Image retargeting is used as the focal solution to this problem which results in images with appropriate sizes. Enormously mounting demand of image retargeting expedites the rate of increment in computational load. This research paper expatiate and experiments a dynamic load balancing based three phase image retargeting methodology using pipeline architecture. In the first phase of image retargeting resize operation is performed on input image which results in multiple sized image copies of the same image. In the second phase resized images undergo quantization operation. In the final phase lossless compression is performed to have an expedient image. In the proposed exhibit think, we have done statistical analysis of results obtained, to confirm an impartial dynamic load balancing with a better degree of underlying resource utilization. We extend the approach to achieve significant storage optimization using three phase image retargeting

Decentralized load balancing in heterogeneous computational grids

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research