Data-intensive Applications In Cloud Research Articles

An innovativefractal architecture model for implementing MapReduce in an open multiprocessing parallel environment

One of the infrastructure applications that cloud computing offers as a service is parallel data processing. MapReduce is a type of parallel processing used more and more by data-intensive applications in cloud computing environments. MapReduce is based on a strategy called "divide and conquer," which uses regular computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used with the proposed fractal network model in the MapReduce application. A well-known model, the cube, is used to compare the fractal network model and its work. Where experiments demonstrated that the fractal model is preferable to the cube model. The fractal model achieved an average speedup of 2.7 and an efficiency rate of 67.7%. In contrast, the cube model could only reach an average speedup of 2.5 and an efficiency rate of 60.4%.

A two-phase virtual machine placement policy for data-intensive applications in cloud

Cloud computing and big data are two technologies whose combination can yield many benefits and challenges. One of the most significant challenges is the traffic produced by data-intensive jobs within a data center. A possible way to manage the produced traffic is optimizing the placement of virtual machines (VM) on hosts. However, placing VMs compactly to reduce the communication cost can negatively impact on other aspects such as host utilization and load balancing. In this paper, we aim to make a balance between optimizing the host utilization and the communication cost while considering load balancing. We investigate the VM placement problem by modeling it as the minimum weight K-vertex-connected induced subgraph. We prove the NP-Hardness of the problem and propose a novel two-phase strategy for placing VMs on hosts. At first phase, in order to balance traffic and workload among racks, we rank all racks using a fuzzy inference system and select the best ones based on a linear programming model. At second phase, we introduce a novel greedy algorithm to assign each VM to a host regarding a proposed communication cost metric. We evaluate our approach using CloudSim simulator whose results show our two-phase strategy is able to make a balance between host utilization and network traffic. It keeps more than 80 percent of the traffic rack local while reduces the average network link saturation to almost 40 percent with a low variance. Besides, the number of used hosts increase linearly by increasing the number of VMs which leads to a higher host utilization.

An efficient bandwidth reservation policy for data-intensive applications in cloud

Recently, more and more data-intensive applications have been deployed in cloud platforms due to its cost-effective and elastic capability. Although computing resources (i.e., CPU and RAM) can be provisioned in on-demand manner, the network bandwidth has enforce certain admission controlling for providing desirable quality-of-service (QoS). As a result, data-intensive applications often suffer from high delay during the phase of bulk data transferring between computing nodes. In this paper, we present an integrated bandwidth reservation algorithm, which combines different admission controlling policies and minimises the finishing time for all jobs in an online manner. Performance evaluation on a real-world cloud platform shows that the proposed reservation algorithm can ensure efficient bulk data transferring even when the system is facing heavy workloads.

An efficient bandwidth reservation policy for data-intensive applications in cloud

Recently, more and more data-intensive applications have been deployed in cloud platforms due to its cost-effective and elastic capability. Although computing resources (i.e., CPU and RAM) can be provisioned in on-demand manner, the network bandwidth has enforce certain admission controlling for providing desirable quality-of-service (QoS). As a result, data-intensive applications often suffer from high delay during the phase of bulk data transferring between computing nodes. In this paper, we present an integrated bandwidth reservation algorithm, which combines different admission controlling policies and minimises the finishing time for all jobs in an online manner. Performance evaluation on a real-world cloud platform shows that the proposed reservation algorithm can ensure efficient bulk data transferring even when the system is facing heavy workloads.

Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm

Nowadays data-intensive applications for processing big data are being hosted in the cloud. Since the cloud environment provides virtualized resources for computation, and data-intensive applications require communication between the computing nodes, the placement of Virtual Machines (VMs) and location of data affect the overall computation time. Majority of the research work reported in the current literature consider the selection of physical nodes for placing data and VMs as independent problems. This paper proposes an approach which considers VM placement and data placement hand in hand. The primary objective is to reduce cross network traffic and bandwidth usage, by placing required number of VMs and data in Physical Machines (PMs) which are physically closer. The VM and data placement problem (referred as MinDistVMDataPlacement problem) is defined in this paper and has been proved to be NP- Hard. This paper presents and evaluates a metaheuristic algorithm based on Ant Colony Optimization (ACO), which selects a set of adjacent PMs for placing data and VMs. Data is distributed in the physical storage devices of the selected PMs. According to the processing capacity of each PM, a set of VMs are placed on these PMs to process data stored in them. We use simulation to evaluate our algorithm. The results show that the proposed algorithm selects PMs in close proximity and the jobs executed in the VMs allocated by the proposed scheme outperforms other allocation schemes.

A Data Placement Algorithm for Data Intensive Applications in Cloud

Data layout is an important issue which aims at reducing data movements among data centers to improve the efficiency of the entire cloud system. This paper proposes a dataintensive application oriented data layout algorithm. It is based on hierarchical data correlation clustering and the PSO algorithm. The datasets with fixed location have been considered, and both the offline strategy and the online strategy for data layout have been given. As this proposed strategy is aimed at reducing the global amount of data transmissions, and the special permission of the datasets has been introduced, the cost of data transmission can be measured more reasonable. Simulation results show that compared with two classical strategies, our algorithm can reduce the amount of data transmission more effectively.

DPPACS: A Novel Data Partitioning and Placement Aware Computation Scheduling Scheme for Data-Intensive Cloud Applications

DPPACS: A Novel Data Partitioning and Placement Aware Computation Scheduling Scheme for Data-Intensive Cloud Applications

A Survey of Data Management System for Cloud Computing: Models and Searching Methods

At present, the research of data storage and management in cloud computing mainly focuses on dealing with data expression and search. This study gives a comprehensive survey of numerous models and approaches of data-intensive applications in cloud computing in both academic and industrial communities. We review various approaches and their ideas of design. And then, we attempt to summarize and appraise the open issues.

Autonomous L3 Cache Technolgy for High Responsiveness

Web services and cloud computing paradigms have opened up many new vistas. The data intensive cloud applications usually require huge amounts of data to input and output from secondary storage systems. The outstanding progress in area of network communications has enabled high speed networks and therefore, communication latency bottleneck in cloud and other web applications has been shifted to node/storage level. Moreover, existing cloud solutions focused mainly on the efficient utilization of computing resources through virtualization and issues of storage bottleneck did not receive much attention. Moreover, virtualization based implementation ensures equal priority to all hosted applications, thus, real time applications in cloud environment can't meet their requirements. To meet the demand of overall low latency in cloud and other web services; and particularly to reduce I/O bottleneck at storage level, novel idea of autonomous L3 cache technology is proposed. Autonomous L3 cache technology utilizes local memory space as dedicated block device cache for certain specific application, thus prioritizing it over rest of hosted ones. Evaluation shows performance improvement of 5-8 times in terms of timeliness in given setup.

A Data Placement Strategy for Data-Intensive Applications in Cloud

With the development of information technology,data-intensive applications in cloud have been used in more and more fields.Because of the decentralized data centers in cloud,these applications now are facing some new challenges in data placement which mainly include how to reduce the time cost of data movements between data centers,how to deal with the data dependencies,and how to keep a relative load balancing of data centers.This paper proposes a data placement strategy,the three stages of which address the three challenges above respectively.Simulation shows that the strategy can effectively reduce the time cost of data movements across data centers during the application's execution.